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from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 631
|
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _UpperCAmelCase ( unittest.TestCase ):
def _snake_case ( self : Union[str, Any]):
SCREAMING_SNAKE_CASE_ :Optional[int] = ["a", "b", "c"]
# Defaults to last layer if both are None
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :Dict = get_aligned_output_features_output_indices(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase)
self.assertEqual(UpperCAmelCase , ["c"])
self.assertEqual(UpperCAmelCase , [2])
# Out indices set to match out features
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :Tuple = get_aligned_output_features_output_indices(["a", "c"] , UpperCAmelCase , UpperCAmelCase)
self.assertEqual(UpperCAmelCase , ["a", "c"])
self.assertEqual(UpperCAmelCase , [0, 2])
# Out features set to match out indices
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :int = get_aligned_output_features_output_indices(UpperCAmelCase , [0, 2] , UpperCAmelCase)
self.assertEqual(UpperCAmelCase , ["a", "c"])
self.assertEqual(UpperCAmelCase , [0, 2])
# Out features selected from negative indices
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :Any = get_aligned_output_features_output_indices(UpperCAmelCase , [-3, -1] , UpperCAmelCase)
self.assertEqual(UpperCAmelCase , ["a", "c"])
self.assertEqual(UpperCAmelCase , [-3, -1])
def _snake_case ( self : str):
# Stage names must be set
with self.assertRaises(UpperCAmelCase):
verify_out_features_out_indices(["a", "b"] , (0, 1) , UpperCAmelCase)
# Out features must be a list
with self.assertRaises(UpperCAmelCase):
verify_out_features_out_indices(("a", "b") , (0, 1) , ["a", "b"])
# Out features must be a subset of stage names
with self.assertRaises(UpperCAmelCase):
verify_out_features_out_indices(["a", "b"] , (0, 1) , ["a"])
# Out indices must be a list or tuple
with self.assertRaises(UpperCAmelCase):
verify_out_features_out_indices(UpperCAmelCase , 0 , ["a", "b"])
# Out indices must be a subset of stage names
with self.assertRaises(UpperCAmelCase):
verify_out_features_out_indices(UpperCAmelCase , (0, 1) , ["a"])
# Out features and out indices must be the same length
with self.assertRaises(UpperCAmelCase):
verify_out_features_out_indices(["a", "b"] , (0,) , ["a", "b", "c"])
# Out features should match out indices
with self.assertRaises(UpperCAmelCase):
verify_out_features_out_indices(["a", "b"] , (0, 2) , ["a", "b", "c"])
# Out features and out indices should be in order
with self.assertRaises(UpperCAmelCase):
verify_out_features_out_indices(["b", "a"] , (0, 1) , ["a", "b"])
# Check passes with valid inputs
verify_out_features_out_indices(["a", "b", "d"] , (0, 1, -1) , ["a", "b", "c", "d"])
def _snake_case ( self : Tuple):
SCREAMING_SNAKE_CASE_ :int = BackboneMixin()
SCREAMING_SNAKE_CASE_ :List[str] = ["a", "b", "c"]
SCREAMING_SNAKE_CASE_ :str = ["a", "c"]
SCREAMING_SNAKE_CASE_ :Union[str, Any] = [0, 2]
# Check that the output features and indices are set correctly
self.assertEqual(backbone.out_features , ["a", "c"])
self.assertEqual(backbone.out_indices , [0, 2])
# Check out features and indices are updated correctly
SCREAMING_SNAKE_CASE_ :Tuple = ["a", "b"]
self.assertEqual(backbone.out_features , ["a", "b"])
self.assertEqual(backbone.out_indices , [0, 1])
SCREAMING_SNAKE_CASE_ :List[str] = [-3, -1]
self.assertEqual(backbone.out_features , ["a", "c"])
self.assertEqual(backbone.out_indices , [-3, -1])
| 631
| 1
|
'''simple docstring'''
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 718
|
def _a ( __lowercase , __lowercase = 0 ) -> list:
"""simple docstring"""
__UpperCamelCase = length or len(__lowercase )
__UpperCamelCase = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
__UpperCamelCase , __UpperCamelCase = list_data[i + 1], list_data[i]
__UpperCamelCase = True
return list_data if not swapped else bubble_sort(__lowercase , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 567
| 0
|
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __UpperCamelCase ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = 'hf-internal-testing/tiny-random-t5'
_lowerCAmelCase : Dict = AutoTokenizer.from_pretrained(_A )
_lowerCAmelCase : Union[str, Any] = AutoModelForSeqaSeqLM.from_pretrained(_A )
_lowerCAmelCase : Union[str, Any] = tokenizer('This is me' ,return_tensors='pt' )
_lowerCAmelCase : str = model.to_bettertransformer()
self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
_lowerCAmelCase : Dict = model.generate(**_A )
_lowerCAmelCase : List[str] = model.reverse_bettertransformer()
self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_A )
_lowerCAmelCase : Dict = AutoModelForSeqaSeqLM.from_pretrained(_A )
self.assertFalse(
any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
_lowerCAmelCase : Dict = model_reloaded.generate(**_A )
self.assertTrue(torch.allclose(_A ,_A ) )
def __lowerCamelCase ( self ):
'''simple docstring'''
_lowerCAmelCase : List[str] = 'hf-internal-testing/tiny-random-t5'
_lowerCAmelCase : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_A )
_lowerCAmelCase : Any = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(_A ):
model.save_pretrained(_A )
_lowerCAmelCase : List[str] = model.reverse_bettertransformer()
model.save_pretrained(_A )
| 259
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"""microsoft/swin-tiny-patch4-window7-224""": (
"""https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class __UpperCamelCase ( a__ , a__ ):
_UpperCAmelCase = "swin"
_UpperCAmelCase = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self ,_A=224 ,_A=4 ,_A=3 ,_A=96 ,_A=[2, 2, 6, 2] ,_A=[3, 6, 12, 24] ,_A=7 ,_A=4.0 ,_A=True ,_A=0.0 ,_A=0.0 ,_A=0.1 ,_A="gelu" ,_A=False ,_A=0.0_2 ,_A=1E-5 ,_A=32 ,_A=None ,_A=None ,**_A ,):
'''simple docstring'''
super().__init__(**_A )
_lowerCAmelCase : List[str] = image_size
_lowerCAmelCase : List[str] = patch_size
_lowerCAmelCase : Union[str, Any] = num_channels
_lowerCAmelCase : Union[str, Any] = embed_dim
_lowerCAmelCase : Dict = depths
_lowerCAmelCase : Any = len(_A )
_lowerCAmelCase : Optional[Any] = num_heads
_lowerCAmelCase : List[Any] = window_size
_lowerCAmelCase : str = mlp_ratio
_lowerCAmelCase : Dict = qkv_bias
_lowerCAmelCase : Union[str, Any] = hidden_dropout_prob
_lowerCAmelCase : Any = attention_probs_dropout_prob
_lowerCAmelCase : List[Any] = drop_path_rate
_lowerCAmelCase : List[Any] = hidden_act
_lowerCAmelCase : Tuple = use_absolute_embeddings
_lowerCAmelCase : Tuple = layer_norm_eps
_lowerCAmelCase : Union[str, Any] = initializer_range
_lowerCAmelCase : Dict = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_lowerCAmelCase : List[Any] = int(embed_dim * 2 ** (len(_A ) - 1) )
_lowerCAmelCase : List[str] = ['stem'] + [F"""stage{idx}""" for idx in range(1 ,len(_A ) + 1 )]
_lowerCAmelCase, _lowerCAmelCase : Union[str, Any] = get_aligned_output_features_output_indices(
out_features=_A ,out_indices=_A ,stage_names=self.stage_names )
class __UpperCamelCase ( a__ ):
_UpperCAmelCase = version.parse("1.11" )
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return 1E-4
| 259
| 1
|
"""simple docstring"""
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
assert isinstance(_UpperCAmelCase , _UpperCAmelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : Any = tmp_path / '''cache'''
A_ : Optional[Any] = {'''text''': '''string'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
A_ : List[str] = TextDatasetReader(_UpperCAmelCase , cache_dir=_UpperCAmelCase , keep_in_memory=_UpperCAmelCase ).read()
_check_text_dataset(_UpperCAmelCase , _UpperCAmelCase )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''text''': '''string'''},
{'''text''': '''int32'''},
{'''text''': '''float32'''},
] , )
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : str = tmp_path / '''cache'''
A_ : str = {'''text''': '''string'''}
A_ : List[str] = features.copy() if features else default_expected_features
A_ : Union[str, Any] = (
Features({feature: Value(_UpperCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
A_ : List[str] = TextDatasetReader(_UpperCAmelCase , features=_UpperCAmelCase , cache_dir=_UpperCAmelCase ).read()
_check_text_dataset(_UpperCAmelCase , _UpperCAmelCase )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : Tuple = tmp_path / '''cache'''
A_ : Any = {'''text''': '''string'''}
A_ : List[Any] = TextDatasetReader(_UpperCAmelCase , cache_dir=_UpperCAmelCase , split=_UpperCAmelCase ).read()
_check_text_dataset(_UpperCAmelCase , _UpperCAmelCase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if issubclass(_UpperCAmelCase , _UpperCAmelCase ):
A_ : Tuple = text_path
elif issubclass(_UpperCAmelCase , _UpperCAmelCase ):
A_ : Dict = [text_path]
A_ : List[str] = tmp_path / '''cache'''
A_ : int = {'''text''': '''string'''}
A_ : List[str] = TextDatasetReader(_UpperCAmelCase , cache_dir=_UpperCAmelCase ).read()
_check_text_dataset(_UpperCAmelCase , _UpperCAmelCase )
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=("train",) ):
"""simple docstring"""
assert isinstance(_UpperCAmelCase , _UpperCAmelCase )
for split in splits:
A_ : List[Any] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : Union[str, Any] = tmp_path / '''cache'''
A_ : Tuple = {'''text''': '''string'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
A_ : Tuple = TextDatasetReader({'''train''': text_path} , cache_dir=_UpperCAmelCase , keep_in_memory=_UpperCAmelCase ).read()
_check_text_datasetdict(_UpperCAmelCase , _UpperCAmelCase )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''text''': '''string'''},
{'''text''': '''int32'''},
{'''text''': '''float32'''},
] , )
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : Optional[Any] = tmp_path / '''cache'''
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
A_ : Union[str, Any] = {'''text''': '''string'''}
A_ : Optional[int] = features.copy() if features else default_expected_features
A_ : List[Any] = (
Features({feature: Value(_UpperCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
A_ : int = TextDatasetReader({'''train''': text_path} , features=_UpperCAmelCase , cache_dir=_UpperCAmelCase ).read()
_check_text_datasetdict(_UpperCAmelCase , _UpperCAmelCase )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if split:
A_ : Tuple = {split: text_path}
else:
A_ : Optional[Any] = '''train'''
A_ : Optional[int] = {'''train''': text_path, '''test''': text_path}
A_ : Tuple = tmp_path / '''cache'''
A_ : List[str] = {'''text''': '''string'''}
A_ : int = TextDatasetReader(_UpperCAmelCase , cache_dir=_UpperCAmelCase ).read()
_check_text_datasetdict(_UpperCAmelCase , _UpperCAmelCase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
| 361
|
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class lowercase :
def __init__( self : Dict ):
"""simple docstring"""
A_ : Tuple = {}
def a_ ( self : Union[str, Any] , _lowerCamelCase : List[Any] , _lowerCamelCase : Dict , _lowerCamelCase : List[Any]=1 ):
"""simple docstring"""
if self.graph.get(_lowerCamelCase ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
A_ : int = [[w, v]]
if not self.graph.get(_lowerCamelCase ):
A_ : List[Any] = []
def a_ ( self : Optional[int] ):
"""simple docstring"""
return list(self.graph )
def a_ ( self : Dict , _lowerCamelCase : str , _lowerCamelCase : Tuple ):
"""simple docstring"""
if self.graph.get(_lowerCamelCase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_lowerCamelCase )
def a_ ( self : Union[str, Any] , _lowerCamelCase : Optional[int]=-2 , _lowerCamelCase : Tuple=-1 ):
"""simple docstring"""
if s == d:
return []
A_ : Union[str, Any] = []
A_ : Optional[int] = []
if s == -2:
A_ : int = list(self.graph )[0]
stack.append(_lowerCamelCase )
visited.append(_lowerCamelCase )
A_ : Tuple = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : Optional[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(_lowerCamelCase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
A_ : Union[str, Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(_lowerCamelCase ) != 0:
A_ : Optional[int] = stack[len(_lowerCamelCase ) - 1]
else:
A_ : List[Any] = ss
# check if se have reached the starting point
if len(_lowerCamelCase ) == 0:
return visited
def a_ ( self : Dict , _lowerCamelCase : List[Any]=-1 ):
"""simple docstring"""
if c == -1:
A_ : List[Any] = floor(random() * 1_00_00 ) + 10
for i in range(_lowerCamelCase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
A_ : Tuple = floor(random() * c ) + 1
if n != i:
self.add_pair(_lowerCamelCase , _lowerCamelCase , 1 )
def a_ ( self : Optional[int] , _lowerCamelCase : Union[str, Any]=-2 ):
"""simple docstring"""
A_ : List[str] = deque()
A_ : int = []
if s == -2:
A_ : Any = list(self.graph )[0]
d.append(_lowerCamelCase )
visited.append(_lowerCamelCase )
while d:
A_ : List[Any] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def a_ ( self : Union[str, Any] , _lowerCamelCase : Tuple ):
"""simple docstring"""
A_ : List[Any] = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def a_ ( self : Any , _lowerCamelCase : Optional[Any] ):
"""simple docstring"""
return len(self.graph[u] )
def a_ ( self : Union[str, Any] , _lowerCamelCase : Tuple=-2 ):
"""simple docstring"""
A_ : int = []
A_ : Optional[Any] = []
if s == -2:
A_ : List[str] = list(self.graph )[0]
stack.append(_lowerCamelCase )
visited.append(_lowerCamelCase )
A_ : str = s
A_ : List[Any] = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : str = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A_ : List[Any] = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(_lowerCamelCase ) != 0:
A_ : List[str] = stack[len(_lowerCamelCase ) - 1]
else:
A_ : int = ss
# check if se have reached the starting point
if len(_lowerCamelCase ) == 0:
return sorted_nodes
def a_ ( self : Dict ):
"""simple docstring"""
A_ : str = []
A_ : Dict = []
A_ : Dict = list(self.graph )[0]
stack.append(_lowerCamelCase )
visited.append(_lowerCamelCase )
A_ : Any = -2
A_ : List[str] = []
A_ : Dict = s
A_ : Tuple = False
A_ : List[str] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : Dict = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A_ : Tuple = len(_lowerCamelCase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A_ : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A_ : Optional[int] = True
if len(_lowerCamelCase ) != 0:
A_ : Union[str, Any] = stack[len(_lowerCamelCase ) - 1]
else:
A_ : int = False
indirect_parents.append(_lowerCamelCase )
A_ : Dict = s
A_ : Optional[int] = ss
# check if se have reached the starting point
if len(_lowerCamelCase ) == 0:
return list(_lowerCamelCase )
def a_ ( self : List[str] ):
"""simple docstring"""
A_ : Dict = []
A_ : Dict = []
A_ : Optional[Any] = list(self.graph )[0]
stack.append(_lowerCamelCase )
visited.append(_lowerCamelCase )
A_ : Optional[Any] = -2
A_ : List[str] = []
A_ : List[Any] = s
A_ : int = False
A_ : Any = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : Union[str, Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A_ : Dict = len(_lowerCamelCase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A_ : Any = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A_ : str = True
if len(_lowerCamelCase ) != 0:
A_ : Optional[int] = stack[len(_lowerCamelCase ) - 1]
else:
A_ : Tuple = False
indirect_parents.append(_lowerCamelCase )
A_ : int = s
A_ : List[Any] = ss
# check if se have reached the starting point
if len(_lowerCamelCase ) == 0:
return False
def a_ ( self : Tuple , _lowerCamelCase : Union[str, Any]=-2 , _lowerCamelCase : int=-1 ):
"""simple docstring"""
A_ : int = time()
self.dfs(_lowerCamelCase , _lowerCamelCase )
A_ : int = time()
return end - begin
def a_ ( self : Optional[Any] , _lowerCamelCase : str=-2 ):
"""simple docstring"""
A_ : int = time()
self.bfs(_lowerCamelCase )
A_ : Union[str, Any] = time()
return end - begin
class lowercase :
def __init__( self : Any ):
"""simple docstring"""
A_ : Tuple = {}
def a_ ( self : List[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[int]=1 ):
"""simple docstring"""
if self.graph.get(_lowerCamelCase ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
A_ : Union[str, Any] = [[w, v]]
# add the other way
if self.graph.get(_lowerCamelCase ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
A_ : Tuple = [[w, u]]
def a_ ( self : Dict , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Dict ):
"""simple docstring"""
if self.graph.get(_lowerCamelCase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_lowerCamelCase )
# the other way round
if self.graph.get(_lowerCamelCase ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(_lowerCamelCase )
def a_ ( self : Any , _lowerCamelCase : str=-2 , _lowerCamelCase : int=-1 ):
"""simple docstring"""
if s == d:
return []
A_ : Optional[Any] = []
A_ : List[Any] = []
if s == -2:
A_ : Optional[int] = list(self.graph )[0]
stack.append(_lowerCamelCase )
visited.append(_lowerCamelCase )
A_ : Union[str, Any] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : Any = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(_lowerCamelCase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
A_ : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(_lowerCamelCase ) != 0:
A_ : List[Any] = stack[len(_lowerCamelCase ) - 1]
else:
A_ : str = ss
# check if se have reached the starting point
if len(_lowerCamelCase ) == 0:
return visited
def a_ ( self : Optional[Any] , _lowerCamelCase : Union[str, Any]=-1 ):
"""simple docstring"""
if c == -1:
A_ : List[Any] = floor(random() * 1_00_00 ) + 10
for i in range(_lowerCamelCase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
A_ : List[str] = floor(random() * c ) + 1
if n != i:
self.add_pair(_lowerCamelCase , _lowerCamelCase , 1 )
def a_ ( self : Dict , _lowerCamelCase : List[Any]=-2 ):
"""simple docstring"""
A_ : Dict = deque()
A_ : Tuple = []
if s == -2:
A_ : List[str] = list(self.graph )[0]
d.append(_lowerCamelCase )
visited.append(_lowerCamelCase )
while d:
A_ : str = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def a_ ( self : Any , _lowerCamelCase : int ):
"""simple docstring"""
return len(self.graph[u] )
def a_ ( self : Any ):
"""simple docstring"""
A_ : Dict = []
A_ : Optional[int] = []
A_ : Union[str, Any] = list(self.graph )[0]
stack.append(_lowerCamelCase )
visited.append(_lowerCamelCase )
A_ : str = -2
A_ : int = []
A_ : Optional[Any] = s
A_ : str = False
A_ : int = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : Tuple = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A_ : Tuple = len(_lowerCamelCase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A_ : str = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A_ : Union[str, Any] = True
if len(_lowerCamelCase ) != 0:
A_ : Tuple = stack[len(_lowerCamelCase ) - 1]
else:
A_ : str = False
indirect_parents.append(_lowerCamelCase )
A_ : Union[str, Any] = s
A_ : List[str] = ss
# check if se have reached the starting point
if len(_lowerCamelCase ) == 0:
return list(_lowerCamelCase )
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
A_ : List[str] = []
A_ : int = []
A_ : List[Any] = list(self.graph )[0]
stack.append(_lowerCamelCase )
visited.append(_lowerCamelCase )
A_ : int = -2
A_ : Tuple = []
A_ : Any = s
A_ : Tuple = False
A_ : Union[str, Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : Any = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A_ : Optional[int] = len(_lowerCamelCase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A_ : Optional[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A_ : Optional[int] = True
if len(_lowerCamelCase ) != 0:
A_ : str = stack[len(_lowerCamelCase ) - 1]
else:
A_ : List[Any] = False
indirect_parents.append(_lowerCamelCase )
A_ : List[Any] = s
A_ : List[Any] = ss
# check if se have reached the starting point
if len(_lowerCamelCase ) == 0:
return False
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
return list(self.graph )
def a_ ( self : Tuple , _lowerCamelCase : Union[str, Any]=-2 , _lowerCamelCase : str=-1 ):
"""simple docstring"""
A_ : Optional[int] = time()
self.dfs(_lowerCamelCase , _lowerCamelCase )
A_ : Union[str, Any] = time()
return end - begin
def a_ ( self : Tuple , _lowerCamelCase : str=-2 ):
"""simple docstring"""
A_ : Optional[int] = time()
self.bfs(_lowerCamelCase )
A_ : List[str] = time()
return end - begin
| 361
| 1
|
from manim import *
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = Rectangle(height=0.5 , width=0.5 )
lowerCamelCase_ = Rectangle(height=0.2_5 , width=0.2_5 )
lowerCamelCase_ = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0 )
lowerCamelCase_ = [mem.copy() for i in range(6 )]
lowerCamelCase_ = [mem.copy() for i in range(6 )]
lowerCamelCase_ = VGroup(*lowercase ).arrange(lowercase , buff=0 )
lowerCamelCase_ = VGroup(*lowercase ).arrange(lowercase , buff=0 )
lowerCamelCase_ = VGroup(lowercase , lowercase ).arrange(lowercase , buff=0 )
lowerCamelCase_ = Text("CPU" , font_size=24 )
lowerCamelCase_ = Group(lowercase , lowercase ).arrange(lowercase , buff=0.5 , aligned_edge=lowercase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowercase )
lowerCamelCase_ = [mem.copy() for i in range(4 )]
lowerCamelCase_ = VGroup(*lowercase ).arrange(lowercase , buff=0 )
lowerCamelCase_ = Text("GPU" , font_size=24 )
lowerCamelCase_ = Group(lowercase , lowercase ).arrange(lowercase , buff=0.5 , aligned_edge=lowercase )
gpu.move_to([-1, -1, 0] )
self.add(lowercase )
lowerCamelCase_ = [mem.copy() for i in range(6 )]
lowerCamelCase_ = VGroup(*lowercase ).arrange(lowercase , buff=0 )
lowerCamelCase_ = Text("Model" , font_size=24 )
lowerCamelCase_ = Group(lowercase , lowercase ).arrange(lowercase , buff=0.5 , aligned_edge=lowercase )
model.move_to([3, -1.0, 0] )
self.add(lowercase )
lowerCamelCase_ = []
lowerCamelCase_ = []
lowerCamelCase_ = []
for i, rect in enumerate(lowercase ):
rect.set_stroke(lowercase )
lowerCamelCase_ = Rectangle(height=0.4_6 / 4 , width=0.4_6 / 3 ).set_stroke(width=0.0 ).set_fill(lowercase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.0_2 , direction=lowercase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=lowercase , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=lowercase , buff=0.0 )
self.add(lowercase )
model_cpu_arr.append(lowercase )
self.add(*lowercase , *lowercase , *lowercase )
lowerCamelCase_ = [mem.copy() for i in range(6 )]
lowerCamelCase_ = VGroup(*lowercase ).arrange(lowercase , buff=0 )
lowerCamelCase_ = Text("Loaded Checkpoint" , font_size=24 )
lowerCamelCase_ = Group(lowercase , lowercase ).arrange(lowercase , buff=0.5 , aligned_edge=lowercase )
checkpoint.move_to([3, 0.5, 0] )
self.add(lowercase )
lowerCamelCase_ = []
lowerCamelCase_ = []
for i, rect in enumerate(lowercase ):
lowerCamelCase_ = fill.copy().set_fill(lowercase , opacity=0.7 )
target.move_to(lowercase )
ckpt_arr.append(lowercase )
lowerCamelCase_ = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(lowercase )
self.add(*lowercase , *lowercase )
lowerCamelCase_ = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowerCamelCase_ = MarkupText(
f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(lowercase , lowercase )
lowerCamelCase_ = MarkupText(
f'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint' , font_size=18 , )
blue_text.next_to(lowercase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(lowercase )
lowerCamelCase_ = MarkupText(
f'Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.' , font_size=24 , )
step_a.move_to([2, 2, 0] )
lowerCamelCase_ = [meta_mem.copy() for i in range(6 )]
lowerCamelCase_ = [meta_mem.copy() for i in range(6 )]
lowerCamelCase_ = VGroup(*lowercase ).arrange(lowercase , buff=0 )
lowerCamelCase_ = VGroup(*lowercase ).arrange(lowercase , buff=0 )
lowerCamelCase_ = VGroup(lowercase , lowercase ).arrange(lowercase , buff=0 )
lowerCamelCase_ = Text("Disk" , font_size=24 )
lowerCamelCase_ = Group(lowercase , lowercase ).arrange(lowercase , buff=0.5 , aligned_edge=lowercase )
disk.move_to([-4.0, -1.2_5, 0] )
self.play(Write(lowercase , run_time=3 ) , Write(lowercase , run_time=1 ) , Create(lowercase , run_time=1 ) )
lowerCamelCase_ = []
for i, rect in enumerate(lowercase ):
lowerCamelCase_ = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(lowercase , run_time=1.5 ) )
self.play(*lowercase )
self.play(FadeOut(lowercase ) )
lowerCamelCase_ = MarkupText(f'Then, the checkpoint is removed from memory\nthrough garbage collection.' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(lowercase , run_time=3 ) )
self.play(
FadeOut(lowercase , lowercase , *lowercase , *lowercase ) , )
self.wait()
| 463
|
from math import isqrt
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = False
return [i for i in range(2 , lowerCamelCase__ ) if is_prime[i]]
def lowerCamelCase_ ( lowerCamelCase__ = 1_0**8 ):
lowerCamelCase_ = calculate_prime_numbers(max_number // 2 )
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = len(lowerCamelCase__ ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F"""{solution() = }""")
| 463
| 1
|
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCamelCase = HUGGINGFACE_HUB_CACHE
lowerCamelCase = "config.json"
lowerCamelCase = "diffusion_pytorch_model.bin"
lowerCamelCase = "diffusion_flax_model.msgpack"
lowerCamelCase = "model.onnx"
lowerCamelCase = "diffusion_pytorch_model.safetensors"
lowerCamelCase = "weights.pb"
lowerCamelCase = "https://huggingface.co"
lowerCamelCase = default_cache_path
lowerCamelCase = "diffusers_modules"
lowerCamelCase = os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowerCamelCase = ["fp16", "non-ema"]
lowerCamelCase = ".self_attn"
| 704
|
def SCREAMING_SNAKE_CASE( __UpperCamelCase ) -> float:
a__ : Optional[Any] = 0
while len(__UpperCamelCase ) > 1:
a__ : str = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
a__ : List[str] = files.index(min(__UpperCamelCase ) )
temp += files[min_index]
files.pop(__UpperCamelCase )
files.append(__UpperCamelCase )
optimal_merge_cost += temp
return optimal_merge_cost
if __name__ == "__main__":
import doctest
doctest.testmod()
| 207
| 0
|
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class __lowerCamelCase (unittest.TestCase ):
@parameterized.expand([(None,), ('foo.json',)] )
def snake_case_ ( self: int,A_: int ):
'''simple docstring'''
__UpperCamelCase = GenerationConfig(
do_sample=A_,temperature=0.7,length_penalty=1.0,bad_words_ids=[[1, 2, 3], [4, 5]],)
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(A_,config_name=A_ )
__UpperCamelCase = GenerationConfig.from_pretrained(A_,config_name=A_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample,A_ )
self.assertEqual(loaded_config.temperature,0.7 )
self.assertEqual(loaded_config.length_penalty,1.0 )
self.assertEqual(loaded_config.bad_words_ids,[[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k,50 )
self.assertEqual(loaded_config.max_length,20 )
self.assertEqual(loaded_config.max_time,A_ )
def snake_case_ ( self: Tuple ):
'''simple docstring'''
__UpperCamelCase = AutoConfig.from_pretrained('gpt2' )
__UpperCamelCase = GenerationConfig.from_model_config(A_ )
__UpperCamelCase = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(A_,A_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id,default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id,model_config.eos_token_id )
def snake_case_ ( self: Any ):
'''simple docstring'''
__UpperCamelCase = GenerationConfig()
__UpperCamelCase = {
'max_new_tokens': 1024,
'foo': 'bar',
}
__UpperCamelCase = copy.deepcopy(A_ )
__UpperCamelCase = generation_config.update(**A_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(A_,A_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens,1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(A_,{'foo': 'bar'} )
def snake_case_ ( self: List[Any] ):
'''simple docstring'''
__UpperCamelCase = GenerationConfig()
__UpperCamelCase = 'bar'
with tempfile.TemporaryDirectory('test-generation-config' ) as tmp_dir:
generation_config.save_pretrained(A_ )
__UpperCamelCase = GenerationConfig.from_pretrained(A_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo,'bar' )
__UpperCamelCase = GenerationConfig.from_model_config(A_ )
assert not hasattr(A_,'foo' ) # no new kwargs should be initialized if from config
def snake_case_ ( self: List[Any] ):
'''simple docstring'''
__UpperCamelCase = GenerationConfig()
self.assertEqual(default_config.temperature,1.0 )
self.assertEqual(default_config.do_sample,A_ )
self.assertEqual(default_config.num_beams,1 )
__UpperCamelCase = GenerationConfig(
do_sample=A_,temperature=0.7,length_penalty=1.0,bad_words_ids=[[1, 2, 3], [4, 5]],)
self.assertEqual(config.temperature,0.7 )
self.assertEqual(config.do_sample,A_ )
self.assertEqual(config.num_beams,1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(A_ )
__UpperCamelCase = GenerationConfig.from_pretrained(A_,temperature=1.0 )
self.assertEqual(loaded_config.temperature,1.0 )
self.assertEqual(loaded_config.do_sample,A_ )
self.assertEqual(loaded_config.num_beams,1 ) # default value
@is_staging_test
class __lowerCamelCase (unittest.TestCase ):
@classmethod
def snake_case_ ( cls: int ):
'''simple docstring'''
__UpperCamelCase = TOKEN
HfFolder.save_token(A_ )
@classmethod
def snake_case_ ( cls: int ):
'''simple docstring'''
try:
delete_repo(token=cls._token,repo_id='test-generation-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token,repo_id='valid_org/test-generation-config-org' )
except HTTPError:
pass
def snake_case_ ( self: int ):
'''simple docstring'''
__UpperCamelCase = GenerationConfig(
do_sample=A_,temperature=0.7,length_penalty=1.0,)
config.push_to_hub('test-generation-config',use_auth_token=self._token )
__UpperCamelCase = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(A_,getattr(A_,A_ ) )
# Reset repo
delete_repo(token=self._token,repo_id='test-generation-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
A_,repo_id='test-generation-config',push_to_hub=A_,use_auth_token=self._token )
__UpperCamelCase = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(A_,getattr(A_,A_ ) )
def snake_case_ ( self: Dict ):
'''simple docstring'''
__UpperCamelCase = GenerationConfig(
do_sample=A_,temperature=0.7,length_penalty=1.0,)
config.push_to_hub('valid_org/test-generation-config-org',use_auth_token=self._token )
__UpperCamelCase = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(A_,getattr(A_,A_ ) )
# Reset repo
delete_repo(token=self._token,repo_id='valid_org/test-generation-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
A_,repo_id='valid_org/test-generation-config-org',push_to_hub=A_,use_auth_token=self._token )
__UpperCamelCase = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(A_,getattr(A_,A_ ) )
| 1
|
'''simple docstring'''
def __a ( A__ = 1000 ) -> int:
lowerCAmelCase = 3
lowerCAmelCase = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(f"{solution() = }")
| 649
| 0
|
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Union[str, Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' )
SCREAMING_SNAKE_CASE =sd_pipe.to(snake_case )
sd_pipe.set_progress_bar_config(disable=snake_case )
sd_pipe.set_scheduler('sample_euler' )
SCREAMING_SNAKE_CASE ='A painting of a squirrel eating a burger'
SCREAMING_SNAKE_CASE =torch.manual_seed(0 )
SCREAMING_SNAKE_CASE =sd_pipe([prompt] ,generator=snake_case ,guidance_scale=9.0 ,num_inference_steps=20 ,output_type='np' )
SCREAMING_SNAKE_CASE =output.images
SCREAMING_SNAKE_CASE =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE =np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
SCREAMING_SNAKE_CASE =sd_pipe.to(snake_case )
sd_pipe.set_progress_bar_config(disable=snake_case )
sd_pipe.set_scheduler('sample_euler' )
SCREAMING_SNAKE_CASE ='A painting of a squirrel eating a burger'
SCREAMING_SNAKE_CASE =torch.manual_seed(0 )
SCREAMING_SNAKE_CASE =sd_pipe([prompt] ,generator=snake_case ,guidance_scale=9.0 ,num_inference_steps=20 ,output_type='np' )
SCREAMING_SNAKE_CASE =output.images
SCREAMING_SNAKE_CASE =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE =np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
SCREAMING_SNAKE_CASE =sd_pipe.to(snake_case )
sd_pipe.set_progress_bar_config(disable=snake_case )
sd_pipe.set_scheduler('sample_dpmpp_2m' )
SCREAMING_SNAKE_CASE ='A painting of a squirrel eating a burger'
SCREAMING_SNAKE_CASE =torch.manual_seed(0 )
SCREAMING_SNAKE_CASE =sd_pipe(
[prompt] ,generator=snake_case ,guidance_scale=7.5 ,num_inference_steps=15 ,output_type='np' ,use_karras_sigmas=snake_case ,)
SCREAMING_SNAKE_CASE =output.images
SCREAMING_SNAKE_CASE =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE =np.array(
[0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 703
|
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class a_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[Any] ,snake_case : Tuple ,snake_case : Tuple=13 ,snake_case : Any=7 ,snake_case : Dict=True ,snake_case : str=True ,snake_case : Optional[Any]=True ,snake_case : Optional[int]=True ,snake_case : List[Any]=99 ,snake_case : Optional[int]=32 ,snake_case : str=5 ,snake_case : Union[str, Any]=4 ,snake_case : str=37 ,snake_case : List[str]="gelu" ,snake_case : Union[str, Any]=0.1 ,snake_case : Optional[int]=0.1 ,snake_case : Optional[Any]=512 ,snake_case : Optional[Any]=16 ,snake_case : str=2 ,snake_case : int=0.02 ,snake_case : int=4 ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =seq_length
SCREAMING_SNAKE_CASE =is_training
SCREAMING_SNAKE_CASE =use_attention_mask
SCREAMING_SNAKE_CASE =use_token_type_ids
SCREAMING_SNAKE_CASE =use_labels
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =type_vocab_size
SCREAMING_SNAKE_CASE =type_sequence_label_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =num_choices
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE =None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE =DistilBertConfig(
vocab_size=self.vocab_size ,dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,hidden_dim=self.intermediate_size ,hidden_act=self.hidden_act ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,tie_weights_=snake_case ,)
return config, input_ids, attention_mask
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =config_and_inputs
SCREAMING_SNAKE_CASE ={'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class a_ ( lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =FlaxDistilBertModelTester(self )
@slow
def _lowerCAmelCase ( self : Optional[int] ):
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class_name.from_pretrained('distilbert-base-uncased' )
SCREAMING_SNAKE_CASE =model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case )
@require_flax
class a_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =FlaxDistilBertModel.from_pretrained('distilbert-base-uncased' )
SCREAMING_SNAKE_CASE =np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE =np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case )[0]
SCREAMING_SNAKE_CASE =(1, 11, 768)
self.assertEqual(output.shape ,snake_case )
SCREAMING_SNAKE_CASE =np.array([[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,snake_case ,atol=1e-4 ) )
| 252
| 0
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
a = logging.get_logger(__name__)
def __magic_name__ ( __UpperCAmelCase ) -> List[List[ImageInput]]:
'''simple docstring'''
if isinstance(__UpperCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(__UpperCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(__UpperCAmelCase ):
return [[videos]]
raise ValueError(f"""Could not make batched video from {videos}""" )
class __a ( _snake_case ):
__UpperCamelCase : Any = ['pixel_values']
def __init__( self : Dict ,lowerCamelCase : bool = True ,lowerCamelCase : Dict[str, int] = None ,lowerCamelCase : PILImageResampling = PILImageResampling.BILINEAR ,lowerCamelCase : bool = True ,lowerCamelCase : Dict[str, int] = None ,lowerCamelCase : bool = True ,lowerCamelCase : Union[int, float] = 1 / 255 ,lowerCamelCase : bool = True ,lowerCamelCase : Optional[Union[float, List[float]]] = None ,lowerCamelCase : Optional[Union[float, List[float]]] = None ,**lowerCamelCase : Union[str, Any] ,):
'''simple docstring'''
super().__init__(**lowerCamelCase )
__SCREAMING_SNAKE_CASE = size if size is not None else {"""shortest_edge""": 224}
__SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase ,default_to_square=lowerCamelCase )
__SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
__SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase ,param_name="""crop_size""" )
__SCREAMING_SNAKE_CASE = do_resize
__SCREAMING_SNAKE_CASE = size
__SCREAMING_SNAKE_CASE = do_center_crop
__SCREAMING_SNAKE_CASE = crop_size
__SCREAMING_SNAKE_CASE = resample
__SCREAMING_SNAKE_CASE = do_rescale
__SCREAMING_SNAKE_CASE = rescale_factor
__SCREAMING_SNAKE_CASE = do_normalize
__SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__SCREAMING_SNAKE_CASE = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCAmelCase__ ( self : Tuple ,lowerCamelCase : np.ndarray ,lowerCamelCase : Dict[str, int] ,lowerCamelCase : PILImageResampling = PILImageResampling.BILINEAR ,lowerCamelCase : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase : List[str] ,):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase ,default_to_square=lowerCamelCase )
if "shortest_edge" in size:
__SCREAMING_SNAKE_CASE = get_resize_output_image_size(lowerCamelCase ,size["""shortest_edge"""] ,default_to_square=lowerCamelCase )
elif "height" in size and "width" in size:
__SCREAMING_SNAKE_CASE = (size["""height"""], size["""width"""])
else:
raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
return resize(lowerCamelCase ,size=lowerCamelCase ,resample=lowerCamelCase ,data_format=lowerCamelCase ,**lowerCamelCase )
def UpperCAmelCase__ ( self : Optional[Any] ,lowerCamelCase : np.ndarray ,lowerCamelCase : Dict[str, int] ,lowerCamelCase : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase : List[str] ,):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(lowerCamelCase ,size=(size["""height"""], size["""width"""]) ,data_format=lowerCamelCase ,**lowerCamelCase )
def UpperCAmelCase__ ( self : Dict ,lowerCamelCase : np.ndarray ,lowerCamelCase : Union[int, float] ,lowerCamelCase : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase : str ,):
'''simple docstring'''
return rescale(lowerCamelCase ,scale=lowerCamelCase ,data_format=lowerCamelCase ,**lowerCamelCase )
def UpperCAmelCase__ ( self : Optional[Any] ,lowerCamelCase : np.ndarray ,lowerCamelCase : Union[float, List[float]] ,lowerCamelCase : Union[float, List[float]] ,lowerCamelCase : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase : Union[str, Any] ,):
'''simple docstring'''
return normalize(lowerCamelCase ,mean=lowerCamelCase ,std=lowerCamelCase ,data_format=lowerCamelCase ,**lowerCamelCase )
def UpperCAmelCase__ ( self : Optional[int] ,lowerCamelCase : ImageInput ,lowerCamelCase : bool = None ,lowerCamelCase : Dict[str, int] = None ,lowerCamelCase : PILImageResampling = None ,lowerCamelCase : bool = None ,lowerCamelCase : Dict[str, int] = None ,lowerCamelCase : bool = None ,lowerCamelCase : float = None ,lowerCamelCase : bool = None ,lowerCamelCase : Optional[Union[float, List[float]]] = None ,lowerCamelCase : Optional[Union[float, List[float]]] = None ,lowerCamelCase : Optional[ChannelDimension] = ChannelDimension.FIRST ,):
'''simple docstring'''
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
__SCREAMING_SNAKE_CASE = to_numpy_array(lowerCamelCase )
if do_resize:
__SCREAMING_SNAKE_CASE = self.resize(image=lowerCamelCase ,size=lowerCamelCase ,resample=lowerCamelCase )
if do_center_crop:
__SCREAMING_SNAKE_CASE = self.center_crop(lowerCamelCase ,size=lowerCamelCase )
if do_rescale:
__SCREAMING_SNAKE_CASE = self.rescale(image=lowerCamelCase ,scale=lowerCamelCase )
if do_normalize:
__SCREAMING_SNAKE_CASE = self.normalize(image=lowerCamelCase ,mean=lowerCamelCase ,std=lowerCamelCase )
__SCREAMING_SNAKE_CASE = to_channel_dimension_format(lowerCamelCase ,lowerCamelCase )
return image
def UpperCAmelCase__ ( self : Optional[int] ,lowerCamelCase : ImageInput ,lowerCamelCase : bool = None ,lowerCamelCase : Dict[str, int] = None ,lowerCamelCase : PILImageResampling = None ,lowerCamelCase : bool = None ,lowerCamelCase : Dict[str, int] = None ,lowerCamelCase : bool = None ,lowerCamelCase : float = None ,lowerCamelCase : bool = None ,lowerCamelCase : Optional[Union[float, List[float]]] = None ,lowerCamelCase : Optional[Union[float, List[float]]] = None ,lowerCamelCase : Optional[Union[str, TensorType]] = None ,lowerCamelCase : ChannelDimension = ChannelDimension.FIRST ,**lowerCamelCase : Optional[int] ,):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = do_resize if do_resize is not None else self.do_resize
__SCREAMING_SNAKE_CASE = resample if resample is not None else self.resample
__SCREAMING_SNAKE_CASE = do_center_crop if do_center_crop is not None else self.do_center_crop
__SCREAMING_SNAKE_CASE = do_rescale if do_rescale is not None else self.do_rescale
__SCREAMING_SNAKE_CASE = rescale_factor if rescale_factor is not None else self.rescale_factor
__SCREAMING_SNAKE_CASE = do_normalize if do_normalize is not None else self.do_normalize
__SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else self.image_mean
__SCREAMING_SNAKE_CASE = image_std if image_std is not None else self.image_std
__SCREAMING_SNAKE_CASE = size if size is not None else self.size
__SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase ,default_to_square=lowerCamelCase )
__SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else self.crop_size
__SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase ,param_name="""crop_size""" )
if not valid_images(lowerCamelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
__SCREAMING_SNAKE_CASE = make_batched(lowerCamelCase )
__SCREAMING_SNAKE_CASE = [
[
self._preprocess_image(
image=lowerCamelCase ,do_resize=lowerCamelCase ,size=lowerCamelCase ,resample=lowerCamelCase ,do_center_crop=lowerCamelCase ,crop_size=lowerCamelCase ,do_rescale=lowerCamelCase ,rescale_factor=lowerCamelCase ,do_normalize=lowerCamelCase ,image_mean=lowerCamelCase ,image_std=lowerCamelCase ,data_format=lowerCamelCase ,)
for img in video
]
for video in videos
]
__SCREAMING_SNAKE_CASE = {"""pixel_values""": videos}
return BatchFeature(data=lowerCamelCase ,tensor_type=lowerCamelCase )
| 109
|
"""simple docstring"""
def lowercase_ ( _lowercase : int ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
raise TypeError("only integers accepted as input" )
else:
UpperCAmelCase : Optional[int] = str(abs(_lowercase ) )
UpperCAmelCase : Union[str, Any] = [list(_lowercase ) for char in range(len(_lowercase ) )]
for index in range(len(_lowercase ) ):
num_transpositions[index].pop(_lowercase )
return max(
int("".join(list(_lowercase ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 595
| 0
|
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class __a ( lowerCAmelCase__ ):
def __init__( self ):
# test for the above condition
self.test()
def snake_case_ ( self ):
_lowerCamelCase = 0
_lowerCamelCase = False
while not completed:
if counter == 1:
self.reset()
_lowerCamelCase = self.advance()
if not self.does_advance(UpperCamelCase_ ):
raise Exception(
'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.' )
_lowerCamelCase = self.update(UpperCamelCase_ )
counter += 1
if counter > 1_00_00:
raise Exception('update() does not fulfill the constraint.' )
if self.remaining() != 0:
raise Exception('Custom Constraint is not defined correctly.' )
@abstractmethod
def snake_case_ ( self ):
raise NotImplementedError(
F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def snake_case_ ( self , a__ ):
raise NotImplementedError(
F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def snake_case_ ( self , a__ ):
raise NotImplementedError(
F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def snake_case_ ( self ):
raise NotImplementedError(
F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def snake_case_ ( self ):
raise NotImplementedError(
F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def snake_case_ ( self , a__=False ):
raise NotImplementedError(
F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
class __a ( lowerCAmelCase__ ):
def __init__( self , a__ ):
super(UpperCamelCase_ , self ).__init__()
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or len(UpperCamelCase_ ) == 0:
raise ValueError(F'`token_ids` has to be a non-empty list, but is {token_ids}.' )
if any((not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or token_id < 0) for token_id in token_ids ):
raise ValueError(F'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.' )
_lowerCamelCase = token_ids
_lowerCamelCase = len(self.token_ids )
_lowerCamelCase = -1 # the index of the currently fulfilled step
_lowerCamelCase = False
def snake_case_ ( self ):
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def snake_case_ ( self , a__ ):
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(F'`token_id` has to be an `int`, but is {token_id} of type {type(UpperCamelCase_ )}' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def snake_case_ ( self , a__ ):
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(F'`token_id` has to be an `int`, but is {token_id} of type {type(UpperCamelCase_ )}' )
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
if self.does_advance(UpperCamelCase_ ):
self.fulfilled_idx += 1
_lowerCamelCase = True
if self.fulfilled_idx == (self.seqlen - 1):
_lowerCamelCase = True
_lowerCamelCase = completed
else:
# failed to make progress.
_lowerCamelCase = True
self.reset()
return stepped, completed, reset
def snake_case_ ( self ):
_lowerCamelCase = False
_lowerCamelCase = 0
def snake_case_ ( self ):
return self.seqlen - (self.fulfilled_idx + 1)
def snake_case_ ( self , a__=False ):
_lowerCamelCase = PhrasalConstraint(self.token_ids )
if stateful:
_lowerCamelCase = self.seqlen
_lowerCamelCase = self.fulfilled_idx
_lowerCamelCase = self.completed
return new_constraint
class __a :
def __init__( self , a__ , a__=True ):
_lowerCamelCase = max([len(UpperCamelCase_ ) for one in nested_token_ids] )
_lowerCamelCase = {}
for token_ids in nested_token_ids:
_lowerCamelCase = root
for tidx, token_id in enumerate(UpperCamelCase_ ):
if token_id not in level:
_lowerCamelCase = {}
_lowerCamelCase = level[token_id]
if no_subsets and self.has_subsets(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(
'Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'
F' {nested_token_ids}.' )
_lowerCamelCase = root
def snake_case_ ( self , a__ ):
_lowerCamelCase = self.trie
for current_token in current_seq:
_lowerCamelCase = start[current_token]
_lowerCamelCase = list(start.keys() )
return next_tokens
def snake_case_ ( self , a__ ):
_lowerCamelCase = self.next_tokens(UpperCamelCase_ )
return len(UpperCamelCase_ ) == 0
def snake_case_ ( self , a__ ):
_lowerCamelCase = list(root.values() )
if len(UpperCamelCase_ ) == 0:
return 1
else:
return sum([self.count_leaves(UpperCamelCase_ ) for nn in next_nodes] )
def snake_case_ ( self , a__ , a__ ):
_lowerCamelCase = self.count_leaves(UpperCamelCase_ )
return len(UpperCamelCase_ ) != leaf_count
class __a ( lowerCAmelCase__ ):
def __init__( self , a__ ):
super(UpperCamelCase_ , self ).__init__()
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or len(UpperCamelCase_ ) == 0:
raise ValueError(F'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.' )
if any(not isinstance(UpperCamelCase_ , UpperCamelCase_ ) for token_ids in nested_token_ids ):
raise ValueError(F'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.' )
if any(
any((not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
F'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.' )
_lowerCamelCase = DisjunctiveTrie(UpperCamelCase_ )
_lowerCamelCase = nested_token_ids
_lowerCamelCase = self.trie.max_height
_lowerCamelCase = []
_lowerCamelCase = False
def snake_case_ ( self ):
_lowerCamelCase = self.trie.next_tokens(self.current_seq )
if len(UpperCamelCase_ ) == 0:
return None
else:
return token_list
def snake_case_ ( self , a__ ):
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(F'`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCamelCase_ )}' )
_lowerCamelCase = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def snake_case_ ( self , a__ ):
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(F'`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCamelCase_ )}' )
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
if self.does_advance(UpperCamelCase_ ):
self.current_seq.append(UpperCamelCase_ )
_lowerCamelCase = True
else:
_lowerCamelCase = True
self.reset()
_lowerCamelCase = self.trie.reached_leaf(self.current_seq )
_lowerCamelCase = completed
return stepped, completed, reset
def snake_case_ ( self ):
_lowerCamelCase = False
_lowerCamelCase = []
def snake_case_ ( self ):
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def snake_case_ ( self , a__=False ):
_lowerCamelCase = DisjunctiveConstraint(self.token_ids )
if stateful:
_lowerCamelCase = self.seqlen
_lowerCamelCase = self.current_seq
_lowerCamelCase = self.completed
return new_constraint
class __a :
def __init__( self , a__ ):
_lowerCamelCase = constraints
# max # of steps required to fulfill a given constraint
_lowerCamelCase = max([c.seqlen for c in constraints] )
_lowerCamelCase = len(UpperCamelCase_ )
_lowerCamelCase = False
self.init_state()
def snake_case_ ( self ):
_lowerCamelCase = []
_lowerCamelCase = None
_lowerCamelCase = [constraint.copy(stateful=UpperCamelCase_ ) for constraint in self.constraints]
def snake_case_ ( self ):
_lowerCamelCase = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def snake_case_ ( self ):
_lowerCamelCase = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
_lowerCamelCase = constraint.advance()
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
token_list.append(UpperCamelCase_ )
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ):
token_list.extend(UpperCamelCase_ )
else:
_lowerCamelCase = self.inprogress_constraint.advance()
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
token_list.append(UpperCamelCase_ )
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ):
token_list.extend(UpperCamelCase_ )
if len(UpperCamelCase_ ) == 0:
return None
else:
return token_list
def snake_case_ ( self , a__ ):
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
_lowerCamelCase = self.add(UpperCamelCase_ )
# the entire list of constraints are fulfilled
if self.completed:
break
def snake_case_ ( self , a__ ):
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(F'`token_id` should be an `int`, but is `{token_id}`.' )
_lowerCamelCase = False, False
if self.completed:
_lowerCamelCase = True
_lowerCamelCase = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
_lowerCamelCase = self.inprogress_constraint.update(UpperCamelCase_ )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=UpperCamelCase_ ) )
_lowerCamelCase = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
_lowerCamelCase = None
if len(self.pending_constraints ) == 0:
# we're done!
_lowerCamelCase = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(UpperCamelCase_ ):
_lowerCamelCase = pending_constraint.update(UpperCamelCase_ )
if not stepped:
raise Exception(
'`constraint.update(token_id)` is not yielding incremental progress, '
'even though `constraint.does_advance(token_id)` is true.' )
if complete:
self.complete_constraints.append(UpperCamelCase_ )
_lowerCamelCase = None
if not complete and stepped:
_lowerCamelCase = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
_lowerCamelCase = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
_lowerCamelCase = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def snake_case_ ( self , a__=True ):
_lowerCamelCase = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
_lowerCamelCase = [
constraint.copy(stateful=UpperCamelCase_ ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
_lowerCamelCase = self.inprogress_constraint.copy(stateful=UpperCamelCase_ )
_lowerCamelCase = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 710
|
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
A_ : Any =logging.get_logger(__name__)
A_ : Dict ={"""vocab_file""": """vocab.txt"""}
A_ : Optional[int] ={
"""vocab_file""": {
"""facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
"""facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
},
}
A_ : Any ={
"""facebook/esm2_t6_8M_UR50D""": 1_0_2_4,
"""facebook/esm2_t12_35M_UR50D""": 1_0_2_4,
}
def SCREAMING_SNAKE_CASE_ ( snake_case : Optional[int] )-> Tuple:
with open(snake_case , 'r' ) as f:
_lowerCamelCase = f.read().splitlines()
return [l.strip() for l in lines]
class __a ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : str = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ : List[str] = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ : Optional[int] = ["input_ids", "attention_mask"]
def __init__( self , a__ , a__="<unk>" , a__="<cls>" , a__="<pad>" , a__="<mask>" , a__="<eos>" , **a__ , ):
super().__init__(**a__ )
_lowerCamelCase = load_vocab_file(a__ )
_lowerCamelCase = dict(enumerate(self.all_tokens ) )
_lowerCamelCase = {tok: ind for ind, tok in enumerate(self.all_tokens )}
_lowerCamelCase = unk_token
_lowerCamelCase = cls_token
_lowerCamelCase = pad_token
_lowerCamelCase = mask_token
_lowerCamelCase = eos_token
_lowerCamelCase = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def snake_case_ ( self , a__ ):
return self._id_to_token.get(a__ , self.unk_token )
def snake_case_ ( self , a__ ):
return self._token_to_id.get(a__ , self._token_to_id.get(self.unk_token ) )
def snake_case_ ( self , a__ , **a__ ):
return text.split()
def snake_case_ ( self , a__=False ):
return len(self._id_to_token )
def snake_case_ ( self ):
return {token: i for i, token in enumerate(self.all_tokens )}
def snake_case_ ( self , a__ ):
return self._token_to_id.get(a__ , self._token_to_id.get(self.unk_token ) )
def snake_case_ ( self , a__ ):
return self._id_to_token.get(a__ , self.unk_token )
def snake_case_ ( self , a__ , a__ = None ):
_lowerCamelCase = [self.cls_token_id]
_lowerCamelCase = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def snake_case_ ( self , a__ , a__ = None , a__ = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
_lowerCamelCase = [1] + ([0] * len(a__ )) + [1]
if token_ids_a is not None:
mask += [0] * len(a__ ) + [1]
return mask
def snake_case_ ( self , a__ , a__ ):
_lowerCamelCase = os.path.join(a__ , (filename_prefix + '-' if filename_prefix else '') + 'vocab.txt' )
with open(a__ , 'w' ) as f:
f.write('\n'.join(self.all_tokens ) )
return (vocab_file,)
@property
def snake_case_ ( self ):
return self.get_vocab_size(with_added_tokens=a__ )
def snake_case_ ( self , a__ , a__ = False ):
return super()._add_tokens(a__ , special_tokens=a__ )
| 222
| 0
|
"""simple docstring"""
import logging
import os
from .state import PartialState
class lowerCAmelCase ( logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def __A ( lowerCAmelCase__ ) -> Any:
SCREAMING_SNAKE_CASE = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> Union[str, Any]:
if PartialState._shared_state == {}:
raise RuntimeError(
'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.' )
SCREAMING_SNAKE_CASE = kwargs.pop('main_process_only' , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = kwargs.pop('in_order' , lowerCAmelCase__ )
if self.isEnabledFor(lowerCAmelCase__ ):
if self._should_log(lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.process(lowerCAmelCase__ , lowerCAmelCase__ )
self.logger.log(lowerCAmelCase__ , lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__ )
elif in_order:
SCREAMING_SNAKE_CASE = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.process(lowerCAmelCase__ , lowerCAmelCase__ )
self.logger.log(lowerCAmelCase__ , lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__ )
state.wait_for_everyone()
def lowercase (SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str = None ) -> Optional[Any]:
if log_level is None:
SCREAMING_SNAKE_CASE = os.environ.get('ACCELERATE_LOG_LEVEL' , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = logging.getLogger(SCREAMING_SNAKE_CASE_ )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(SCREAMING_SNAKE_CASE_ , {} )
| 247
|
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
__UpperCamelCase = '''platform'''
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class lowerCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Dict = PegasusConfig
SCREAMING_SNAKE_CASE_ : Optional[Any] = {}
SCREAMING_SNAKE_CASE_ : Dict = """gelu"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=13 , lowerCAmelCase__=7 , lowerCAmelCase__=True , lowerCAmelCase__=False , lowerCAmelCase__=99 , lowerCAmelCase__=32 , lowerCAmelCase__=5 , lowerCAmelCase__=4 , lowerCAmelCase__=37 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=20 , lowerCAmelCase__=2 , lowerCAmelCase__=1 , lowerCAmelCase__=0 , ) -> Tuple:
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = seq_length
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = eos_token_id
SCREAMING_SNAKE_CASE = pad_token_id
SCREAMING_SNAKE_CASE = bos_token_id
def __A ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
SCREAMING_SNAKE_CASE = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
SCREAMING_SNAKE_CASE = np.concatenate([input_ids, eos_tensor] , axis=1 )
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
SCREAMING_SNAKE_CASE = prepare_pegasus_inputs_dict(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
return config, inputs_dict
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]:
SCREAMING_SNAKE_CASE = 20
SCREAMING_SNAKE_CASE = model_class_name(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = model.encode(inputs_dict['input_ids'] )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
SCREAMING_SNAKE_CASE = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='i4' )
SCREAMING_SNAKE_CASE = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
SCREAMING_SNAKE_CASE = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase__ , decoder_attention_mask=lowerCAmelCase__ , past_key_values=lowerCAmelCase__ , decoder_position_ids=lowerCAmelCase__ , )
SCREAMING_SNAKE_CASE = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4' )
SCREAMING_SNAKE_CASE = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase__ , decoder_attention_mask=lowerCAmelCase__ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCAmelCase__ , )
SCREAMING_SNAKE_CASE = model.decode(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=F'Max diff is {diff}' )
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[int]:
SCREAMING_SNAKE_CASE = 20
SCREAMING_SNAKE_CASE = model_class_name(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = model.encode(inputs_dict['input_ids'] )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
SCREAMING_SNAKE_CASE = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
SCREAMING_SNAKE_CASE = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
SCREAMING_SNAKE_CASE = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase__ , decoder_attention_mask=lowerCAmelCase__ , past_key_values=lowerCAmelCase__ , decoder_position_ids=lowerCAmelCase__ , )
SCREAMING_SNAKE_CASE = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4' )
SCREAMING_SNAKE_CASE = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase__ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCAmelCase__ , decoder_position_ids=lowerCAmelCase__ , )
SCREAMING_SNAKE_CASE = model.decode(lowerCAmelCase__ , lowerCAmelCase__ , decoder_attention_mask=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=F'Max diff is {diff}' )
def lowercase (SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[Any]=None , SCREAMING_SNAKE_CASE_ : Tuple=None , ) -> Union[str, Any]:
if attention_mask is None:
SCREAMING_SNAKE_CASE = np.not_equal(SCREAMING_SNAKE_CASE_ , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class lowerCAmelCase ( lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[int] = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
SCREAMING_SNAKE_CASE_ : Any = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
SCREAMING_SNAKE_CASE_ : Tuple = True
SCREAMING_SNAKE_CASE_ : List[Any] = False
SCREAMING_SNAKE_CASE_ : Dict = False
SCREAMING_SNAKE_CASE_ : List[Any] = False
def __A ( self ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE = FlaxPegasusModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=lowerCAmelCase__ )
def __A ( self ) -> Any:
self.config_tester.run_common_tests()
def __A ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def __A ( self ) -> str:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def __A ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE = self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = model_class(lowerCAmelCase__ )
@jax.jit
def encode_jitted(lowerCAmelCase__ , lowerCAmelCase__=None , **lowerCAmelCase__ ):
return model.encode(input_ids=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )
with self.subTest('JIT Enabled' ):
SCREAMING_SNAKE_CASE = encode_jitted(**lowerCAmelCase__ ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE = encode_jitted(**lowerCAmelCase__ ).to_tuple()
self.assertEqual(len(lowerCAmelCase__ ) , len(lowerCAmelCase__ ) )
for jitted_output, output in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
self.assertEqual(jitted_output.shape , output.shape )
def __A ( self ) -> Tuple:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE = model_class(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = model.encode(inputs_dict['input_ids'] , inputs_dict['attention_mask'] )
SCREAMING_SNAKE_CASE = {
'decoder_input_ids': inputs_dict['decoder_input_ids'],
'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
'encoder_outputs': encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
return model.decode(
decoder_input_ids=lowerCAmelCase__ , decoder_attention_mask=lowerCAmelCase__ , encoder_outputs=lowerCAmelCase__ , )
with self.subTest('JIT Enabled' ):
SCREAMING_SNAKE_CASE = decode_jitted(**lowerCAmelCase__ ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE = decode_jitted(**lowerCAmelCase__ ).to_tuple()
self.assertEqual(len(lowerCAmelCase__ ) , len(lowerCAmelCase__ ) )
for jitted_output, output in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def __A ( self ) -> Union[str, Any]:
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class_name.from_pretrained('google/pegasus-large' , from_pt=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = np.ones((1, 1) )
SCREAMING_SNAKE_CASE = model(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
@slow
def __A ( self ) -> Any:
SCREAMING_SNAKE_CASE = FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum' )
SCREAMING_SNAKE_CASE = PegasusTokenizer.from_pretrained('google/pegasus-xsum' )
SCREAMING_SNAKE_CASE = [
' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
]
SCREAMING_SNAKE_CASE = [
'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
]
SCREAMING_SNAKE_CASE = tokenizer(lowerCAmelCase__ , return_tensors='np' , truncation=lowerCAmelCase__ , max_length=512 , padding=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = model.generate(**lowerCAmelCase__ , num_beams=2 ).sequences
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )
assert tgt_text == decoded
| 247
| 1
|
import math
def lowerCamelCase_ ( UpperCamelCase_ ):
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
_a : Dict = f"""Input value of [number={number}] must be an integer"""
raise TypeError(UpperCamelCase_ )
if number < 1:
_a : Optional[Any] = f"""Input value of [number={number}] must be > 0"""
raise ValueError(UpperCamelCase_ )
elif number == 1:
return 3
elif number == 2:
return 5
else:
_a : Optional[int] = int(math.log(number // 3 , 2 ) ) + 2
_a : int = [3, 5]
_a : List[str] = 2
_a : Dict = 3
for block in range(1 , UpperCamelCase_ ):
for _ in range(UpperCamelCase_ ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
__UpperCAmelCase : List[str] = 0
try:
__UpperCAmelCase : str = proth(number)
except ValueError:
print(f'''ValueError: there is no {number}th Proth number''')
continue
print(f'''The {number}th Proth number: {value}''')
| 716
|
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__UpperCAmelCase : Any = 0B1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__UpperCAmelCase : Optional[Any] = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class lowerCamelCase :
def __init__( self : Tuple ) -> List[Any]:
_a : List[Any] = WATERMARK_BITS
_a : List[str] = WatermarkEncoder()
self.encoder.set_watermark('''bits''' , self.watermark )
def snake_case_ ( self : Dict , __snake_case : torch.FloatTensor ) -> Optional[Any]:
# can't encode images that are smaller than 256
if images.shape[-1] < 256:
return images
_a : Union[str, Any] = (255 * (images / 2 + 0.5)).cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
_a : Any = [self.encoder.encode(__snake_case , '''dwtDct''' ) for image in images]
_a : Optional[int] = torch.from_numpy(np.array(__snake_case ) ).permute(0 , 3 , 1 , 2 )
_a : Any = torch.clamp(2 * (images / 255 - 0.5) , min=-1.0 , max=1.0 )
return images
| 249
| 0
|
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
def lowercase__ ( self : int , __snake_case : List[str] ) -> Union[str, Any]:
_lowerCAmelCase = 3
_lowerCAmelCase = 2_50
_lowerCAmelCase = ids_tensor((batch_size, length) , __snake_case )
_lowerCAmelCase = torch.ones((batch_size, length) , device=__snake_case , dtype=torch.float ) / length
return input_ids, scores
def lowercase__ ( self : Optional[Any] ) -> Union[str, Any]:
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(5 )
_lowerCAmelCase = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=10 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(__snake_case , __snake_case ) )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(9 )
self.assertFalse(criteria(__snake_case , __snake_case ) )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(10 )
self.assertTrue(criteria(__snake_case , __snake_case ) )
def lowercase__ ( self : Any ) -> Union[str, Any]:
_lowerCAmelCase = MaxLengthCriteria(max_length=10 )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(5 )
self.assertFalse(criteria(__snake_case , __snake_case ) )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(9 )
self.assertFalse(criteria(__snake_case , __snake_case ) )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(10 )
self.assertTrue(criteria(__snake_case , __snake_case ) )
def lowercase__ ( self : Optional[int] ) -> int:
_lowerCAmelCase = MaxNewTokensCriteria(start_length=5 , max_new_tokens=5 )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(5 )
self.assertFalse(criteria(__snake_case , __snake_case ) )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(9 )
self.assertFalse(criteria(__snake_case , __snake_case ) )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(10 )
self.assertTrue(criteria(__snake_case , __snake_case ) )
_lowerCAmelCase = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length , 10 )
def lowercase__ ( self : int ) -> str:
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(5 )
_lowerCAmelCase = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(__snake_case , __snake_case ) )
_lowerCAmelCase = MaxTimeCriteria(max_time=0.1 , initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(__snake_case , __snake_case ) )
def lowercase__ ( self : Union[str, Any] ) -> Tuple:
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 10 )
with self.assertWarns(__snake_case ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 11 )
_lowerCAmelCase = validate_stopping_criteria(StoppingCriteriaList() , 11 )
self.assertEqual(len(__snake_case ) , 1 )
| 207
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ : Any =logging.get_logger(__name__)
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = DPTConfig()
if "large" in checkpoint_url:
_lowerCAmelCase = 10_24
_lowerCAmelCase = 40_96
_lowerCAmelCase = 24
_lowerCAmelCase = 16
_lowerCAmelCase = [5, 11, 17, 23]
_lowerCAmelCase = [2_56, 5_12, 10_24, 10_24]
_lowerCAmelCase = (1, 3_84, 3_84)
if "ade" in checkpoint_url:
_lowerCAmelCase = True
_lowerCAmelCase = 1_50
_lowerCAmelCase = """huggingface/label-files"""
_lowerCAmelCase = """ade20k-id2label.json"""
_lowerCAmelCase = json.load(open(cached_download(hf_hub_url(lowerCAmelCase , lowerCAmelCase , repo_type="""dataset""" ) ) , """r""" ) )
_lowerCAmelCase = {int(lowerCAmelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase = idalabel
_lowerCAmelCase = {v: k for k, v in idalabel.items()}
_lowerCAmelCase = [1, 1_50, 4_80, 4_80]
return config, expected_shape
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(lowerCAmelCase , lowerCAmelCase )
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
_lowerCAmelCase = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
_lowerCAmelCase = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
_lowerCAmelCase = name.replace("""patch_embed""" , """patch_embeddings""" )
if "pos_embed" in name:
_lowerCAmelCase = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
_lowerCAmelCase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
_lowerCAmelCase = name.replace("""proj""" , """projection""" )
if "blocks" in name:
_lowerCAmelCase = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
_lowerCAmelCase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
_lowerCAmelCase = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name:
_lowerCAmelCase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
_lowerCAmelCase = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
_lowerCAmelCase = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
_lowerCAmelCase = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
_lowerCAmelCase = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
_lowerCAmelCase = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
_lowerCAmelCase = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
_lowerCAmelCase = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
_lowerCAmelCase = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
_lowerCAmelCase = name.replace(f"refinenet{layer_idx}" , f"fusion_stage.layers.{abs(layer_idx-4 )}" )
if "out_conv" in name:
_lowerCAmelCase = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
_lowerCAmelCase = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
_lowerCAmelCase = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
_lowerCAmelCase = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
_lowerCAmelCase = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
_lowerCAmelCase = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
_lowerCAmelCase = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
_lowerCAmelCase = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
_lowerCAmelCase = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
_lowerCAmelCase = name.replace("""auxlayer""" , """auxiliary_head.head""" )
return name
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowerCAmelCase = state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight" )
_lowerCAmelCase = state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
_lowerCAmelCase = in_proj_weight[: config.hidden_size, :]
_lowerCAmelCase = in_proj_bias[: config.hidden_size]
_lowerCAmelCase = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowerCAmelCase = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_lowerCAmelCase = in_proj_weight[
-config.hidden_size :, :
]
_lowerCAmelCase = in_proj_bias[-config.hidden_size :]
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_lowerCAmelCase = Image.open(requests.get(lowerCAmelCase , stream=lowerCAmelCase ).raw )
return im
@torch.no_grad()
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = get_dpt_config(lowerCAmelCase )
# load original state_dict from URL
_lowerCAmelCase = torch.hub.load_state_dict_from_url(lowerCAmelCase , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(lowerCAmelCase )
# rename keys
for key in state_dict.copy().keys():
_lowerCAmelCase = state_dict.pop(lowerCAmelCase )
_lowerCAmelCase = val
# read in qkv matrices
read_in_q_k_v(lowerCAmelCase , lowerCAmelCase )
# load HuggingFace model
_lowerCAmelCase = DPTForSemanticSegmentation(lowerCAmelCase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase )
model.load_state_dict(lowerCAmelCase )
model.eval()
# Check outputs on an image
_lowerCAmelCase = 4_80 if """ade""" in checkpoint_url else 3_84
_lowerCAmelCase = DPTImageProcessor(size=lowerCAmelCase )
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(lowerCAmelCase , return_tensors="""pt""" )
# forward pass
_lowerCAmelCase = model(**lowerCAmelCase ).logits if """ade""" in checkpoint_url else model(**lowerCAmelCase ).predicted_depth
# Assert logits
_lowerCAmelCase = torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]] )
if "ade" in checkpoint_url:
_lowerCAmelCase = torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]] )
assert outputs.shape == torch.Size(lowerCAmelCase )
assert (
torch.allclose(outputs[0, 0, :3, :3] , lowerCAmelCase , atol=1e-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] , lowerCAmelCase )
)
Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
print(f"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(lowerCAmelCase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(lowerCAmelCase )
if push_to_hub:
print("""Pushing model to hub...""" )
model.push_to_hub(
repo_path_or_name=Path(lowerCAmelCase , lowerCAmelCase ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=lowerCAmelCase , )
image_processor.push_to_hub(
repo_path_or_name=Path(lowerCAmelCase , lowerCAmelCase ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=lowerCAmelCase , )
if __name__ == "__main__":
A__ : Optional[int] =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
type=str,
help='''URL of the original DPT checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
)
parser.add_argument(
'''--model_name''',
default='''dpt-large''',
type=str,
help='''Name of the model, in case you\'re pushing to the hub.''',
)
A__ : Optional[int] =parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 207
| 1
|
'''simple docstring'''
import math
import qiskit
def __snake_case ( UpperCAmelCase_ : int = 1 , UpperCAmelCase_ : int = 1 , UpperCAmelCase_ : int = 1 ):
if (
isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
or isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
or isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
):
raise TypeError("inputs must be integers." )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError("inputs must be positive." )
if (
(math.floor(UpperCAmelCase_ ) != input_a)
or (math.floor(UpperCAmelCase_ ) != input_a)
or (math.floor(UpperCAmelCase_ ) != carry_in)
):
raise ValueError("inputs must be exact integers." )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError("inputs must be less or equal to 2." )
# build registers
lowerCamelCase_ = qiskit.QuantumRegister(4 , "qr" )
lowerCamelCase_ = qiskit.ClassicalRegister(2 , "cr" )
# list the entries
lowerCamelCase_ = [input_a, input_a, carry_in]
lowerCamelCase_ = qiskit.QuantumCircuit(UpperCAmelCase_ , UpperCAmelCase_ )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(UpperCAmelCase_ ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(UpperCAmelCase_ ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(UpperCAmelCase_ ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , UpperCAmelCase_ ) # measure the last two qbits
lowerCamelCase_ = qiskit.Aer.get_backend("aer_simulator" )
lowerCamelCase_ = qiskit.execute(UpperCAmelCase_ , UpperCAmelCase_ , shots=1000 )
return job.result().get_counts(UpperCAmelCase_ )
if __name__ == "__main__":
print(f'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')
| 717
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ : List[Any] = {
"""configuration_owlvit""": [
"""OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""OwlViTConfig""",
"""OwlViTOnnxConfig""",
"""OwlViTTextConfig""",
"""OwlViTVisionConfig""",
],
"""processing_owlvit""": ["""OwlViTProcessor"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : int = ["""OwlViTFeatureExtractor"""]
a_ : Any = ["""OwlViTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Tuple = [
"""OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""OwlViTModel""",
"""OwlViTPreTrainedModel""",
"""OwlViTTextModel""",
"""OwlViTVisionModel""",
"""OwlViTForObjectDetection""",
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
a_ : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 445
| 0
|
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 78
|
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
SCREAMING_SNAKE_CASE_: Optional[int] =logging.get_logger(__name__) # pylint: disable=invalid-name
class __A ( UpperCamelCase__ ):
def __init__(self : Any , __a : CLIPSegForImageSegmentation , __a : CLIPSegProcessor , __a : AutoencoderKL , __a : CLIPTextModel , __a : CLIPTokenizer , __a : UNetaDConditionModel , __a : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , __a : StableDiffusionSafetyChecker , __a : CLIPImageProcessor , ):
super().__init__()
if hasattr(scheduler.config , "steps_offset" ) and scheduler.config.steps_offset != 1:
UpperCAmelCase_ = (
f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1" , "1.0.0" , __a , standard_warn=__a )
UpperCAmelCase_ = dict(scheduler.config )
UpperCAmelCase_ = 1
UpperCAmelCase_ = FrozenDict(__a )
if hasattr(scheduler.config , "skip_prk_steps" ) and scheduler.config.skip_prk_steps is False:
UpperCAmelCase_ = (
f"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
" Hub, it would be very nice if you could open a Pull request for the"
" `scheduler/scheduler_config.json` file"
)
deprecate("skip_prk_steps not set" , "1.0.0" , __a , standard_warn=__a )
UpperCAmelCase_ = dict(scheduler.config )
UpperCAmelCase_ = True
UpperCAmelCase_ = FrozenDict(__a )
if safety_checker is None:
logger.warning(
f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." )
self.register_modules(
segmentation_model=__a , segmentation_processor=__a , vae=__a , text_encoder=__a , tokenizer=__a , unet=__a , scheduler=__a , safety_checker=__a , feature_extractor=__a , )
def _lowercase (self : str , __a : Optional[Union[str, int]] = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase_ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__a )
def _lowercase (self : int ):
self.enable_attention_slicing(__a )
def _lowercase (self : Optional[Any] ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
UpperCAmelCase_ = torch.device("cuda" )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(__a , __a )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _lowercase (self : Optional[int] ):
if self.device != torch.device("meta" ) or not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(__a , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
def __call__(self : Dict , __a : Union[str, List[str]] , __a : Union[torch.FloatTensor, PIL.Image.Image] , __a : str , __a : int = 512 , __a : int = 512 , __a : int = 50 , __a : float = 7.5 , __a : Optional[Union[str, List[str]]] = None , __a : Optional[int] = 1 , __a : float = 0.0 , __a : Optional[torch.Generator] = None , __a : Optional[torch.FloatTensor] = None , __a : Optional[str] = "pil" , __a : bool = True , __a : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __a : int = 1 , **__a : int , ):
UpperCAmelCase_ = self.segmentation_processor(
text=[text] , images=[image] , padding="max_length" , return_tensors="pt" ).to(self.device )
UpperCAmelCase_ = self.segmentation_model(**__a )
UpperCAmelCase_ = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
UpperCAmelCase_ = self.numpy_to_pil(__a )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
UpperCAmelCase_ = StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=__a , image=__a , mask_image=__a , height=__a , width=__a , num_inference_steps=__a , guidance_scale=__a , negative_prompt=__a , num_images_per_prompt=__a , eta=__a , generator=__a , latents=__a , output_type=__a , return_dict=__a , callback=__a , callback_steps=__a , )
| 78
| 1
|
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_lowerCamelCase : List[Any] = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n journal = {Transactions of the Association for Computational Linguistics},\n volume = {4},\n year={2016},\n url = {https://www.aclweb.org/anthology/Q16-1029},\n pages = {401--415\n},\n@inproceedings{post-2018-call,\n title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n author = \"Post, Matt\",\n booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n month = oct,\n year = \"2018\",\n address = \"Belgium, Brussels\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W18-6319\",\n pages = \"186--191\",\n}\n"
_lowerCamelCase : str = "\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"
_lowerCamelCase : str = "\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n sources: list of source sentences where each sentence should be a string.\n predictions: list of predicted sentences where each sentence should be a string.\n references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n sari: sari score\n sacrebleu: sacrebleu score\n exact: exact score\n\nExamples:\n >>> sources=[\"About 95 species are currently accepted .\"]\n >>> predictions=[\"About 95 you now get in .\"]\n >>> references=[[\"About 95 species are currently known .\"]]\n >>> wiki_split = datasets.load_metric(\"wiki_split\")\n >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n >>> print(results)\n {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"
def lowercase_ ( _UpperCAmelCase ):
"""simple docstring"""
def remove_articles(_UpperCAmelCase ):
A_ : Tuple = re.compile(r'''\b(a|an|the)\b''' , re.UNICODE )
return re.sub(_UpperCAmelCase , ''' ''' , _UpperCAmelCase )
def white_space_fix(_UpperCAmelCase ):
return " ".join(text.split() )
def remove_punc(_UpperCAmelCase ):
A_ : Optional[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_UpperCAmelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase ) ) ) )
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
return int(normalize_answer(_UpperCAmelCase ) == normalize_answer(_UpperCAmelCase ) )
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : List[Any] = [any(compute_exact(_UpperCAmelCase , _UpperCAmelCase ) for ref in refs ) for pred, refs in zip(_UpperCAmelCase , _UpperCAmelCase )]
return (sum(_UpperCAmelCase ) / len(_UpperCAmelCase )) * 100
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : int = [rgram for rgrams in rgramslist for rgram in rgrams]
A_ : int = Counter(_UpperCAmelCase )
A_ : str = Counter(_UpperCAmelCase )
A_ : Union[str, Any] = Counter()
for sgram, scount in sgramcounter.items():
A_ : List[str] = scount * numref
A_ : Tuple = Counter(_UpperCAmelCase )
A_ : int = Counter()
for cgram, ccount in cgramcounter.items():
A_ : List[str] = ccount * numref
# KEEP
A_ : Tuple = sgramcounter_rep & cgramcounter_rep
A_ : Any = keepgramcounter_rep & rgramcounter
A_ : Union[str, Any] = sgramcounter_rep & rgramcounter
A_ : Optional[int] = 0
A_ : List[Any] = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
A_ : int = 1
A_ : List[Any] = 1
if len(_UpperCAmelCase ) > 0:
A_ : str = keeptmpscorea / len(_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
A_ : List[str] = keeptmpscorea / sum(keepgramcounterall_rep.values() )
A_ : str = 0
if keepscore_precision > 0 or keepscore_recall > 0:
A_ : Any = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
A_ : List[Any] = sgramcounter_rep - cgramcounter_rep
A_ : Tuple = delgramcounter_rep - rgramcounter
A_ : int = sgramcounter_rep - rgramcounter
A_ : Any = 0
A_ : List[Any] = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
A_ : Union[str, Any] = 1
if len(_UpperCAmelCase ) > 0:
A_ : Dict = deltmpscorea / len(_UpperCAmelCase )
# ADDITION
A_ : List[Any] = set(_UpperCAmelCase ) - set(_UpperCAmelCase )
A_ : List[Any] = set(_UpperCAmelCase ) & set(_UpperCAmelCase )
A_ : Tuple = set(_UpperCAmelCase ) - set(_UpperCAmelCase )
A_ : List[str] = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
A_ : Optional[Any] = 1
A_ : int = 1
if len(_UpperCAmelCase ) > 0:
A_ : Tuple = addtmpscore / len(_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
A_ : str = addtmpscore / len(_UpperCAmelCase )
A_ : Tuple = 0
if addscore_precision > 0 or addscore_recall > 0:
A_ : Tuple = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : Optional[Any] = len(_UpperCAmelCase )
A_ : List[str] = ssent.split(''' ''' )
A_ : List[Any] = csent.split(''' ''' )
A_ : int = []
A_ : Optional[int] = []
A_ : str = []
A_ : Dict = []
A_ : Optional[Any] = []
A_ : List[str] = []
A_ : int = []
A_ : Optional[Any] = []
A_ : Optional[Any] = []
A_ : Optional[Any] = []
for rsent in rsents:
A_ : List[Any] = rsent.split(''' ''' )
A_ : List[str] = []
A_ : Optional[int] = []
A_ : Dict = []
ragramslist.append(_UpperCAmelCase )
for i in range(0 , len(_UpperCAmelCase ) - 1 ):
if i < len(_UpperCAmelCase ) - 1:
A_ : int = ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(_UpperCAmelCase )
if i < len(_UpperCAmelCase ) - 2:
A_ : str = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(_UpperCAmelCase )
if i < len(_UpperCAmelCase ) - 3:
A_ : List[Any] = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(_UpperCAmelCase )
ragramslist.append(_UpperCAmelCase )
ragramslist.append(_UpperCAmelCase )
ragramslist.append(_UpperCAmelCase )
for i in range(0 , len(_UpperCAmelCase ) - 1 ):
if i < len(_UpperCAmelCase ) - 1:
A_ : List[Any] = sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(_UpperCAmelCase )
if i < len(_UpperCAmelCase ) - 2:
A_ : Optional[int] = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(_UpperCAmelCase )
if i < len(_UpperCAmelCase ) - 3:
A_ : Any = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(_UpperCAmelCase )
for i in range(0 , len(_UpperCAmelCase ) - 1 ):
if i < len(_UpperCAmelCase ) - 1:
A_ : Dict = cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(_UpperCAmelCase )
if i < len(_UpperCAmelCase ) - 2:
A_ : str = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(_UpperCAmelCase )
if i < len(_UpperCAmelCase ) - 3:
A_ : str = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(_UpperCAmelCase )
(A_) : Union[str, Any] = SARIngram(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
(A_) : Optional[Any] = SARIngram(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
(A_) : Dict = SARIngram(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
(A_) : Tuple = SARIngram(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
A_ : Union[str, Any] = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
A_ : int = sum([delascore, delascore, delascore, delascore] ) / 4
A_ : Optional[int] = sum([addascore, addascore, addascore, addascore] ) / 4
A_ : List[Any] = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase = True , _UpperCAmelCase = "13a" , _UpperCAmelCase = True ):
"""simple docstring"""
if lowercase:
A_ : Optional[Any] = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
A_ : List[str] = sacrebleu.metrics.bleu._get_tokenizer(_UpperCAmelCase )()(_UpperCAmelCase )
else:
A_ : Optional[Any] = sacrebleu.TOKENIZERS[tokenizer]()(_UpperCAmelCase )
elif tokenizer == "moses":
A_ : Tuple = sacremoses.MosesTokenizer().tokenize(_UpperCAmelCase , return_str=_UpperCAmelCase , escape=_UpperCAmelCase )
elif tokenizer == "penn":
A_ : Dict = sacremoses.MosesTokenizer().penn_tokenize(_UpperCAmelCase , return_str=_UpperCAmelCase )
else:
A_ : List[Any] = sentence
if not return_str:
A_ : str = normalized_sent.split()
return normalized_sent
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if not (len(_UpperCAmelCase ) == len(_UpperCAmelCase ) == len(_UpperCAmelCase )):
raise ValueError('''Sources length must match predictions and references lengths.''' )
A_ : List[Any] = 0
for src, pred, refs in zip(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
sari_score += SARIsent(normalize(_UpperCAmelCase ) , normalize(_UpperCAmelCase ) , [normalize(_UpperCAmelCase ) for sent in refs] )
A_ : List[Any] = sari_score / len(_UpperCAmelCase )
return 100 * sari_score
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase="exp" , _UpperCAmelCase=None , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=False , ):
"""simple docstring"""
A_ : List[Any] = len(references[0] )
if any(len(_UpperCAmelCase ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
A_ : str = [[refs[i] for refs in references] for i in range(_UpperCAmelCase )]
A_ : Any = sacrebleu.corpus_bleu(
_UpperCAmelCase , _UpperCAmelCase , smooth_method=_UpperCAmelCase , smooth_value=_UpperCAmelCase , force=_UpperCAmelCase , lowercase=_UpperCAmelCase , use_effective_order=_UpperCAmelCase , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class lowercase ( datasets.Metric):
def a_ ( self : List[str] ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=[
'''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
'''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
'''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
'''https://github.com/mjpost/sacreBLEU''',
] , reference_urls=[
'''https://www.aclweb.org/anthology/Q16-1029.pdf''',
'''https://github.com/mjpost/sacreBLEU''',
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def a_ ( self : str , _lowerCamelCase : int , _lowerCamelCase : str , _lowerCamelCase : Optional[Any] ):
"""simple docstring"""
A_ : List[Any] = {}
result.update({'''sari''': compute_sari(sources=lowerCamelCase__ , predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'''sacrebleu''': compute_sacrebleu(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'''exact''': compute_em(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
return result
| 718
|
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
_lowerCamelCase : Optional[Any] = {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class lowercase ( unittest.TestCase):
__lowerCAmelCase : Optional[Any] = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__lowerCAmelCase : str = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
__lowerCAmelCase : int = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
__lowerCAmelCase : List[Any] = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def a_ ( self : Optional[Any] , _lowerCamelCase : Any , _lowerCamelCase : List[Any] , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : int = ZeroShotClassificationPipeline(
model=_lowerCamelCase , tokenizer=_lowerCamelCase , candidate_labels=['''polics''', '''health'''] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def a_ ( self : List[str] , _lowerCamelCase : Any , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ : List[Any] = classifier('''Who are you voting for in 2020?''' , candidate_labels='''politics''' )
self.assertEqual(_lowerCamelCase , {'''sequence''': ANY(_lowerCamelCase ), '''labels''': [ANY(_lowerCamelCase )], '''scores''': [ANY(_lowerCamelCase )]} )
# No kwarg
A_ : Tuple = classifier('''Who are you voting for in 2020?''' , ['''politics'''] )
self.assertEqual(_lowerCamelCase , {'''sequence''': ANY(_lowerCamelCase ), '''labels''': [ANY(_lowerCamelCase )], '''scores''': [ANY(_lowerCamelCase )]} )
A_ : List[Any] = classifier('''Who are you voting for in 2020?''' , candidate_labels=['''politics'''] )
self.assertEqual(_lowerCamelCase , {'''sequence''': ANY(_lowerCamelCase ), '''labels''': [ANY(_lowerCamelCase )], '''scores''': [ANY(_lowerCamelCase )]} )
A_ : Union[str, Any] = classifier('''Who are you voting for in 2020?''' , candidate_labels='''politics, public health''' )
self.assertEqual(
_lowerCamelCase , {'''sequence''': ANY(_lowerCamelCase ), '''labels''': [ANY(_lowerCamelCase ), ANY(_lowerCamelCase )], '''scores''': [ANY(_lowerCamelCase ), ANY(_lowerCamelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['''scores'''] ) ) , 1.0 )
A_ : Tuple = classifier('''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health'''] )
self.assertEqual(
_lowerCamelCase , {'''sequence''': ANY(_lowerCamelCase ), '''labels''': [ANY(_lowerCamelCase ), ANY(_lowerCamelCase )], '''scores''': [ANY(_lowerCamelCase ), ANY(_lowerCamelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['''scores'''] ) ) , 1.0 )
A_ : List[str] = classifier(
'''Who are you voting for in 2020?''' , candidate_labels='''politics''' , hypothesis_template='''This text is about {}''' )
self.assertEqual(_lowerCamelCase , {'''sequence''': ANY(_lowerCamelCase ), '''labels''': [ANY(_lowerCamelCase )], '''scores''': [ANY(_lowerCamelCase )]} )
# https://github.com/huggingface/transformers/issues/13846
A_ : str = classifier(['''I am happy'''] , ['''positive''', '''negative'''] )
self.assertEqual(
_lowerCamelCase , [
{'''sequence''': ANY(_lowerCamelCase ), '''labels''': [ANY(_lowerCamelCase ), ANY(_lowerCamelCase )], '''scores''': [ANY(_lowerCamelCase ), ANY(_lowerCamelCase )]}
for i in range(1 )
] , )
A_ : str = classifier(['''I am happy''', '''I am sad'''] , ['''positive''', '''negative'''] )
self.assertEqual(
_lowerCamelCase , [
{'''sequence''': ANY(_lowerCamelCase ), '''labels''': [ANY(_lowerCamelCase ), ANY(_lowerCamelCase )], '''scores''': [ANY(_lowerCamelCase ), ANY(_lowerCamelCase )]}
for i in range(2 )
] , )
with self.assertRaises(_lowerCamelCase ):
classifier('''''' , candidate_labels='''politics''' )
with self.assertRaises(_lowerCamelCase ):
classifier(_lowerCamelCase , candidate_labels='''politics''' )
with self.assertRaises(_lowerCamelCase ):
classifier('''Who are you voting for in 2020?''' , candidate_labels='''''' )
with self.assertRaises(_lowerCamelCase ):
classifier('''Who are you voting for in 2020?''' , candidate_labels=_lowerCamelCase )
with self.assertRaises(_lowerCamelCase ):
classifier(
'''Who are you voting for in 2020?''' , candidate_labels='''politics''' , hypothesis_template='''Not formatting template''' , )
with self.assertRaises(_lowerCamelCase ):
classifier(
'''Who are you voting for in 2020?''' , candidate_labels='''politics''' , hypothesis_template=_lowerCamelCase , )
self.run_entailment_id(_lowerCamelCase )
def a_ ( self : Any , _lowerCamelCase : Pipeline ):
"""simple docstring"""
A_ : int = zero_shot_classifier.model.config
A_ : Dict = config.labelaid
A_ : Optional[int] = zero_shot_classifier.entailment_id
A_ : Optional[Any] = {'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
A_ : Union[str, Any] = {'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
A_ : int = {'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
A_ : Optional[Any] = {'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
A_ : List[Any] = original_labelaid
self.assertEqual(_lowerCamelCase , zero_shot_classifier.entailment_id )
@require_torch
def a_ ( self : List[Any] ):
"""simple docstring"""
A_ : List[str] = pipeline(
'''zero-shot-classification''' , model='''sshleifer/tiny-distilbert-base-cased-distilled-squad''' , framework='''pt''' , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
'''Who are you voting for in 2020?''' * 1_00 , candidate_labels=['''politics''', '''public health''', '''science'''] )
@require_torch
def a_ ( self : Dict ):
"""simple docstring"""
A_ : Optional[Any] = pipeline(
'''zero-shot-classification''' , model='''sshleifer/tiny-distilbert-base-cased-distilled-squad''' , framework='''pt''' , )
A_ : Optional[Any] = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(_lowerCamelCase ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''science''', '''public health''', '''politics'''],
'''scores''': [0.333, 0.333, 0.333],
} , )
@require_tf
def a_ ( self : Dict ):
"""simple docstring"""
A_ : List[str] = pipeline(
'''zero-shot-classification''' , model='''sshleifer/tiny-distilbert-base-cased-distilled-squad''' , framework='''tf''' , )
A_ : List[str] = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(_lowerCamelCase ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''science''', '''public health''', '''politics'''],
'''scores''': [0.333, 0.333, 0.333],
} , )
@slow
@require_torch
def a_ ( self : int ):
"""simple docstring"""
A_ : Union[str, Any] = pipeline('''zero-shot-classification''' , model='''roberta-large-mnli''' , framework='''pt''' )
A_ : str = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(_lowerCamelCase ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''politics''', '''public health''', '''science'''],
'''scores''': [0.976, 0.015, 0.009],
} , )
A_ : str = zero_shot_classifier(
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
''' English-to-German translation task, improving over the existing best results, including ensembles by'''
''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
''' large and limited training data.''' , candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision'''] , multi_label=_lowerCamelCase , )
self.assertEqual(
nested_simplify(_lowerCamelCase ) , {
'''sequence''': (
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
''' networks in an encoder-decoder configuration. The best performing models also connect the'''
''' encoder and decoder through an attention mechanism. We propose a new simple network'''
''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
''' superior in quality while being more parallelizable and requiring significantly less time to'''
''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
''' other tasks by applying it successfully to English constituency parsing both with large and'''
''' limited training data.'''
),
'''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
'''scores''': [0.817, 0.713, 0.018, 0.018],
} , )
@slow
@require_tf
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Tuple = pipeline('''zero-shot-classification''' , model='''roberta-large-mnli''' , framework='''tf''' )
A_ : str = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(_lowerCamelCase ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''politics''', '''public health''', '''science'''],
'''scores''': [0.976, 0.015, 0.009],
} , )
A_ : Tuple = zero_shot_classifier(
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
''' English-to-German translation task, improving over the existing best results, including ensembles by'''
''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
''' large and limited training data.''' , candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision'''] , multi_label=_lowerCamelCase , )
self.assertEqual(
nested_simplify(_lowerCamelCase ) , {
'''sequence''': (
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
''' networks in an encoder-decoder configuration. The best performing models also connect the'''
''' encoder and decoder through an attention mechanism. We propose a new simple network'''
''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
''' superior in quality while being more parallelizable and requiring significantly less time to'''
''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
''' other tasks by applying it successfully to English constituency parsing both with large and'''
''' limited training data.'''
),
'''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
'''scores''': [0.817, 0.713, 0.018, 0.018],
} , )
| 361
| 0
|
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DonutImageProcessor
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Dict=7 , SCREAMING_SNAKE_CASE__ : int=3 , SCREAMING_SNAKE_CASE__ : Optional[int]=1_8 , SCREAMING_SNAKE_CASE__ : int=3_0 , SCREAMING_SNAKE_CASE__ : Optional[Any]=4_0_0 , SCREAMING_SNAKE_CASE__ : Optional[int]=True , SCREAMING_SNAKE_CASE__ : int=None , SCREAMING_SNAKE_CASE__ : List[str]=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]=False , SCREAMING_SNAKE_CASE__ : Any=True , SCREAMING_SNAKE_CASE__ : Optional[int]=True , SCREAMING_SNAKE_CASE__ : Tuple=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE__ : Any=[0.5, 0.5, 0.5] , ) -> Tuple:
a_ : List[str] = parent
a_ : Union[str, Any] = batch_size
a_ : Optional[int] = num_channels
a_ : int = image_size
a_ : int = min_resolution
a_ : Optional[Any] = max_resolution
a_ : Any = do_resize
a_ : int = size if size is not None else {'''height''': 1_8, '''width''': 2_0}
a_ : int = do_thumbnail
a_ : Optional[Any] = do_align_axis
a_ : Tuple = do_pad
a_ : Dict = do_normalize
a_ : int = image_mean
a_ : Optional[int] = image_std
def SCREAMING_SNAKE_CASE ( self : int ) -> Optional[Any]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( a_ , unittest.TestCase ):
snake_case__ : str = DonutImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[str]:
a_ : Any = DonutImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Any:
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE ( self : int ) -> Any:
a_ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_snake_case , 'do_resize' ) )
self.assertTrue(hasattr(_snake_case , 'size' ) )
self.assertTrue(hasattr(_snake_case , 'do_thumbnail' ) )
self.assertTrue(hasattr(_snake_case , 'do_align_long_axis' ) )
self.assertTrue(hasattr(_snake_case , 'do_pad' ) )
self.assertTrue(hasattr(_snake_case , 'do_normalize' ) )
self.assertTrue(hasattr(_snake_case , 'image_mean' ) )
self.assertTrue(hasattr(_snake_case , 'image_std' ) )
def SCREAMING_SNAKE_CASE ( self : Any ) -> int:
a_ : int = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 1_8, 'width': 2_0} )
a_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 )
self.assertEqual(image_processor.size , {'height': 4_2, 'width': 4_2} )
# Previous config had dimensions in (width, height) order
a_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=(4_2, 8_4) )
self.assertEqual(image_processor.size , {'height': 8_4, 'width': 4_2} )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[Any]:
pass
@is_flaky()
def SCREAMING_SNAKE_CASE ( self : Any ) -> List[Any]:
# Initialize image_processing
a_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
a_ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=_snake_case )
for image in image_inputs:
self.assertIsInstance(_snake_case , Image.Image )
# Test not batched input
a_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
a_ : Tuple = image_processing(_snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[Any]:
# Initialize image_processing
a_ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
a_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_snake_case , numpify=_snake_case )
for image in image_inputs:
self.assertIsInstance(_snake_case , np.ndarray )
# Test not batched input
a_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
a_ : Union[str, Any] = image_processing(_snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Any:
# Initialize image_processing
a_ : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
a_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_snake_case , torchify=_snake_case )
for image in image_inputs:
self.assertIsInstance(_snake_case , torch.Tensor )
# Test not batched input
a_ : Any = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
a_ : Any = image_processing(_snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
| 570
|
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_UpperCAmelCase : Optional[int] = pytest.mark.integration
@pytest.mark.parametrize('''path''' ,['''paws''', '''csv'''] )
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> Dict:
inspect_dataset(UpperCamelCase ,UpperCamelCase )
_UpperCamelCase : Optional[Any] = path + '''.py'''
assert script_name in os.listdir(UpperCamelCase )
assert "__pycache__" not in os.listdir(UpperCamelCase )
@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning''' )
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning''' )
@pytest.mark.parametrize('''path''' ,['''accuracy'''] )
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> int:
inspect_metric(UpperCamelCase ,UpperCamelCase )
_UpperCamelCase : List[str] = path + '''.py'''
assert script_name in os.listdir(UpperCamelCase )
assert "__pycache__" not in os.listdir(UpperCamelCase )
@pytest.mark.parametrize(
'''path, config_name, expected_splits''' ,[
('''squad''', '''plain_text''', ['''train''', '''validation''']),
('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
] ,)
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> int:
_UpperCamelCase : List[str] = get_dataset_config_info(UpperCamelCase ,config_name=UpperCamelCase )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
'''path, config_name, expected_exception''' ,[
('''paws''', None, ValueError),
] ,)
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> List[str]:
with pytest.raises(UpperCamelCase ):
get_dataset_config_info(UpperCamelCase ,config_name=UpperCamelCase )
@pytest.mark.parametrize(
'''path, expected''' ,[
('''squad''', '''plain_text'''),
('''acronym_identification''', '''default'''),
('''lhoestq/squad''', '''plain_text'''),
('''lhoestq/test''', '''default'''),
('''lhoestq/demo1''', '''lhoestq--demo1'''),
('''dalle-mini/wit''', '''dalle-mini--wit'''),
] ,)
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> Union[str, Any]:
_UpperCamelCase : int = get_dataset_config_names(UpperCamelCase )
assert expected in config_names
@pytest.mark.parametrize(
'''path, expected_configs, expected_splits_in_first_config''' ,[
('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
] ,)
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> Dict:
_UpperCamelCase : Dict = get_dataset_infos(UpperCamelCase )
assert list(infos.keys() ) == expected_configs
_UpperCamelCase : Dict = expected_configs[0]
assert expected_config in infos
_UpperCamelCase : Any = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
'''path, expected_config, expected_splits''' ,[
('''squad''', '''plain_text''', ['''train''', '''validation''']),
('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
] ,)
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> Union[str, Any]:
_UpperCamelCase : List[Any] = get_dataset_infos(UpperCamelCase )
assert expected_config in infos
_UpperCamelCase : Union[str, Any] = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
'''path, config_name, expected_exception''' ,[
('''paws''', None, ValueError),
] ,)
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> List[Any]:
with pytest.raises(UpperCamelCase ):
get_dataset_split_names(UpperCamelCase ,config_name=UpperCamelCase )
| 683
| 0
|
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Any = AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""" )
lowercase_ : List[str] = AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""" )
model.to(lowercase_ )
from datasets import load_dataset
lowercase_ : Any = load_dataset("""nielsr/rvlcdip-demo""" )
lowercase_ : Optional[Any] = dataset["""train"""][0]["""image"""].convert("""RGB""" )
lowercase_ : str = image_processor(lowercase_ , return_tensors="""pt""" ).to(lowercase_ )
# forward pass
with torch.no_grad():
lowercase_ : Optional[Any] = model(**lowercase_ )
lowercase_ : Dict = outputs.logits
lowercase_ : Optional[int] = torch.Size((1, 16) )
self.assertEqual(logits.shape , lowercase_ )
lowercase_ : List[str] = torch.tensor(
[-0.41_58, -0.40_92, -0.43_47] , device=lowercase_ , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , lowercase_ , atol=1E-4 ) )
| 705
|
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __magic_name__ :
def __init__( self : int , lowercase_ : Optional[Any] , lowercase_ : List[Any]=13 , lowercase_ : List[str]=10 , lowercase_ : Union[str, Any]=3 , lowercase_ : str=2 , lowercase_ : Optional[Any]=2 , lowercase_ : int=True , lowercase_ : List[Any]=True , lowercase_ : Union[str, Any]=32 , lowercase_ : Union[str, Any]=5 , lowercase_ : str=4 , lowercase_ : Dict=37 , lowercase_ : Tuple="gelu" , lowercase_ : int=0.1 , lowercase_ : Optional[Any]=0.1 , lowercase_ : Any=10 , lowercase_ : Tuple=0.02 , lowercase_ : Any="divided_space_time" , lowercase_ : Tuple=None , ):
lowercase_ : int = parent
lowercase_ : str = batch_size
lowercase_ : List[str] = image_size
lowercase_ : str = num_channels
lowercase_ : List[Any] = patch_size
lowercase_ : Optional[Any] = num_frames
lowercase_ : Dict = is_training
lowercase_ : int = use_labels
lowercase_ : List[str] = hidden_size
lowercase_ : Dict = num_hidden_layers
lowercase_ : Dict = num_attention_heads
lowercase_ : Any = intermediate_size
lowercase_ : Optional[int] = hidden_act
lowercase_ : Optional[Any] = hidden_dropout_prob
lowercase_ : List[Any] = attention_probs_dropout_prob
lowercase_ : Any = attention_type
lowercase_ : Union[str, Any] = initializer_range
lowercase_ : List[str] = scope
lowercase_ : Optional[int] = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
lowercase_ : Dict = (image_size // patch_size) ** 2
lowercase_ : List[Any] = (num_frames) * self.num_patches_per_frame + 1
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Optional[Any] = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
lowercase_ : int = None
if self.use_labels:
lowercase_ : Optional[int] = ids_tensor([self.batch_size] , self.num_labels )
lowercase_ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : int = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , attention_type=self.attention_type , )
lowercase_ : Any = self.num_labels
return config
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : List[str] ):
lowercase_ : Optional[Any] = TimesformerModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : int = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : str ):
lowercase_ : Dict = TimesformerForVideoClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : int = model(lowercase_ )
# verify the logits shape
lowercase_ : List[Any] = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : List[str] = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ : int = config_and_inputs
lowercase_ : List[Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
UpperCamelCase__ = (
{'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
if is_torch_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = TimesformerModelTester(self )
lowercase_ : Union[str, Any] = ConfigTester(
self , config_class=lowercase_ , has_text_modality=lowercase_ , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Tuple=False ):
lowercase_ : List[Any] = copy.deepcopy(lowercase_ )
if return_labels:
if model_class in get_values(lowercase_ ):
lowercase_ : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowercase_ )
return inputs_dict
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""TimeSformer does not use inputs_embeds""" )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ , lowercase_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : str = model_class(lowercase_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase_ : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase_ , nn.Linear ) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ , lowercase_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : Dict = model_class(lowercase_ )
lowercase_ : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ : Union[str, Any] = [*signature.parameters.keys()]
lowercase_ : str = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Any ):
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : Any = TimesformerModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
if not self.has_attentions:
pass
else:
lowercase_ , lowercase_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ : List[str] = True
for model_class in self.all_model_classes:
lowercase_ : str = self.model_tester.seq_length
lowercase_ : int = self.model_tester.num_frames
lowercase_ : int = True
lowercase_ : Any = False
lowercase_ : str = True
lowercase_ : int = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
lowercase_ : List[Any] = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
lowercase_ : List[str] = outputs.attentions
self.assertEqual(len(lowercase_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase_ : List[str] = True
lowercase_ : str = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
lowercase_ : Dict = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
lowercase_ : int = outputs.attentions
self.assertEqual(len(lowercase_ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
lowercase_ : Optional[Any] = len(lowercase_ )
# Check attention is always last and order is fine
lowercase_ : Tuple = True
lowercase_ : Dict = True
lowercase_ : str = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
lowercase_ : str = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
self.assertEqual(out_len + 1 , len(lowercase_ ) )
lowercase_ : Optional[Any] = outputs.attentions
self.assertEqual(len(lowercase_ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
def check_hidden_states_output(lowercase_ : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : Dict ):
lowercase_ : List[str] = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
lowercase_ : Optional[Any] = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
lowercase_ : Dict = outputs.hidden_states
lowercase_ : List[Any] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(lowercase_ ) , lowercase_ )
lowercase_ : List[Any] = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowercase_ , lowercase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : List[str] = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ : Optional[int] = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
def lowerCamelCase ( ) -> Optional[int]:
lowercase_ : List[str] = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
lowercase_ : List[Any] = np.load(UpperCAmelCase__ )
return list(UpperCAmelCase__ )
@require_torch
@require_vision
class __magic_name__ ( unittest.TestCase):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : str ):
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = TimesformerForVideoClassification.from_pretrained("""facebook/timesformer-base-finetuned-k400""" ).to(
lowercase_ )
lowercase_ : Optional[Any] = self.default_image_processor
lowercase_ : Any = prepare_video()
lowercase_ : Optional[int] = image_processor(video[:8] , return_tensors="""pt""" ).to(lowercase_ )
# forward pass
with torch.no_grad():
lowercase_ : Optional[Any] = model(**lowercase_ )
# verify the logits
lowercase_ : Any = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , lowercase_ )
lowercase_ : int = torch.tensor([-0.30_16, -0.77_13, -0.42_05] ).to(lowercase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase_ , atol=1E-4 ) )
| 30
| 0
|
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase ) -> list[int]:
'''simple docstring'''
return [ord(__UpperCAmelCase ) - 96 for elem in plain]
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
return "".join(chr(elem + 96 ) for elem in encoded )
def __magic_name__ ( ) -> None:
'''simple docstring'''
snake_case_ = encode(input('''-> ''' ).strip().lower() )
print('''Encoded: ''', __UpperCAmelCase )
print('''Decoded:''', decode(__UpperCAmelCase ) )
if __name__ == "__main__":
main()
| 640
|
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
snake_case_ = "linear"
snake_case_ = "cosine"
snake_case_ = "cosine_with_restarts"
snake_case_ = "polynomial"
snake_case_ = "constant"
snake_case_ = "constant_with_warmup"
snake_case_ = "piecewise_constant"
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = -1 ) -> Dict:
'''simple docstring'''
return LambdaLR(__UpperCAmelCase, lambda __UpperCAmelCase : 1, last_epoch=__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = -1 ) -> Dict:
'''simple docstring'''
def lr_lambda(__UpperCAmelCase ):
if current_step < num_warmup_steps:
return float(__UpperCAmelCase ) / float(max(1.0, __UpperCAmelCase ) )
return 1.0
return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, last_epoch=__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = -1 ) -> Any:
'''simple docstring'''
snake_case_ = {}
snake_case_ = step_rules.split(''',''' )
for rule_str in rule_list[:-1]:
snake_case_ ,snake_case_ = rule_str.split(''':''' )
snake_case_ = int(__UpperCAmelCase )
snake_case_ = float(__UpperCAmelCase )
snake_case_ = value
snake_case_ = float(rule_list[-1] )
def create_rules_function(__UpperCAmelCase, __UpperCAmelCase ):
def rule_func(__UpperCAmelCase ) -> float:
snake_case_ = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(__UpperCAmelCase ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
snake_case_ = create_rules_function(__UpperCAmelCase, __UpperCAmelCase )
return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, last_epoch=__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=-1 ) -> List[Any]:
'''simple docstring'''
def lr_lambda(__UpperCAmelCase ):
if current_step < num_warmup_steps:
return float(__UpperCAmelCase ) / float(max(1, __UpperCAmelCase ) )
return max(
0.0, float(num_training_steps - current_step ) / float(max(1, num_training_steps - num_warmup_steps ) ) )
return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.5, __UpperCAmelCase = -1 ) -> Optional[Any]:
'''simple docstring'''
def lr_lambda(__UpperCAmelCase ):
if current_step < num_warmup_steps:
return float(__UpperCAmelCase ) / float(max(1, __UpperCAmelCase ) )
snake_case_ = float(current_step - num_warmup_steps ) / float(max(1, num_training_steps - num_warmup_steps ) )
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(__UpperCAmelCase ) * 2.0 * progress )) )
return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 1, __UpperCAmelCase = -1 ) -> Dict:
'''simple docstring'''
def lr_lambda(__UpperCAmelCase ):
if current_step < num_warmup_steps:
return float(__UpperCAmelCase ) / float(max(1, __UpperCAmelCase ) )
snake_case_ = float(current_step - num_warmup_steps ) / float(max(1, num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(__UpperCAmelCase ) * progress) % 1.0) )) )
return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=1e-7, __UpperCAmelCase=1.0, __UpperCAmelCase=-1 ) -> str:
'''simple docstring'''
snake_case_ = optimizer.defaults['''lr''']
if not (lr_init > lr_end):
raise ValueError(F"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})" )
def lr_lambda(__UpperCAmelCase ):
if current_step < num_warmup_steps:
return float(__UpperCAmelCase ) / float(max(1, __UpperCAmelCase ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
snake_case_ = lr_init - lr_end
snake_case_ = num_training_steps - num_warmup_steps
snake_case_ = 1 - (current_step - num_warmup_steps) / decay_steps
snake_case_ = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase )
a : Tuple = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = 1, __UpperCAmelCase = 1.0, __UpperCAmelCase = -1, ) -> int:
'''simple docstring'''
snake_case_ = SchedulerType(__UpperCAmelCase )
snake_case_ = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(__UpperCAmelCase, last_epoch=__UpperCAmelCase )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(__UpperCAmelCase, step_rules=__UpperCAmelCase, last_epoch=__UpperCAmelCase )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F"{name} requires `num_warmup_steps`, please provide that argument." )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(__UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, last_epoch=__UpperCAmelCase )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F"{name} requires `num_training_steps`, please provide that argument." )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
__UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, num_training_steps=__UpperCAmelCase, num_cycles=__UpperCAmelCase, last_epoch=__UpperCAmelCase, )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
__UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, num_training_steps=__UpperCAmelCase, power=__UpperCAmelCase, last_epoch=__UpperCAmelCase, )
return schedule_func(
__UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, num_training_steps=__UpperCAmelCase, last_epoch=__UpperCAmelCase )
| 640
| 1
|
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any]=1_3 , lowerCAmelCase_ : List[str]=7 , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : List[Any]=9_9 , lowerCAmelCase_ : Tuple=3_2 , lowerCAmelCase_ : Any=5 , lowerCAmelCase_ : Any=4 , lowerCAmelCase_ : Dict=3_7 , lowerCAmelCase_ : Tuple="gelu" , lowerCAmelCase_ : List[Any]=0.1 , lowerCAmelCase_ : str=0.1 , lowerCAmelCase_ : str=5_1_2 , lowerCAmelCase_ : Dict=1_6 , lowerCAmelCase_ : Tuple=2 , lowerCAmelCase_ : Optional[int]=0.02 , lowerCAmelCase_ : Optional[int]=4 , ) -> List[Any]:
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = seq_length
__lowerCAmelCase = is_training
__lowerCAmelCase = use_attention_mask
__lowerCAmelCase = use_token_type_ids
__lowerCAmelCase = use_labels
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = type_vocab_size
__lowerCAmelCase = type_sequence_label_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = num_choices
def lowercase ( self : List[str] ) -> Optional[int]:
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase = None
if self.use_attention_mask:
__lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase = None
if self.use_token_type_ids:
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowercase ( self : Dict ) -> Dict:
__lowerCAmelCase = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = config_and_inputs
__lowerCAmelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def lowercase ( self : Tuple ) -> int:
__lowerCAmelCase = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = config_and_inputs
__lowerCAmelCase = True
__lowerCAmelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class _UpperCAmelCase ( _UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
a_ = True
a_ = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowercase ( self : Any ) -> Dict:
__lowerCAmelCase = FlaxRobertaPreLayerNormModelTester(self )
@slow
def lowercase ( self : Tuple ) -> List[str]:
for model_class_name in self.all_model_classes:
__lowerCAmelCase = model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCAmelCase_ )
__lowerCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCAmelCase_ )
@require_flax
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase ( self : int ) -> int:
__lowerCAmelCase = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCAmelCase_ )
__lowerCAmelCase = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] , dtype=jnp.intaa )
__lowerCAmelCase = model(lowerCAmelCase_ )[0]
__lowerCAmelCase = [1, 1_1, 5_0_2_6_5]
self.assertEqual(list(output.shape ) , lowerCAmelCase_ )
# compare the actual values for a slice.
__lowerCAmelCase = np.array(
[[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCAmelCase_ , atol=1e-4 ) )
@slow
def lowercase ( self : Union[str, Any] ) -> Tuple:
__lowerCAmelCase = FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCAmelCase_ )
__lowerCAmelCase = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] , dtype=jnp.intaa )
__lowerCAmelCase = model(lowerCAmelCase_ )[0]
# compare the actual values for a slice.
__lowerCAmelCase = np.array(
[[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCAmelCase_ , atol=1e-4 ) )
| 709
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : List[Any] = '▁'
_snake_case : Tuple = {'vocab_file': 'spiece.model'}
_snake_case : Optional[int] = {
'vocab_file': {
'google/reformer-crime-and-punishment': (
'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
)
}
}
_snake_case : Union[str, Any] = {
'google/reformer-crime-and-punishment': 524288,
}
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self : Any , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Any="</s>" , lowerCAmelCase_ : Any="<unk>" , lowerCAmelCase_ : List[Any]=[] , lowerCAmelCase_ : Optional[Dict[str, Any]] = None , **lowerCAmelCase_ : Any , ) -> None:
__lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , additional_special_tokens=lowerCAmelCase_ , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase_ , )
__lowerCAmelCase = vocab_file
__lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCAmelCase_ )
@property
def lowercase ( self : Any ) -> Any:
return self.sp_model.get_piece_size()
def lowercase ( self : int ) -> Dict[str, int]:
__lowerCAmelCase = {self.convert_ids_to_tokens(lowerCAmelCase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : str ) -> Any:
__lowerCAmelCase = self.__dict__.copy()
__lowerCAmelCase = None
return state
def __setstate__( self : Dict , lowerCAmelCase_ : str ) -> str:
__lowerCAmelCase = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
__lowerCAmelCase = {}
__lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowercase ( self : int , lowerCAmelCase_ : str ) -> List[str]:
return self.sp_model.encode(lowerCAmelCase_ , out_type=lowerCAmelCase_ )
def lowercase ( self : Dict , lowerCAmelCase_ : Dict ) -> Tuple:
return self.sp_model.piece_to_id(lowerCAmelCase_ )
def lowercase ( self : Any , lowerCAmelCase_ : int ) -> Optional[int]:
if index < self.sp_model.get_piece_size():
__lowerCAmelCase = self.sp_model.IdToPiece(lowerCAmelCase_ )
return token
def lowercase ( self : Optional[int] , lowerCAmelCase_ : Tuple ) -> List[str]:
__lowerCAmelCase = []
__lowerCAmelCase = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(lowerCAmelCase_ ) + token
__lowerCAmelCase = []
else:
current_sub_tokens.append(lowerCAmelCase_ )
out_string += self.sp_model.decode(lowerCAmelCase_ )
return out_string.strip()
def lowercase ( self : Tuple , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(lowerCAmelCase_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCAmelCase = os.path.join(
lowerCAmelCase_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCAmelCase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCAmelCase_ , 'wb' ) as fi:
__lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase_ )
return (out_vocab_file,)
| 421
| 0
|
import functools
def __UpperCAmelCase ( lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[str] ) -> int:
"""simple docstring"""
if not isinstance(a_ , a_ ) or not all(isinstance(a_ , a_ ) for day in days ):
raise ValueError('The parameter days should be a list of integers' )
if len(a_ ) != 3 or not all(isinstance(a_ , a_ ) for cost in costs ):
raise ValueError('The parameter costs should be a list of three integers' )
if len(a_ ) == 0:
return 0
if min(a_ ) <= 0:
raise ValueError('All days elements should be greater than 0' )
if max(a_ ) >= 3_66:
raise ValueError('All days elements should be less than 366' )
SCREAMING_SNAKE_CASE_ : Optional[int] = set(a_ )
@functools.cache
def dynamic_programming(lowerCamelCase_ : List[Any] ) -> int:
if index > 3_65:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 105
|
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class a_ :
'''simple docstring'''
def __init__( self , lowercase_ , lowercase_=1_3 , lowercase_=7 , lowercase_=True , lowercase_=True , lowercase_=True , lowercase_=True , lowercase_=9_9 , lowercase_=6_4 , lowercase_=5 , lowercase_=4 , lowercase_=3_7 , lowercase_="gelu" , lowercase_=0.1 , lowercase_=0.1 , lowercase_=5_1_2 , lowercase_=1_6 , lowercase_=2 , lowercase_=0.02 , lowercase_=3 , lowercase_=4 , lowercase_=None , ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ = parent
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = seq_length
lowerCAmelCase_ = is_training
lowerCAmelCase_ = use_input_mask
lowerCAmelCase_ = use_token_type_ids
lowerCAmelCase_ = use_labels
lowerCAmelCase_ = vocab_size
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = hidden_dropout_prob
lowerCAmelCase_ = attention_probs_dropout_prob
lowerCAmelCase_ = max_position_embeddings
lowerCAmelCase_ = type_vocab_size
lowerCAmelCase_ = type_sequence_label_size
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = num_labels
lowerCAmelCase_ = num_choices
lowerCAmelCase_ = scope
lowerCAmelCase_ = vocab_size - 1
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase_ = None
if self.use_input_mask:
lowerCAmelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ = None
if self.use_labels:
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase_ = self.get_config()
return config, input_ids, input_mask, token_labels
def _lowercase ( self ) -> Optional[int]:
'''simple docstring'''
return GPTNeoXConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowercase_ , initializer_range=self.initializer_range , pad_token_id=self.pad_token_id , )
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = self.prepare_config_and_inputs()
lowerCAmelCase_ = True
return config, input_ids, input_mask, token_labels
def _lowercase ( self , lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
lowerCAmelCase_ = GPTNeoXModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowerCAmelCase_ = model(lowercase_ , attention_mask=lowercase_ )
lowerCAmelCase_ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self , lowercase_ , lowercase_ , lowercase_ ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ = True
lowerCAmelCase_ = GPTNeoXModel(lowercase_ )
model.to(lowercase_ )
model.eval()
lowerCAmelCase_ = model(lowercase_ , attention_mask=lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
lowerCAmelCase_ = GPTNeoXForCausalLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowerCAmelCase_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ = self.num_labels
lowerCAmelCase_ = GPTNeoXForQuestionAnswering(lowercase_ )
model.to(lowercase_ )
model.eval()
lowerCAmelCase_ = model(lowercase_ , attention_mask=lowercase_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowercase ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ = self.num_labels
lowerCAmelCase_ = GPTNeoXForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
lowerCAmelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ = self.num_labels
lowerCAmelCase_ = GPTNeoXForTokenClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
lowerCAmelCase_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self , lowercase_ , lowercase_ , lowercase_ ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ = True
lowerCAmelCase_ = GPTNeoXForCausalLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
# first forward pass
lowerCAmelCase_ = model(lowercase_ , attention_mask=lowercase_ , use_cache=lowercase_ )
lowerCAmelCase_ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCAmelCase_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCAmelCase_ = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowerCAmelCase_ = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCAmelCase_ = torch.cat([input_mask, next_mask] , dim=-1 )
lowerCAmelCase_ = model(lowercase_ , attention_mask=lowercase_ , output_hidden_states=lowercase_ )
lowerCAmelCase_ = output_from_no_past['hidden_states'][0]
lowerCAmelCase_ = model(
lowercase_ , attention_mask=lowercase_ , past_key_values=lowercase_ , output_hidden_states=lowercase_ , )['hidden_states'][0]
# select random slice
lowerCAmelCase_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCAmelCase_ = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCAmelCase_ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowercase_ , lowercase_ , atol=1e-3 ) )
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = config_and_inputs
lowerCAmelCase_ = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( a_ , a_ , a_ , unittest.TestCase ):
'''simple docstring'''
__a: str = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
__a: Optional[Any] = (GPTNeoXForCausalLM,) if is_torch_available() else ()
__a: List[str] = (
{
'''feature-extraction''': GPTNeoXModel,
'''question-answering''': GPTNeoXForQuestionAnswering,
'''text-classification''': GPTNeoXForSequenceClassification,
'''text-generation''': GPTNeoXForCausalLM,
'''token-classification''': GPTNeoXForTokenClassification,
'''zero-shot''': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
__a: Any = False
__a: Tuple = False
__a: List[Any] = False
__a: Optional[int] = False
def _lowercase ( self ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ = GPTNeoXModelTester(self )
lowerCAmelCase_ = ConfigTester(self , config_class=lowercase_ , hidden_size=6_4 , num_attention_heads=8 )
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def _lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowercase_ , lowercase_ , lowercase_ )
def _lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowercase_ , lowercase_ , lowercase_ )
def _lowercase ( self ) -> str:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_decoder()
lowerCAmelCase_ = None
self.model_tester.create_and_check_model_as_decoder(lowercase_ , lowercase_ , lowercase_ )
def _lowercase ( self ) -> Any:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase_ , lowercase_ , lowercase_ )
def _lowercase ( self ) -> int:
'''simple docstring'''
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowercase_ )
def _lowercase ( self ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase_ )
def _lowercase ( self ) -> str:
'''simple docstring'''
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase_ )
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase_ )
@unittest.skip(reason='Feed forward chunking is not implemented' )
def _lowercase ( self ) -> str:
'''simple docstring'''
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def _lowercase ( self , lowercase_ ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ = ids_tensor([1, 1_0] , config.vocab_size )
lowerCAmelCase_ = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase_ = GPTNeoXModel(lowercase_ )
original_model.to(lowercase_ )
original_model.eval()
lowerCAmelCase_ = original_model(lowercase_ ).last_hidden_state
lowerCAmelCase_ = original_model(lowercase_ ).last_hidden_state
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase_ = {'type': scaling_type, 'factor': 10.0}
lowerCAmelCase_ = GPTNeoXModel(lowercase_ )
scaled_model.to(lowercase_ )
scaled_model.eval()
lowerCAmelCase_ = scaled_model(lowercase_ ).last_hidden_state
lowerCAmelCase_ = scaled_model(lowercase_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowercase_ , lowercase_ , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(lowercase_ , lowercase_ , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowercase_ , lowercase_ , atol=1e-5 ) )
@require_torch
class a_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowercase ( self ) -> Any:
'''simple docstring'''
lowerCAmelCase_ = AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped' )
for checkpointing in [True, False]:
lowerCAmelCase_ = GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped' )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(lowercase_ )
lowerCAmelCase_ = tokenizer('My favorite food is' , return_tensors='pt' ).to(lowercase_ )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
lowerCAmelCase_ = 'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'
lowerCAmelCase_ = model.generate(**lowercase_ , do_sample=lowercase_ , max_new_tokens=2_0 )
lowerCAmelCase_ = tokenizer.batch_decode(lowercase_ )[0]
self.assertEqual(lowercase_ , lowercase_ )
| 318
| 0
|
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
_lowerCAmelCase = logging.getLogger(__name__)
torch.set_grad_enabled(False)
_lowerCAmelCase = 'cuda' if torch.cuda.is_available() else 'cpu'
def a__ ( a , a=1_0_0 , a=" " ) -> List[str]:
A_ : Optional[int] = text.split(a )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(a ) , a )]
def a__ ( a ) -> dict:
A_ , A_ : Optional[Any] = [], []
for title, text in zip(documents['''title'''] , documents['''text'''] ):
if text is not None:
for passage in split_text(a ):
titles.append(title if title is not None else '''''' )
texts.append(a )
return {"title": titles, "text": texts}
def a__ ( a , a , a ) -> dict:
A_ : Optional[Any] = ctx_tokenizer(
documents['''title'''] , documents['''text'''] , truncation=a , padding='''longest''' , return_tensors='''pt''' )['''input_ids''']
A_ : List[Any] = ctx_encoder(input_ids.to(device=a ) , return_dict=a ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def a__ ( a , a , a , ) -> Optional[int]:
######################################
logger.info('''Step 1 - Create the dataset''' )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
A_ : Union[str, Any] = load_dataset(
'''csv''' , data_files=[rag_example_args.csv_path] , split='''train''' , delimiter='''\t''' , column_names=['''title''', '''text'''] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
A_ : int = dataset.map(a , batched=a , num_proc=processing_args.num_proc )
# And compute the embeddings
A_ : Tuple = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=a )
A_ : Tuple = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
A_ : Dict = Features(
{'''text''': Value('''string''' ), '''title''': Value('''string''' ), '''embeddings''': Sequence(Value('''float32''' ) )} ) # optional, save as float32 instead of float64 to save space
A_ : Union[str, Any] = dataset.map(
partial(a , ctx_encoder=a , ctx_tokenizer=a ) , batched=a , batch_size=processing_args.batch_size , features=a , )
# And finally save your dataset
A_ : Any = os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset''' )
dataset.save_to_disk(a )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info('''Step 2 - Index the dataset''' )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
A_ : Optional[Any] = faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index('''embeddings''' , custom_index=a )
# And save the index
A_ : int = os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset_hnsw_index.faiss''' )
dataset.get_index('''embeddings''' ).save(a )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class __UpperCAmelCase:
"""simple docstring"""
__magic_name__ = field(
default=str(Path(A__ ).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv""" ) , metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""} , )
__magic_name__ = field(
default=A__ , metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""} , )
__magic_name__ = field(
default="""facebook/rag-sequence-nq""" , metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""} , )
__magic_name__ = field(
default="""facebook/dpr-ctx_encoder-multiset-base""" , metadata={
"""help""": (
"""The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"""
""" 'facebook/dpr-ctx_encoder-multiset-base'"""
)
} , )
__magic_name__ = field(
default=str(Path(A__ ).parent / """test_run""" / """dummy-kb""" ) , metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""} , )
@dataclass
class __UpperCAmelCase:
"""simple docstring"""
__magic_name__ = field(
default=A__ , metadata={
"""help""": """The number of processes to use to split the documents into passages. Default is single process."""
} , )
__magic_name__ = field(
default=16 , metadata={
"""help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
} , )
@dataclass
class __UpperCAmelCase:
"""simple docstring"""
__magic_name__ = field(
default=768 , metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""} , )
__magic_name__ = field(
default=128 , metadata={
"""help""": (
"""The number of bi-directional links created for every new element during the HNSW index construction."""
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
_lowerCAmelCase = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
_lowerCAmelCase = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 236
|
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : str = 10
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : Optional[Any] = [1, 2, 3, 4]
A_ : Union[str, Any] = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(__magic_name__ , self.block_size , 0 ) , __magic_name__ )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : List[Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
A_ : Union[str, Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__magic_name__ , self.block_size , 0 ) , __magic_name__ )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : Tuple = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
A_ : Union[str, Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__magic_name__ , self.block_size , 0 ) , __magic_name__ )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : Optional[Any] = '''It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this.'''
A_ , A_ : Optional[Any] = process_story(__magic_name__ )
self.assertEqual(__magic_name__ , [] )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : int = ''''''
A_ , A_ : Union[str, Any] = process_story(__magic_name__ )
self.assertEqual(__magic_name__ , [] )
self.assertEqual(__magic_name__ , [] )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : int = (
'''It was the year of Our Lord one thousand seven hundred and '''
'''seventy-five\n\nSpiritual revelations were conceded to England '''
'''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
)
A_ , A_ : Optional[int] = process_story(__magic_name__ )
A_ : List[str] = [
'''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
'''Spiritual revelations were conceded to England at that favoured period, as at this.''',
]
self.assertEqual(__magic_name__ , __magic_name__ )
A_ : Union[str, Any] = ['''It was the best of times.''']
self.assertEqual(__magic_name__ , __magic_name__ )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : List[str] = torch.tensor([1, 2, 3, 4] )
A_ : Any = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(__magic_name__ , 0 ).numpy() , expected.numpy() )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : Any = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
A_ : str = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__magic_name__ , 23 ).numpy() , expected.numpy() )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : Optional[int] = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
A_ : Union[str, Any] = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__magic_name__ , 1 ).numpy() , expected.numpy() )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : int = 101
A_ : List[str] = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
A_ : Tuple = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
A_ : Optional[Any] = compute_token_type_ids(__magic_name__ , __magic_name__ )
np.testing.assert_array_equal(__magic_name__ , __magic_name__ )
| 236
| 1
|
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> str:
if number > 0:
raise ValueError("input must be a negative integer" )
UpperCAmelCase_ = len(bin(snake_case__ )[3:] )
UpperCAmelCase_ = bin(abs(snake_case__ ) - (1 << binary_number_length) )[3:]
UpperCAmelCase_ = (
(
"""1"""
+ """0""" * (binary_number_length - len(snake_case__ ))
+ twos_complement_number
)
if number < 0
else """0"""
)
return "0b" + twos_complement_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 579
|
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(snake_case__ , snake_case__ )
def a__ ( snake_case__ : int = 20 ):
_UpperCAmelCase : Union[str, Any] = 1
for i in range(1 , n + 1 ):
_UpperCAmelCase : Dict = lcm(snake_case__ , snake_case__ )
return g
if __name__ == "__main__":
print(F'{solution() = }')
| 643
| 0
|
'''simple docstring'''
from __future__ import annotations
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:Optional[int] = str(__snake_case )
return n == n[::-1]
def A_ ( snake_case = 1000000 ):
SCREAMING_SNAKE_CASE:str = 0
for i in range(1 , __snake_case ):
if is_palindrome(__snake_case ) and is_palindrome(bin(__snake_case ).split("b" )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 712
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case ( metaclass=_a ):
_A : Any = ['''torch''', '''torchsde''']
def __init__( self : Any ,*SCREAMING_SNAKE_CASE__ : int ,**SCREAMING_SNAKE_CASE__ : Tuple ):
requires_backends(self ,["torch", "torchsde"] )
@classmethod
def __UpperCamelCase ( cls : Dict ,*SCREAMING_SNAKE_CASE__ : Dict ,**SCREAMING_SNAKE_CASE__ : Optional[Any] ):
requires_backends(cls ,["torch", "torchsde"] )
@classmethod
def __UpperCamelCase ( cls : List[str] ,*SCREAMING_SNAKE_CASE__ : int ,**SCREAMING_SNAKE_CASE__ : Optional[Any] ):
requires_backends(cls ,["torch", "torchsde"] )
| 465
| 0
|
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def __lowerCAmelCase ( __UpperCamelCase : Callable ):
'''simple docstring'''
@wraps(__UpperCamelCase )
def _inner_fn(*__UpperCamelCase : List[str] , **__UpperCamelCase : Optional[Any] ):
warnings.warn(
(F'\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.') , __UpperCamelCase , )
return fn(*__UpperCamelCase , **__UpperCamelCase )
return _inner_fn
| 58
|
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
# General docstring
SCREAMING_SNAKE_CASE = """MobileNetV1Config"""
# Base docstring
SCREAMING_SNAKE_CASE = """google/mobilenet_v1_1.0_224"""
SCREAMING_SNAKE_CASE = [1, 1_024, 7, 7]
# Image classification docstring
SCREAMING_SNAKE_CASE = """google/mobilenet_v1_1.0_224"""
SCREAMING_SNAKE_CASE = """tabby, tabby cat"""
SCREAMING_SNAKE_CASE = [
"""google/mobilenet_v1_1.0_224""",
"""google/mobilenet_v1_0.75_192""",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=None )-> int:
"""simple docstring"""
UpperCamelCase = {}
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCamelCase = model.mobilenet_va
else:
UpperCamelCase = model
UpperCamelCase = "MobilenetV1/Conv2d_0/"
UpperCamelCase = backbone.conv_stem.convolution.weight
UpperCamelCase = backbone.conv_stem.normalization.bias
UpperCamelCase = backbone.conv_stem.normalization.weight
UpperCamelCase = backbone.conv_stem.normalization.running_mean
UpperCamelCase = backbone.conv_stem.normalization.running_var
for i in range(13 ):
UpperCamelCase = i + 1
UpperCamelCase = i * 2
UpperCamelCase = backbone.layer[pt_index]
UpperCamelCase = F"MobilenetV1/Conv2d_{tf_index}_depthwise/"
UpperCamelCase = pointer.convolution.weight
UpperCamelCase = pointer.normalization.bias
UpperCamelCase = pointer.normalization.weight
UpperCamelCase = pointer.normalization.running_mean
UpperCamelCase = pointer.normalization.running_var
UpperCamelCase = backbone.layer[pt_index + 1]
UpperCamelCase = F"MobilenetV1/Conv2d_{tf_index}_pointwise/"
UpperCamelCase = pointer.convolution.weight
UpperCamelCase = pointer.normalization.bias
UpperCamelCase = pointer.normalization.weight
UpperCamelCase = pointer.normalization.running_mean
UpperCamelCase = pointer.normalization.running_var
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCamelCase = "MobilenetV1/Logits/Conv2d_1c_1x1/"
UpperCamelCase = model.classifier.weight
UpperCamelCase = model.classifier.bias
return tf_to_pt_map
def lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )-> List[str]:
"""simple docstring"""
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions." )
raise
# Load weights from TF model
UpperCamelCase = tf.train.list_variables(UpperCAmelCase_ )
UpperCamelCase = {}
for name, shape in init_vars:
logger.info(F"Loading TF weight {name} with shape {shape}" )
UpperCamelCase = tf.train.load_variable(UpperCAmelCase_ , UpperCAmelCase_ )
UpperCamelCase = array
# Build TF to PyTorch weights loading map
UpperCamelCase = _build_tf_to_pytorch_map(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
for name, pointer in tf_to_pt_map.items():
logger.info(F"Importing {name}" )
if name not in tf_weights:
logger.info(F"{name} not in tf pre-trained weights, skipping" )
continue
UpperCamelCase = tf_weights[name]
if "depthwise_weights" in name:
logger.info("Transposing depthwise" )
UpperCamelCase = np.transpose(UpperCAmelCase_ , (2, 3, 0, 1) )
elif "weights" in name:
logger.info("Transposing" )
if len(pointer.shape ) == 2: # copying into linear layer
UpperCamelCase = array.squeeze().transpose()
else:
UpperCamelCase = np.transpose(UpperCAmelCase_ , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F"Pointer shape {pointer.shape} and array shape {array.shape} mismatched" )
logger.info(F"Initialize PyTorch weight {name} {array.shape}" )
UpperCamelCase = torch.from_numpy(UpperCAmelCase_ )
tf_weights.pop(UpperCAmelCase_ , UpperCAmelCase_ )
tf_weights.pop(name + "/RMSProp" , UpperCAmelCase_ )
tf_weights.pop(name + "/RMSProp_1" , UpperCAmelCase_ )
tf_weights.pop(name + "/ExponentialMovingAverage" , UpperCAmelCase_ )
logger.info(F"Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}" )
return model
def lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ )-> torch.Tensor:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = features.shape[-2:]
UpperCamelCase , UpperCamelCase = conv_layer.stride
UpperCamelCase , UpperCamelCase = conv_layer.kernel_size
if in_height % stride_height == 0:
UpperCamelCase = max(kernel_height - stride_height , 0 )
else:
UpperCamelCase = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
UpperCamelCase = max(kernel_width - stride_width , 0 )
else:
UpperCamelCase = max(kernel_width - (in_width % stride_width) , 0 )
UpperCamelCase = pad_along_width // 2
UpperCamelCase = pad_along_width - pad_left
UpperCamelCase = pad_along_height // 2
UpperCamelCase = pad_along_height - pad_top
UpperCamelCase = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(UpperCAmelCase_ , UpperCAmelCase_ , "constant" , 0.0 )
class __a ( nn.Module ):
def __init__( self : List[Any] , UpperCAmelCase_ : MobileNetVaConfig , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] = 1 , UpperCAmelCase_ : Optional[int] = 1 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional[bool] = True , UpperCAmelCase_ : Optional[bool or str] = True , )-> None:
"""simple docstring"""
super().__init__()
UpperCamelCase = config
if in_channels % groups != 0:
raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups." )
if out_channels % groups != 0:
raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups." )
UpperCamelCase = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
UpperCamelCase = nn.Convad(
in_channels=UpperCAmelCase_ , out_channels=UpperCAmelCase_ , kernel_size=UpperCAmelCase_ , stride=UpperCAmelCase_ , padding=UpperCAmelCase_ , groups=UpperCAmelCase_ , bias=UpperCAmelCase_ , padding_mode="zeros" , )
if use_normalization:
UpperCamelCase = nn.BatchNormad(
num_features=UpperCAmelCase_ , eps=config.layer_norm_eps , momentum=0.9997 , affine=UpperCAmelCase_ , track_running_stats=UpperCAmelCase_ , )
else:
UpperCamelCase = None
if use_activation:
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCamelCase = ACTaFN[use_activation]
elif isinstance(config.hidden_act , UpperCAmelCase_ ):
UpperCamelCase = ACTaFN[config.hidden_act]
else:
UpperCamelCase = config.hidden_act
else:
UpperCamelCase = None
def _SCREAMING_SNAKE_CASE ( self : Dict , UpperCAmelCase_ : torch.Tensor )-> torch.Tensor:
"""simple docstring"""
if self.config.tf_padding:
UpperCamelCase = apply_tf_padding(UpperCAmelCase_ , self.convolution )
UpperCamelCase = self.convolution(UpperCAmelCase_ )
if self.normalization is not None:
UpperCamelCase = self.normalization(UpperCAmelCase_ )
if self.activation is not None:
UpperCamelCase = self.activation(UpperCAmelCase_ )
return features
class __a ( _lowerCAmelCase ):
UpperCamelCase_ : List[str] = MobileNetVaConfig
UpperCamelCase_ : Dict = load_tf_weights_in_mobilenet_va
UpperCamelCase_ : List[str] = '''mobilenet_v1'''
UpperCamelCase_ : Optional[int] = '''pixel_values'''
UpperCamelCase_ : List[Any] = False
def _SCREAMING_SNAKE_CASE ( self : Any , UpperCAmelCase_ : Union[nn.Linear, nn.Convad] )-> None:
"""simple docstring"""
if isinstance(UpperCAmelCase_ , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(UpperCAmelCase_ , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
SCREAMING_SNAKE_CASE = R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
SCREAMING_SNAKE_CASE = R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.''' , _lowerCAmelCase , )
class __a ( _lowerCAmelCase ):
def __init__( self : str , UpperCAmelCase_ : MobileNetVaConfig , UpperCAmelCase_ : bool = True )-> Optional[int]:
"""simple docstring"""
super().__init__(UpperCAmelCase_ )
UpperCamelCase = config
UpperCamelCase = 32
UpperCamelCase = max(int(depth * config.depth_multiplier ) , config.min_depth )
UpperCamelCase = MobileNetVaConvLayer(
UpperCAmelCase_ , in_channels=config.num_channels , out_channels=UpperCAmelCase_ , kernel_size=3 , stride=2 , )
UpperCamelCase = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
UpperCamelCase = nn.ModuleList()
for i in range(13 ):
UpperCamelCase = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
UpperCamelCase = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
UpperCAmelCase_ , in_channels=UpperCAmelCase_ , out_channels=UpperCAmelCase_ , kernel_size=3 , stride=strides[i] , groups=UpperCAmelCase_ , ) )
self.layer.append(
MobileNetVaConvLayer(
UpperCAmelCase_ , in_channels=UpperCAmelCase_ , out_channels=UpperCAmelCase_ , kernel_size=1 , ) )
UpperCamelCase = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , UpperCAmelCase_ : Dict )-> List[str]:
"""simple docstring"""
raise NotImplementedError
@add_start_docstrings_to_model_forward(UpperCAmelCase_ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=UpperCAmelCase_ , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _SCREAMING_SNAKE_CASE ( self : Tuple , UpperCAmelCase_ : Optional[torch.Tensor] = None , UpperCAmelCase_ : Optional[bool] = None , UpperCAmelCase_ : Optional[bool] = None , )-> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
"""simple docstring"""
UpperCamelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCamelCase = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values" )
UpperCamelCase = self.conv_stem(UpperCAmelCase_ )
UpperCamelCase = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
UpperCamelCase = layer_module(UpperCAmelCase_ )
if output_hidden_states:
UpperCamelCase = all_hidden_states + (hidden_states,)
UpperCamelCase = hidden_states
if self.pooler is not None:
UpperCamelCase = torch.flatten(self.pooler(UpperCAmelCase_ ) , start_dim=1 )
else:
UpperCamelCase = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=UpperCAmelCase_ , pooler_output=UpperCAmelCase_ , hidden_states=UpperCAmelCase_ , )
@add_start_docstrings(
'''
MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''' , _lowerCAmelCase , )
class __a ( _lowerCAmelCase ):
def __init__( self : str , UpperCAmelCase_ : MobileNetVaConfig )-> None:
"""simple docstring"""
super().__init__(UpperCAmelCase_ )
UpperCamelCase = config.num_labels
UpperCamelCase = MobileNetVaModel(UpperCAmelCase_ )
UpperCamelCase = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
UpperCamelCase = nn.Dropout(config.classifier_dropout_prob , inplace=UpperCAmelCase_ )
UpperCamelCase = nn.Linear(UpperCAmelCase_ , config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCAmelCase_ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=UpperCAmelCase_ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _SCREAMING_SNAKE_CASE ( self : List[str] , UpperCAmelCase_ : Optional[torch.Tensor] = None , UpperCAmelCase_ : Optional[bool] = None , UpperCAmelCase_ : Optional[torch.Tensor] = None , UpperCAmelCase_ : Optional[bool] = None , )-> Union[tuple, ImageClassifierOutputWithNoAttention]:
"""simple docstring"""
UpperCamelCase = return_dict if return_dict is not None else self.config.use_return_dict
UpperCamelCase = self.mobilenet_va(UpperCAmelCase_ , output_hidden_states=UpperCAmelCase_ , return_dict=UpperCAmelCase_ )
UpperCamelCase = outputs.pooler_output if return_dict else outputs[1]
UpperCamelCase = self.classifier(self.dropout(UpperCAmelCase_ ) )
UpperCamelCase = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
UpperCamelCase = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
UpperCamelCase = "single_label_classification"
else:
UpperCamelCase = "multi_label_classification"
if self.config.problem_type == "regression":
UpperCamelCase = MSELoss()
if self.num_labels == 1:
UpperCamelCase = loss_fct(logits.squeeze() , labels.squeeze() )
else:
UpperCamelCase = loss_fct(UpperCAmelCase_ , UpperCAmelCase_ )
elif self.config.problem_type == "single_label_classification":
UpperCamelCase = CrossEntropyLoss()
UpperCamelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
UpperCamelCase = BCEWithLogitsLoss()
UpperCamelCase = loss_fct(UpperCAmelCase_ , UpperCAmelCase_ )
if not return_dict:
UpperCamelCase = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=UpperCAmelCase_ , logits=UpperCAmelCase_ , hidden_states=outputs.hidden_states , )
| 554
| 0
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__A =logging.get_logger(__name__)
class _snake_case ( a__ ):
lowerCAmelCase :Optional[int] = ['''pixel_values''']
def __init__( self , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = PILImageResampling.BICUBIC , _lowerCamelCase = True , _lowerCamelCase = 1 / 255 , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = True , **_lowerCamelCase , ):
super().__init__(**_lowerCamelCase)
UpperCAmelCase__ : List[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCAmelCase__ : int = get_size_dict(_lowerCamelCase , default_to_square=_lowerCamelCase)
UpperCAmelCase__ : Union[str, Any] = do_resize
UpperCAmelCase__ : int = size
UpperCAmelCase__ : Union[str, Any] = resample
UpperCAmelCase__ : Union[str, Any] = do_rescale
UpperCAmelCase__ : Any = rescale_factor
UpperCAmelCase__ : Optional[int] = do_normalize
UpperCAmelCase__ : str = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
UpperCAmelCase__ : Any = image_std if image_std is not None else OPENAI_CLIP_STD
UpperCAmelCase__ : int = do_convert_rgb
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = PILImageResampling.BICUBIC , _lowerCamelCase = None , **_lowerCamelCase , ):
UpperCAmelCase__ : List[str] = get_size_dict(_lowerCamelCase , default_to_square=_lowerCamelCase)
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''')
UpperCAmelCase__ : List[Any] = (size["""height"""], size["""width"""])
return resize(_lowerCamelCase , size=_lowerCamelCase , resample=_lowerCamelCase , data_format=_lowerCamelCase , **_lowerCamelCase)
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , **_lowerCamelCase , ):
return rescale(_lowerCamelCase , scale=_lowerCamelCase , data_format=_lowerCamelCase , **_lowerCamelCase)
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , **_lowerCamelCase , ):
return normalize(_lowerCamelCase , mean=_lowerCamelCase , std=_lowerCamelCase , data_format=_lowerCamelCase , **_lowerCamelCase)
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = ChannelDimension.FIRST , **_lowerCamelCase , ):
UpperCAmelCase__ : List[Any] = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ : List[str] = resample if resample is not None else self.resample
UpperCAmelCase__ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ : List[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ : int = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ : Optional[int] = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ : Union[str, Any] = image_std if image_std is not None else self.image_std
UpperCAmelCase__ : Dict = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCAmelCase__ : Optional[int] = size if size is not None else self.size
UpperCAmelCase__ : Optional[Any] = get_size_dict(_lowerCamelCase , default_to_square=_lowerCamelCase)
UpperCAmelCase__ : Dict = make_list_of_images(_lowerCamelCase)
if not valid_images(_lowerCamelCase):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""")
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""")
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""")
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""")
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCAmelCase__ : List[Any] = [convert_to_rgb(_lowerCamelCase) for image in images]
# All transformations expect numpy arrays.
UpperCAmelCase__ : int = [to_numpy_array(_lowerCamelCase) for image in images]
if do_resize:
UpperCAmelCase__ : Tuple = [self.resize(image=_lowerCamelCase , size=_lowerCamelCase , resample=_lowerCamelCase) for image in images]
if do_rescale:
UpperCAmelCase__ : Tuple = [self.rescale(image=_lowerCamelCase , scale=_lowerCamelCase) for image in images]
if do_normalize:
UpperCAmelCase__ : List[str] = [self.normalize(image=_lowerCamelCase , mean=_lowerCamelCase , std=_lowerCamelCase) for image in images]
UpperCAmelCase__ : Tuple = [to_channel_dimension_format(_lowerCamelCase , _lowerCamelCase) for image in images]
UpperCAmelCase__ : Dict = BatchFeature(data={"""pixel_values""": images} , tensor_type=_lowerCamelCase)
return encoded_outputs
| 113
|
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _UpperCamelCase ( UpperCamelCase__ ):
@wraps(UpperCamelCase__ )
def _inner_fn(*UpperCamelCase__ , **UpperCamelCase__ ):
warnings.warn(
(f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''') , UpperCamelCase__ , )
return fn(*UpperCamelCase__ , **UpperCamelCase__ )
return _inner_fn
| 113
| 1
|
'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
UpperCamelCase_ = getLogger(__name__)
UpperCamelCase_ = '''cuda''' if torch.cuda.is_available() else '''cpu'''
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : int = 8 , UpperCAmelCase__ : str = DEFAULT_DEVICE , UpperCAmelCase__ : List[str]=False , UpperCAmelCase__ : Any="summarization" , UpperCAmelCase__ : Dict=None , **UpperCAmelCase__ : Optional[Any] , ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :Any = Path(UpperCAmelCase__ ).open('w' , encoding='utf-8' )
SCREAMING_SNAKE_CASE__ :str = str(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase__ ).to(UpperCAmelCase__ )
if fpaa:
SCREAMING_SNAKE_CASE__ :Optional[int] = model.half()
SCREAMING_SNAKE_CASE__ :Optional[Any] = AutoTokenizer.from_pretrained(UpperCAmelCase__ )
logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}''' ) # if this is wrong, check config.model_type.
SCREAMING_SNAKE_CASE__ :Dict = time.time()
# update config with task specific params
use_task_specific_params(UpperCAmelCase__ , UpperCAmelCase__ )
if prefix is None:
SCREAMING_SNAKE_CASE__ :List[str] = prefix or getattr(model.config , 'prefix' , '' ) or ''
for examples_chunk in tqdm(list(chunks(UpperCAmelCase__ , UpperCAmelCase__ ) ) ):
SCREAMING_SNAKE_CASE__ :Tuple = [prefix + text for text in examples_chunk]
SCREAMING_SNAKE_CASE__ :Tuple = tokenizer(UpperCAmelCase__ , return_tensors='pt' , truncation=UpperCAmelCase__ , padding='longest' ).to(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :Optional[int] = model.generate(
input_ids=batch.input_ids , attention_mask=batch.attention_mask , **UpperCAmelCase__ , )
SCREAMING_SNAKE_CASE__ :Optional[int] = tokenizer.batch_decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ , clean_up_tokenization_spaces=UpperCAmelCase__ )
for hypothesis in dec:
fout.write(hypothesis + '\n' )
fout.flush()
fout.close()
SCREAMING_SNAKE_CASE__ :List[Any] = int(time.time() - start_time ) # seconds
SCREAMING_SNAKE_CASE__ :int = len(UpperCAmelCase__ )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )}
def lowerCamelCase ( ) -> List[str]:
'''simple docstring'''
return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S' )
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any]=True ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :str = argparse.ArgumentParser()
parser.add_argument('model_name' , type=UpperCAmelCase__ , help='like facebook/bart-large-cnn,t5-base, etc.' )
parser.add_argument('input_path' , type=UpperCAmelCase__ , help='like cnn_dm/test.source' )
parser.add_argument('save_path' , type=UpperCAmelCase__ , help='where to save summaries' )
parser.add_argument('--reference_path' , type=UpperCAmelCase__ , required=UpperCAmelCase__ , help='like cnn_dm/test.target' )
parser.add_argument('--score_path' , type=UpperCAmelCase__ , required=UpperCAmelCase__ , default='metrics.json' , help='where to save metrics' )
parser.add_argument('--device' , type=UpperCAmelCase__ , required=UpperCAmelCase__ , default=UpperCAmelCase__ , help='cuda, cuda:1, cpu etc.' )
parser.add_argument(
'--prefix' , type=UpperCAmelCase__ , required=UpperCAmelCase__ , default=UpperCAmelCase__ , help='will be added to the begininng of src examples' )
parser.add_argument('--task' , type=UpperCAmelCase__ , default='summarization' , help='used for task_specific_params + metrics' )
parser.add_argument('--bs' , type=UpperCAmelCase__ , default=8 , required=UpperCAmelCase__ , help='batch size' )
parser.add_argument(
'--n_obs' , type=UpperCAmelCase__ , default=-1 , required=UpperCAmelCase__ , help='How many observations. Defaults to all.' )
parser.add_argument('--fp16' , action='store_true' )
parser.add_argument('--dump-args' , action='store_true' , help='print the custom hparams with the results' )
parser.add_argument(
'--info' , nargs='?' , type=UpperCAmelCase__ , const=datetime_now() , help=(
'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
' lang=en-ru. If no value is passed, the current datetime string will be used.'
) , )
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ :Tuple = parser.parse_known_args()
SCREAMING_SNAKE_CASE__ :Any = parse_numeric_n_bool_cl_kwargs(UpperCAmelCase__ )
if parsed_args and verbose:
print(F'''parsed the following generate kwargs: {parsed_args}''' )
SCREAMING_SNAKE_CASE__ :Optional[int] = [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
SCREAMING_SNAKE_CASE__ :Any = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=UpperCAmelCase__ )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.''' )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('Can\'t mix --fp16 and --device cpu' )
SCREAMING_SNAKE_CASE__ :Dict = generate_summaries_or_translations(
UpperCAmelCase__ , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **UpperCAmelCase__ , )
if args.reference_path is None:
return {}
# Compute scores
SCREAMING_SNAKE_CASE__ :Tuple = calculate_bleu if 'translation' in args.task else calculate_rouge
SCREAMING_SNAKE_CASE__ :List[str] = [x.rstrip() for x in open(args.save_path ).readlines()]
SCREAMING_SNAKE_CASE__ :int = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(UpperCAmelCase__ )]
SCREAMING_SNAKE_CASE__ :dict = score_fn(UpperCAmelCase__ , UpperCAmelCase__ )
scores.update(UpperCAmelCase__ )
if args.dump_args:
scores.update(UpperCAmelCase__ )
if args.info:
SCREAMING_SNAKE_CASE__ :Union[str, Any] = args.info
if verbose:
print(UpperCAmelCase__ )
if args.score_path is not None:
json.dump(UpperCAmelCase__ , open(args.score_path , 'w' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 209
|
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase_ = get_tests_dir('''fixtures/test_sentencepiece.model''')
UpperCamelCase_ = get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')
UpperCamelCase_ = '''pt''' if is_torch_available() else '''tf'''
@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
A_ : Tuple = CamembertTokenizer
A_ : Dict = CamembertTokenizerFast
A_ : Any = True
A_ : List[Any] = True
def __lowerCamelCase ( self : int ) -> Tuple:
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ :Optional[int] = CamembertTokenizer(UpperCamelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCamelCase ( self : int ) -> int:
SCREAMING_SNAKE_CASE__ :List[Any] = '<pad>'
SCREAMING_SNAKE_CASE__ :Union[str, Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_ ) , UpperCamelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_ ) , UpperCamelCase_ )
def __lowerCamelCase ( self : int ) -> int:
SCREAMING_SNAKE_CASE__ :Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>NOTUSED' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(UpperCamelCase_ ) , 10_04 )
def __lowerCamelCase ( self : List[str] ) -> List[str]:
self.assertEqual(self.get_tokenizer().vocab_size , 10_05 )
def __lowerCamelCase ( self : Union[str, Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ :Any = CamembertTokenizer(UpperCamelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ :Dict = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ :Optional[int] = 'I was born in 92000, and this is falsé.'
SCREAMING_SNAKE_CASE__ :List[str] = tokenizer.encode(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :str = rust_tokenizer.encode(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :int = tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Dict = rust_tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
SCREAMING_SNAKE_CASE__ :int = tokenizer.convert_ids_to_tokens(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Tuple = rust_tokenizer.tokenize(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
def __lowerCamelCase ( self : Optional[Any] ) -> List[str]:
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE__ :List[str] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ :Dict = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ :Optional[Any] = 'I was born in 92000, and this is falsé.'
SCREAMING_SNAKE_CASE__ :Optional[int] = tokenizer.tokenize(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :str = rust_tokenizer.tokenize(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Optional[Any] = tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Optional[int] = rust_tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ :Union[str, Any] = tokenizer.encode(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Optional[Any] = rust_tokenizer.encode(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
@slow
def __lowerCamelCase ( self : Union[str, Any] ) -> Any:
# fmt: off
SCREAMING_SNAKE_CASE__ :Union[str, Any] = {'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
SCREAMING_SNAKE_CASE__ :Union[str, Any] = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=UpperCamelCase_ , model_name='camembert-base' , revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf' , sequences=UpperCamelCase_ , )
| 209
| 1
|
"""simple docstring"""
import os
def a_ ( ):
'''simple docstring'''
with open(os.path.dirname(_lowerCAmelCase ) + '/p022_names.txt' ) as file:
lowercase__ : Union[str, Any] = str(file.readlines()[0] )
lowercase__ : Tuple = names.replace('"' , '' ).split(',' )
names.sort()
lowercase__ : Tuple = 0
lowercase__ : Optional[Any] = 0
for i, name in enumerate(_lowerCAmelCase ):
for letter in name:
name_score += ord(_lowerCAmelCase ) - 64
total_score += (i + 1) * name_score
lowercase__ : Tuple = 0
return total_score
if __name__ == "__main__":
print(solution())
| 645
|
"""simple docstring"""
from collections.abc import Sequence
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
lowercase__ : int = 0.0
for coeff in reversed(_lowerCAmelCase ):
lowercase__ : List[Any] = result * x + coeff
return result
if __name__ == "__main__":
_UpperCamelCase : int = (0.0, 0.0, 5.0, 9.3, 7.0)
_UpperCamelCase : Dict = 1_0.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 645
| 1
|
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ : Optional[Any] = logging.get_logger(__name__)
snake_case_ : int = '▁'
snake_case_ : List[Any] = {
'vocab_file': 'vocab.json',
'spm_file': 'sentencepiece.bpe.model',
}
snake_case_ : Dict = {
'vocab_file': {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
),
},
'spm_file': {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
)
},
}
snake_case_ : int = {
'facebook/s2t-small-librispeech-asr': 1_024,
}
snake_case_ : List[str] = ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']
snake_case_ : str = {'mustc': MUSTC_LANGS}
class lowercase__ ( snake_case_ ):
'''simple docstring'''
_snake_case = VOCAB_FILES_NAMES
_snake_case = PRETRAINED_VOCAB_FILES_MAP
_snake_case = MAX_MODEL_INPUT_SIZES
_snake_case = ['''input_ids''', '''attention_mask''']
_snake_case = []
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__="<s>" , lowerCamelCase__="</s>" , lowerCamelCase__="<pad>" , lowerCamelCase__="<unk>" , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__ = None , **lowerCamelCase__ , ):
'''simple docstring'''
UpperCamelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , unk_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , do_upper_case=lowerCamelCase__ , do_lower_case=lowerCamelCase__ , tgt_lang=lowerCamelCase__ , lang_codes=lowerCamelCase__ , sp_model_kwargs=self.sp_model_kwargs , **lowerCamelCase__ , )
UpperCamelCase = do_upper_case
UpperCamelCase = do_lower_case
UpperCamelCase = load_json(lowerCamelCase__ )
UpperCamelCase = {v: k for k, v in self.encoder.items()}
UpperCamelCase = spm_file
UpperCamelCase = load_spm(lowerCamelCase__ , self.sp_model_kwargs )
if lang_codes is not None:
UpperCamelCase = lang_codes
UpperCamelCase = LANGUAGES[lang_codes]
UpperCamelCase = [f'<lang:{lang}>' for lang in self.langs]
UpperCamelCase = {lang: self.sp_model.PieceToId(f'<lang:{lang}>' ) for lang in self.langs}
UpperCamelCase = self.lang_tokens
UpperCamelCase = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
UpperCamelCase = {}
@property
def UpperCAmelCase ( self ):
'''simple docstring'''
return len(self.encoder )
@property
def UpperCAmelCase ( self ):
'''simple docstring'''
return self._tgt_lang
@tgt_lang.setter
def UpperCAmelCase ( self , lowerCamelCase__ ):
'''simple docstring'''
UpperCamelCase = new_tgt_lang
self.set_tgt_lang_special_tokens(lowerCamelCase__ )
def UpperCAmelCase ( self , lowerCamelCase__ ):
'''simple docstring'''
UpperCamelCase = self.lang_code_to_id[tgt_lang]
UpperCamelCase = [lang_code_id]
def UpperCAmelCase ( self , lowerCamelCase__ ):
'''simple docstring'''
return self.sp_model.encode(lowerCamelCase__ , out_type=lowerCamelCase__ )
def UpperCAmelCase ( self , lowerCamelCase__ ):
'''simple docstring'''
return self.encoder.get(lowerCamelCase__ , self.encoder[self.unk_token] )
def UpperCAmelCase ( self , lowerCamelCase__ ):
'''simple docstring'''
return self.decoder.get(lowerCamelCase__ , self.unk_token )
def UpperCAmelCase ( self , lowerCamelCase__ ):
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
UpperCamelCase = self.sp_model.decode(lowerCamelCase__ )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
UpperCamelCase = []
else:
current_sub_tokens.append(lowerCamelCase__ )
UpperCamelCase = self.sp_model.decode(lowerCamelCase__ )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__=None ):
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase__ , token_ids_a=lowerCamelCase__ , already_has_special_tokens=lowerCamelCase__ )
UpperCamelCase = [1] * len(self.prefix_tokens )
UpperCamelCase = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(lowerCamelCase__ )) + suffix_ones
return prefix_ones + ([0] * len(lowerCamelCase__ )) + ([0] * len(lowerCamelCase__ )) + suffix_ones
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
'''simple docstring'''
UpperCamelCase = self.__dict__.copy()
UpperCamelCase = None
return state
def __setstate__( self , lowerCamelCase__ ):
'''simple docstring'''
UpperCamelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
UpperCamelCase = {}
UpperCamelCase = load_spm(self.spm_file , self.sp_model_kwargs )
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
'''simple docstring'''
UpperCamelCase = Path(lowerCamelCase__ )
assert save_dir.is_dir(), f'{save_directory} should be a directory'
UpperCamelCase = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
)
UpperCamelCase = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
)
save_json(self.encoder , lowerCamelCase__ )
if os.path.abspath(self.spm_file ) != os.path.abspath(lowerCamelCase__ ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , lowerCamelCase__ )
elif not os.path.isfile(self.spm_file ):
with open(lowerCamelCase__ , '''wb''' ) as fi:
UpperCamelCase = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase__ )
return (str(lowerCamelCase__ ), str(lowerCamelCase__ ))
def __snake_case ( _UpperCAmelCase : str, _UpperCAmelCase : Dict[str, Any]):
UpperCamelCase = sentencepiece.SentencePieceProcessor(**_UpperCAmelCase)
spm.Load(str(_UpperCAmelCase))
return spm
def __snake_case ( _UpperCAmelCase : str):
with open(_UpperCAmelCase, '''r''') as f:
return json.load(_UpperCAmelCase)
def __snake_case ( _UpperCAmelCase : Union[str, Any], _UpperCAmelCase : str):
with open(_UpperCAmelCase, '''w''') as f:
json.dump(_UpperCAmelCase, _UpperCAmelCase, indent=2)
| 212
|
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = tempfile.mkdtemp()
UpperCamelCase = BlipImageProcessor()
UpperCamelCase = BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel''' )
UpperCamelCase = BlipProcessor(lowerCamelCase__ , lowerCamelCase__ )
processor.save_pretrained(self.tmpdirname )
def UpperCAmelCase ( self , **lowerCamelCase__ ):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase__ ).tokenizer
def UpperCAmelCase ( self , **lowerCamelCase__ ):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase__ ).image_processor
def UpperCAmelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
UpperCamelCase = [Image.fromarray(np.moveaxis(lowerCamelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
UpperCamelCase = self.get_image_processor(do_normalize=lowerCamelCase__ , padding_value=1.0 )
UpperCamelCase = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=lowerCamelCase__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCamelCase__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCamelCase__ )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = BlipProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = image_processor(lowerCamelCase__ , return_tensors='''np''' )
UpperCamelCase = processor(images=lowerCamelCase__ , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = BlipProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
UpperCamelCase = '''lower newer'''
UpperCamelCase = processor(text=lowerCamelCase__ )
UpperCamelCase = tokenizer(lowerCamelCase__ , return_token_type_ids=lowerCamelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = BlipProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
UpperCamelCase = '''lower newer'''
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(text=lowerCamelCase__ , images=lowerCamelCase__ )
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase__ ):
processor()
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = BlipProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
UpperCamelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCamelCase = processor.batch_decode(lowerCamelCase__ )
UpperCamelCase = tokenizer.batch_decode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = BlipProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
UpperCamelCase = '''lower newer'''
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(text=lowerCamelCase__ , images=lowerCamelCase__ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
| 212
| 1
|
import numpy as np
import qiskit
def lowerCamelCase_ ( _lowercase = 8 , _lowercase = None ) -> int:
__A : Any = np.random.default_rng(seed=lowercase__ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
__A : Optional[int] = 6 * key_len
# Measurement basis for Alice's qubits.
__A : str = rng.integers(2 , size=lowercase__ )
# The set of states Alice will prepare.
__A : Union[str, Any] = rng.integers(2 , size=lowercase__ )
# Measurement basis for Bob's qubits.
__A : Dict = rng.integers(2 , size=lowercase__ )
# Quantum Circuit to simulate BB84
__A : List[Any] = qiskit.QuantumCircuit(lowercase__ , name="BB84" )
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(lowercase__ ):
if alice_state[index] == 1:
bbaa_circ.x(lowercase__ )
if alice_basis[index] == 1:
bbaa_circ.h(lowercase__ )
bbaa_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(lowercase__ ):
if bob_basis[index] == 1:
bbaa_circ.h(lowercase__ )
bbaa_circ.barrier()
bbaa_circ.measure_all()
# Simulate the quantum circuit.
__A : List[Any] = qiskit.Aer.get_backend("aer_simulator" )
# We only need to run one shot because the key is unique.
# Multiple shots will produce the same key.
__A : Union[str, Any] = qiskit.execute(lowercase__ , lowercase__ , shots=1 , seed_simulator=lowercase__ )
# Returns the result of measurement.
__A : List[Any] = job.result().get_counts(lowercase__ ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
__A : int = "".join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
lowercase__ , lowercase__ , lowercase__ )
if alice_basis_bit == bob_basis_bit
] )
# Get final key. Pad with 0 if too short, otherwise truncate.
__A : Union[str, Any] = gen_key[:key_len] if len(lowercase__ ) >= key_len else gen_key.ljust(lowercase__ , "0" )
return key
if __name__ == "__main__":
print(F'''The generated key is : {bbaa(8, seed=0)}''')
from doctest import testmod
testmod()
| 719
|
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'vocab_file': 'spiece.model'}
UpperCamelCase = {
'vocab_file': {
't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
}
}
# TODO(PVP) - this should be removed in Transformers v5
UpperCamelCase = {
't5-small': 512,
't5-base': 512,
't5-large': 512,
't5-3b': 512,
't5-11b': 512,
}
UpperCamelCase = '▁'
class _a ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : Tuple = VOCAB_FILES_NAMES
lowerCamelCase_ : List[str] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase_ : List[str] = ["""input_ids""", """attention_mask"""]
def __init__( self , __UpperCAmelCase , __UpperCAmelCase="</s>" , __UpperCAmelCase="<unk>" , __UpperCAmelCase="<pad>" , __UpperCAmelCase=100 , __UpperCAmelCase=None , __UpperCAmelCase = None , __UpperCAmelCase=True , **__UpperCAmelCase , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__A : Dict = [F"<extra_id_{i}>" for i in range(__UpperCAmelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__A : Any = len(set(filter(lambda __UpperCAmelCase : bool("extra_id" in str(__UpperCAmelCase ) ) , __UpperCAmelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
" tokens" )
if legacy:
logger.warning_once(
F"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"
" read the related pull request available at https://github.com/huggingface/transformers/pull/24565" )
__A : Tuple = legacy
__A : Tuple = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , extra_ids=__UpperCAmelCase , additional_special_tokens=__UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=__UpperCAmelCase , **__UpperCAmelCase , )
__A : Optional[Any] = vocab_file
__A : List[str] = extra_ids
__A : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
@staticmethod
def __UpperCAmelCase( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
__A : Union[str, Any] = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
"This tokenizer was incorrectly instantiated with a model max length of"
F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
F" {pretrained_model_name_or_path} automatically truncating your input to"
F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
" instantiate this tokenizer with `model_max_length` set to your preferred value." , __UpperCAmelCase , )
return max_model_length
@property
def __UpperCAmelCase( self ):
return self.sp_model.get_piece_size() + self._extra_ids
def __UpperCAmelCase( self ):
__A : List[str] = {self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCAmelCase , token_ids_a=__UpperCAmelCase , already_has_special_tokens=__UpperCAmelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(__UpperCAmelCase )) + [1]
return ([0] * len(__UpperCAmelCase )) + [1] + ([0] * len(__UpperCAmelCase )) + [1]
def __UpperCAmelCase( self ):
return list(
set(filter(lambda __UpperCAmelCase : bool(re.search(r"<extra_id_\d+>" , __UpperCAmelCase ) ) is not None , self.additional_special_tokens ) ) )
def __UpperCAmelCase( self ):
return [self._convert_token_to_id(__UpperCAmelCase ) for token in self.get_sentinel_tokens()]
def __UpperCAmelCase( self , __UpperCAmelCase ):
if len(__UpperCAmelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
" eos tokens being added." )
return token_ids
else:
return token_ids + [self.eos_token_id]
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase = None ):
__A : List[str] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase = None ):
__A : List[Any] = self._add_eos_if_not_present(__UpperCAmelCase )
if token_ids_a is None:
return token_ids_a
else:
__A : str = self._add_eos_if_not_present(__UpperCAmelCase )
return token_ids_a + token_ids_a
def __getstate__( self ):
__A : Any = self.__dict__.copy()
__A : List[str] = None
return state
def __setstate__( self , __UpperCAmelCase ):
__A : int = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
__A : Optional[int] = {}
__A : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __UpperCAmelCase( self , __UpperCAmelCase , **__UpperCAmelCase ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
__A : List[str] = SPIECE_UNDERLINE + text.replace(__UpperCAmelCase , " " )
return super().tokenize(__UpperCAmelCase , **__UpperCAmelCase )
def __UpperCAmelCase( self , __UpperCAmelCase , **__UpperCAmelCase ):
if not self.legacy:
__A : Tuple = text.startswith(__UpperCAmelCase )
if is_first:
__A : Optional[int] = text[1:]
__A : Optional[Any] = self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
if not self.legacy and not is_first and not text.startswith(" " ) and tokens[0].startswith(__UpperCAmelCase ):
__A : Tuple = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def __UpperCAmelCase( self , __UpperCAmelCase ):
if token.startswith("<extra_id_" ):
__A : Optional[Any] = re.match(r"<extra_id_(\d+)>" , __UpperCAmelCase )
__A : Optional[Any] = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(__UpperCAmelCase )
def __UpperCAmelCase( self , __UpperCAmelCase ):
if index < self.sp_model.get_piece_size():
__A : Union[str, Any] = self.sp_model.IdToPiece(__UpperCAmelCase )
else:
__A : List[Any] = F"<extra_id_{self.vocab_size - 1 - index}>"
return token
def __UpperCAmelCase( self , __UpperCAmelCase ):
__A : int = []
__A : List[Any] = ""
__A : str = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__UpperCAmelCase ) + token
__A : Tuple = True
__A : Any = []
else:
current_sub_tokens.append(__UpperCAmelCase )
__A : Optional[int] = False
out_string += self.sp_model.decode(__UpperCAmelCase )
return out_string.strip()
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase = None ):
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
__A : Union[str, Any] = os.path.join(
__UpperCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCAmelCase , "wb" ) as fi:
__A : Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
| 387
| 0
|
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : Dict):
if any(not isinstance(_UpperCAmelCase, _UpperCAmelCase) or x < 0 for x in sequence):
raise TypeError('''Sequence must be list of non-negative integers''')
for _ in range(len(_UpperCAmelCase)):
for i, (rod_upper, rod_lower) in enumerate(zip(_UpperCAmelCase, sequence[1:])):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 212
|
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class _lowerCAmelCase ( __UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Any = 'time_series_transformer'
SCREAMING_SNAKE_CASE_: List[str] = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
'num_hidden_layers': 'encoder_layers',
}
def __init__( self , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = "student_t" , lowerCAmelCase_ = "nll" , lowerCAmelCase_ = 1 , lowerCAmelCase_ = [1, 2, 3, 4, 5, 6, 7] , lowerCAmelCase_ = "mean" , lowerCAmelCase_ = 0 , lowerCAmelCase_ = 0 , lowerCAmelCase_ = 0 , lowerCAmelCase_ = 0 , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = 3_2 , lowerCAmelCase_ = 3_2 , lowerCAmelCase_ = 2 , lowerCAmelCase_ = 2 , lowerCAmelCase_ = 2 , lowerCAmelCase_ = 2 , lowerCAmelCase_ = True , lowerCAmelCase_ = "gelu" , lowerCAmelCase_ = 6_4 , lowerCAmelCase_ = 0.1 , lowerCAmelCase_ = 0.1 , lowerCAmelCase_ = 0.1 , lowerCAmelCase_ = 0.1 , lowerCAmelCase_ = 0.1 , lowerCAmelCase_ = 1_0_0 , lowerCAmelCase_ = 0.02 , lowerCAmelCase_=True , **lowerCAmelCase_ , ) -> str:
# time series specific configuration
_SCREAMING_SNAKE_CASE : List[str] = prediction_length
_SCREAMING_SNAKE_CASE : Tuple = context_length or prediction_length
_SCREAMING_SNAKE_CASE : Any = distribution_output
_SCREAMING_SNAKE_CASE : List[str] = loss
_SCREAMING_SNAKE_CASE : Any = input_size
_SCREAMING_SNAKE_CASE : Any = num_time_features
_SCREAMING_SNAKE_CASE : List[Any] = lags_sequence
_SCREAMING_SNAKE_CASE : Dict = scaling
_SCREAMING_SNAKE_CASE : Any = num_dynamic_real_features
_SCREAMING_SNAKE_CASE : List[Any] = num_static_real_features
_SCREAMING_SNAKE_CASE : List[Any] = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase_ ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
_SCREAMING_SNAKE_CASE : List[str] = cardinality
else:
_SCREAMING_SNAKE_CASE : Dict = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase_ ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
_SCREAMING_SNAKE_CASE : int = embedding_dimension
else:
_SCREAMING_SNAKE_CASE : List[str] = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality]
_SCREAMING_SNAKE_CASE : str = num_parallel_samples
# Transformer architecture configuration
_SCREAMING_SNAKE_CASE : Optional[Any] = input_size * len(lowerCAmelCase_ ) + self._number_of_features
_SCREAMING_SNAKE_CASE : int = d_model
_SCREAMING_SNAKE_CASE : str = encoder_attention_heads
_SCREAMING_SNAKE_CASE : Optional[Any] = decoder_attention_heads
_SCREAMING_SNAKE_CASE : List[Any] = encoder_ffn_dim
_SCREAMING_SNAKE_CASE : Dict = decoder_ffn_dim
_SCREAMING_SNAKE_CASE : Union[str, Any] = encoder_layers
_SCREAMING_SNAKE_CASE : Tuple = decoder_layers
_SCREAMING_SNAKE_CASE : Dict = dropout
_SCREAMING_SNAKE_CASE : Union[str, Any] = attention_dropout
_SCREAMING_SNAKE_CASE : Tuple = activation_dropout
_SCREAMING_SNAKE_CASE : Union[str, Any] = encoder_layerdrop
_SCREAMING_SNAKE_CASE : List[str] = decoder_layerdrop
_SCREAMING_SNAKE_CASE : Dict = activation_function
_SCREAMING_SNAKE_CASE : str = init_std
_SCREAMING_SNAKE_CASE : Tuple = use_cache
super().__init__(is_encoder_decoder=lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def A ( self ) -> int:
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 621
| 0
|
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class A :
def __init__( self, UpperCamelCase__, UpperCamelCase__ = 13, UpperCamelCase__ = 64, UpperCamelCase__ = 2, UpperCamelCase__ = 3, UpperCamelCase__ = 3, UpperCamelCase__ = True, UpperCamelCase__ = True, UpperCamelCase__ = 128, UpperCamelCase__=[16, 32, 64, 128], UpperCamelCase__ = 7, UpperCamelCase__ = 4, UpperCamelCase__ = 37, UpperCamelCase__ = "gelu", UpperCamelCase__ = 0.1, UpperCamelCase__ = 0.1, UpperCamelCase__ = 10, UpperCamelCase__ = 0.02, UpperCamelCase__ = 2, UpperCamelCase__ = 1, UpperCamelCase__ = 128, UpperCamelCase__ = [2, 2, 2, 2], UpperCamelCase__ = 2, UpperCamelCase__ = 2, ):
"""simple docstring"""
lowerCAmelCase_ = parent
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = image_size
lowerCAmelCase_ = patch_size
lowerCAmelCase_ = num_channels
lowerCAmelCase_ = is_training
lowerCAmelCase_ = use_labels
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = hidden_dropout_prob
lowerCAmelCase_ = attention_probs_dropout_prob
lowerCAmelCase_ = type_sequence_label_size
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = encoder_stride
lowerCAmelCase_ = num_attention_outputs
lowerCAmelCase_ = embed_dim
lowerCAmelCase_ = embed_dim + 1
lowerCAmelCase_ = resolution
lowerCAmelCase_ = depths
lowerCAmelCase_ = hidden_sizes
lowerCAmelCase_ = dim
lowerCAmelCase_ = mlp_expansion_ratio
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ = None
if self.use_labels:
lowerCAmelCase_ = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCAmelCase_ = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return EfficientFormerConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=UpperCamelCase__, initializer_range=self.initializer_range, encoder_stride=self.encoder_stride, resolution=self.resolution, depths=self.depths, hidden_sizes=self.hidden_sizes, dim=self.dim, mlp_expansion_ratio=self.mlp_expansion_ratio, )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = TFEfficientFormerModel(config=UpperCamelCase__ )
lowerCAmelCase_ = model(UpperCamelCase__, training=UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = self.type_sequence_label_size
lowerCAmelCase_ = TFEfficientFormerForImageClassification(UpperCamelCase__ )
lowerCAmelCase_ = model(UpperCamelCase__, labels=UpperCamelCase__, training=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCAmelCase_ = 1
lowerCAmelCase_ = TFEfficientFormerForImageClassification(UpperCamelCase__ )
lowerCAmelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase_ = model(UpperCamelCase__, labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = config_and_inputs
lowerCAmelCase_ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class A ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
__snake_case = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
__snake_case = (
{
'feature-extraction': TFEfficientFormerModel,
'image-classification': (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = False
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = TFEfficientFormerModelTester(self )
lowerCAmelCase_ = ConfigTester(
self, config_class=UpperCamelCase__, has_text_modality=UpperCamelCase__, hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''EfficientFormer does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='''EfficientFormer does not support input and output embeddings''' )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ = model_class(UpperCamelCase__ )
lowerCAmelCase_ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ = [*signature.parameters.keys()]
lowerCAmelCase_ = ['''pixel_values''']
self.assertListEqual(arg_names[:1], UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
def check_hidden_states_output(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
lowerCAmelCase_ = model_class(UpperCamelCase__ )
lowerCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__, UpperCamelCase__ ), training=UpperCamelCase__ )
lowerCAmelCase_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCAmelCase_ = getattr(
self.model_tester, '''expected_num_hidden_layers''', self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(UpperCamelCase__ ), UpperCamelCase__ )
if hasattr(self.model_tester, '''encoder_seq_length''' ):
lowerCAmelCase_ = self.model_tester.encoder_seq_length
if hasattr(self.model_tester, '''chunk_length''' ) and self.model_tester.chunk_length > 1:
lowerCAmelCase_ = seq_length * self.model_tester.chunk_length
else:
lowerCAmelCase_ = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ), [seq_length, self.model_tester.hidden_size], )
if config.is_encoder_decoder:
lowerCAmelCase_ = outputs.decoder_hidden_states
self.asseretIsInstance(UpperCamelCase__, (list, tuple) )
self.assertEqual(len(UpperCamelCase__ ), UpperCamelCase__ )
lowerCAmelCase_ = getattr(self.model_tester, '''seq_length''', UpperCamelCase__ )
lowerCAmelCase_ = getattr(self.model_tester, '''decoder_seq_length''', UpperCamelCase__ )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ), [decoder_seq_length, self.model_tester.hidden_size], )
lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ = True
check_hidden_states_output(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ = True
check_hidden_states_output(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__=False ):
"""simple docstring"""
lowerCAmelCase_ = super()._prepare_for_class(UpperCamelCase__, UpperCamelCase__, return_labels=UpperCamelCase__ )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
@unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet''' )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ = TFEfficientFormerModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ = True
lowerCAmelCase_ = getattr(self.model_tester, '''seq_length''', UpperCamelCase__ )
lowerCAmelCase_ = getattr(self.model_tester, '''encoder_seq_length''', UpperCamelCase__ )
lowerCAmelCase_ = getattr(self.model_tester, '''key_length''', UpperCamelCase__ )
lowerCAmelCase_ = getattr(self.model_tester, '''chunk_length''', UpperCamelCase__ )
if chunk_length is not None and hasattr(self.model_tester, '''num_hashes''' ):
lowerCAmelCase_ = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
lowerCAmelCase_ = True
lowerCAmelCase_ = False
lowerCAmelCase_ = True
lowerCAmelCase_ = model_class(UpperCamelCase__ )
lowerCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__, UpperCamelCase__ ), training=UpperCamelCase__ )
lowerCAmelCase_ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCamelCase__ ), self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCAmelCase_ = True
lowerCAmelCase_ = model_class(UpperCamelCase__ )
lowerCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__, UpperCamelCase__ ), training=UpperCamelCase__ )
lowerCAmelCase_ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCamelCase__ ), self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ), [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length], )
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
lowerCAmelCase_ = model_class(UpperCamelCase__ )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
lowerCAmelCase_ = {
key: tf.keras.Input(shape=val.shape[1:], dtype=val.dtype, name=UpperCamelCase__ )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
lowerCAmelCase_ = model(UpperCamelCase__ )
self.assertTrue(outputs_dict is not None )
def __UpperCamelCase ( ):
lowerCAmelCase_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class A ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return (
EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300''' )
if is_vision_available()
else None
)
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = TFEfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300''' )
lowerCAmelCase_ = self.default_image_processor
lowerCAmelCase_ = prepare_img()
lowerCAmelCase_ = image_processor(images=UpperCamelCase__, return_tensors='''tf''' )
# forward pass
lowerCAmelCase_ = model(**UpperCamelCase__, training=UpperCamelCase__ )
# verify the logits
lowerCAmelCase_ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape, UpperCamelCase__ )
lowerCAmelCase_ = tf.constant([-0.0_555, 0.4_825, -0.0_852] )
self.assertTrue(np.allclose(outputs.logits[0, :3], UpperCamelCase__, atol=1E-4 ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
'''snap-research/efficientformer-l1-300''' )
lowerCAmelCase_ = self.default_image_processor
lowerCAmelCase_ = prepare_img()
lowerCAmelCase_ = image_processor(images=UpperCamelCase__, return_tensors='''tf''' )
# forward pass
lowerCAmelCase_ = model(**UpperCamelCase__, training=UpperCamelCase__ )
# verify the logits
lowerCAmelCase_ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape, UpperCamelCase__ )
lowerCAmelCase_ = tf.constant([-0.1_312, 0.4_353, -1.0_499] )
self.assertTrue(np.allclose(outputs.logits[0, :3], UpperCamelCase__, atol=1E-4 ) )
| 325
|
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A ( __UpperCAmelCase , unittest.TestCase ):
__snake_case = GPTSanJapaneseTokenizer
__snake_case = False
__snake_case = {'do_clean_text': False, 'add_prefix_space': False}
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
super().setUp()
# fmt: off
lowerCAmelCase_ = ['''こん''', '''こんに''', '''にちは''', '''ばんは''', '''世界,㔺界''', '''、''', '''。''', '''<BR>''', '''<SP>''', '''<TAB>''', '''<URL>''', '''<EMAIL>''', '''<TEL>''', '''<DATE>''', '''<PRICE>''', '''<BLOCK>''', '''<KIGOU>''', '''<U2000U2BFF>''', '''<|emoji1|>''', '''<unk>''', '''<|bagoftoken|>''', '''<|endoftext|>''']
# fmt: on
lowerCAmelCase_ = {'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}} # 😀
lowerCAmelCase_ = {'''unk_token''': '''<unk>'''}
lowerCAmelCase_ = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCAmelCase_ = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''emoji_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
with open(self.emoji_file, '''w''' ) as emoji_writer:
emoji_writer.write(json.dumps(UpperCamelCase__ ) )
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname, **UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = '''こんにちは、世界。 \nこんばんは、㔺界。😀'''
lowerCAmelCase_ = '''こんにちは、世界。 \nこんばんは、世界。😀'''
return input_text, output_text
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ , lowerCAmelCase_ = self.get_input_output_texts(UpperCamelCase__ )
lowerCAmelCase_ = tokenizer.encode(UpperCamelCase__, add_special_tokens=UpperCamelCase__ )
lowerCAmelCase_ = tokenizer.decode(UpperCamelCase__, clean_up_tokenization_spaces=UpperCamelCase__ )
return text, ids
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass # TODO add if relevant
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass # TODO add if relevant
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass # TODO add if relevant
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCAmelCase_ = '''こんにちは、世界。 こんばんは、㔺界。'''
lowerCAmelCase_ = ['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
lowerCAmelCase_ = tokenizer.tokenize(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__, UpperCamelCase__ )
# Testing conversion to ids without special tokens
lowerCAmelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
lowerCAmelCase_ = tokenizer.convert_tokens_to_ids(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__, UpperCamelCase__ )
# Testing conversion to ids with special tokens
lowerCAmelCase_ = tokens + [tokenizer.unk_token]
lowerCAmelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
lowerCAmelCase_ = tokenizer.convert_tokens_to_ids(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__, UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCAmelCase_ = '''こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'''
lowerCAmelCase_ = '''こんにちは、、、、世界。こんばんは、、、、世界。'''
lowerCAmelCase_ = tokenizer.encode(UpperCamelCase__ )
lowerCAmelCase_ = tokenizer.decode(UpperCamelCase__ )
self.assertEqual(UpperCamelCase__, UpperCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''' )
# Testing tokenization
lowerCAmelCase_ = '''こんにちは、世界。'''
lowerCAmelCase_ = '''こんばんは、㔺界。😀'''
lowerCAmelCase_ = '''こんにちは、世界。こんばんは、世界。😀'''
lowerCAmelCase_ = tokenizer.encode(prefix_text + input_text )
lowerCAmelCase_ = tokenizer.encode('''''', prefix_text=prefix_text + input_text )
lowerCAmelCase_ = tokenizer.encode(UpperCamelCase__, prefix_text=UpperCamelCase__ )
lowerCAmelCase_ = tokenizer.decode(UpperCamelCase__ )
lowerCAmelCase_ = tokenizer.decode(UpperCamelCase__ )
lowerCAmelCase_ = tokenizer.decode(UpperCamelCase__ )
self.assertEqual(UpperCamelCase__, UpperCamelCase__ )
self.assertEqual(UpperCamelCase__, UpperCamelCase__ )
self.assertEqual(UpperCamelCase__, UpperCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''' )
# Testing tokenization
lowerCAmelCase_ = '''こんにちは、世界。'''
lowerCAmelCase_ = '''こんばんは、㔺界。😀'''
lowerCAmelCase_ = len(tokenizer.encode(UpperCamelCase__ ) ) - 2
lowerCAmelCase_ = len(tokenizer.encode(UpperCamelCase__ ) ) - 2
lowerCAmelCase_ = [1] + [0] * (len_prefix + len_text + 1)
lowerCAmelCase_ = [1] * (len_prefix + len_text + 1) + [0]
lowerCAmelCase_ = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
lowerCAmelCase_ = tokenizer(prefix_text + input_text ).token_type_ids
lowerCAmelCase_ = tokenizer('''''', prefix_text=prefix_text + input_text ).token_type_ids
lowerCAmelCase_ = tokenizer(UpperCamelCase__, prefix_text=UpperCamelCase__ ).token_type_ids
self.assertListEqual(UpperCamelCase__, UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__, UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__, UpperCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''' )
lowerCAmelCase_ = tokenizer.encode('''あンいワ''' )
lowerCAmelCase_ = tokenizer.encode('''''', prefix_text='''あンいワ''' )
lowerCAmelCase_ = tokenizer.encode('''いワ''', prefix_text='''あン''' )
self.assertEqual(tokenizer.decode(UpperCamelCase__ ), tokenizer.decode(UpperCamelCase__ ) )
self.assertEqual(tokenizer.decode(UpperCamelCase__ ), tokenizer.decode(UpperCamelCase__ ) )
self.assertNotEqual(UpperCamelCase__, UpperCamelCase__ )
self.assertNotEqual(UpperCamelCase__, UpperCamelCase__ )
self.assertEqual(x_token_a[1], x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1], x_token_a[3] ) # SEG token
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''' )
lowerCAmelCase_ = [['''武田信玄''', '''は、'''], ['''織田信長''', '''の配下の、''']]
lowerCAmelCase_ = tokenizer(UpperCamelCase__, padding=UpperCamelCase__ )
lowerCAmelCase_ = tokenizer.batch_encode_plus(UpperCamelCase__, padding=UpperCamelCase__ )
# fmt: off
lowerCAmelCase_ = [[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
lowerCAmelCase_ = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
lowerCAmelCase_ = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids, UpperCamelCase__ )
self.assertListEqual(x_token.token_type_ids, UpperCamelCase__ )
self.assertListEqual(x_token.attention_mask, UpperCamelCase__ )
self.assertListEqual(x_token_a.input_ids, UpperCamelCase__ )
self.assertListEqual(x_token_a.token_type_ids, UpperCamelCase__ )
self.assertListEqual(x_token_a.attention_mask, UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass
| 325
| 1
|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__UpperCAmelCase = 16
__UpperCAmelCase = 32
def __UpperCamelCase ( lowercase__ : Accelerator , lowercase__ : int = 16 ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowerCAmelCase_ : int = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowercase__ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase_ : List[Any] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase__ , max_length=lowercase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowerCAmelCase_ : Union[str, Any] = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCAmelCase_ : Tuple = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase__ : Optional[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowerCAmelCase_ : Dict = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowerCAmelCase_ : int = 16
elif accelerator.mixed_precision != "no":
lowerCAmelCase_ : int = 8
else:
lowerCAmelCase_ : str = None
return tokenizer.pad(
lowercase__ , padding="""longest""" , max_length=lowercase__ , pad_to_multiple_of=lowercase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowerCAmelCase_ : Optional[int] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
lowerCAmelCase_ : Any = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__UpperCAmelCase = mocked_dataloaders # noqa: F811
def __UpperCamelCase ( lowercase__ : List[Any] , lowercase__ : Optional[Any] ) -> Any:
'''simple docstring'''
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , lowercase__ ) == "1":
lowerCAmelCase_ : Any = 2
# Initialize accelerator
lowerCAmelCase_ : List[str] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCAmelCase_ : Any = config["""lr"""]
lowerCAmelCase_ : str = int(config["""num_epochs"""] )
lowerCAmelCase_ : Dict = int(config["""seed"""] )
lowerCAmelCase_ : Optional[Any] = int(config["""batch_size"""] )
lowerCAmelCase_ : Optional[int] = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
lowerCAmelCase_ : Optional[int] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
lowerCAmelCase_ : Any = batch_size // MAX_GPU_BATCH_SIZE
lowerCAmelCase_ : List[Any] = MAX_GPU_BATCH_SIZE
set_seed(lowercase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = get_dataloaders(lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase_ : Optional[Any] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowercase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowerCAmelCase_ : Union[str, Any] = model.to(accelerator.device )
# Instantiate optimizer
lowerCAmelCase_ : List[Any] = AdamW(params=model.parameters() , lr=lowercase__ )
# Instantiate scheduler
lowerCAmelCase_ : Tuple = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=100 , num_training_steps=(len(lowercase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Now we train the model
for epoch in range(lowercase__ ):
model.train()
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowerCAmelCase_ : str = model(**lowercase__ )
lowerCAmelCase_ : Optional[Any] = outputs.loss
lowerCAmelCase_ : int = loss / gradient_accumulation_steps
accelerator.backward(lowercase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
lowerCAmelCase_ : str = 0
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCAmelCase_ : List[Any] = model(**lowercase__ )
lowerCAmelCase_ : int = outputs.logits.argmax(dim=-1 )
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = accelerator.gather((predictions, batch["""labels"""]) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(lowercase__ ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
lowerCAmelCase_ : Optional[Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
lowerCAmelCase_ : Optional[int] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
lowerCAmelCase_ : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , lowercase__ )
def __UpperCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowercase__ , default=lowercase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
lowerCAmelCase_ : str = parser.parse_args()
lowerCAmelCase_ : Union[str, Any] = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main()
| 600
|
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
def __UpperCamelCase ( lowercase__ : Tuple , lowercase__ : Tuple , lowercase__ : Optional[int] , lowercase__ : Optional[int] , lowercase__ : Any ) -> Tuple:
'''simple docstring'''
for attribute in key.split(""".""" ):
lowerCAmelCase_ : List[str] = getattr(lowercase__ , lowercase__ )
if weight_type is not None:
lowerCAmelCase_ : Optional[int] = getattr(lowercase__ , lowercase__ ).shape
else:
lowerCAmelCase_ : Union[str, Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}'
)
if weight_type == "weight":
lowerCAmelCase_ : Union[str, Any] = value
elif weight_type == "weight_g":
lowerCAmelCase_ : Any = value
elif weight_type == "weight_v":
lowerCAmelCase_ : Dict = value
elif weight_type == "bias":
lowerCAmelCase_ : List[Any] = value
else:
lowerCAmelCase_ : Any = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def __UpperCamelCase ( lowercase__ : List[str] , lowercase__ : List[str] , lowercase__ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = []
lowerCAmelCase_ : Dict = fairseq_model.state_dict()
lowerCAmelCase_ : Optional[int] = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
lowerCAmelCase_ : Dict = False
if "conv_layers" in name:
load_conv_layer(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , hf_model.config.feat_extract_norm == """group""" , )
lowerCAmelCase_ : Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
lowerCAmelCase_ : Union[str, Any] = """hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key
if key in name or (key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0] and not is_finetuned):
lowerCAmelCase_ : Dict = True
if "*" in mapped_key:
lowerCAmelCase_ : Any = name.split(lowercase__ )[0].split(""".""" )[-2]
lowerCAmelCase_ : Optional[Any] = mapped_key.replace("""*""" , lowercase__ )
if "weight_g" in name:
lowerCAmelCase_ : Optional[int] = """weight_g"""
elif "weight_v" in name:
lowerCAmelCase_ : Optional[int] = """weight_v"""
elif "weight" in name:
lowerCAmelCase_ : List[str] = """weight"""
elif "bias" in name:
lowerCAmelCase_ : int = """bias"""
else:
lowerCAmelCase_ : List[str] = None
set_recursively(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
continue
if not is_used:
unused_weights.append(lowercase__ )
logger.warning(f'Unused weights: {unused_weights}' )
def __UpperCamelCase ( lowercase__ : List[Any] , lowercase__ : Optional[Any] , lowercase__ : int , lowercase__ : int , lowercase__ : str ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : int = full_name.split("""conv_layers.""" )[-1]
lowerCAmelCase_ : Dict = name.split(""".""" )
lowerCAmelCase_ : Optional[int] = int(items[0] )
lowerCAmelCase_ : Union[str, Any] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
lowerCAmelCase_ : str = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
lowerCAmelCase_ : List[str] = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
lowerCAmelCase_ : Union[str, Any] = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
lowerCAmelCase_ : str = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(lowercase__ )
@torch.no_grad()
def __UpperCamelCase ( lowercase__ : List[Any] , lowercase__ : Dict , lowercase__ : Union[str, Any]=None , lowercase__ : List[Any]=None , lowercase__ : int=True ) -> Any:
'''simple docstring'''
if config_path is not None:
lowerCAmelCase_ : Union[str, Any] = HubertConfig.from_pretrained(lowercase__ )
else:
lowerCAmelCase_ : str = HubertConfig()
if is_finetuned:
if dict_path:
lowerCAmelCase_ : Tuple = Dictionary.load(lowercase__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowerCAmelCase_ : str = target_dict.pad_index
lowerCAmelCase_ : Optional[int] = target_dict.bos_index
lowerCAmelCase_ : Optional[Any] = target_dict.eos_index
lowerCAmelCase_ : Union[str, Any] = len(target_dict.symbols )
lowerCAmelCase_ : Any = os.path.join(lowercase__ , """vocab.json""" )
if not os.path.isdir(lowercase__ ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase__ ) )
return
os.makedirs(lowercase__ , exist_ok=lowercase__ )
with open(lowercase__ , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices , lowercase__ )
lowerCAmelCase_ : List[str] = WavaVecaCTCTokenizer(
lowercase__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=lowercase__ , )
lowerCAmelCase_ : List[str] = True if config.feat_extract_norm == """layer""" else False
lowerCAmelCase_ : str = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=lowercase__ , return_attention_mask=lowercase__ , )
lowerCAmelCase_ : str = WavaVecaProcessor(feature_extractor=lowercase__ , tokenizer=lowercase__ )
processor.save_pretrained(lowercase__ )
lowerCAmelCase_ : Optional[int] = HubertForCTC(lowercase__ )
else:
lowerCAmelCase_ : List[Any] = HubertModel(lowercase__ )
if is_finetuned:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : int = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
lowerCAmelCase_ : Tuple = model[0].eval()
recursively_load_weights(lowercase__ , lowercase__ , lowercase__ )
hf_wavavec.save_pretrained(lowercase__ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
__UpperCAmelCase = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 600
| 1
|
'''simple docstring'''
import cmath
import math
def __a ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float ):
a__ : Union[str, Any] = math.radians(_lowerCamelCase )
a__ : List[str] = math.radians(_lowerCamelCase )
# Convert voltage and current to rectangular form
a__ : List[Any] = cmath.rect(_lowerCamelCase , _lowerCamelCase )
a__ : Union[str, Any] = cmath.rect(_lowerCamelCase , _lowerCamelCase )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 706
|
'''simple docstring'''
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] , A__ : list[int] ) -> None:
'''simple docstring'''
a__ : Union[str, Any] = len(A__ )
a__ : Tuple = [0] * len_array
if len_array > 0:
a__ : Dict = array[0]
for i in range(1 , A__ ):
a__ : Optional[Any] = self.prefix_sum[i - 1] + array[i]
def __lowerCAmelCase ( self : int , A__ : int , A__ : int ) -> int:
'''simple docstring'''
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def __lowerCAmelCase ( self : Tuple , A__ : int ) -> bool:
'''simple docstring'''
a__ : Tuple = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(A__ )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 340
| 0
|
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = prime_factors(_lowercase )
if is_square_free(_lowercase ):
return -1 if len(_lowercase ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 462
|
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class snake_case__ :
"""simple docstring"""
def __init__( self , __lowercase ) -> Optional[Any]:
"""simple docstring"""
if isinstance(__lowercase , __lowercase ):
# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
# modified it, it will not be accepted here again, since `auto` values would have been overridden
a__ : int = deepcopy(__lowercase )
elif os.path.exists(__lowercase ):
with io.open(__lowercase , """r""" , encoding="""utf-8""" ) as f:
a__ : str = json.load(__lowercase )
else:
try:
a__ : Union[str, Any] = baseaa.urlsafe_baadecode(__lowercase ).decode("""utf-8""" )
a__ : Tuple = json.loads(__lowercase )
except (UnicodeDecodeError, AttributeError, ValueError):
raise ValueError(
F'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}''' )
a__ : Tuple = config
self.set_stage_and_offload()
def SCREAMING_SNAKE_CASE__( self ) -> Tuple:
"""simple docstring"""
a__ : List[Any] = self.get_value("""zero_optimization.stage""" , -1 )
# offload
a__ : Tuple = False
if self.is_zeroa() or self.is_zeroa():
a__ : Any = set(["""cpu""", """nvme"""] )
a__ : List[str] = set(
[
self.get_value("""zero_optimization.offload_optimizer.device""" ),
self.get_value("""zero_optimization.offload_param.device""" ),
] )
if len(offload_devices & offload_devices_valid ) > 0:
a__ : Tuple = True
def SCREAMING_SNAKE_CASE__( self , __lowercase ) -> Tuple:
"""simple docstring"""
a__ : Optional[Any] = self.config
# find the config node of interest if it exists
a__ : Any = ds_key_long.split(""".""" )
a__ : List[Any] = nodes.pop()
for node in nodes:
a__ : Any = config.get(__lowercase )
if config is None:
return None, ds_key
return config, ds_key
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase=None ) -> Any:
"""simple docstring"""
a__ , a__ : int = self.find_config_node(__lowercase )
if config is None:
return default
return config.get(__lowercase , __lowercase )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase=False ) -> Dict:
"""simple docstring"""
a__ : Dict = self.config
# find the config node of interest if it exists
a__ : Dict = ds_key_long.split(""".""" )
for node in nodes:
a__ : Any = config
a__ : Optional[Any] = config.get(__lowercase )
if config is None:
if must_exist:
raise ValueError(F'''Can\'t find {ds_key_long} entry in the config: {self.config}''' )
else:
return
# if found remove it
if parent_config is not None:
parent_config.pop(__lowercase )
def SCREAMING_SNAKE_CASE__( self , __lowercase ) -> Optional[Any]:
"""simple docstring"""
a__ : Tuple = self.get_value(__lowercase )
return False if value is None else bool(__lowercase )
def SCREAMING_SNAKE_CASE__( self , __lowercase ) -> Tuple:
"""simple docstring"""
a__ : int = self.get_value(__lowercase )
return False if value is None else not bool(__lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docstring"""
return self._stage == 2
def SCREAMING_SNAKE_CASE__( self ) -> List[Any]:
"""simple docstring"""
return self._stage == 3
def SCREAMING_SNAKE_CASE__( self ) -> Union[str, Any]:
"""simple docstring"""
return self._offload
class snake_case__ :
"""simple docstring"""
def __init__( self , __lowercase ) -> Optional[int]:
"""simple docstring"""
a__ : List[str] = engine
def SCREAMING_SNAKE_CASE__( self , __lowercase , **__lowercase ) -> List[str]:
"""simple docstring"""
self.engine.backward(__lowercase , **__lowercase )
# Deepspeed's `engine.step` performs the following operations:
# - gradient accumulation check
# - gradient clipping
# - optimizer step
# - zero grad
# - checking overflow
# - lr_scheduler step (only if engine.lr_scheduler is not None)
self.engine.step()
# and this plugin overrides the above calls with no-ops when Accelerate runs under
# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
# training loop that works transparently under many training regimes.
class snake_case__ (A__ ):
"""simple docstring"""
def __init__( self , __lowercase ) -> int:
"""simple docstring"""
super().__init__(__lowercase , device_placement=__lowercase , scaler=__lowercase )
a__ : Any = hasattr(self.optimizer , """overflow""" )
def SCREAMING_SNAKE_CASE__( self , __lowercase=None ) -> int:
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
def SCREAMING_SNAKE_CASE__( self ) -> Optional[int]:
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
@property
def SCREAMING_SNAKE_CASE__( self ) -> Optional[int]:
"""simple docstring"""
if self.__has_overflow__:
return self.optimizer.overflow
return False
class snake_case__ (A__ ):
"""simple docstring"""
def __init__( self , __lowercase , __lowercase ) -> Dict:
"""simple docstring"""
super().__init__(__lowercase , __lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> Tuple:
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
class snake_case__ :
"""simple docstring"""
def __init__( self , __lowercase , __lowercase=0.0_0_1 , __lowercase=0 , **__lowercase ) -> List[str]:
"""simple docstring"""
a__ : Optional[int] = params
a__ : List[str] = lr
a__ : List[str] = weight_decay
a__ : Tuple = kwargs
class snake_case__ :
"""simple docstring"""
def __init__( self , __lowercase , __lowercase=None , __lowercase=0 , **__lowercase ) -> Optional[int]:
"""simple docstring"""
a__ : List[Any] = optimizer
a__ : List[Any] = total_num_steps
a__ : Optional[Any] = warmup_num_steps
a__ : Optional[int] = kwargs
| 136
| 0
|
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _A , _A=13 , _A=7 , _A=True , _A=True , _A=True , _A=True , _A=99 , _A=32 , _A=5 , _A=4 , _A=37 , _A="gelu" , _A=0.1 , _A=0.1 , _A=512 , _A=16 , _A=2 , _A=0.02 , _A=4 , ) -> Tuple:
SCREAMING_SNAKE_CASE_ = parent
SCREAMING_SNAKE_CASE_ = batch_size
SCREAMING_SNAKE_CASE_ = seq_length
SCREAMING_SNAKE_CASE_ = is_training
SCREAMING_SNAKE_CASE_ = use_attention_mask
SCREAMING_SNAKE_CASE_ = use_token_type_ids
SCREAMING_SNAKE_CASE_ = use_labels
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = type_sequence_label_size
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = num_choices
def _UpperCamelCase ( self ) -> int:
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE_ = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE_ = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE_ = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE_ = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _UpperCamelCase ( self ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = config_and_inputs
SCREAMING_SNAKE_CASE_ = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def _UpperCamelCase ( self ) -> Any:
SCREAMING_SNAKE_CASE_ = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = config_and_inputs
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class UpperCamelCase__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ =True
UpperCAmelCase_ =(
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _UpperCamelCase ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = FlaxBertModelTester(self )
@slow
def _UpperCamelCase ( self ) -> List[str]:
# Only check this for base model, not necessary for all model classes.
# This will also help speed-up tests.
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained('''bert-base-cased''' )
SCREAMING_SNAKE_CASE_ = model(np.ones((1, 1) ) )
self.assertIsNotNone(_A )
| 597
|
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCAmelCase_ =["image_processor", "tokenizer"]
UpperCAmelCase_ ="Pix2StructImageProcessor"
UpperCAmelCase_ =("T5Tokenizer", "T5TokenizerFast")
def __init__( self , _A , _A ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = False
super().__init__(_A , _A )
def __call__( self , _A=None , _A = None , _A = True , _A = False , _A = None , _A = None , _A = 2048 , _A = 0 , _A = None , _A = None , _A = False , _A = False , _A = False , _A = False , _A = False , _A = True , _A = None , **_A , ) -> BatchEncoding:
if images is None and text is None:
raise ValueError('''You have to specify either images or text.''' )
# Get only text
if images is None and not self.image_processor.is_vqa:
SCREAMING_SNAKE_CASE_ = self.tokenizer
SCREAMING_SNAKE_CASE_ = self.tokenizer(
text=_A , add_special_tokens=_A , padding=_A , truncation=_A , max_length=_A , stride=_A , pad_to_multiple_of=_A , return_attention_mask=_A , return_overflowing_tokens=_A , return_special_tokens_mask=_A , return_offsets_mapping=_A , return_token_type_ids=_A , return_length=_A , verbose=_A , return_tensors=_A , **_A , )
return text_encoding
if not self.image_processor.is_vqa:
# add pixel_values
SCREAMING_SNAKE_CASE_ = self.image_processor(
_A , return_tensors=_A , max_patches=_A , **_A )
else:
# add pixel_values and bbox
SCREAMING_SNAKE_CASE_ = self.image_processor(
_A , return_tensors=_A , max_patches=_A , header_text=_A , **_A )
if text is not None and not self.image_processor.is_vqa:
SCREAMING_SNAKE_CASE_ = self.tokenizer(
text=_A , add_special_tokens=_A , padding=_A , truncation=_A , max_length=_A , stride=_A , pad_to_multiple_of=_A , return_attention_mask=_A , return_overflowing_tokens=_A , return_special_tokens_mask=_A , return_offsets_mapping=_A , return_token_type_ids=_A , return_length=_A , verbose=_A , return_tensors=_A , **_A , )
if "attention_mask" in text_encoding:
SCREAMING_SNAKE_CASE_ = text_encoding.pop('''attention_mask''' )
if "input_ids" in text_encoding:
SCREAMING_SNAKE_CASE_ = text_encoding.pop('''input_ids''' )
else:
SCREAMING_SNAKE_CASE_ = None
if text_encoding is not None:
encoding_image_processor.update(_A )
return encoding_image_processor
def _UpperCamelCase ( self , *_A , **_A ) -> int:
return self.tokenizer.batch_decode(*_A , **_A )
def _UpperCamelCase ( self , *_A , **_A ) -> List[str]:
return self.tokenizer.decode(*_A , **_A )
@property
def _UpperCamelCase ( self ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE_ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 597
| 1
|
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class a__( lowerCamelCase__ ):
def lowercase_ ( self : Dict ):
a : int = tempfile.mkdtemp()
a : str = 8
# DPR tok
a : List[str] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
a : Union[str, Any] = os.path.join(self.tmpdirname , 'dpr_tokenizer' )
os.makedirs(__snake_case , exist_ok=__snake_case )
a : Union[str, Any] = os.path.join(__snake_case , DPR_VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
# BART tok
a : Optional[Any] = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
a : List[str] = dict(zip(__snake_case , range(len(__snake_case ) ) ) )
a : Dict = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
a : Optional[int] = {'unk_token': '<unk>'}
a : Dict = os.path.join(self.tmpdirname , 'bart_tokenizer' )
os.makedirs(__snake_case , exist_ok=__snake_case )
a : List[Any] = os.path.join(__snake_case , BART_VOCAB_FILES_NAMES['vocab_file'] )
a : List[str] = os.path.join(__snake_case , BART_VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__snake_case ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__snake_case ) )
def lowercase_ ( self : List[Any] ):
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'dpr_tokenizer' ) )
def lowercase_ ( self : Any ):
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'bart_tokenizer' ) )
def lowercase_ ( self : Union[str, Any] ):
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def lowercase_ ( self : Any ):
a : Dict = os.path.join(self.tmpdirname , 'rag_tokenizer' )
a : str = RagConfig(question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() )
a : Optional[Any] = RagTokenizer(question_encoder=self.get_dpr_tokenizer() , generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(__snake_case )
rag_tokenizer.save_pretrained(__snake_case )
a : Any = RagTokenizer.from_pretrained(__snake_case , config=__snake_case )
self.assertIsInstance(new_rag_tokenizer.question_encoder , __snake_case )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() , rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator , __snake_case )
self.assertEqual(new_rag_tokenizer.generator.get_vocab() , rag_tokenizer.generator.get_vocab() )
@slow
def lowercase_ ( self : int ):
a : Dict = RagTokenizer.from_pretrained('facebook/rag-token-nq' )
a : Union[str, Any] = [
'who got the first nobel prize in physics',
'when is the next deadpool movie being released',
'which mode is used for short wave broadcast service',
'who is the owner of reading football club',
'when is the next scandal episode coming out',
'when is the last time the philadelphia won the superbowl',
'what is the most current adobe flash player version',
'how many episodes are there in dragon ball z',
'what is the first step in the evolution of the eye',
'where is gall bladder situated in human body',
'what is the main mineral in lithium batteries',
'who is the president of usa right now',
'where do the greasers live in the outsiders',
'panda is a national animal of which country',
'what is the name of manchester united stadium',
]
a : Union[str, Any] = tokenizer(__snake_case )
self.assertIsNotNone(__snake_case )
@slow
def lowercase_ ( self : Union[str, Any] ):
a : Optional[int] = RagTokenizer.from_pretrained('facebook/rag-sequence-nq' )
a : Dict = [
'who got the first nobel prize in physics',
'when is the next deadpool movie being released',
'which mode is used for short wave broadcast service',
'who is the owner of reading football club',
'when is the next scandal episode coming out',
'when is the last time the philadelphia won the superbowl',
'what is the most current adobe flash player version',
'how many episodes are there in dragon ball z',
'what is the first step in the evolution of the eye',
'where is gall bladder situated in human body',
'what is the main mineral in lithium batteries',
'who is the president of usa right now',
'where do the greasers live in the outsiders',
'panda is a national animal of which country',
'what is the name of manchester united stadium',
]
a : str = tokenizer(__snake_case )
self.assertIsNotNone(__snake_case )
| 526
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: List[Any] = {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: Dict = ['ReformerTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: Tuple = ['ReformerTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: List[Any] = [
'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'ReformerAttention',
'ReformerForMaskedLM',
'ReformerForQuestionAnswering',
'ReformerForSequenceClassification',
'ReformerLayer',
'ReformerModel',
'ReformerModelWithLMHead',
'ReformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
lowerCAmelCase: str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 526
| 1
|
import argparse
from collections import defaultdict
import yaml
__lowerCamelCase = 'docs/source/en/_toctree.yml'
def UpperCamelCase__ ( UpperCAmelCase ) -> List[str]:
"""simple docstring"""
_a : List[str] = defaultdict(UpperCAmelCase )
for doc in model_doc:
counts[doc["local"]] += 1
_a : Any = [key for key, value in counts.items() if value > 1]
_a : Any = []
for duplicate_key in duplicates:
_a : List[str] = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(UpperCAmelCase ) > 1:
raise ValueError(
F'{duplicate_key} is present several times in the documentation table of content at '
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(UpperCAmelCase , key=lambda UpperCAmelCase : s["title"].lower() )
def UpperCamelCase__ ( UpperCAmelCase=False ) -> str:
"""simple docstring"""
with open(UpperCAmelCase , encoding='''utf-8''' ) as f:
_a : Any = yaml.safe_load(f.read() )
# Get to the API doc
_a : Union[str, Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_a : int = content[api_idx]['''sections''']
# Then to the model doc
_a : str = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
_a : Any = api_doc[model_idx]['''sections''']
_a : Tuple = [(idx, section) for idx, section in enumerate(UpperCAmelCase ) if '''sections''' in section]
_a : Tuple = False
for idx, modality_doc in modalities_docs:
_a : Optional[int] = modality_doc['''sections''']
_a : Optional[Any] = clean_model_doc_toc(UpperCAmelCase )
if old_modality_doc != new_modality_doc:
_a : List[Any] = True
if overwrite:
_a : List[str] = new_modality_doc
if diff:
if overwrite:
_a : Tuple = model_doc
_a : List[str] = api_doc
with open(UpperCAmelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(UpperCAmelCase , allow_unicode=UpperCAmelCase ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__lowerCamelCase = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 307
|
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
__lowerCamelCase = logging.getLogger(__name__)
torch.set_grad_enabled(False)
__lowerCamelCase = 'cuda' if torch.cuda.is_available() else 'cpu'
def UpperCamelCase__ ( UpperCAmelCase , UpperCAmelCase=100 , UpperCAmelCase=" " ) -> List[str]:
"""simple docstring"""
_a : int = text.split(UpperCAmelCase )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(UpperCAmelCase ) , UpperCAmelCase )]
def UpperCamelCase__ ( UpperCAmelCase ) -> dict:
"""simple docstring"""
_a , _a : List[Any] = [], []
for title, text in zip(documents['''title'''] , documents['''text'''] ):
if text is not None:
for passage in split_text(UpperCAmelCase ):
titles.append(title if title is not None else '''''' )
texts.append(UpperCAmelCase )
return {"title": titles, "text": texts}
def UpperCamelCase__ ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> dict:
"""simple docstring"""
_a : str = ctx_tokenizer(
documents['''title'''] , documents['''text'''] , truncation=UpperCAmelCase , padding='''longest''' , return_tensors='''pt''' )['''input_ids''']
_a : int = ctx_encoder(input_ids.to(device=UpperCAmelCase ) , return_dict=UpperCAmelCase ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def UpperCamelCase__ ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ) -> str:
"""simple docstring"""
logger.info('''Step 1 - Create the dataset''' )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
_a : Any = load_dataset(
'''csv''' , data_files=[rag_example_args.csv_path] , split='''train''' , delimiter='''\t''' , column_names=['''title''', '''text'''] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
_a : Union[str, Any] = dataset.map(UpperCAmelCase , batched=UpperCAmelCase , num_proc=processing_args.num_proc )
# And compute the embeddings
_a : Optional[Any] = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=UpperCAmelCase )
_a : Dict = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
_a : Optional[Any] = Features(
{'''text''': Value('''string''' ), '''title''': Value('''string''' ), '''embeddings''': Sequence(Value('''float32''' ) )} ) # optional, save as float32 instead of float64 to save space
_a : str = dataset.map(
partial(UpperCAmelCase , ctx_encoder=UpperCAmelCase , ctx_tokenizer=UpperCAmelCase ) , batched=UpperCAmelCase , batch_size=processing_args.batch_size , features=UpperCAmelCase , )
# And finally save your dataset
_a : List[Any] = os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset''' )
dataset.save_to_disk(UpperCAmelCase )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info('''Step 2 - Index the dataset''' )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
_a : Optional[int] = faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index('''embeddings''' , custom_index=UpperCAmelCase )
# And save the index
_a : List[Any] = os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset_hnsw_index.faiss''' )
dataset.get_index('''embeddings''' ).save(UpperCAmelCase )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class UpperCamelCase_ :
lowercase = field(
default=str(Path(UpperCamelCase ).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv''' ) , metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''} , )
lowercase = field(
default=UpperCamelCase , metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''} , )
lowercase = field(
default='''facebook/rag-sequence-nq''' , metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''} , )
lowercase = field(
default='''facebook/dpr-ctx_encoder-multiset-base''' , metadata={
'''help''': (
'''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
''' \'facebook/dpr-ctx_encoder-multiset-base\''''
)
} , )
lowercase = field(
default=str(Path(UpperCamelCase ).parent / '''test_run''' / '''dummy-kb''' ) , metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''} , )
@dataclass
class UpperCamelCase_ :
lowercase = field(
default=UpperCamelCase , metadata={
'''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
} , )
lowercase = field(
default=16 , metadata={
'''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
} , )
@dataclass
class UpperCamelCase_ :
lowercase = field(
default=768 , metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''} , )
lowercase = field(
default=128 , metadata={
'''help''': (
'''The number of bi-directional links created for every new element during the HNSW index construction.'''
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
__lowerCamelCase = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
__lowerCamelCase = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 307
| 1
|
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = tf.convert_to_tensor(
[
[
8.222_0991, # 3rd highest value; idx. 0
-0.562_0044,
5.2322_9752,
4.038_6393,
-6.879_8378,
-0.5478_5802,
-3.201_2153,
2.9277_7176,
1.8817_1953,
7.3534_1276, # 5th highest value; idx. 9
8.4320_7833, # 2nd highest value; idx. 10
-9.8571_1836,
-5.9620_9236,
-1.1303_9161,
-7.111_5294,
-0.836_9633,
-5.318_6408,
7.0642_7407,
0.8136_9344,
-0.8202_3817,
-5.917_9796,
0.5881_3443,
-6.9977_8438,
4.7155_1189,
-0.1877_1637,
7.4402_0759, # 4th highest value; idx. 25
9.3845_0987, # 1st highest value; idx. 26
2.1266_2941,
-9.3256_2038,
2.3565_2522,
], # cummulative prob of 5 highest values <= 0.6
[
0.5842_5518,
4.5313_9238,
-5.5751_0464,
-6.2803_0699,
-7.1952_9503,
-4.0212_2551,
1.3933_7037,
-6.0670_7057,
1.5948_0517,
-9.64_3119,
0.0390_7799,
0.6723_1762,
-8.8820_6726,
6.2711_5922, # 4th highest value; idx. 13
2.2852_0723,
4.8276_7506,
4.3042_1368,
8.827_5313, # 2nd highest value; idx. 17
5.4402_9958, # 5th highest value; idx. 18
-4.473_5794,
7.3857_9536, # 3rd highest value; idx. 20
-2.9105_1663,
2.6194_6077,
-2.567_4762,
-9.4895_9302,
-4.0292_2645,
-1.3541_6918,
9.6770_2323, # 1st highest value; idx. 27
-5.8947_8553,
1.8537_0467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
snake_case_ : Any = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 1_0], [0, 2_5], [0, 2_6], [1, 1_3], [1, 1_7], [1, 1_8], [1, 2_0], [1, 2_7]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
snake_case_ : Optional[Any] = tf.convert_to_tensor(
[8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023] , dtype=tf.floataa , ) # expected non filtered values as noted above
snake_case_ : Optional[int] = tf_top_k_top_p_filtering(_lowercase , top_k=1_0 , top_p=0.6 , min_tokens_to_keep=4 )
snake_case_ : Union[str, Any] = output[output != -float("""inf""" )]
snake_case_ : Dict = tf.cast(
tf.where(tf.not_equal(_lowercase , tf.constant(-float("""inf""" ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(_lowercase , _lowercase , rtol=1E-12 )
tf.debugging.assert_equal(_lowercase , _lowercase )
@require_tf
class _lowerCAmelCase ( unittest.TestCase , _UpperCAmelCase ):
"""simple docstring"""
if is_tf_available():
_lowerCamelCase = {
'''AutoModelForCausalLM''': TFAutoModelForCausalLM,
'''AutoModelForSpeechSeq2Seq''': TFAutoModelForSpeechSeqaSeq,
'''AutoModelForSeq2SeqLM''': TFAutoModelForSeqaSeqLM,
'''AutoModelForVision2Seq''': TFAutoModelForVisionaSeq,
'''LogitsProcessorList''': TFLogitsProcessorList,
'''MinLengthLogitsProcessor''': TFMinLengthLogitsProcessor,
'''create_tensor_fn''': tf.convert_to_tensor,
'''floats_tensor''': floats_tensor,
'''return_tensors''': '''tf''',
}
@slow
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Any = TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
snake_case_ : List[Any] = 2
snake_case_ : Dict = 2
class _lowerCAmelCase ( tf.Module ):
"""simple docstring"""
def __init__( self , _lowercase ) -> Any:
'''simple docstring'''
super(_lowercase , self ).__init__()
snake_case_ : Tuple = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name="""input_ids""" ),
tf.TensorSpec((None, input_length) , tf.intaa , name="""attention_mask""" ),
) , jit_compile=_lowercase , )
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = self.model.generate(
input_ids=_lowercase , attention_mask=_lowercase , max_new_tokens=_lowercase , return_dict_in_generate=_lowercase , )
return {"sequences": outputs["sequences"]}
snake_case_ : Any = [[2, 0], [1_0_2, 1_0_3]]
snake_case_ : Any = [[1, 0], [1, 1]]
snake_case_ : List[Any] = DummyModel(model=_lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(_lowercase , _lowercase , signatures={"""serving_default""": dummy_model.serving} )
snake_case_ : str = tf.saved_model.load(_lowercase ).signatures['''serving_default''']
for batch_size in range(1 , len(_lowercase ) + 1 ):
snake_case_ : Union[str, Any] = {
'''input_ids''': tf.constant(dummy_input_ids[:batch_size] ),
'''attention_mask''': tf.constant(dummy_attention_masks[:batch_size] ),
}
snake_case_ : Optional[int] = serving_func(**_lowercase )['''sequences''']
snake_case_ : Optional[int] = test_model.generate(**_lowercase , max_new_tokens=_lowercase )
tf.debugging.assert_equal(_lowercase , _lowercase )
@slow
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
snake_case_ : Any = 1
snake_case_ : Tuple = 2
class _lowerCAmelCase ( tf.Module ):
"""simple docstring"""
def __init__( self , _lowercase ) -> Any:
'''simple docstring'''
super(_lowercase , self ).__init__()
snake_case_ : List[str] = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name="""input_ids""" ),
tf.TensorSpec((batch_size, None) , tf.intaa , name="""attention_mask""" ),
) , jit_compile=_lowercase , )
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = self.model.generate(
input_ids=_lowercase , attention_mask=_lowercase , max_new_tokens=_lowercase , return_dict_in_generate=_lowercase , )
return {"sequences": outputs["sequences"]}
snake_case_ : Union[str, Any] = [[2], [1_0_2, 1_0_3]]
snake_case_ : int = [[1], [1, 1]]
snake_case_ : Any = DummyModel(model=_lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(_lowercase , _lowercase , signatures={"""serving_default""": dummy_model.serving} )
snake_case_ : Optional[Any] = tf.saved_model.load(_lowercase ).signatures['''serving_default''']
for input_row in range(len(_lowercase ) ):
snake_case_ : str = {
'''input_ids''': tf.constant([dummy_input_ids[input_row]] ),
'''attention_mask''': tf.constant([dummy_attention_masks[input_row]] ),
}
snake_case_ : List[str] = serving_func(**_lowercase )['''sequences''']
snake_case_ : Optional[int] = test_model.generate(**_lowercase , max_new_tokens=_lowercase )
tf.debugging.assert_equal(_lowercase , _lowercase )
@slow
@require_tensorflow_text
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id="""google/flan-t5-small""" , filename="""spiece.model""" , local_dir=_lowercase )
class _lowerCAmelCase ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self ) -> Optional[int]:
'''simple docstring'''
super().__init__()
snake_case_ : int = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(_lowercase , """spiece.model""" ) , """rb""" ).read() )
snake_case_ : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
def UpperCAmelCase__ ( self , _lowercase , *_lowercase , **_lowercase ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.tokenizer.tokenize(_lowercase )
snake_case_ : Union[str, Any] = text.pad_model_inputs(
_lowercase , max_seq_length=6_4 , pad_value=self.model.config.pad_token_id )
snake_case_ : Optional[int] = self.model.generate(input_ids=_lowercase , attention_mask=_lowercase )
return self.tokenizer.detokenize(_lowercase )
snake_case_ : Any = CompleteSentenceTransformer()
snake_case_ : str = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name="""inputs""" )
snake_case_ : Dict = complete_model(_lowercase )
snake_case_ : Optional[int] = tf.keras.Model(_lowercase , _lowercase )
keras_model.save(_lowercase )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = {
'''do_sample''': True,
'''num_beams''': 1,
'''top_p''': 0.7,
'''top_k''': 1_0,
'''temperature''': 0.7,
}
snake_case_ : Any = 1_4
snake_case_ : Optional[Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
snake_case_ : Union[str, Any] = '''Hello, my dog is cute and'''
snake_case_ : List[str] = tokenizer(_lowercase , return_tensors="""tf""" )
snake_case_ : List[str] = TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
snake_case_ : List[str] = 6_3_8
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(""":/CPU:0""" ):
tf.random.set_seed(0 )
snake_case_ : Dict = model.generate(**_lowercase , eos_token_id=_lowercase , **_lowercase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
snake_case_ : List[str] = [6_3_8, 1_9_8]
with tf.device(""":/CPU:0""" ):
tf.random.set_seed(0 )
snake_case_ : Optional[Any] = model.generate(**_lowercase , eos_token_id=_lowercase , **_lowercase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bart""" )
snake_case_ : List[Any] = '''Hugging Face is a technology company based in New York and Paris.'''
snake_case_ : Optional[int] = bart_tokenizer(_lowercase , return_tensors="""tf""" ).input_ids
snake_case_ : str = TFBartForConditionalGeneration.from_pretrained("""hf-internal-testing/tiny-random-bart""" )
snake_case_ : str = bart_model.generate(_lowercase ).numpy()
class _lowerCAmelCase ( _UpperCAmelCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self , _lowercase , _lowercase=None , **_lowercase ) -> int:
'''simple docstring'''
return super().call(_lowercase , **_lowercase )
snake_case_ : List[Any] = FakeBart.from_pretrained("""hf-internal-testing/tiny-random-bart""" )
snake_case_ : List[Any] = bart_model.generate(_lowercase , foo="""bar""" ).numpy()
self.assertTrue(np.array_equal(_lowercase , _lowercase ) )
class _lowerCAmelCase ( bart_model.model.encoder.__class__ ):
"""simple docstring"""
def UpperCAmelCase__ ( self , _lowercase , **_lowercase ) -> List[Any]:
'''simple docstring'''
return super().call(_lowercase , **_lowercase )
snake_case_ : Dict = FakeEncoder(bart_model.config , bart_model.model.shared )
snake_case_ : List[Any] = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
snake_case_ : Dict = bart_model.generate(_lowercase ).numpy()
with self.assertRaises(_lowercase ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(_lowercase , foo="""bar""" )
| 58
|
def a ( A__ ) -> int:
'''simple docstring'''
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(A__ , A__ ):
raise TypeError('''Input value must be a \'int\' type''' )
return bin(A__ ).count('''1''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 35
| 0
|
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
@dataclass(frozen=_lowerCAmelCase )
class __a :
UpperCamelCase_ : str
UpperCamelCase_ : str
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
@dataclass(frozen=_lowerCAmelCase )
class __a :
UpperCamelCase_ : List[int]
UpperCamelCase_ : Optional[List[int]] = None
UpperCamelCase_ : Optional[List[int]] = None
UpperCamelCase_ : Optional[Union[int, float]] = None
UpperCamelCase_ : Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class __a ( _lowerCAmelCase ):
UpperCamelCase_ : List[InputFeatures]
def __init__( self : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : PreTrainedTokenizer , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : Optional[Any]=False , UpperCAmelCase_ : bool = False , )-> Dict:
"""simple docstring"""
UpperCamelCase = hans_processors[task]()
UpperCamelCase = os.path.join(
UpperCAmelCase_ , "cached_{}_{}_{}_{}".format(
"dev" if evaluate else "train" , tokenizer.__class__.__name__ , str(UpperCAmelCase_ ) , UpperCAmelCase_ , ) , )
UpperCamelCase = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCamelCase , UpperCamelCase = label_list[2], label_list[1]
UpperCamelCase = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
UpperCamelCase = cached_features_file + ".lock"
with FileLock(UpperCAmelCase_ ):
if os.path.exists(UpperCAmelCase_ ) and not overwrite_cache:
logger.info(f"Loading features from cached file {cached_features_file}" )
UpperCamelCase = torch.load(UpperCAmelCase_ )
else:
logger.info(f"Creating features from dataset file at {data_dir}" )
UpperCamelCase = (
processor.get_dev_examples(UpperCAmelCase_ ) if evaluate else processor.get_train_examples(UpperCAmelCase_ )
)
logger.info("Training examples: %s" , len(UpperCAmelCase_ ) )
UpperCamelCase = hans_convert_examples_to_features(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
logger.info("Saving features into cached file %s" , UpperCAmelCase_ )
torch.save(self.features , UpperCAmelCase_ )
def __len__( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
return len(self.features )
def __getitem__( self : Optional[Any] , UpperCAmelCase_ : Any )-> InputFeatures:
"""simple docstring"""
return self.features[i]
def _SCREAMING_SNAKE_CASE ( self : str )-> List[Any]:
"""simple docstring"""
return self.label_list
if is_tf_available():
import tensorflow as tf
class __a :
UpperCamelCase_ : List[InputFeatures]
def __init__( self : int , UpperCAmelCase_ : str , UpperCAmelCase_ : PreTrainedTokenizer , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[int] = 128 , UpperCAmelCase_ : Optional[Any]=False , UpperCAmelCase_ : bool = False , )-> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = hans_processors[task]()
UpperCamelCase = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCamelCase , UpperCamelCase = label_list[2], label_list[1]
UpperCamelCase = label_list
UpperCamelCase = processor.get_dev_examples(UpperCAmelCase_ ) if evaluate else processor.get_train_examples(UpperCAmelCase_ )
UpperCamelCase = hans_convert_examples_to_features(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc="convert examples to features" ):
if ex_index % 10_000 == 0:
logger.info("Writing example %d of %d" % (ex_index, len(UpperCAmelCase_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
UpperCamelCase = tf.data.Dataset.from_generator(
UpperCAmelCase_ , (
{
"example_id": tf.intaa,
"input_ids": tf.intaa,
"attention_mask": tf.intaa,
"token_type_ids": tf.intaa,
},
tf.intaa,
) , (
{
"example_id": tf.TensorShape([] ),
"input_ids": tf.TensorShape([None, None] ),
"attention_mask": tf.TensorShape([None, None] ),
"token_type_ids": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def _SCREAMING_SNAKE_CASE ( self : Tuple )-> Tuple:
"""simple docstring"""
return self.dataset
def __len__( self : List[Any] )-> List[Any]:
"""simple docstring"""
return len(self.features )
def __getitem__( self : Tuple , UpperCAmelCase_ : List[str] )-> InputFeatures:
"""simple docstring"""
return self.features[i]
def _SCREAMING_SNAKE_CASE ( self : List[str] )-> List[str]:
"""simple docstring"""
return self.label_list
class __a ( _lowerCAmelCase ):
def _SCREAMING_SNAKE_CASE ( self : str , UpperCAmelCase_ : Tuple )-> Tuple:
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase_ , "heuristics_train_set.txt" ) ) , "train" )
def _SCREAMING_SNAKE_CASE ( self : int , UpperCAmelCase_ : List[str] )-> Dict:
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase_ , "heuristics_evaluation_set.txt" ) ) , "dev" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] )-> Optional[Any]:
"""simple docstring"""
return ["contradiction", "entailment", "neutral"]
def _SCREAMING_SNAKE_CASE ( self : Dict , UpperCAmelCase_ : Dict , UpperCAmelCase_ : str )-> str:
"""simple docstring"""
UpperCamelCase = []
for i, line in enumerate(UpperCAmelCase_ ):
if i == 0:
continue
UpperCamelCase = "%s-%s" % (set_type, line[0])
UpperCamelCase = line[5]
UpperCamelCase = line[6]
UpperCamelCase = line[7][2:] if line[7].startswith("ex" ) else line[7]
UpperCamelCase = line[0]
examples.append(InputExample(guid=UpperCAmelCase_ , text_a=UpperCAmelCase_ , text_b=UpperCAmelCase_ , label=UpperCAmelCase_ , pairID=UpperCAmelCase_ ) )
return examples
def lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , )-> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = {label: i for i, label in enumerate(UpperCAmelCase_ )}
UpperCamelCase = []
for ex_index, example in tqdm.tqdm(enumerate(UpperCAmelCase_ ) , desc="convert examples to features" ):
if ex_index % 1_00_00 == 0:
logger.info("Writing example %d" % (ex_index) )
UpperCamelCase = tokenizer(
example.text_a , example.text_b , add_special_tokens=UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding="max_length" , truncation=UpperCAmelCase_ , return_overflowing_tokens=UpperCAmelCase_ , )
UpperCamelCase = label_map[example.label] if example.label in label_map else 0
UpperCamelCase = int(example.pairID )
features.append(InputFeatures(**UpperCAmelCase_ , label=UpperCAmelCase_ , pairID=UpperCAmelCase_ ) )
for i, example in enumerate(examples[:5] ):
logger.info("*** Example ***" )
logger.info(F"guid: {example}" )
logger.info(F"features: {features[i]}" )
return features
SCREAMING_SNAKE_CASE = {
"""hans""": 3,
}
SCREAMING_SNAKE_CASE = {
"""hans""": HansProcessor,
}
| 556
|
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __a ( _lowerCAmelCase , unittest.TestCase ):
UpperCamelCase_ : str = FunnelTokenizer
UpperCamelCase_ : Any = FunnelTokenizerFast
UpperCamelCase_ : Union[str, Any] = True
UpperCamelCase_ : Tuple = True
def _SCREAMING_SNAKE_CASE ( self : Tuple )-> Optional[int]:
"""simple docstring"""
super().setUp()
UpperCamelCase = [
"<unk>",
"<cls>",
"<sep>",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , **UpperCAmelCase_ : Optional[Any] )-> str:
"""simple docstring"""
return FunnelTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Dict , **UpperCAmelCase_ : Any )-> Union[str, Any]:
"""simple docstring"""
return FunnelTokenizerFast.from_pretrained(self.tmpdirname , **UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Dict , UpperCAmelCase_ : Tuple )-> str:
"""simple docstring"""
UpperCamelCase = "UNwant\u00E9d,running"
UpperCamelCase = "unwanted, running"
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self : str )-> List[str]:
"""simple docstring"""
UpperCamelCase = self.tokenizer_class(self.vocab_file )
UpperCamelCase = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(UpperCAmelCase_ , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) , [7, 4, 5, 10, 8, 9] )
def _SCREAMING_SNAKE_CASE ( self : List[Any] )-> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.get_tokenizers(do_lower_case=UpperCAmelCase_ )
for tokenizer in tokenizers:
UpperCamelCase = tokenizer("UNwant\u00E9d,running" )
UpperCamelCase = len(inputs["input_ids"] ) - 1
self.assertListEqual(inputs["token_type_ids"] , [2] + [0] * sentence_len )
UpperCamelCase = tokenizer("UNwant\u00E9d,running" , "UNwant\u00E9d,running" )
self.assertListEqual(inputs["token_type_ids"] , [2] + [0] * sentence_len + [1] * sentence_len )
| 556
| 1
|
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.getLogger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] = """Hello world! cécé herlolip"""
SCREAMING_SNAKE_CASE__ : Any = namedtuple(
"""BertAbsConfig""",
[
"""temp_dir""",
"""large""",
"""use_bert_emb""",
"""finetune_bert""",
"""encoder""",
"""share_emb""",
"""max_pos""",
"""enc_layers""",
"""enc_hidden_size""",
"""enc_heads""",
"""enc_ff_size""",
"""enc_dropout""",
"""dec_layers""",
"""dec_hidden_size""",
"""dec_heads""",
"""dec_ff_size""",
"""dec_dropout""",
],
)
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase ) -> Tuple:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = BertAbsConfig(
temp_dir=""".""" , finetune_bert=__lowerCamelCase , large=__lowerCamelCase , share_emb=__lowerCamelCase , use_bert_emb=__lowerCamelCase , encoder="""bert""" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
UpperCAmelCase__ : Any = torch.load(__lowerCamelCase , lambda __lowerCamelCase , __lowerCamelCase : storage )
UpperCAmelCase__ : int = AbsSummarizer(__lowerCamelCase , torch.device("""cpu""" ) , __lowerCamelCase )
original.eval()
UpperCAmelCase__ : Tuple = BertAbsSummarizer(__lowerCamelCase , torch.device("""cpu""" ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("""convert the model""" )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("""Make sure that the models' outputs are identical""" )
UpperCAmelCase__ : Any = BertTokenizer.from_pretrained("""bert-base-uncased""" )
# prepare the model inputs
UpperCAmelCase__ : List[Any] = tokenizer.encode("""This is sample éàalj'-.""" )
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase )) )
UpperCAmelCase__ : Any = torch.tensor(__lowerCamelCase ).unsqueeze(0 )
UpperCAmelCase__ : Tuple = tokenizer.encode("""This is sample 3 éàalj'-.""" )
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase )) )
UpperCAmelCase__ : Dict = torch.tensor(__lowerCamelCase ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
UpperCAmelCase__ : List[str] = encoder_input_ids
UpperCAmelCase__ : List[str] = decoder_input_ids
UpperCAmelCase__ : List[str] = None
UpperCAmelCase__ : Tuple = None
UpperCAmelCase__ : Optional[Any] = None
UpperCAmelCase__ : Optional[Any] = None
UpperCAmelCase__ : Optional[int] = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
UpperCAmelCase__ : Any = original(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )[0]
UpperCAmelCase__ : List[str] = original.generator(__lowerCamelCase )
UpperCAmelCase__ : List[Any] = new_model(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )[0]
UpperCAmelCase__ : int = new_model.generator(__lowerCamelCase )
UpperCAmelCase__ : List[Any] = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(__lowerCamelCase ) )
UpperCAmelCase__ : Any = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(__lowerCamelCase ) )
UpperCAmelCase__ : str = torch.allclose(__lowerCamelCase , __lowerCamelCase , atol=1E-3 )
if are_identical:
logging.info("""all weights are equal up to 1e-3""" )
else:
raise ValueError("""the weights are different. The new model is likely different from the original one.""" )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("""saving the model's state dictionary""" )
torch.save(
new_model.state_dict() , """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
parser.add_argument(
"""--bertabs_checkpoint_path""",
default=None,
type=str,
required=True,
help="""Path the official PyTorch dump.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the output PyTorch model.""",
)
SCREAMING_SNAKE_CASE__ : int = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 79
|
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
}
__magic_name__ = {
'''vocab_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'''},
'''merges_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'''},
}
__magic_name__ = {
'''ctrl''': 256,
}
__magic_name__ = {
'''Pregnancy''': 168_629,
'''Christianity''': 7_675,
'''Explain''': 106_423,
'''Fitness''': 63_440,
'''Saving''': 63_163,
'''Ask''': 27_171,
'''Ass''': 95_985,
'''Joke''': 163_509,
'''Questions''': 45_622,
'''Thoughts''': 49_605,
'''Retail''': 52_342,
'''Feminism''': 164_338,
'''Writing''': 11_992,
'''Atheism''': 192_263,
'''Netflix''': 48_616,
'''Computing''': 39_639,
'''Opinion''': 43_213,
'''Alone''': 44_967,
'''Funny''': 58_917,
'''Gaming''': 40_358,
'''Human''': 4_088,
'''India''': 1_331,
'''Joker''': 77_138,
'''Diet''': 36_206,
'''Legal''': 11_859,
'''Norman''': 4_939,
'''Tip''': 72_689,
'''Weight''': 52_343,
'''Movies''': 46_273,
'''Running''': 23_425,
'''Science''': 2_090,
'''Horror''': 37_793,
'''Confession''': 60_572,
'''Finance''': 12_250,
'''Politics''': 16_360,
'''Scary''': 191_985,
'''Support''': 12_654,
'''Technologies''': 32_516,
'''Teenage''': 66_160,
'''Event''': 32_769,
'''Learned''': 67_460,
'''Notion''': 182_770,
'''Wikipedia''': 37_583,
'''Books''': 6_665,
'''Extract''': 76_050,
'''Confessions''': 102_701,
'''Conspiracy''': 75_932,
'''Links''': 63_674,
'''Narcissus''': 150_425,
'''Relationship''': 54_766,
'''Relationships''': 134_796,
'''Reviews''': 41_671,
'''News''': 4_256,
'''Translation''': 26_820,
'''multilingual''': 128_406,
}
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase ):
snake_case__ = set()
snake_case__ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
snake_case__ = char
snake_case__ = set(__lowerCAmelCase )
return pairs
class _SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
_A : Tuple = VOCAB_FILES_NAMES
_A : str = PRETRAINED_VOCAB_FILES_MAP
_A : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A : List[Any] = CONTROL_CODES
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase="<unk>" , **lowerCamelCase ):
super().__init__(unk_token=lowerCamelCase , **lowerCamelCase )
with open(lowerCamelCase , encoding="utf-8" ) as vocab_handle:
snake_case__ = json.load(lowerCamelCase )
snake_case__ = {v: k for k, v in self.encoder.items()}
with open(lowerCamelCase , encoding="utf-8" ) as merges_handle:
snake_case__ = merges_handle.read().split("\n" )[1:-1]
snake_case__ = [tuple(merge.split() ) for merge in merges]
snake_case__ = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
snake_case__ = {}
@property
def A_ ( self ):
return len(self.encoder )
def A_ ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def A_ ( self , lowerCamelCase ):
if token in self.cache:
return self.cache[token]
snake_case__ = tuple(lowerCamelCase )
snake_case__ = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
snake_case__ = get_pairs(lowerCamelCase )
if not pairs:
return token
while True:
snake_case__ = min(lowerCamelCase , key=lambda lowerCamelCase : self.bpe_ranks.get(lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
snake_case__ , snake_case__ = bigram
snake_case__ = []
snake_case__ = 0
while i < len(lowerCamelCase ):
try:
snake_case__ = word.index(lowerCamelCase , lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
snake_case__ = j
if word[i] == first and i < len(lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
snake_case__ = tuple(lowerCamelCase )
snake_case__ = new_word
if len(lowerCamelCase ) == 1:
break
else:
snake_case__ = get_pairs(lowerCamelCase )
snake_case__ = "@@ ".join(lowerCamelCase )
snake_case__ = word[:-4]
snake_case__ = word
return word
def A_ ( self , lowerCamelCase ):
snake_case__ = []
snake_case__ = re.findall(r"\S+\n?" , lowerCamelCase )
for token in words:
split_tokens.extend(list(self.bpe(lowerCamelCase ).split(" " ) ) )
return split_tokens
def A_ ( self , lowerCamelCase ):
return self.encoder.get(lowerCamelCase , self.encoder.get(self.unk_token ) )
def A_ ( self , lowerCamelCase ):
return self.decoder.get(lowerCamelCase , self.unk_token )
def A_ ( self , lowerCamelCase ):
snake_case__ = " ".join(lowerCamelCase ).replace("@@ " , "" ).strip()
return out_string
def A_ ( self , lowerCamelCase , lowerCamelCase = None ):
if not os.path.isdir(lowerCamelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case__ = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
snake_case__ = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase , ensure_ascii=lowerCamelCase ) + "\n" )
snake_case__ = 0
with open(lowerCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
" Please check that the tokenizer is not corrupted!" )
snake_case__ = token_index
writer.write(" ".join(lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
| 276
| 0
|
from manim import *
class __A ( UpperCamelCase__ ):
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =Rectangle(height=0.5 , width=0.5 )
__magic_name__ : Dict =Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
__magic_name__ : Dict =[mem.copy() for i in range(6 )]
__magic_name__ : str =[mem.copy() for i in range(6 )]
__magic_name__ : Optional[Any] =VGroup(*__snake_case ).arrange(__snake_case , buff=0 )
__magic_name__ : Union[str, Any] =VGroup(*__snake_case ).arrange(__snake_case , buff=0 )
__magic_name__ : Optional[int] =VGroup(__snake_case , __snake_case ).arrange(__snake_case , buff=0 )
__magic_name__ : Dict =Text("""CPU""" , font_size=24 )
__magic_name__ : Union[str, Any] =Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__snake_case )
__magic_name__ : Optional[int] =[mem.copy() for i in range(4 )]
__magic_name__ : str =VGroup(*__snake_case ).arrange(__snake_case , buff=0 )
__magic_name__ : int =Text("""GPU""" , font_size=24 )
__magic_name__ : Optional[Any] =Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case )
gpu.move_to([-1, -1, 0] )
self.add(__snake_case )
__magic_name__ : Optional[Any] =[mem.copy() for i in range(6 )]
__magic_name__ : Dict =VGroup(*__snake_case ).arrange(__snake_case , buff=0 )
__magic_name__ : List[str] =Text("""Model""" , font_size=24 )
__magic_name__ : List[str] =Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case )
model.move_to([3, -1.0, 0] )
self.add(__snake_case )
__magic_name__ : Dict =[]
for i, rect in enumerate(__snake_case ):
rect.set_stroke(__snake_case )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
__magic_name__ : str =Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__snake_case , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__snake_case )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=__snake_case , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=__snake_case , buff=0.0 )
self.add(__snake_case )
cpu_targs.append(__snake_case )
__magic_name__ : List[Any] =[mem.copy() for i in range(6 )]
__magic_name__ : Union[str, Any] =VGroup(*__snake_case ).arrange(__snake_case , buff=0 )
__magic_name__ : int =Text("""Loaded Checkpoint""" , font_size=24 )
__magic_name__ : int =Group(__snake_case , __snake_case ).arrange(__snake_case , aligned_edge=__snake_case , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
__magic_name__ : str =Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
__magic_name__ : int =MarkupText(
f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__snake_case , __snake_case )
__magic_name__ : Optional[Any] =MarkupText(
f"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(__snake_case , DOWN * 2.4 , aligned_edge=key_text.get_left() )
__magic_name__ : Tuple =MarkupText(
f"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__snake_case ) , Write(__snake_case ) )
self.play(Write(__snake_case , run_time=1 ) , Create(__snake_case , run_time=1 ) )
__magic_name__ : Dict =[]
__magic_name__ : Dict =[]
for i, rect in enumerate(__snake_case ):
__magic_name__ : Dict =fill.copy().set_fill(__snake_case , opacity=0.7 )
target.move_to(__snake_case )
first_animations.append(GrowFromCenter(__snake_case , run_time=1 ) )
__magic_name__ : Optional[int] =target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(__snake_case , run_time=1.5 ) )
self.play(*__snake_case )
self.play(*__snake_case )
self.wait()
| 719
|
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
UpperCAmelCase_ : str = "3"
print("Python version:", sys.version)
print("transformers version:", transformers.__version__)
try:
import torch
print("Torch version:", torch.__version__)
print("Cuda available:", torch.cuda.is_available())
print("Cuda version:", torch.version.cuda)
print("CuDNN version:", torch.backends.cudnn.version())
print("Number of GPUs available:", torch.cuda.device_count())
print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
print("Torch version:", None)
try:
import deepspeed
print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
print("DeepSpeed version:", None)
try:
import tensorflow as tf
print("TensorFlow version:", tf.__version__)
print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
print("TensorFlow version:", None)
| 367
| 0
|
"""simple docstring"""
snake_case = 6_5_5_2_1
def snake_case ( lowerCAmelCase_ ) -> int:
_snake_case = 1
_snake_case = 0
for plain_chr in plain_text:
_snake_case = (a + ord(lowerCAmelCase_ )) % MOD_ADLER
_snake_case = (b + a) % MOD_ADLER
return (b << 16) | a
| 103
|
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(snake_case__ , snake_case__ )
def a__ ( snake_case__ : int = 20 ):
_UpperCAmelCase : Union[str, Any] = 1
for i in range(1 , n + 1 ):
_UpperCAmelCase : Dict = lcm(snake_case__ , snake_case__ )
return g
if __name__ == "__main__":
print(F'{solution() = }')
| 643
| 0
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : Optional[int] = {
'''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
'''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
'''kssteven/ibert-roberta-large-mnli''': (
'''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
),
}
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A : List[Any] = 'ibert'
def __init__( self , _SCREAMING_SNAKE_CASE=3_0522 , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=3072 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=1e-12 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE="absolute" , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="none" , **_SCREAMING_SNAKE_CASE , ) -> Tuple:
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
snake_case_ : int = vocab_size
snake_case_ : Optional[int] = hidden_size
snake_case_ : str = num_hidden_layers
snake_case_ : List[str] = num_attention_heads
snake_case_ : int = hidden_act
snake_case_ : List[Any] = intermediate_size
snake_case_ : Tuple = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[Any] = max_position_embeddings
snake_case_ : Dict = type_vocab_size
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Optional[int] = layer_norm_eps
snake_case_ : Tuple = position_embedding_type
snake_case_ : Union[str, Any] = quant_mode
snake_case_ : List[str] = force_dequant
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
@property
def _lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
snake_case_ : Any = {0: "batch", 1: "choice", 2: "sequence"}
else:
snake_case_ : Dict = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 114
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase : str = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
'''tokenization_biogpt''': ['''BioGptTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : List[str] = [
'''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BioGptForCausalLM''',
'''BioGptForTokenClassification''',
'''BioGptForSequenceClassification''',
'''BioGptModel''',
'''BioGptPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
lowercase : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 114
| 1
|
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _SCREAMING_SNAKE_CASE:
def __init__( self : str ) -> None:
SCREAMING_SNAKE_CASE__ :list[Any] = []
SCREAMING_SNAKE_CASE__ :int = 0
SCREAMING_SNAKE_CASE__ :int = 0
def __lowerCamelCase ( self : Any ) -> bool:
return self.head == self.tail
def __lowerCamelCase ( self : Any , UpperCamelCase_ : Any ) -> None:
self.data.append(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :int = self.tail + 1
def __lowerCamelCase ( self : Tuple ) -> Any:
SCREAMING_SNAKE_CASE__ :Union[str, Any] = self.data[self.head]
SCREAMING_SNAKE_CASE__ :Union[str, Any] = self.head + 1
return ret
def __lowerCamelCase ( self : str ) -> int:
return self.tail - self.head
def __lowerCamelCase ( self : Optional[int] ) -> None:
print(self.data )
print('**************' )
print(self.data[self.head : self.tail] )
class _SCREAMING_SNAKE_CASE:
def __init__( self : List[str] , UpperCamelCase_ : Any ) -> None:
SCREAMING_SNAKE_CASE__ :Tuple = data
SCREAMING_SNAKE_CASE__ :MyNode | None = None
SCREAMING_SNAKE_CASE__ :MyNode | None = None
SCREAMING_SNAKE_CASE__ :int = 1
def __lowerCamelCase ( self : Union[str, Any] ) -> Any:
return self.data
def __lowerCamelCase ( self : Optional[int] ) -> MyNode | None:
return self.left
def __lowerCamelCase ( self : List[str] ) -> MyNode | None:
return self.right
def __lowerCamelCase ( self : List[Any] ) -> int:
return self.height
def __lowerCamelCase ( self : Optional[Any] , UpperCamelCase_ : Any ) -> None:
SCREAMING_SNAKE_CASE__ :List[str] = data
def __lowerCamelCase ( self : Dict , UpperCamelCase_ : MyNode | None ) -> None:
SCREAMING_SNAKE_CASE__ :Dict = node
def __lowerCamelCase ( self : Optional[int] , UpperCamelCase_ : MyNode | None ) -> None:
SCREAMING_SNAKE_CASE__ :Union[str, Any] = node
def __lowerCamelCase ( self : Union[str, Any] , UpperCamelCase_ : int ) -> None:
SCREAMING_SNAKE_CASE__ :Dict = height
def lowerCamelCase ( UpperCAmelCase__ : MyNode | None ) -> int:
'''simple docstring'''
if node is None:
return 0
return node.get_height()
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> int:
'''simple docstring'''
if a > b:
return a
return b
def lowerCamelCase ( UpperCAmelCase__ : MyNode ) -> MyNode:
'''simple docstring'''
print('left rotation node:' , node.get_data() )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :Any = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :Dict = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(UpperCAmelCase__ )
return ret
def lowerCamelCase ( UpperCAmelCase__ : MyNode ) -> MyNode:
'''simple docstring'''
print('right rotation node:' , node.get_data() )
SCREAMING_SNAKE_CASE__ :Tuple = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :int = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(UpperCAmelCase__ )
return ret
def lowerCamelCase ( UpperCAmelCase__ : MyNode ) -> MyNode:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :str = node.get_left()
assert left_child is not None
node.set_left(left_rotation(UpperCAmelCase__ ) )
return right_rotation(UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : MyNode ) -> MyNode:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :Optional[Any] = node.get_right()
assert right_child is not None
node.set_right(right_rotation(UpperCAmelCase__ ) )
return left_rotation(UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : MyNode | None , UpperCAmelCase__ : Any ) -> MyNode | None:
'''simple docstring'''
if node is None:
return MyNode(UpperCAmelCase__ )
if data < node.get_data():
node.set_left(insert_node(node.get_left() , UpperCAmelCase__ ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
SCREAMING_SNAKE_CASE__ :Union[str, Any] = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
SCREAMING_SNAKE_CASE__ :Dict = right_rotation(UpperCAmelCase__ )
else:
SCREAMING_SNAKE_CASE__ :str = lr_rotation(UpperCAmelCase__ )
else:
node.set_right(insert_node(node.get_right() , UpperCAmelCase__ ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
SCREAMING_SNAKE_CASE__ :Optional[int] = node.get_right()
assert right_child is not None
if data < right_child.get_data():
SCREAMING_SNAKE_CASE__ :Optional[Any] = rl_rotation(UpperCAmelCase__ )
else:
SCREAMING_SNAKE_CASE__ :Union[str, Any] = left_rotation(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :Tuple = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(UpperCAmelCase__ )
return node
def lowerCamelCase ( UpperCAmelCase__ : MyNode ) -> Any:
'''simple docstring'''
while True:
SCREAMING_SNAKE_CASE__ :Optional[int] = root.get_right()
if right_child is None:
break
SCREAMING_SNAKE_CASE__ :str = right_child
return root.get_data()
def lowerCamelCase ( UpperCAmelCase__ : MyNode ) -> Any:
'''simple docstring'''
while True:
SCREAMING_SNAKE_CASE__ :Optional[int] = root.get_left()
if left_child is None:
break
SCREAMING_SNAKE_CASE__ :List[Any] = left_child
return root.get_data()
def lowerCamelCase ( UpperCAmelCase__ : MyNode , UpperCAmelCase__ : Any ) -> MyNode | None:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :List[Any] = root.get_left()
SCREAMING_SNAKE_CASE__ :Optional[int] = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
SCREAMING_SNAKE_CASE__ :int = get_left_most(UpperCAmelCase__ )
root.set_data(UpperCAmelCase__ )
root.set_right(del_node(UpperCAmelCase__ , UpperCAmelCase__ ) )
elif left_child is not None:
SCREAMING_SNAKE_CASE__ :Union[str, Any] = left_child
elif right_child is not None:
SCREAMING_SNAKE_CASE__ :Optional[int] = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print('No such data' )
return root
else:
root.set_left(del_node(UpperCAmelCase__ , UpperCAmelCase__ ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(UpperCAmelCase__ , UpperCAmelCase__ ) )
if get_height(UpperCAmelCase__ ) - get_height(UpperCAmelCase__ ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
SCREAMING_SNAKE_CASE__ :Any = left_rotation(UpperCAmelCase__ )
else:
SCREAMING_SNAKE_CASE__ :int = rl_rotation(UpperCAmelCase__ )
elif get_height(UpperCAmelCase__ ) - get_height(UpperCAmelCase__ ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
SCREAMING_SNAKE_CASE__ :Any = right_rotation(UpperCAmelCase__ )
else:
SCREAMING_SNAKE_CASE__ :str = lr_rotation(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :Optional[Any] = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1
root.set_height(UpperCAmelCase__ )
return root
class _SCREAMING_SNAKE_CASE:
def __init__( self : List[Any] ) -> None:
SCREAMING_SNAKE_CASE__ :MyNode | None = None
def __lowerCamelCase ( self : Optional[Any] ) -> int:
return get_height(self.root )
def __lowerCamelCase ( self : int , UpperCamelCase_ : Any ) -> None:
print('insert:' + str(UpperCamelCase_ ) )
SCREAMING_SNAKE_CASE__ :Dict = insert_node(self.root , UpperCamelCase_ )
def __lowerCamelCase ( self : str , UpperCamelCase_ : Any ) -> None:
print('delete:' + str(UpperCamelCase_ ) )
if self.root is None:
print('Tree is empty!' )
return
SCREAMING_SNAKE_CASE__ :List[Any] = del_node(self.root , UpperCamelCase_ )
def __str__( self : List[Any] , ) -> str: # a level traversale, gives a more intuitive look on the tree
SCREAMING_SNAKE_CASE__ :List[str] = ''
SCREAMING_SNAKE_CASE__ :Optional[Any] = MyQueue()
q.push(self.root )
SCREAMING_SNAKE_CASE__ :int = self.get_height()
if layer == 0:
return output
SCREAMING_SNAKE_CASE__ :str = 0
while not q.is_empty():
SCREAMING_SNAKE_CASE__ :Optional[int] = q.pop()
SCREAMING_SNAKE_CASE__ :List[str] = ' ' * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(UpperCamelCase_ )
q.push(UpperCamelCase_ )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
SCREAMING_SNAKE_CASE__ :Tuple = cnt + 1
for i in range(1_00 ):
if cnt == math.pow(2 , UpperCamelCase_ ) - 1:
SCREAMING_SNAKE_CASE__ :Optional[int] = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def lowerCamelCase ( ) -> None:
'''simple docstring'''
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
UpperCamelCase_ = AVLtree()
UpperCamelCase_ = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 209
|
'''simple docstring'''
from __future__ import annotations
UpperCamelCase_ = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCamelCase ( UpperCAmelCase__ : list[list[int]] , UpperCAmelCase__ : list[int] , UpperCAmelCase__ : list[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : list[list[int]] , ) -> tuple[list[list[int]], list[list[int]]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :str = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCAmelCase__ ) )
] # the reference grid
SCREAMING_SNAKE_CASE__ :Any = 1
SCREAMING_SNAKE_CASE__ :Dict = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCAmelCase__ ) )
] # the action grid
SCREAMING_SNAKE_CASE__ :int = init[0]
SCREAMING_SNAKE_CASE__ :Optional[Any] = init[1]
SCREAMING_SNAKE_CASE__ :List[str] = 0
SCREAMING_SNAKE_CASE__ :List[Any] = g + heuristic[x][y] # cost from starting cell to destination cell
SCREAMING_SNAKE_CASE__ :List[Any] = [[f, g, x, y]]
SCREAMING_SNAKE_CASE__ :Any = False # flag that is set when search is complete
SCREAMING_SNAKE_CASE__ :str = False # flag set if we can't find expand
while not found and not resign:
if len(UpperCAmelCase__ ) == 0:
raise ValueError('Algorithm is unable to find solution' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
SCREAMING_SNAKE_CASE__ :List[Any] = cell.pop()
SCREAMING_SNAKE_CASE__ :Optional[int] = next_cell[2]
SCREAMING_SNAKE_CASE__ :Any = next_cell[3]
SCREAMING_SNAKE_CASE__ :Dict = next_cell[1]
if x == goal[0] and y == goal[1]:
SCREAMING_SNAKE_CASE__ :Tuple = True
else:
for i in range(len(UpperCAmelCase__ ) ): # to try out different valid actions
SCREAMING_SNAKE_CASE__ :Optional[int] = x + DIRECTIONS[i][0]
SCREAMING_SNAKE_CASE__ :int = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(UpperCAmelCase__ ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
SCREAMING_SNAKE_CASE__ :str = g + cost
SCREAMING_SNAKE_CASE__ :Union[str, Any] = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = 1
SCREAMING_SNAKE_CASE__ :Any = i
SCREAMING_SNAKE_CASE__ :int = []
SCREAMING_SNAKE_CASE__ :Union[str, Any] = goal[0]
SCREAMING_SNAKE_CASE__ :Optional[int] = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
SCREAMING_SNAKE_CASE__ :Optional[Any] = x - DIRECTIONS[action[x][y]][0]
SCREAMING_SNAKE_CASE__ :Optional[Any] = y - DIRECTIONS[action[x][y]][1]
SCREAMING_SNAKE_CASE__ :Optional[int] = xa
SCREAMING_SNAKE_CASE__ :int = ya
invpath.append([x, y] )
SCREAMING_SNAKE_CASE__ :int = []
for i in range(len(UpperCAmelCase__ ) ):
path.append(invpath[len(UpperCAmelCase__ ) - 1 - i] )
return path, action
if __name__ == "__main__":
UpperCamelCase_ = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
UpperCamelCase_ = [0, 0]
# all coordinates are given in format [y,x]
UpperCamelCase_ = [len(grid) - 1, len(grid[0]) - 1]
UpperCamelCase_ = 1
# the cost map which pushes the path closer to the goal
UpperCamelCase_ = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
UpperCamelCase_ = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
UpperCamelCase_ = 99
UpperCamelCase_ , UpperCamelCase_ = search(grid, init, goal, cost, heuristic)
print('''ACTION MAP''')
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 209
| 1
|
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
_lowerCamelCase ="%20".join(argv[1:]) if len(argv) > 1 else quote(str(input("Search: ")))
print("Googling.....")
_lowerCamelCase =f'https://www.google.com/search?q={query}&num=100'
_lowerCamelCase =requests.get(
url,
headers={"User-Agent": str(UserAgent().random)},
)
try:
_lowerCamelCase =(
BeautifulSoup(res.text, "html.parser")
.find("div", attrs={"class": "yuRUbf"})
.find("a")
.get("href")
)
except AttributeError:
_lowerCamelCase =parse_qs(
BeautifulSoup(res.text, "html.parser")
.find("div", attrs={"class": "kCrYT"})
.find("a")
.get("href")
)["url"][0]
webbrowser.open(link)
| 252
|
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCamelCase =16
_lowerCamelCase =32
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = 16 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('bert-base-cased' )
SCREAMING_SNAKE_CASE =DatasetDict(
{
'train': dataset['train'].select(lowerCAmelCase_ ),
'validation': dataset['train'].select(lowerCAmelCase_ ),
'test': dataset['validation'],
} )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE =tokenizer(examples['sentence1'], examples['sentence2'], truncation=lowerCAmelCase_, max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE =datasets.map(
lowerCAmelCase_, batched=lowerCAmelCase_, remove_columns=['idx', 'sentence1', 'sentence2'], )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE =tokenized_datasets.rename_column('label', 'labels' )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE =128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE =16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE =8
else:
SCREAMING_SNAKE_CASE =None
return tokenizer.pad(
lowerCAmelCase_, padding='longest', max_length=lowerCAmelCase_, pad_to_multiple_of=lowerCAmelCase_, return_tensors='pt', )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['train'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['validation'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['test'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader, test_dataloader
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
# Download the dataset
SCREAMING_SNAKE_CASE =load_dataset('glue', 'mrpc' )
# Create our splits
SCREAMING_SNAKE_CASE =StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
SCREAMING_SNAKE_CASE =Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE =config['lr']
SCREAMING_SNAKE_CASE =int(config['num_epochs'] )
SCREAMING_SNAKE_CASE =int(config['seed'] )
SCREAMING_SNAKE_CASE =int(config['batch_size'] )
SCREAMING_SNAKE_CASE =evaluate.load('glue', 'mrpc' )
# If the batch size is too big we use gradient accumulation
SCREAMING_SNAKE_CASE =1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
SCREAMING_SNAKE_CASE =batch_size // MAX_GPU_BATCH_SIZE
SCREAMING_SNAKE_CASE =MAX_GPU_BATCH_SIZE
set_seed(lowerCAmelCase_ )
# New Code #
# Create our folds:
SCREAMING_SNAKE_CASE =kfold.split(np.zeros(datasets['train'].num_rows ), datasets['train']['label'] )
SCREAMING_SNAKE_CASE =[]
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =get_fold_dataloaders(
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE =AutoModelForSequenceClassification.from_pretrained('bert-base-cased', return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE =model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE =AdamW(params=model.parameters(), lr=lowerCAmelCase_ )
# Instantiate scheduler
SCREAMING_SNAKE_CASE =get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_, num_warmup_steps=100, num_training_steps=(len(lowerCAmelCase_ ) * num_epochs) // gradient_accumulation_steps, )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.loss
SCREAMING_SNAKE_CASE =loss / gradient_accumulation_steps
accelerator.backward(lowerCAmelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowerCAmelCase_, references=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:', lowerCAmelCase_ )
# New Code #
# We also run predictions on the test set at the very end
SCREAMING_SNAKE_CASE =[]
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.logits
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.gather_for_metrics((predictions, batch['labels']) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(lowerCAmelCase_, dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
SCREAMING_SNAKE_CASE =torch.cat(lowerCAmelCase_, dim=0 )
SCREAMING_SNAKE_CASE =torch.stack(lowerCAmelCase_, dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
SCREAMING_SNAKE_CASE =metric.compute(predictions=lowerCAmelCase_, references=lowerCAmelCase_ )
accelerator.print('Average test metrics from all folds:', lowerCAmelCase_ )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision', type=lowerCAmelCase_, default=lowerCAmelCase_, choices=['no', 'fp16', 'bf16', 'fp8'], help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.', )
parser.add_argument('--cpu', action='store_true', help='If passed, will train on the CPU.' )
# New Code #
parser.add_argument('--num_folds', type=lowerCAmelCase_, default=3, help='The number of splits to perform across the dataset' )
SCREAMING_SNAKE_CASE =parser.parse_args()
SCREAMING_SNAKE_CASE ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(lowerCAmelCase_, lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 252
| 1
|
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCamelCase : Union[str, Any] = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase : Optional[int] = importlib.util.spec_from_file_location(
'''transformers''',
os.path.join(PATH_TO_TRANSFORMERS, '''__init__.py'''),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
__lowerCamelCase : List[Any] = spec.loader.load_module()
__lowerCamelCase : Union[str, Any] = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__lowerCamelCase : Dict = re.compile('''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')
__lowerCamelCase : Union[str, Any] = {
'''CLIPConfigMixin''',
'''DecisionTransformerConfigMixin''',
'''EncoderDecoderConfigMixin''',
'''RagConfigMixin''',
'''SpeechEncoderDecoderConfigMixin''',
'''VisionEncoderDecoderConfigMixin''',
'''VisionTextDualEncoderConfigMixin''',
}
def _snake_case ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = []
for config_class in list(CONFIG_MAPPING.values() ):
SCREAMING_SNAKE_CASE_ : Dict = False
# source code of `config_class`
SCREAMING_SNAKE_CASE_ : Union[str, Any] = inspect.getsource(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[int] = _re_checkpoint.findall(lowerCAmelCase )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
SCREAMING_SNAKE_CASE_ : Any = f'https://huggingface.co/{ckpt_name}'
if ckpt_link == ckpt_link_from_name:
SCREAMING_SNAKE_CASE_ : int = True
break
SCREAMING_SNAKE_CASE_ : List[Any] = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(lowerCAmelCase )
if len(lowerCAmelCase ) > 0:
SCREAMING_SNAKE_CASE_ : Optional[Any] = "\n".join(sorted(lowerCAmelCase ) )
raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 216
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
__lowerCamelCase : int = {'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Union[str, Any] = ['''HerbertTokenizerFast''']
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
__lowerCamelCase : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 216
| 1
|
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
lowercase : List[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
lowercase : Tuple = "\n Examples:\n ```py\n >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")\n >>> pipe_prior.to(\"cuda\")\n\n >>> prompt = \"red cat, 4k photo\"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> negative_image_emb = out.negative_image_embeds\n\n >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")\n >>> pipe.to(\"cuda\")\n\n >>> image = pipe(\n ... prompt,\n ... image_embeds=image_emb,\n ... negative_image_embeds=negative_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... ).images\n\n >>> image[0].save(\"cat.png\")\n ```\n"
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=8 ):
A : Any = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
A : List[Any] = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class __lowercase ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) -> Tuple:
super().__init__()
self.register_modules(
text_encoder=__UpperCAmelCase , tokenizer=__UpperCAmelCase , unet=__UpperCAmelCase , scheduler=__UpperCAmelCase , movq=__UpperCAmelCase , )
A : Optional[Any] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
if latents is None:
A : List[Any] = randn_tensor(__UpperCAmelCase , generator=__UpperCAmelCase , device=__UpperCAmelCase , dtype=__UpperCAmelCase )
else:
if latents.shape != shape:
raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}' )
A : str = latents.to(__UpperCAmelCase )
A : Union[str, Any] = latents * scheduler.init_noise_sigma
return latents
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None , ) -> int:
A : Optional[int] = len(__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else 1
# get prompt text embeddings
A : List[Any] = self.tokenizer(
__UpperCAmelCase , padding='''max_length''' , truncation=__UpperCAmelCase , max_length=77 , return_attention_mask=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , return_tensors='''pt''' , )
A : int = text_inputs.input_ids
A : Dict = self.tokenizer(__UpperCAmelCase , padding='''longest''' , return_tensors='''pt''' ).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__UpperCAmelCase , __UpperCAmelCase ):
A : Dict = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
f' {self.tokenizer.model_max_length} tokens: {removed_text}' )
A : Optional[int] = text_input_ids.to(__UpperCAmelCase )
A : List[str] = text_inputs.attention_mask.to(__UpperCAmelCase )
A , A : Tuple = self.text_encoder(
input_ids=__UpperCAmelCase , attention_mask=__UpperCAmelCase )
A : Optional[int] = prompt_embeds.repeat_interleave(__UpperCAmelCase , dim=0 )
A : Dict = text_encoder_hidden_states.repeat_interleave(__UpperCAmelCase , dim=0 )
A : Tuple = text_mask.repeat_interleave(__UpperCAmelCase , dim=0 )
if do_classifier_free_guidance:
A : List[str]
if negative_prompt is None:
A : Optional[Any] = [''''''] * batch_size
elif type(__UpperCAmelCase ) is not type(__UpperCAmelCase ):
raise TypeError(
f'`negative_prompt` should be the same type to `prompt`, but got {type(__UpperCAmelCase )} !='
f' {type(__UpperCAmelCase )}.' )
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ):
A : Optional[int] = [negative_prompt]
elif batch_size != len(__UpperCAmelCase ):
raise ValueError(
f'`negative_prompt`: {negative_prompt} has batch size {len(__UpperCAmelCase )}, but `prompt`:'
f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
''' the batch size of `prompt`.''' )
else:
A : str = negative_prompt
A : Tuple = self.tokenizer(
__UpperCAmelCase , padding='''max_length''' , max_length=77 , truncation=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , return_tensors='''pt''' , )
A : Optional[int] = uncond_input.input_ids.to(__UpperCAmelCase )
A : Optional[Any] = uncond_input.attention_mask.to(__UpperCAmelCase )
A , A : Union[str, Any] = self.text_encoder(
input_ids=__UpperCAmelCase , attention_mask=__UpperCAmelCase )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
A : Union[str, Any] = negative_prompt_embeds.shape[1]
A : Optional[Any] = negative_prompt_embeds.repeat(1 , __UpperCAmelCase )
A : List[str] = negative_prompt_embeds.view(batch_size * num_images_per_prompt , __UpperCAmelCase )
A : Optional[Any] = uncond_text_encoder_hidden_states.shape[1]
A : Union[str, Any] = uncond_text_encoder_hidden_states.repeat(1 , __UpperCAmelCase , 1 )
A : Any = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt , __UpperCAmelCase , -1 )
A : Optional[Any] = uncond_text_mask.repeat_interleave(__UpperCAmelCase , dim=0 )
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
A : str = torch.cat([negative_prompt_embeds, prompt_embeds] )
A : int = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
A : Any = torch.cat([uncond_text_mask, text_mask] )
return prompt_embeds, text_encoder_hidden_states, text_mask
def snake_case ( self , __UpperCAmelCase=0 ) -> Tuple:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
A : str = torch.device(f'cuda:{gpu_id}' )
A : str = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__UpperCAmelCase , __UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase=0 ) -> Optional[int]:
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
A : List[Any] = torch.device(f'cuda:{gpu_id}' )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=__UpperCAmelCase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
A : Optional[Any] = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
A , A : Tuple = cpu_offload_with_hook(__UpperCAmelCase , __UpperCAmelCase , prev_module_hook=__UpperCAmelCase )
if self.safety_checker is not None:
A , A : Any = cpu_offload_with_hook(self.safety_checker , __UpperCAmelCase , prev_module_hook=__UpperCAmelCase )
# We'll offload the last model manually.
A : Dict = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def snake_case ( self ) -> Any:
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(__UpperCAmelCase , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__UpperCAmelCase )
def __call__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = 5_12 , __UpperCAmelCase = 5_12 , __UpperCAmelCase = 1_00 , __UpperCAmelCase = 4.0 , __UpperCAmelCase = 1 , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = "pil" , __UpperCAmelCase = True , ) -> Optional[int]:
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
A : Union[str, Any] = 1
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ):
A : Tuple = len(__UpperCAmelCase )
else:
raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(__UpperCAmelCase )}' )
A : str = self._execution_device
A : str = batch_size * num_images_per_prompt
A : Tuple = guidance_scale > 1.0
A , A , A : List[str] = self._encode_prompt(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
A : Any = torch.cat(__UpperCAmelCase , dim=0 )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
A : List[Any] = torch.cat(__UpperCAmelCase , dim=0 )
if do_classifier_free_guidance:
A : Optional[int] = image_embeds.repeat_interleave(__UpperCAmelCase , dim=0 )
A : int = negative_image_embeds.repeat_interleave(__UpperCAmelCase , dim=0 )
A : Dict = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(
dtype=prompt_embeds.dtype , device=__UpperCAmelCase )
self.scheduler.set_timesteps(__UpperCAmelCase , device=__UpperCAmelCase )
A : Any = self.scheduler.timesteps
A : List[Any] = self.unet.config.in_channels
A , A : int = get_new_h_w(__UpperCAmelCase , __UpperCAmelCase , self.movq_scale_factor )
# create initial latent
A : List[Any] = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , text_encoder_hidden_states.dtype , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , self.scheduler , )
for i, t in enumerate(self.progress_bar(__UpperCAmelCase ) ):
# expand the latents if we are doing classifier free guidance
A : int = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
A : str = {'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
A : List[str] = self.unet(
sample=__UpperCAmelCase , timestep=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , added_cond_kwargs=__UpperCAmelCase , return_dict=__UpperCAmelCase , )[0]
if do_classifier_free_guidance:
A , A : List[Any] = noise_pred.split(latents.shape[1] , dim=1 )
A , A : int = noise_pred.chunk(2 )
A , A : List[Any] = variance_pred.chunk(2 )
A : str = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
A : Union[str, Any] = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
A , A : Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
A : Any = self.scheduler.step(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , generator=__UpperCAmelCase , ).prev_sample
# post-processing
A : str = self.movq.decode(__UpperCAmelCase , force_not_quantize=__UpperCAmelCase )['''sample''']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )
if output_type in ["np", "pil"]:
A : Any = image * 0.5 + 0.5
A : List[str] = image.clamp(0 , 1 )
A : List[str] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
A : Tuple = self.numpy_to_pil(__UpperCAmelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__UpperCAmelCase )
| 423
|
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
lowercase : int = logging.get_logger(__name__)
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
A : str = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(lowerCamelCase_ ) == len(lowerCamelCase_ ), F'{len(lowerCamelCase_ )} != {len(lowerCamelCase_ )}'
dest_layers.load_state_dict(layers_to_copy.state_dict() )
lowercase : Optional[int] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
lowercase : Union[str, Any] = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
try:
A : Dict = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'
F' {n_student}' )
return list(range(lowerCamelCase_ ) )
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
if n_student > n_teacher:
raise ValueError(F'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}' )
elif n_teacher == n_student:
return list(range(lowerCamelCase_ ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ = "student" , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_=False , lowerCamelCase_=None , lowerCamelCase_=None , **lowerCamelCase_ , ):
A : List[str] = '''encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'''
assert (e is not None) or (d is not None), _msg
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
AutoTokenizer.from_pretrained(lowerCamelCase_ ).save_pretrained(lowerCamelCase_ ) # purely for convenience
A : List[Any] = AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_ ).eval()
else:
assert isinstance(lowerCamelCase_ , lowerCamelCase_ ), F'teacher must be a model or string got type {type(lowerCamelCase_ )}'
A : Tuple = teacher.config.to_diff_dict()
try:
A , A : str = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
A : str = teacher_e
if d is None:
A : List[str] = teacher_d
init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d} )
except AttributeError: # T5
if hasattr(teacher.config , '''num_encoder_layers''' ):
A , A : str = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
A , A : List[str] = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
A : Union[str, Any] = teacher_e
if d is None:
A : Any = teacher_d
if hasattr(teacher.config , '''num_encoder_layers''' ):
init_kwargs.update({'''num_encoder_layers''': e, '''num_decoder_layers''': d} )
else:
init_kwargs.update({'''num_layers''': e, '''num_decoder_layers''': d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(lowerCamelCase_ )
# Copy weights
A : Dict = teacher.config_class(**lowerCamelCase_ )
A : List[str] = AutoModelForSeqaSeqLM.from_config(lowerCamelCase_ )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
A : int = student.load_state_dict(teacher.state_dict() , strict=lowerCamelCase_ )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
A , A : Tuple = list(range(lowerCamelCase_ ) ), list(range(lowerCamelCase_ ) )
logger.info(
F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'
F' {save_path}' )
student.save_pretrained(lowerCamelCase_ )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
A : List[int] = pick_layers_to_copy(lowerCamelCase_ , lowerCamelCase_ )
if d_layers_to_copy is None:
A : List[int] = pick_layers_to_copy(lowerCamelCase_ , lowerCamelCase_ )
try:
if hasattr(
lowerCamelCase_ , '''prophetnet''' ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , lowerCamelCase_ )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , lowerCamelCase_ )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , lowerCamelCase_ )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , lowerCamelCase_ )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , lowerCamelCase_ )
copy_layers(teacher.decoder.block , student.decoder.block , lowerCamelCase_ )
logger.info(
F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}' )
A : Tuple = {
'''teacher_type''': teacher.config.model_type,
'''copied_encoder_layers''': e_layers_to_copy,
'''copied_decoder_layers''': d_layers_to_copy,
}
student.save_pretrained(lowerCamelCase_ )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 423
| 1
|
"""simple docstring"""
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = logging.get_logger()
# the current default level is logging.WARNING
lowerCAmelCase = logging.get_verbosity()
logging.set_verbosity_error()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_warning()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_info()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_debug()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
# restore to the original level
logging.set_verbosity(snake_case_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = logging.get_verbosity()
lowerCAmelCase = logging.get_logger('transformers.models.bart.tokenization_bart' )
lowerCAmelCase = """Testing 1, 2, 3"""
# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
if level_origin <= logging.WARNING:
with CaptureLogger(snake_case_ ) as cl:
logger.warning(snake_case_ )
self.assertEqual(cl.out , msg + '\n' )
# this is setting the level for all of `transformers.*` loggers
logging.set_verbosity_error()
# should not be able to log warnings
with CaptureLogger(snake_case_ ) as cl:
logger.warning(snake_case_ )
self.assertEqual(cl.out , '' )
# should be able to log warnings again
logging.set_verbosity_warning()
with CaptureLogger(snake_case_ ) as cl:
logger.warning(snake_case_ )
self.assertEqual(cl.out , msg + '\n' )
# restore to the original level
logging.set_verbosity(snake_case_ )
@mockenv(TRANSFORMERS_VERBOSITY='error' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
transformers.utils.logging._reset_library_root_logger()
# this action activates the env var
lowerCAmelCase = logging.get_logger('transformers.models.bart.tokenization_bart' )
lowerCAmelCase = os.getenv('TRANSFORMERS_VERBOSITY' , snake_case_ )
lowerCAmelCase = logging.log_levels[env_level_str]
lowerCAmelCase = logging.get_verbosity()
self.assertEqual(
snake_case_ , snake_case_ , F'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}' , )
# restore to the original level
lowerCAmelCase = """"""
transformers.utils.logging._reset_library_root_logger()
@mockenv(TRANSFORMERS_VERBOSITY='super-error' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
transformers.utils.logging._reset_library_root_logger()
lowerCAmelCase = logging.logging.getLogger()
with CaptureLogger(snake_case_ ) as cl:
# this action activates the env var
logging.get_logger('transformers.models.bart.tokenization_bart' )
self.assertIn('Unknown option TRANSFORMERS_VERBOSITY=super-error' , cl.out )
# no need to restore as nothing was changed
def UpperCamelCase__ ( self ):
"""simple docstring"""
transformers.utils.logging._reset_library_root_logger()
lowerCAmelCase = logging.get_logger('transformers.models.bart.tokenization_bart' )
lowerCAmelCase = """Testing 1, 2, 3"""
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='1' ):
# nothing should be logged as env var disables this method
with CaptureLogger(snake_case_ ) as cl:
logger.warning_advice(snake_case_ )
self.assertEqual(cl.out , '' )
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='' ):
# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
with CaptureLogger(snake_case_ ) as cl:
logger.warning_advice(snake_case_ )
self.assertEqual(cl.out , msg + '\n' )
def _SCREAMING_SNAKE_CASE ():
disable_progress_bar()
assert are_progress_bars_disabled()
enable_progress_bar()
assert not are_progress_bars_disabled()
| 4
|
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
__a = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
lowercase = 42
lowercase = 42
lowercase = 42
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
lowercase = 42
lowercase = 42
lowercase = None
lowercase = None
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = "train"
lowercase = "dev"
lowercase = "test"
class UpperCAmelCase_ :
"""simple docstring"""
@staticmethod
def lowerCamelCase ( snake_case_ : Optional[Any] , snake_case_ : Union[Split, str] ):
raise NotImplementedError
@staticmethod
def lowerCamelCase ( snake_case_ : str ):
raise NotImplementedError
@staticmethod
def lowerCamelCase ( snake_case_ : List[InputExample] , snake_case_ : List[str] , snake_case_ : int , snake_case_ : PreTrainedTokenizer , snake_case_ : Optional[int]=False , snake_case_ : Dict="[CLS]" , snake_case_ : str=1 , snake_case_ : Dict="[SEP]" , snake_case_ : List[str]=False , snake_case_ : int=False , snake_case_ : Tuple=0 , snake_case_ : Union[str, Any]=0 , snake_case_ : List[Any]=-100 , snake_case_ : Any=0 , snake_case_ : Union[str, Any]=True , ):
snake_case__ : int = {label: i for i, label in enumerate(snake_case_ )}
snake_case__ : List[Any] = []
for ex_index, example in enumerate(snake_case_ ):
if ex_index % 10_000 == 0:
logger.info("""Writing example %d of %d""" , snake_case_ , len(snake_case_ ) )
snake_case__ : Tuple = []
snake_case__ : Dict = []
for word, label in zip(example.words , example.labels ):
snake_case__ : Any = tokenizer.tokenize(snake_case_ )
# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
if len(snake_case_ ) > 0:
tokens.extend(snake_case_ )
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(snake_case_ ) - 1) )
# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
snake_case__ : Dict = tokenizer.num_special_tokens_to_add()
if len(snake_case_ ) > max_seq_length - special_tokens_count:
snake_case__ : Tuple = tokens[: (max_seq_length - special_tokens_count)]
snake_case__ : Tuple = label_ids[: (max_seq_length - special_tokens_count)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens += [sep_token]
label_ids += [pad_token_label_id]
if sep_token_extra:
# roberta uses an extra separator b/w pairs of sentences
tokens += [sep_token]
label_ids += [pad_token_label_id]
snake_case__ : Dict = [sequence_a_segment_id] * len(snake_case_ )
if cls_token_at_end:
tokens += [cls_token]
label_ids += [pad_token_label_id]
segment_ids += [cls_token_segment_id]
else:
snake_case__ : str = [cls_token] + tokens
snake_case__ : Union[str, Any] = [pad_token_label_id] + label_ids
snake_case__ : Any = [cls_token_segment_id] + segment_ids
snake_case__ : Union[str, Any] = tokenizer.convert_tokens_to_ids(snake_case_ )
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
snake_case__ : Tuple = [1 if mask_padding_with_zero else 0] * len(snake_case_ )
# Zero-pad up to the sequence length.
snake_case__ : Dict = max_seq_length - len(snake_case_ )
if pad_on_left:
snake_case__ : Optional[Any] = ([pad_token] * padding_length) + input_ids
snake_case__ : Union[str, Any] = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
snake_case__ : Union[str, Any] = ([pad_token_segment_id] * padding_length) + segment_ids
snake_case__ : Union[str, Any] = ([pad_token_label_id] * padding_length) + label_ids
else:
input_ids += [pad_token] * padding_length
input_mask += [0 if mask_padding_with_zero else 1] * padding_length
segment_ids += [pad_token_segment_id] * padding_length
label_ids += [pad_token_label_id] * padding_length
assert len(snake_case_ ) == max_seq_length
assert len(snake_case_ ) == max_seq_length
assert len(snake_case_ ) == max_seq_length
assert len(snake_case_ ) == max_seq_length
if ex_index < 5:
logger.info("""*** Example ***""" )
logger.info("""guid: %s""" , example.guid )
logger.info("""tokens: %s""" , """ """.join([str(snake_case_ ) for x in tokens] ) )
logger.info("""input_ids: %s""" , """ """.join([str(snake_case_ ) for x in input_ids] ) )
logger.info("""input_mask: %s""" , """ """.join([str(snake_case_ ) for x in input_mask] ) )
logger.info("""segment_ids: %s""" , """ """.join([str(snake_case_ ) for x in segment_ids] ) )
logger.info("""label_ids: %s""" , """ """.join([str(snake_case_ ) for x in label_ids] ) )
if "token_type_ids" not in tokenizer.model_input_names:
snake_case__ : Tuple = None
features.append(
InputFeatures(
input_ids=snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , label_ids=snake_case_ ) )
return features
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = 42
lowercase = nn.CrossEntropyLoss().ignore_index
def __init__( self : int , snake_case_ : TokenClassificationTask , snake_case_ : str , snake_case_ : PreTrainedTokenizer , snake_case_ : List[str] , snake_case_ : str , snake_case_ : Optional[int] = None , snake_case_ : Tuple=False , snake_case_ : Split = Split.train , ):
# Load data features from cache or dataset file
snake_case__ : Tuple = os.path.join(
snake_case_ , """cached_{}_{}_{}""".format(mode.value , tokenizer.__class__.__name__ , str(snake_case_ ) ) , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case__ : Dict = cached_features_file + """.lock"""
with FileLock(snake_case_ ):
if os.path.exists(snake_case_ ) and not overwrite_cache:
logger.info(f"Loading features from cached file {cached_features_file}" )
snake_case__ : Tuple = torch.load(snake_case_ )
else:
logger.info(f"Creating features from dataset file at {data_dir}" )
snake_case__ : Any = token_classification_task.read_examples_from_file(snake_case_ , snake_case_ )
# TODO clean up all this to leverage built-in features of tokenizers
snake_case__ : Any = token_classification_task.convert_examples_to_features(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , cls_token_at_end=bool(model_type in ["""xlnet"""] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=snake_case_ , pad_on_left=bool(tokenizer.padding_side == """left""" ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info(f"Saving features into cached file {cached_features_file}" )
torch.save(self.features , snake_case_ )
def __len__( self : str ):
return len(self.features )
def __getitem__( self : int , snake_case_ : Dict ):
return self.features[i]
if is_tf_available():
import tensorflow as tf
class UpperCAmelCase_ :
"""simple docstring"""
lowercase = 42
lowercase = -1_00
def __init__( self : List[str] , snake_case_ : TokenClassificationTask , snake_case_ : str , snake_case_ : PreTrainedTokenizer , snake_case_ : List[str] , snake_case_ : str , snake_case_ : Optional[int] = None , snake_case_ : Any=False , snake_case_ : Split = Split.train , ):
snake_case__ : int = token_classification_task.read_examples_from_file(snake_case_ , snake_case_ )
# TODO clean up all this to leverage built-in features of tokenizers
snake_case__ : Optional[Any] = token_classification_task.convert_examples_to_features(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , cls_token_at_end=bool(model_type in ["""xlnet"""] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=snake_case_ , pad_on_left=bool(tokenizer.padding_side == """left""" ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
def gen():
for ex in self.features:
if ex.token_type_ids is None:
yield (
{"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
ex.label_ids,
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label_ids,
)
if "token_type_ids" not in tokenizer.model_input_names:
snake_case__ : str = tf.data.Dataset.from_generator(
snake_case_ , ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa) , (
{"""input_ids""": tf.TensorShape([None] ), """attention_mask""": tf.TensorShape([None] )},
tf.TensorShape([None] ),
) , )
else:
snake_case__ : str = tf.data.Dataset.from_generator(
snake_case_ , ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa) , (
{
"""input_ids""": tf.TensorShape([None] ),
"""attention_mask""": tf.TensorShape([None] ),
"""token_type_ids""": tf.TensorShape([None] ),
},
tf.TensorShape([None] ),
) , )
def lowerCamelCase ( self : Optional[Any] ):
snake_case__ : Optional[Any] = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )
return self.dataset
def __len__( self : Optional[Any] ):
return len(self.features )
def __getitem__( self : str , snake_case_ : List[str] ):
return self.features[i]
| 374
| 0
|
'''simple docstring'''
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def snake_case ( a_ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = [
"""decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(a_ , a_ )
def snake_case ( a_ : int ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : List[str] = emb.weight.shape
UpperCamelCase_ : Optional[Any] = nn.Linear(a_ , a_ , bias=a_ )
UpperCamelCase_ : Union[str, Any] = emb.weight.data
return lin_layer
def snake_case ( a_ : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : str = torch.load(a_ , map_location="""cpu""" )
UpperCamelCase_ : str = Namespace(**checkpoint["""cfg"""]["""model"""] )
UpperCamelCase_ : Optional[int] = checkpoint["""model"""]
remove_ignore_keys_(a_ )
UpperCamelCase_ : Any = state_dict["""decoder.embed_tokens.weight"""].shape[0]
UpperCamelCase_ : Optional[int] = {key.replace("""decoder""" , """model""" ): val for key, val in state_dict.items()}
UpperCamelCase_ : List[Any] = XGLMConfig(
vocab_size=a_ , max_position_embeddings=args.max_target_positions , num_layers=args.decoder_layers , attention_heads=args.decoder_attention_heads , ffn_dim=args.decoder_ffn_embed_dim , d_model=args.decoder_embed_dim , layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="""gelu""" , scale_embedding=not args.no_scale_embedding , tie_word_embeddings=args.share_decoder_input_output_embed , )
UpperCamelCase_ : int = XGLMForCausalLM(a_ )
UpperCamelCase_ : Tuple = model.load_state_dict(a_ , strict=a_ )
print(a_ )
UpperCamelCase_ : str = make_linear_from_emb(model.model.embed_tokens )
return model
if __name__ == "__main__":
UpperCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCamelCase =parser.parse_args()
UpperCamelCase =convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
model.save_pretrained(args.pytorch_dump_folder_path)
| 717
|
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
UpperCamelCase =logging.get_logger(__name__)
UpperCamelCase ={
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
UpperCamelCase =[
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def snake_case ( a_ : str , a_ : Any , a_ : Dict , a_ : Optional[int] , a_ : Optional[int] ) -> int:
"""simple docstring"""
for attribute in key.split(""".""" ):
UpperCamelCase_ : Union[str, Any] = getattr(a_ , a_ )
if weight_type is not None:
UpperCamelCase_ : Dict = getattr(a_ , a_ ).shape
else:
UpperCamelCase_ : int = hf_pointer.shape
assert hf_shape == value.shape, (
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}"
)
if weight_type == "weight":
UpperCamelCase_ : str = value
elif weight_type == "weight_g":
UpperCamelCase_ : str = value
elif weight_type == "weight_v":
UpperCamelCase_ : Optional[int] = value
elif weight_type == "bias":
UpperCamelCase_ : Any = value
else:
UpperCamelCase_ : Dict = value
logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def snake_case ( a_ : Optional[int] , a_ : int ) -> int:
"""simple docstring"""
UpperCamelCase_ : int = []
UpperCamelCase_ : Dict = fairseq_model.state_dict()
UpperCamelCase_ : Union[str, Any] = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
UpperCamelCase_ : Optional[int] = None
for name, value in fairseq_dict.items():
UpperCamelCase_ : Optional[int] = False
if "conv_layers" in name:
load_conv_layer(
a_ , a_ , a_ , a_ , hf_model.config.feat_extract_norm == """group""" , )
UpperCamelCase_ : Any = True
elif name.split(""".""" )[0] == "proj":
UpperCamelCase_ : Tuple = fairseq_model.proj
UpperCamelCase_ : Any = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
UpperCamelCase_ : Dict = True
if "*" in mapped_key:
UpperCamelCase_ : Optional[int] = name.split(a_ )[0].split(""".""" )[-2]
UpperCamelCase_ : Any = mapped_key.replace("""*""" , a_ )
if "weight_g" in name:
UpperCamelCase_ : Tuple = """weight_g"""
elif "weight_v" in name:
UpperCamelCase_ : Union[str, Any] = """weight_v"""
elif "bias" in name:
UpperCamelCase_ : int = """bias"""
elif "weight" in name:
UpperCamelCase_ : Optional[int] = """weight"""
else:
UpperCamelCase_ : int = None
set_recursively(a_ , a_ , a_ , a_ , a_ )
continue
if not is_used:
unused_weights.append(a_ )
logger.warning(f"Unused weights: {unused_weights}" )
return proj_weight
def snake_case ( a_ : Any , a_ : Any , a_ : Union[str, Any] , a_ : Dict , a_ : Tuple ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = full_name.split("""conv_layers.""" )[-1]
UpperCamelCase_ : Optional[Any] = name.split(""".""" )
UpperCamelCase_ : List[Any] = int(items[0] )
UpperCamelCase_ : Optional[Any] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
UpperCamelCase_ : List[Any] = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
UpperCamelCase_ : Dict = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
" found."
)
UpperCamelCase_ : List[str] = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
)
UpperCamelCase_ : str = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(a_ )
def snake_case ( a_ : str ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ , UpperCamelCase_ : List[str] = emb.weight.shape
UpperCamelCase_ : str = nn.Linear(a_ , a_ , bias=a_ )
UpperCamelCase_ : Optional[int] = emb.weight.data
return lin_layer
def snake_case ( a_ : Any ) -> Tuple:
"""simple docstring"""
with open(a_ , """r""" , encoding="""utf-8""" ) as f:
UpperCamelCase_ : Optional[int] = f.readlines()
UpperCamelCase_ : Union[str, Any] = [line.split(""" """ )[0] for line in lines]
UpperCamelCase_ : List[Any] = len(a_ )
UpperCamelCase_ : Union[str, Any] = {
"""<s>""": 0,
"""<pad>""": 1,
"""</s>""": 2,
"""<unk>""": 3,
}
vocab_dict.update(dict(zip(a_ , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def snake_case ( a_ : List[Any] , a_ : str , a_ : int , a_ : int , a_ : Optional[int] , a_ : Any , a_ : Tuple , ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ : Dict = WavaVecaConfig.from_pretrained(a_ )
UpperCamelCase_ : Any = SpeechaTextaConfig.from_pretrained(
a_ , vocab_size=a_ , decoder_layers=a_ , do_stable_layer_norm=a_ )
UpperCamelCase_ : Optional[Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=a_ , return_attention_mask=a_ , )
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ : Dict = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
UpperCamelCase_ : Union[str, Any] = model[0].eval()
# set weights for wav2vec2 encoder
UpperCamelCase_ : Tuple = WavaVecaModel(a_ )
UpperCamelCase_ : Any = recursively_load_weights_wavaveca(model.encoder , a_ )
UpperCamelCase_ : Any = SpeechaTextaForCausalLM(a_ )
UpperCamelCase_ , UpperCamelCase_ : Optional[Any] = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=a_ )
# set output linear layer
unexpected_keys.remove("""embed_out""" )
UpperCamelCase_ : Tuple = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}" )
logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}" )
UpperCamelCase_ : Optional[Any] = SpeechEncoderDecoderModel(encoder=a_ , decoder=a_ )
UpperCamelCase_ : Dict = False
# add projection layer
UpperCamelCase_ : Any = nn.Parameter(projection_layer.weight )
UpperCamelCase_ : Optional[Any] = nn.Parameter(projection_layer.bias )
UpperCamelCase_ : Dict = create_vocab_dict(a_ )
with open(os.path.join(a_ , """vocab.json""" ) , """w""" ) as fp:
json.dump(a_ , a_ )
UpperCamelCase_ : Optional[int] = SpeechaTextaTokenizer(os.path.join(a_ , """vocab.json""" ) )
tokenizer.save_pretrained(a_ )
UpperCamelCase_ : Optional[int] = hf_wavavec.config.to_dict()
UpperCamelCase_ : Union[str, Any] = tokenizer.pad_token_id
UpperCamelCase_ : List[Any] = tokenizer.bos_token_id
UpperCamelCase_ : str = tokenizer.eos_token_id
UpperCamelCase_ : Dict = """speech_to_text_2"""
UpperCamelCase_ : Optional[Any] = """wav2vec2"""
UpperCamelCase_ : Dict = SpeechEncoderDecoderConfig.from_dict(a_ )
hf_wavavec.save_pretrained(a_ )
feature_extractor.save_pretrained(a_ )
if __name__ == "__main__":
UpperCamelCase =argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument(
"--encoder_config_path",
default="facebook/wav2vec2-large-lv60",
type=str,
help="Path to hf encoder wav2vec2 checkpoint config",
)
parser.add_argument(
"--decoder_config_path",
default="facebook/s2t-small-mustc-en-fr-st",
type=str,
help="Path to hf decoder s2t checkpoint config",
)
parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")
UpperCamelCase =parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
)
| 543
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'microsoft/swin-tiny-patch4-window7-224': (
'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class _lowerCAmelCase ( __snake_case , __snake_case ):
'''simple docstring'''
lowerCAmelCase_ = "swin"
lowerCAmelCase_ = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__(self , UpperCAmelCase=224 , UpperCAmelCase=4 , UpperCAmelCase=3 , UpperCAmelCase=96 , UpperCAmelCase=[2, 2, 6, 2] , UpperCAmelCase=[3, 6, 12, 24] , UpperCAmelCase=7 , UpperCAmelCase=4.0 , UpperCAmelCase=True , UpperCAmelCase=0.0 , UpperCAmelCase=0.0 , UpperCAmelCase=0.1 , UpperCAmelCase="gelu" , UpperCAmelCase=False , UpperCAmelCase=0.02 , UpperCAmelCase=1e-5 , UpperCAmelCase=32 , UpperCAmelCase=None , UpperCAmelCase=None , **UpperCAmelCase , ) -> Tuple:
super().__init__(**SCREAMING_SNAKE_CASE__ )
_snake_case = image_size
_snake_case = patch_size
_snake_case = num_channels
_snake_case = embed_dim
_snake_case = depths
_snake_case = len(SCREAMING_SNAKE_CASE__ )
_snake_case = num_heads
_snake_case = window_size
_snake_case = mlp_ratio
_snake_case = qkv_bias
_snake_case = hidden_dropout_prob
_snake_case = attention_probs_dropout_prob
_snake_case = drop_path_rate
_snake_case = hidden_act
_snake_case = use_absolute_embeddings
_snake_case = layer_norm_eps
_snake_case = initializer_range
_snake_case = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_snake_case = int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__ ) - 1) )
_snake_case = ["""stem"""] + [f"""stage{idx}""" for idx in range(1 , len(SCREAMING_SNAKE_CASE__ ) + 1 )]
_snake_case, _snake_case = get_aligned_output_features_output_indices(
out_features=SCREAMING_SNAKE_CASE__ , out_indices=SCREAMING_SNAKE_CASE__ , stage_names=self.stage_names )
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCAmelCase_ = version.parse("1.11" )
@property
def lowercase (self ) -> int:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowercase (self ) -> int:
return 1e-4
| 585
|
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _lowerCAmelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] =None
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_dict )
UpperCamelCase = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase = os.path.join(SCREAMING_SNAKE_CASE__ , 'feat_extract.json' )
feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE__ )
UpperCamelCase = self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase = feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE__ )[0]
check_json_file_has_correct_format(SCREAMING_SNAKE_CASE__ )
UpperCamelCase = self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class()
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
| 282
| 0
|
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
_lowerCAmelCase = logging.get_logger(__name__)
class A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A = ["input_features", "is_longer"]
def __init__(self , _UpperCAmelCase=6_4 , _UpperCAmelCase=4_8_0_0_0 , _UpperCAmelCase=4_8_0 , _UpperCAmelCase=1_0 , _UpperCAmelCase=1_0_2_4 , _UpperCAmelCase=0.0 , _UpperCAmelCase=False , _UpperCAmelCase = 0 , _UpperCAmelCase = 1_4_0_0_0 , _UpperCAmelCase = None , _UpperCAmelCase = "fusion" , _UpperCAmelCase = "repeatpad" , **_UpperCAmelCase , ) -> List[str]:
super().__init__(
feature_size=_UpperCAmelCase , sampling_rate=_UpperCAmelCase , padding_value=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , **_UpperCAmelCase , )
__UpperCamelCase : Tuple = top_db
__UpperCamelCase : Tuple = truncation
__UpperCamelCase : int = padding
__UpperCamelCase : Union[str, Any] = fft_window_size
__UpperCamelCase : Tuple = (fft_window_size >> 1) + 1
__UpperCamelCase : Union[str, Any] = hop_length
__UpperCamelCase : Optional[Any] = max_length_s
__UpperCamelCase : Optional[int] = max_length_s * sampling_rate
__UpperCamelCase : int = sampling_rate
__UpperCamelCase : Union[str, Any] = frequency_min
__UpperCamelCase : Optional[int] = frequency_max
__UpperCamelCase : Optional[int] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=_UpperCAmelCase , min_frequency=_UpperCAmelCase , max_frequency=_UpperCAmelCase , sampling_rate=_UpperCAmelCase , norm=_UpperCAmelCase , mel_scale="htk" , )
__UpperCamelCase : Optional[int] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=_UpperCAmelCase , min_frequency=_UpperCAmelCase , max_frequency=_UpperCAmelCase , sampling_rate=_UpperCAmelCase , norm="slaney" , mel_scale="slaney" , )
def a_ (self ) -> Dict[str, Any]:
__UpperCamelCase : Optional[Any] = copy.deepcopy(self.__dict__ )
__UpperCamelCase : Union[str, Any] = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def a_ (self , _UpperCAmelCase , _UpperCAmelCase = None ) -> np.ndarray:
__UpperCamelCase : Optional[int] = spectrogram(
_UpperCAmelCase , window_function(self.fft_window_size , "hann" ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=_UpperCAmelCase , log_mel="dB" , )
return log_mel_spectrogram.T
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Optional[Any]:
__UpperCamelCase : List[str] = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
__UpperCamelCase : Union[str, Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
__UpperCamelCase : List[str] = [0]
# randomly choose index for each part
__UpperCamelCase : Optional[int] = np.random.choice(ranges[0] )
__UpperCamelCase : Optional[Any] = np.random.choice(ranges[1] )
__UpperCamelCase : Union[str, Any] = np.random.choice(ranges[2] )
__UpperCamelCase : List[str] = mel[idx_front : idx_front + chunk_frames, :]
__UpperCamelCase : Any = mel[idx_middle : idx_middle + chunk_frames, :]
__UpperCamelCase : int = mel[idx_back : idx_back + chunk_frames, :]
__UpperCamelCase : Tuple = torch.tensor(mel[None, None, :] )
__UpperCamelCase : int = torch.nn.functional.interpolate(
_UpperCAmelCase , size=[chunk_frames, 6_4] , mode="bilinear" , align_corners=_UpperCAmelCase )
__UpperCamelCase : Any = mel_shrink[0][0].numpy()
__UpperCamelCase : List[str] = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> np.array:
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
__UpperCamelCase : Union[str, Any] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
__UpperCamelCase : int = len(_UpperCAmelCase ) - max_length
__UpperCamelCase : Tuple = np.random.randint(0 , overflow + 1 )
__UpperCamelCase : Union[str, Any] = waveform[idx : idx + max_length]
__UpperCamelCase : List[str] = self._np_extract_fbank_features(_UpperCAmelCase , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
__UpperCamelCase : Dict = self._np_extract_fbank_features(_UpperCAmelCase , self.mel_filters )
__UpperCamelCase : Union[str, Any] = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
__UpperCamelCase : Optional[int] = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
__UpperCamelCase : Optional[int] = np.stack([mel, mel, mel, mel] , axis=0 )
__UpperCamelCase : Any = False
else:
__UpperCamelCase : str = self._random_mel_fusion(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
__UpperCamelCase : Tuple = True
else:
raise NotImplementedError(f"data_truncating {truncation} not implemented" )
else:
__UpperCamelCase : List[Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
__UpperCamelCase : List[Any] = int(max_length / len(_UpperCAmelCase ) )
__UpperCamelCase : str = np.stack(np.tile(_UpperCAmelCase , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
__UpperCamelCase : int = int(max_length / len(_UpperCAmelCase ) )
__UpperCamelCase : Any = np.stack(np.tile(_UpperCAmelCase , _UpperCAmelCase ) )
__UpperCamelCase : List[Any] = np.pad(_UpperCAmelCase , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0 )
if truncation == "fusion":
__UpperCamelCase : Any = self._np_extract_fbank_features(_UpperCAmelCase , self.mel_filters )
__UpperCamelCase : List[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
__UpperCamelCase : Union[str, Any] = self._np_extract_fbank_features(_UpperCAmelCase , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__(self , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , **_UpperCAmelCase , ) -> BatchFeature:
__UpperCamelCase : List[str] = truncation if truncation is not None else self.truncation
__UpperCamelCase : Tuple = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
f" was sampled with {self.sampling_rate} and not {sampling_rate}." )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
__UpperCamelCase : Optional[Any] = isinstance(_UpperCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"Only mono-channel audio is supported for input to {self}" )
__UpperCamelCase : Union[str, Any] = is_batched_numpy or (
isinstance(_UpperCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__UpperCamelCase : Any = [np.asarray(_UpperCAmelCase , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_UpperCAmelCase , np.ndarray ):
__UpperCamelCase : Optional[int] = np.asarray(_UpperCAmelCase , dtype=np.floataa )
elif isinstance(_UpperCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__UpperCamelCase : str = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__UpperCamelCase : List[str] = [np.asarray(_UpperCAmelCase )]
# convert to mel spectrogram, truncate and pad if needed.
__UpperCamelCase : List[Any] = [
self._get_input_mel(_UpperCAmelCase , max_length if max_length else self.nb_max_samples , _UpperCAmelCase , _UpperCAmelCase )
for waveform in raw_speech
]
__UpperCamelCase : Dict = []
__UpperCamelCase : Optional[int] = []
for mel, longer in padded_inputs:
input_mel.append(_UpperCAmelCase )
is_longer.append(_UpperCAmelCase )
if truncation == "fusion" and sum(_UpperCAmelCase ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
__UpperCamelCase : int = np.random.randint(0 , len(_UpperCAmelCase ) )
__UpperCamelCase : Optional[int] = True
if isinstance(input_mel[0] , _UpperCAmelCase ):
__UpperCamelCase : Optional[int] = [np.asarray(_UpperCAmelCase , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
__UpperCamelCase : int = [[longer] for longer in is_longer]
__UpperCamelCase : List[str] = {"input_features": input_mel, "is_longer": is_longer}
__UpperCamelCase : int = BatchFeature(_UpperCAmelCase )
if return_tensors is not None:
__UpperCamelCase : List[str] = input_features.convert_to_tensors(_UpperCAmelCase )
return input_features
| 710
|
'''simple docstring'''
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class A :
'''simple docstring'''
def __init__(self , _UpperCAmelCase , _UpperCAmelCase=1_3 , _UpperCAmelCase=7 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=9_9 , _UpperCAmelCase=3_2 , _UpperCAmelCase=5 , _UpperCAmelCase=4 , _UpperCAmelCase=3_7 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=5_0 , _UpperCAmelCase=0.02 , _UpperCAmelCase=True , _UpperCAmelCase=None , ) -> Any:
__UpperCamelCase : Union[str, Any] = parent
__UpperCamelCase : Dict = batch_size
__UpperCamelCase : Dict = seq_length
__UpperCamelCase : Optional[int] = is_training
__UpperCamelCase : Optional[Any] = use_input_mask
__UpperCamelCase : Optional[Any] = vocab_size
__UpperCamelCase : Tuple = hidden_size
__UpperCamelCase : Optional[Any] = num_hidden_layers
__UpperCamelCase : Optional[Any] = num_attention_heads
__UpperCamelCase : Union[str, Any] = intermediate_size
__UpperCamelCase : List[str] = hidden_act
__UpperCamelCase : Optional[int] = hidden_dropout_prob
__UpperCamelCase : Any = attention_probs_dropout_prob
__UpperCamelCase : Dict = max_position_embeddings
__UpperCamelCase : List[str] = initializer_range
__UpperCamelCase : Union[str, Any] = use_labels
__UpperCamelCase : Optional[Any] = scope
def a_ (self ) -> Tuple:
__UpperCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase : Any = None
if self.use_input_mask:
__UpperCamelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
__UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase : Any = self.get_config()
return config, input_ids, input_mask, token_labels
def a_ (self ) -> Tuple:
return BertGenerationConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , )
def a_ (self ) -> Dict:
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : Any = self.prepare_config_and_inputs()
__UpperCamelCase : int = True
__UpperCamelCase : Tuple = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__UpperCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , ) -> Optional[int]:
__UpperCamelCase : Union[str, Any] = BertGenerationEncoder(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : int = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )
__UpperCamelCase : List[Any] = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , ) -> Any:
__UpperCamelCase : Any = True
__UpperCamelCase : Optional[Any] = BertGenerationEncoder(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : Tuple = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , )
__UpperCamelCase : Any = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , ) -> Optional[int]:
__UpperCamelCase : Optional[int] = True
__UpperCamelCase : Optional[int] = True
__UpperCamelCase : Dict = BertGenerationDecoder(config=_UpperCAmelCase ).to(_UpperCAmelCase ).eval()
# first forward pass
__UpperCamelCase : Tuple = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , use_cache=_UpperCAmelCase , )
__UpperCamelCase : Optional[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__UpperCamelCase : List[str] = ids_tensor((self.batch_size, 3) , config.vocab_size )
__UpperCamelCase : str = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
__UpperCamelCase : Optional[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
__UpperCamelCase : Any = torch.cat([input_mask, next_mask] , dim=-1 )
__UpperCamelCase : Any = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , output_hidden_states=_UpperCAmelCase , )["hidden_states"][0]
__UpperCamelCase : str = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , past_key_values=_UpperCAmelCase , output_hidden_states=_UpperCAmelCase , )["hidden_states"][0]
# select random slice
__UpperCamelCase : Optional[Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__UpperCamelCase : int = output_from_no_past[:, -3:, random_slice_idx].detach()
__UpperCamelCase : int = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , *_UpperCAmelCase , ) -> Optional[Any]:
__UpperCamelCase : List[Any] = BertGenerationDecoder(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : List[str] = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ (self ) -> Dict:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase : str = self.prepare_config_and_inputs()
__UpperCamelCase : Any = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class A ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
A = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
A = (BertGenerationDecoder,) if is_torch_available() else ()
A = (
{"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
if is_torch_available()
else {}
)
def a_ (self ) -> Tuple:
__UpperCamelCase : Optional[Any] = BertGenerationEncoderTester(self )
__UpperCamelCase : Optional[Any] = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=3_7 )
def a_ (self ) -> List[Any]:
self.config_tester.run_common_tests()
def a_ (self ) -> List[str]:
__UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def a_ (self ) -> Union[str, Any]:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
__UpperCamelCase : List[Any] = "bert"
self.model_tester.create_and_check_model(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def a_ (self ) -> Any:
__UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_UpperCAmelCase )
def a_ (self ) -> Optional[int]:
__UpperCamelCase : int = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*_UpperCAmelCase )
def a_ (self ) -> Tuple:
# This regression test was failing with PyTorch < 1.3
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
__UpperCamelCase : Optional[int] = None
self.model_tester.create_and_check_model_as_decoder(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
def a_ (self ) -> Dict:
__UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*_UpperCAmelCase )
@slow
def a_ (self ) -> int:
__UpperCamelCase : Dict = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
self.assertIsNotNone(_UpperCAmelCase )
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a_ (self ) -> Tuple:
__UpperCamelCase : List[str] = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
__UpperCamelCase : List[str] = torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]] )
with torch.no_grad():
__UpperCamelCase : Any = model(_UpperCAmelCase )[0]
__UpperCamelCase : List[Any] = torch.Size([1, 8, 1_0_2_4] )
self.assertEqual(output.shape , _UpperCAmelCase )
__UpperCamelCase : List[Any] = torch.tensor(
[[[0.1_775, 0.0_083, -0.0_321], [1.6_002, 0.1_287, 0.3_912], [2.1_473, 0.5_791, 0.6_066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=1E-4 ) )
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a_ (self ) -> Tuple:
__UpperCamelCase : Any = BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
__UpperCamelCase : str = torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]] )
with torch.no_grad():
__UpperCamelCase : Tuple = model(_UpperCAmelCase )[0]
__UpperCamelCase : Tuple = torch.Size([1, 8, 5_0_3_5_8] )
self.assertEqual(output.shape , _UpperCAmelCase )
__UpperCamelCase : int = torch.tensor(
[[[-0.5_788, -2.5_994, -3.7_054], [0.0_438, 4.7_997, 1.8_795], [1.5_862, 6.6_409, 4.4_638]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=1E-4 ) )
| 399
| 0
|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'InformerForPrediction',
'InformerModel',
'InformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 253
|
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _a ( lowerCAmelCase__ ):
'''simple docstring'''
def __UpperCAmelCase( self ):
__A : List[str] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__UpperCAmelCase , "embed_dim" ) )
self.parent.assertTrue(hasattr(__UpperCAmelCase , "num_heads" ) )
class _a :
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=64 , __UpperCAmelCase=3 , __UpperCAmelCase=[16, 48, 96] , __UpperCAmelCase=[1, 3, 6] , __UpperCAmelCase=[1, 2, 10] , __UpperCAmelCase=[7, 3, 3] , __UpperCAmelCase=[4, 2, 2] , __UpperCAmelCase=[2, 1, 1] , __UpperCAmelCase=[2, 2, 2] , __UpperCAmelCase=[False, False, True] , __UpperCAmelCase=[0.0, 0.0, 0.0] , __UpperCAmelCase=0.02 , __UpperCAmelCase=1e-12 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=2 , ):
__A : Union[str, Any] = parent
__A : Union[str, Any] = batch_size
__A : Optional[int] = image_size
__A : List[Any] = patch_sizes
__A : Optional[int] = patch_stride
__A : Dict = patch_padding
__A : int = is_training
__A : Tuple = use_labels
__A : List[str] = num_labels
__A : Tuple = num_channels
__A : Tuple = embed_dim
__A : Optional[int] = num_heads
__A : int = stride_kv
__A : Optional[int] = depth
__A : int = cls_token
__A : Optional[Any] = attention_drop_rate
__A : Tuple = initializer_range
__A : Any = layer_norm_eps
def __UpperCAmelCase( self ):
__A : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__A : Tuple = None
if self.use_labels:
# create a random int32 tensor of given shape
__A : Union[str, Any] = ids_tensor([self.batch_size] , self.num_labels )
__A : Optional[int] = self.get_config()
return config, pixel_values, labels
def __UpperCAmelCase( self ):
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
__A : Optional[Any] = TFCvtModel(config=__UpperCAmelCase )
__A : List[str] = model(__UpperCAmelCase , training=__UpperCAmelCase )
__A : Any = (self.image_size, self.image_size)
__A , __A : Any = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
__A : str = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
__A : List[str] = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
__A : List[str] = self.num_labels
__A : Optional[int] = TFCvtForImageClassification(__UpperCAmelCase )
__A : Optional[int] = model(__UpperCAmelCase , labels=__UpperCAmelCase , training=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCAmelCase( self ):
__A : List[Any] = self.prepare_config_and_inputs()
__A , __A , __A : Optional[Any] = config_and_inputs
__A : List[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class _a ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ : Union[str, Any] = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
lowerCamelCase_ : str = (
{"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
if is_tf_available()
else {}
)
lowerCamelCase_ : int = False
lowerCamelCase_ : Any = False
lowerCamelCase_ : Tuple = False
lowerCamelCase_ : str = False
lowerCamelCase_ : Dict = False
def __UpperCAmelCase( self ):
__A : Optional[int] = TFCvtModelTester(self )
__A : str = TFCvtConfigTester(self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase , hidden_size=37 )
def __UpperCAmelCase( self ):
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason="Cvt does not output attentions" )
def __UpperCAmelCase( self ):
pass
@unittest.skip(reason="Cvt does not use inputs_embeds" )
def __UpperCAmelCase( self ):
pass
@unittest.skip(reason="Cvt does not support input and output embeddings" )
def __UpperCAmelCase( self ):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU" ) ) == 0 , reason="TF does not support backprop for grouped convolutions on CPU." , )
def __UpperCAmelCase( self ):
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU" ) ) == 0 , reason="TF does not support backprop for grouped convolutions on CPU." , )
@slow
def __UpperCAmelCase( self ):
super().test_keras_fit()
@unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8" )
def __UpperCAmelCase( self ):
__A : int = tf.keras.mixed_precision.Policy("mixed_float16" )
tf.keras.mixed_precision.set_global_policy(__UpperCAmelCase )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy("float32" )
def __UpperCAmelCase( self ):
__A , __A : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A : int = model_class(__UpperCAmelCase )
__A : str = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__A : Optional[Any] = [*signature.parameters.keys()]
__A : Optional[int] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def __UpperCAmelCase( self ):
def check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
__A : Dict = model_class(__UpperCAmelCase )
__A : List[Any] = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__A : Optional[int] = outputs.hidden_states
__A : int = len(self.model_tester.depth )
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
__A , __A : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A : int = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__A : Dict = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def __UpperCAmelCase( self ):
__A : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def __UpperCAmelCase( self ):
__A : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase )
@slow
def __UpperCAmelCase( self ):
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__A : int = TFCvtModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def lowerCamelCase_ ( ) -> List[Any]:
__A : int = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class _a ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __UpperCAmelCase( self ):
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def __UpperCAmelCase( self ):
__A : Any = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
__A : int = self.default_image_processor
__A : Dict = prepare_img()
__A : Dict = image_processor(images=__UpperCAmelCase , return_tensors="tf" )
# forward pass
__A : Optional[Any] = model(**__UpperCAmelCase )
# verify the logits
__A : int = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
__A : Optional[Any] = tf.constant([0.92_85, 0.90_15, -0.31_50] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , __UpperCAmelCase , atol=1e-4 ) )
| 520
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|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class lowerCamelCase ( unittest.TestCase ):
def __init__( self : Any , __snake_case : Tuple , __snake_case : Optional[int]=7 , __snake_case : Tuple=3 , __snake_case : Dict=18 , __snake_case : List[Any]=30 , __snake_case : List[str]=4_00 , __snake_case : int=True , __snake_case : str=None , __snake_case : int=True , __snake_case : Any=None , __snake_case : Optional[int]=True , __snake_case : Optional[Any]=[0.48_145_466, 0.4_578_275, 0.40_821_073] , __snake_case : int=[0.26_862_954, 0.26_130_258, 0.27_577_711] , __snake_case : List[Any]=True , ):
'''simple docstring'''
_snake_case: str = size if size is not None else {'height': 2_24, 'width': 2_24}
_snake_case: str = crop_size if crop_size is not None else {'height': 18, 'width': 18}
_snake_case: Dict = parent
_snake_case: List[str] = batch_size
_snake_case: Optional[int] = num_channels
_snake_case: Tuple = image_size
_snake_case: Optional[Any] = min_resolution
_snake_case: str = max_resolution
_snake_case: Dict = do_resize
_snake_case: str = size
_snake_case: int = do_center_crop
_snake_case: int = crop_size
_snake_case: str = do_normalize
_snake_case: str = image_mean
_snake_case: Optional[Any] = image_std
_snake_case: Optional[int] = do_convert_rgb
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def SCREAMING_SNAKE_CASE_ ( self : Dict , __snake_case : Tuple=False , __snake_case : List[Any]=False , __snake_case : List[Any]=False ):
'''simple docstring'''
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
_snake_case: str = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
2_55 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta ) )
else:
_snake_case: Optional[Any] = []
for i in range(self.batch_size ):
_snake_case: Union[str, Any] = np.random.choice(np.arange(self.min_resolution , self.max_resolution ) , 2 )
image_inputs.append(np.random.randint(2_55 , size=(self.num_channels, width, height) , dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
_snake_case: List[Any] = [Image.fromarray(np.moveaxis(__snake_case , 0 , -1 ) ) for x in image_inputs]
if torchify:
_snake_case: List[Any] = [torch.from_numpy(__snake_case ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class lowerCamelCase ( __UpperCAmelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE = ChineseCLIPImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
'''simple docstring'''
_snake_case: Optional[int] = ChineseCLIPImageProcessingTester(self , do_center_crop=__snake_case )
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
_snake_case: List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__snake_case , 'do_resize' ) )
self.assertTrue(hasattr(__snake_case , 'size' ) )
self.assertTrue(hasattr(__snake_case , 'do_center_crop' ) )
self.assertTrue(hasattr(__snake_case , 'center_crop' ) )
self.assertTrue(hasattr(__snake_case , 'do_normalize' ) )
self.assertTrue(hasattr(__snake_case , 'image_mean' ) )
self.assertTrue(hasattr(__snake_case , 'image_std' ) )
self.assertTrue(hasattr(__snake_case , 'do_convert_rgb' ) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
_snake_case: int = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 2_24, 'width': 2_24} )
self.assertEqual(image_processor.crop_size , {'height': 18, 'width': 18} )
_snake_case: Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'shortest_edge': 42} )
self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84} )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
_snake_case: Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_snake_case: Any = self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , Image.Image )
# Test not batched input
_snake_case: str = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_snake_case: Union[str, Any] = image_processing(__snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
'''simple docstring'''
_snake_case: Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_snake_case: Optional[int] = self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case , numpify=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , np.ndarray )
# Test not batched input
_snake_case: Optional[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_snake_case: Union[str, Any] = image_processing(__snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
_snake_case: Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_snake_case: Union[str, Any] = self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case , torchify=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , torch.Tensor )
# Test not batched input
_snake_case: Dict = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_snake_case: str = image_processing(__snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
@require_torch
@require_vision
class lowerCamelCase ( __UpperCAmelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE = ChineseCLIPImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
_snake_case: str = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=__snake_case )
_snake_case: Optional[int] = 3
@property
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Any ):
'''simple docstring'''
_snake_case: List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__snake_case , 'do_resize' ) )
self.assertTrue(hasattr(__snake_case , 'size' ) )
self.assertTrue(hasattr(__snake_case , 'do_center_crop' ) )
self.assertTrue(hasattr(__snake_case , 'center_crop' ) )
self.assertTrue(hasattr(__snake_case , 'do_normalize' ) )
self.assertTrue(hasattr(__snake_case , 'image_mean' ) )
self.assertTrue(hasattr(__snake_case , 'image_std' ) )
self.assertTrue(hasattr(__snake_case , 'do_convert_rgb' ) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
_snake_case: List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_snake_case: str = self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , Image.Image )
# Test not batched input
_snake_case: Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_snake_case: Union[str, Any] = image_processing(__snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 720
|
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
A : str = TypeVar('T')
class lowerCamelCase ( Generic[T] ):
_SCREAMING_SNAKE_CASE = 42 # Cache store of keys
_SCREAMING_SNAKE_CASE = 42 # References of the keys in cache
_SCREAMING_SNAKE_CASE = 10 # Maximum capacity of cache
def __init__( self : List[Any] , __snake_case : int ):
'''simple docstring'''
_snake_case: Dict = deque()
_snake_case: Union[str, Any] = set()
if not n:
_snake_case: Optional[int] = sys.maxsize
elif n < 0:
raise ValueError('n should be an integer greater than 0.' )
else:
_snake_case: Tuple = n
def SCREAMING_SNAKE_CASE_ ( self : Any , __snake_case : T ):
'''simple docstring'''
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
_snake_case: int = self.dq_store.pop()
self.key_reference.remove(__snake_case )
else:
self.dq_store.remove(__snake_case )
self.dq_store.appendleft(__snake_case )
self.key_reference.add(__snake_case )
def SCREAMING_SNAKE_CASE_ ( self : int ):
'''simple docstring'''
for k in self.dq_store:
print(__snake_case )
def __repr__( self : List[Any] ):
'''simple docstring'''
return f'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}'''
if __name__ == "__main__":
import doctest
doctest.testmod()
A : LRUCache[str | int] = LRUCache(4)
lru_cache.refer('A')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('A')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 273
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|
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
__UpperCamelCase : Any = 'scheduler_config.json'
class _UpperCamelCase ( A ):
'''simple docstring'''
a_ : Dict = 1
a_ : List[str] = 2
a_ : Optional[int] = 3
a_ : Optional[int] = 4
a_ : Union[str, Any] = 5
@dataclass
class _UpperCamelCase ( A ):
'''simple docstring'''
a_ : jnp.ndarray
class _UpperCamelCase :
'''simple docstring'''
a_ : Any = SCHEDULER_CONFIG_NAME
a_ : List[str] = ["dtype"]
a_ : Optional[int] = []
a_ : Union[str, Any] = True
@classmethod
def _snake_case ( cls : Optional[int] , _lowerCamelCase : Dict[str, Any] = None , _lowerCamelCase : Optional[str] = None , _lowerCamelCase : Optional[Any]=False , **_lowerCamelCase : Any , ):
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase : Dict = cls.load_config(
pretrained_model_name_or_path=_lowerCamelCase , subfolder=_lowerCamelCase , return_unused_kwargs=_lowerCamelCase , **_lowerCamelCase , )
__lowerCamelCase , __lowerCamelCase : Tuple = cls.from_config(_lowerCamelCase , return_unused_kwargs=_lowerCamelCase , **_lowerCamelCase )
if hasattr(_lowerCamelCase , """create_state""" ) and getattr(_lowerCamelCase , """has_state""" , _lowerCamelCase ):
__lowerCamelCase : Optional[int] = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def _snake_case ( self : List[Any] , _lowerCamelCase : Union[str, os.PathLike] , _lowerCamelCase : bool = False , **_lowerCamelCase : Tuple ):
'''simple docstring'''
self.save_config(save_directory=_lowerCamelCase , push_to_hub=_lowerCamelCase , **_lowerCamelCase )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return self._get_compatibles()
@classmethod
def _snake_case ( cls : str ):
'''simple docstring'''
__lowerCamelCase : str = list(set([cls.__name__] + cls._compatibles ) )
__lowerCamelCase : Union[str, Any] = importlib.import_module(__name__.split(""".""" )[0] )
__lowerCamelCase : Optional[int] = [
getattr(_lowerCamelCase , _lowerCamelCase ) for c in compatible_classes_str if hasattr(_lowerCamelCase , _lowerCamelCase )
]
return compatible_classes
def _UpperCAmelCase ( UpperCAmelCase : jnp.ndarray , UpperCAmelCase : Tuple[int] ):
"""simple docstring"""
assert len(UpperCAmelCase ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCAmelCase ) - x.ndim) ) , UpperCAmelCase )
def _UpperCAmelCase ( UpperCAmelCase : int , UpperCAmelCase : Union[str, Any]=0.9_9_9 , UpperCAmelCase : Optional[int]=jnp.floataa ):
"""simple docstring"""
def alpha_bar(UpperCAmelCase : Union[str, Any] ):
return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
__lowerCamelCase : Dict = []
for i in range(UpperCAmelCase ):
__lowerCamelCase : int = i / num_diffusion_timesteps
__lowerCamelCase : str = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(UpperCAmelCase ) / alpha_bar(UpperCAmelCase ) , UpperCAmelCase ) )
return jnp.array(UpperCAmelCase , dtype=UpperCAmelCase )
@flax.struct.dataclass
class _UpperCamelCase :
'''simple docstring'''
a_ : jnp.ndarray
a_ : jnp.ndarray
a_ : jnp.ndarray
@classmethod
def _snake_case ( cls : Optional[int] , _lowerCamelCase : Optional[Any] ):
'''simple docstring'''
__lowerCamelCase : Tuple = scheduler.config
if config.trained_betas is not None:
__lowerCamelCase : Union[str, Any] = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
__lowerCamelCase : Dict = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__lowerCamelCase : List[str] = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__lowerCamelCase : Any = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
F"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}""" )
__lowerCamelCase : Optional[Any] = 1.0 - betas
__lowerCamelCase : List[Any] = jnp.cumprod(_lowerCamelCase , axis=0 )
return cls(
alphas=_lowerCamelCase , betas=_lowerCamelCase , alphas_cumprod=_lowerCamelCase , )
def _UpperCAmelCase ( UpperCAmelCase : CommonSchedulerState , UpperCAmelCase : jnp.ndarray , UpperCAmelCase : jnp.ndarray , UpperCAmelCase : jnp.ndarray ):
"""simple docstring"""
__lowerCamelCase : Optional[Any] = state.alphas_cumprod
__lowerCamelCase : int = alphas_cumprod[timesteps] ** 0.5
__lowerCamelCase : Union[str, Any] = sqrt_alpha_prod.flatten()
__lowerCamelCase : Optional[Any] = broadcast_to_shape_from_left(UpperCAmelCase , original_samples.shape )
__lowerCamelCase : List[str] = (1 - alphas_cumprod[timesteps]) ** 0.5
__lowerCamelCase : str = sqrt_one_minus_alpha_prod.flatten()
__lowerCamelCase : int = broadcast_to_shape_from_left(UpperCAmelCase , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def _UpperCAmelCase ( UpperCAmelCase : CommonSchedulerState , UpperCAmelCase : jnp.ndarray , UpperCAmelCase : jnp.ndarray , UpperCAmelCase : jnp.ndarray ):
"""simple docstring"""
__lowerCamelCase , __lowerCamelCase : Tuple = get_sqrt_alpha_prod(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCamelCase : str = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def _UpperCAmelCase ( UpperCAmelCase : CommonSchedulerState , UpperCAmelCase : jnp.ndarray , UpperCAmelCase : jnp.ndarray , UpperCAmelCase : jnp.ndarray ):
"""simple docstring"""
__lowerCamelCase , __lowerCamelCase : List[Any] = get_sqrt_alpha_prod(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCamelCase : Tuple = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 519
|
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
__UpperCamelCase : List[Any] = ['bert-base-uncased', 'bert-base-cased']
__UpperCamelCase : int = 'hf-internal-testing/tiny-bert-tf-only'
if is_tf_available():
class _UpperCamelCase ( tf.keras.Model ):
'''simple docstring'''
def __init__( self : int , _lowerCamelCase : int ):
'''simple docstring'''
super().__init__()
__lowerCamelCase : Optional[Any] = tokenizer
__lowerCamelCase : List[str] = AutoConfig.from_pretrained(_lowerCamelCase )
__lowerCamelCase : Union[str, Any] = TFAutoModel.from_config(_lowerCamelCase )
def _snake_case ( self : List[str] , _lowerCamelCase : Optional[int] ):
'''simple docstring'''
__lowerCamelCase : List[Any] = self.tokenizer(_lowerCamelCase )
__lowerCamelCase : Optional[int] = self.bert(**_lowerCamelCase )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
super().setUp()
__lowerCamelCase : Dict = [
BertTokenizer.from_pretrained(_lowerCamelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
__lowerCamelCase : int = [TFBertTokenizer.from_pretrained(_lowerCamelCase ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(_lowerCamelCase , use_fast_bert_tokenizer=_lowerCamelCase )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
__lowerCamelCase : Optional[int] = [
"""This is a straightforward English test sentence.""",
"""This one has some weird characters\rto\nsee\r\nif those\u00E9break things.""",
"""Now we're going to add some Chinese: 一 二 三 一二三""",
"""And some much more rare Chinese: 齉 堃 齉堃""",
"""Je vais aussi écrire en français pour tester les accents""",
"""Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
]
__lowerCamelCase : Union[str, Any] = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
__lowerCamelCase : List[str] = tokenizer(_lowerCamelCase , return_tensors="""tf""" , padding="""longest""" )
__lowerCamelCase : List[str] = tf_tokenizer(_lowerCamelCase )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) )
@slow
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
__lowerCamelCase : Union[str, Any] = tf_tokenizer(self.paired_sentences )
__lowerCamelCase : List[Any] = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) )
@slow
def _snake_case ( self : Tuple ):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
__lowerCamelCase : Dict = tf.function(_lowerCamelCase )
for test_inputs in (self.test_sentences, self.paired_sentences):
__lowerCamelCase : List[Any] = tf.constant(_lowerCamelCase )
__lowerCamelCase : Dict = compiled_tokenizer(_lowerCamelCase )
__lowerCamelCase : Union[str, Any] = tf_tokenizer(_lowerCamelCase )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
__lowerCamelCase : List[Any] = ModelToSave(tokenizer=_lowerCamelCase )
__lowerCamelCase : Union[str, Any] = tf.convert_to_tensor(self.test_sentences )
__lowerCamelCase : Optional[Any] = model(_lowerCamelCase ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
__lowerCamelCase : List[str] = Path(_lowerCamelCase ) / """saved.model"""
model.save(_lowerCamelCase )
__lowerCamelCase : Optional[int] = tf.keras.models.load_model(_lowerCamelCase )
__lowerCamelCase : str = loaded_model(_lowerCamelCase )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1E-5 )
| 519
| 1
|
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowerCAmelCase : int = logging.get_logger(__name__)
class _A ( __magic_name__):
SCREAMING_SNAKE_CASE : str = ['''input_values''', '''attention_mask''']
def __init__( self , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = 1_6000 , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = 80 , _SCREAMING_SNAKE_CASE = 16 , _SCREAMING_SNAKE_CASE = 64 , _SCREAMING_SNAKE_CASE = "hann_window" , _SCREAMING_SNAKE_CASE = 1.0 , _SCREAMING_SNAKE_CASE = 80 , _SCREAMING_SNAKE_CASE = 7600 , _SCREAMING_SNAKE_CASE = 1e-10 , _SCREAMING_SNAKE_CASE = 2 , _SCREAMING_SNAKE_CASE = True , **_SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
super().__init__(feature_size=_SCREAMING_SNAKE_CASE , sampling_rate=_SCREAMING_SNAKE_CASE , padding_value=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ : int = do_normalize
SCREAMING_SNAKE_CASE_ : List[str] = return_attention_mask
SCREAMING_SNAKE_CASE_ : str = num_mel_bins
SCREAMING_SNAKE_CASE_ : Optional[Any] = hop_length
SCREAMING_SNAKE_CASE_ : Optional[int] = win_length
SCREAMING_SNAKE_CASE_ : Optional[Any] = win_function
SCREAMING_SNAKE_CASE_ : Any = frame_signal_scale
SCREAMING_SNAKE_CASE_ : Optional[Any] = fmin
SCREAMING_SNAKE_CASE_ : Any = fmax
SCREAMING_SNAKE_CASE_ : Union[str, Any] = mel_floor
SCREAMING_SNAKE_CASE_ : str = reduction_factor
SCREAMING_SNAKE_CASE_ : Any = win_length * sampling_rate // 1000
SCREAMING_SNAKE_CASE_ : Any = hop_length * sampling_rate // 1000
SCREAMING_SNAKE_CASE_ : int = optimal_fft_length(self.sample_size )
SCREAMING_SNAKE_CASE_ : str = (self.n_fft // 2) + 1
SCREAMING_SNAKE_CASE_ : Tuple = window_function(window_length=self.sample_size , name=self.win_function , periodic=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ : List[Any] = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm='slaney' , mel_scale='slaney' , )
if frame_signal_scale != 1.0:
warnings.warn(
'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers' , _SCREAMING_SNAKE_CASE , )
if reduction_factor != 2.0:
warnings.warn(
'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers' , _SCREAMING_SNAKE_CASE , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def UpperCAmelCase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0.0 ):
"""simple docstring"""
if attention_mask is not None:
SCREAMING_SNAKE_CASE_ : str = np.array(_SCREAMING_SNAKE_CASE , np.intaa )
SCREAMING_SNAKE_CASE_ : Optional[Any] = []
for vector, length in zip(_SCREAMING_SNAKE_CASE , attention_mask.sum(-1 ) ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = padding_value
normed_input_values.append(_SCREAMING_SNAKE_CASE )
else:
SCREAMING_SNAKE_CASE_ : List[Any] = [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = spectrogram(
_SCREAMING_SNAKE_CASE , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel='log10' , )
return log_mel_spec.T
def __call__( self , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
if audio is None and audio_target is None:
raise ValueError('You must provide either `audio` or `audio_target` values.' )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
f" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
f" {self.sampling_rate} and not {sampling_rate}." )
else:
logger.warning(
'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
if audio is not None:
SCREAMING_SNAKE_CASE_ : List[str] = self._process_audio(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
else:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = None
if audio_target is not None:
SCREAMING_SNAKE_CASE_ : int = self._process_audio(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
if inputs is None:
return inputs_target
else:
SCREAMING_SNAKE_CASE_ : str = inputs_target['input_values']
SCREAMING_SNAKE_CASE_ : Dict = inputs_target.get('attention_mask' )
if decoder_attention_mask is not None:
SCREAMING_SNAKE_CASE_ : Dict = decoder_attention_mask
return inputs
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(f"Only mono-channel audio is supported for input to {self}" )
SCREAMING_SNAKE_CASE_ : str = is_batched_numpy or (
isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
SCREAMING_SNAKE_CASE_ : str = [np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ):
SCREAMING_SNAKE_CASE_ : Optional[int] = np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa )
elif isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
SCREAMING_SNAKE_CASE_ : Dict = speech.astype(np.floataa )
# always return batch
if not is_batched:
SCREAMING_SNAKE_CASE_ : Any = [speech]
# needed to make pad() work on spectrogram inputs
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.feature_size
# convert into correct format for padding
if is_target:
SCREAMING_SNAKE_CASE_ : str = [self._extract_mel_features(_SCREAMING_SNAKE_CASE ) for waveform in speech]
SCREAMING_SNAKE_CASE_ : List[Any] = BatchFeature({'input_values': features} )
SCREAMING_SNAKE_CASE_ : List[str] = self.num_mel_bins
else:
SCREAMING_SNAKE_CASE_ : Optional[Any] = BatchFeature({'input_values': speech} )
SCREAMING_SNAKE_CASE_ : List[str] = self.pad(
_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , pad_to_multiple_of=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
SCREAMING_SNAKE_CASE_ : Any = feature_size_hack
# convert input values to correct format
SCREAMING_SNAKE_CASE_ : Optional[Any] = padded_inputs['input_values']
if not isinstance(input_values[0] , np.ndarray ):
SCREAMING_SNAKE_CASE_ : Optional[int] = [np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(_SCREAMING_SNAKE_CASE , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
SCREAMING_SNAKE_CASE_ : List[Any] = [array.astype(np.floataa ) for array in input_values]
elif isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
SCREAMING_SNAKE_CASE_ : int = input_values.astype(np.floataa )
# convert attention_mask to correct format
SCREAMING_SNAKE_CASE_ : Optional[int] = padded_inputs.get('attention_mask' )
if attention_mask is not None:
SCREAMING_SNAKE_CASE_ : List[Any] = [np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
SCREAMING_SNAKE_CASE_ : Optional[Any] = (
attention_mask
if self._get_padding_strategies(_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE ) is not PaddingStrategy.DO_NOT_PAD
else None
)
SCREAMING_SNAKE_CASE_ : List[str] = self.zero_mean_unit_var_norm(
padded_inputs['input_values'] , attention_mask=_SCREAMING_SNAKE_CASE , padding_value=self.padding_value )
if return_tensors is not None:
SCREAMING_SNAKE_CASE_ : List[Any] = padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE )
return padded_inputs
def UpperCAmelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = super().to_dict()
# Don't serialize these as they are derived from the other properties.
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
for name in names:
if name in output:
del output[name]
return output
| 353
|
def A_ ( a , a ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 353
| 1
|
"""simple docstring"""
import numpy as np
from PIL import Image
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case ):
UpperCAmelCase__ : Tuple = np.array(_snake_case )
if arr.shape[0] != arr.shape[1]:
raise ValueError('The input array is not a square matrix' )
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Any = 0
UpperCAmelCase__ : List[str] = 0
UpperCAmelCase__ : Optional[int] = 0
# compute the shape of the output matrix
UpperCAmelCase__ : Optional[Any] = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
UpperCAmelCase__ : Optional[Any] = np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
UpperCAmelCase__ : Optional[Any] = np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
UpperCAmelCase__ : int = 0
UpperCAmelCase__ : Union[str, Any] = 0
return updated_arr
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case ):
UpperCAmelCase__ : Union[str, Any] = np.array(_snake_case )
if arr.shape[0] != arr.shape[1]:
raise ValueError('The input array is not a square matrix' )
UpperCAmelCase__ : Dict = 0
UpperCAmelCase__ : str = 0
UpperCAmelCase__ : Optional[Any] = 0
UpperCAmelCase__ : List[str] = 0
# compute the shape of the output matrix
UpperCAmelCase__ : Any = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
UpperCAmelCase__ : Union[str, Any] = np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
UpperCAmelCase__ : Dict = int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
UpperCAmelCase__ : Any = 0
UpperCAmelCase__ : List[str] = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name='avgpooling', verbose=True)
# Loading the image
UpperCamelCase__ = Image.open('path_to_image')
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 110
|
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : int = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'decoder.output_projection.weight',
'_float_tensor',
'encoder.embed_positions._float_tensor',
'decoder.embed_positions._float_tensor',
]
for k in ignore_keys:
state_dict.pop(_snake_case ,_snake_case )
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = emb.weight.shape
UpperCAmelCase__ : List[str] = nn.Linear(_snake_case ,_snake_case ,bias=_snake_case )
UpperCAmelCase__ : Any = emb.weight.data
return lin_layer
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : Optional[int] = torch.load(_snake_case ,map_location='cpu' )
UpperCAmelCase__ : Any = mam_aaa['args'] or mam_aaa['cfg']['model']
UpperCAmelCase__ : Optional[int] = mam_aaa['model']
remove_ignore_keys_(_snake_case )
UpperCAmelCase__ : Optional[int] = state_dict['encoder.embed_tokens.weight'].shape[0]
UpperCAmelCase__ : List[str] = MaMaaaConfig(
vocab_size=_snake_case ,max_position_embeddings=1024 ,encoder_layers=args.encoder_layers ,decoder_layers=args.decoder_layers ,encoder_attention_heads=args.encoder_attention_heads ,decoder_attention_heads=args.decoder_attention_heads ,encoder_ffn_dim=args.encoder_ffn_embed_dim ,decoder_ffn_dim=args.decoder_ffn_embed_dim ,d_model=args.encoder_embed_dim ,encoder_layerdrop=args.encoder_layerdrop ,decoder_layerdrop=args.decoder_layerdrop ,dropout=args.dropout ,attention_dropout=args.attention_dropout ,activation_dropout=args.activation_dropout ,activation_function='relu' ,)
UpperCAmelCase__ : Optional[int] = state_dict['decoder.embed_tokens.weight']
UpperCAmelCase__ : Union[str, Any] = MaMaaaForConditionalGeneration(_snake_case )
model.model.load_state_dict(_snake_case ,strict=_snake_case )
UpperCAmelCase__ : Optional[Any] = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
UpperCamelCase__ = parser.parse_args()
UpperCamelCase__ = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 110
| 1
|
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {'''vocab_file''': '''spiece.model'''}
UpperCAmelCase_ = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_ = {
'''t5-small''': 512,
'''t5-base''': 512,
'''t5-large''': 512,
'''t5-3b''': 512,
'''t5-11b''': 512,
}
UpperCAmelCase_ = '''▁'''
class lowerCAmelCase ( _a ):
_SCREAMING_SNAKE_CASE : List[str] =VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE : Tuple =PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE : int =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE : Union[str, Any] =["""input_ids""", """attention_mask"""]
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__="</s>" , lowerCAmelCase__="<unk>" , lowerCAmelCase__="<pad>" , lowerCAmelCase__=100 , lowerCAmelCase__=None , lowerCAmelCase__ = None , lowerCAmelCase__=True , **lowerCAmelCase__ , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
_A= [f"<extra_id_{i}>" for i in range(lowerCAmelCase__ )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
_A= len(set(filter(lambda lowerCAmelCase__ : bool('extra_id' in str(lowerCAmelCase__ ) ) , lowerCAmelCase__ ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
' tokens' )
if legacy:
logger.warning_once(
f"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"
' read the related pull request available at https://github.com/huggingface/transformers/pull/24565' )
_A= legacy
_A= {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , extra_ids=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , sp_model_kwargs=self.sp_model_kwargs , legacy=lowerCAmelCase__ , **lowerCAmelCase__ , )
_A= vocab_file
_A= extra_ids
_A= spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCAmelCase__ )
@staticmethod
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
_A= TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'This tokenizer was incorrectly instantiated with a model max length of'
f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
f" {pretrained_model_name_or_path} automatically truncating your input to"
f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
' instantiate this tokenizer with `model_max_length` set to your preferred value.' , lowerCAmelCase__ , )
return max_model_length
@property
def a__ ( self ):
return self.sp_model.get_piece_size() + self._extra_ids
def a__ ( self ):
_A= {self.convert_ids_to_tokens(lowerCAmelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def a__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase__ , token_ids_a=lowerCAmelCase__ , already_has_special_tokens=lowerCAmelCase__ )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(lowerCAmelCase__ )) + [1]
return ([0] * len(lowerCAmelCase__ )) + [1] + ([0] * len(lowerCAmelCase__ )) + [1]
def a__ ( self ):
return list(
set(filter(lambda lowerCAmelCase__ : bool(re.search(r'<extra_id_\d+>' , lowerCAmelCase__ ) ) is not None , self.additional_special_tokens ) ) )
def a__ ( self ):
return [self._convert_token_to_id(lowerCAmelCase__ ) for token in self.get_sentinel_tokens()]
def a__ ( self , lowerCAmelCase__ ):
if len(lowerCAmelCase__ ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
' eos tokens being added.' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def a__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ):
_A= [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def a__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ):
_A= self._add_eos_if_not_present(lowerCAmelCase__ )
if token_ids_a is None:
return token_ids_a
else:
_A= self._add_eos_if_not_present(lowerCAmelCase__ )
return token_ids_a + token_ids_a
def __getstate__( self ):
_A= self.__dict__.copy()
_A= None
return state
def __setstate__( self , lowerCAmelCase__ ):
_A= d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
_A= {}
_A= spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def a__ ( self , lowerCAmelCase__ , **lowerCAmelCase__ ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
_A= SPIECE_UNDERLINE + text.replace(lowerCAmelCase__ , ' ' )
return super().tokenize(lowerCAmelCase__ , **lowerCAmelCase__ )
def a__ ( self , lowerCAmelCase__ , **lowerCAmelCase__ ):
if not self.legacy:
_A= text.startswith(lowerCAmelCase__ )
if is_first:
_A= text[1:]
_A= self.sp_model.encode(lowerCAmelCase__ , out_type=lowerCAmelCase__ )
if not self.legacy and not is_first and not text.startswith(' ' ) and tokens[0].startswith(lowerCAmelCase__ ):
_A= ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def a__ ( self , lowerCAmelCase__ ):
if token.startswith('<extra_id_' ):
_A= re.match(r'<extra_id_(\d+)>' , lowerCAmelCase__ )
_A= int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(lowerCAmelCase__ )
def a__ ( self , lowerCAmelCase__ ):
if index < self.sp_model.get_piece_size():
_A= self.sp_model.IdToPiece(lowerCAmelCase__ )
else:
_A= f"<extra_id_{self.vocab_size - 1 - index}>"
return token
def a__ ( self , lowerCAmelCase__ ):
_A= []
_A= ''
_A= False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCAmelCase__ ) + token
_A= True
_A= []
else:
current_sub_tokens.append(lowerCAmelCase__ )
_A= False
out_string += self.sp_model.decode(lowerCAmelCase__ )
return out_string.strip()
def a__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ):
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
_A= os.path.join(
lowerCAmelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCAmelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCAmelCase__ , 'wb' ) as fi:
_A= self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase__ )
return (out_vocab_file,)
| 476
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class lowerCAmelCase ( _a ):
_SCREAMING_SNAKE_CASE : List[str] ="""convbert"""
def __init__( self , lowerCAmelCase__=30522 , lowerCAmelCase__=768 , lowerCAmelCase__=12 , lowerCAmelCase__=12 , lowerCAmelCase__=3072 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=512 , lowerCAmelCase__=2 , lowerCAmelCase__=0.02 , lowerCAmelCase__=1E-12 , lowerCAmelCase__=1 , lowerCAmelCase__=0 , lowerCAmelCase__=2 , lowerCAmelCase__=768 , lowerCAmelCase__=2 , lowerCAmelCase__=9 , lowerCAmelCase__=1 , lowerCAmelCase__=None , **lowerCAmelCase__ , ):
super().__init__(
pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ , )
_A= vocab_size
_A= hidden_size
_A= num_hidden_layers
_A= num_attention_heads
_A= intermediate_size
_A= hidden_act
_A= hidden_dropout_prob
_A= attention_probs_dropout_prob
_A= max_position_embeddings
_A= type_vocab_size
_A= initializer_range
_A= layer_norm_eps
_A= embedding_size
_A= head_ratio
_A= conv_kernel_size
_A= num_groups
_A= classifier_dropout
class lowerCAmelCase ( _a ):
@property
def a__ ( self ):
if self.task == "multiple-choice":
_A= {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_A= {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 476
| 1
|
'''simple docstring'''
lowerCAmelCase__ : List[Any] = 8.3144598
def _a ( __lowerCAmelCase : float , __lowerCAmelCase : float ):
"""simple docstring"""
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
raise Exception('''Molar mass cannot be less than or equal to 0 kg/mol''' )
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# example
lowerCAmelCase__ : Union[str, Any] = 300
lowerCAmelCase__ : Optional[Any] = 28
lowerCAmelCase__ : str = rms_speed_of_molecule(temperature, molar_mass)
print(f"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")
| 347
|
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowercase__ ( __snake_case : Optional[Any] ):
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase_ : Optional[int] = orig_key.replace('model.' , '' )
if "norm1" in orig_key:
UpperCAmelCase_ : Optional[Any] = orig_key.replace('norm1' , 'attention.output.LayerNorm' )
if "norm2" in orig_key:
UpperCAmelCase_ : List[str] = orig_key.replace('norm2' , 'output.LayerNorm' )
if "norm" in orig_key:
UpperCAmelCase_ : Dict = orig_key.replace('norm' , 'LayerNorm' )
if "transformer" in orig_key:
UpperCAmelCase_ : Any = orig_key.split('.' )[0].split('_' )[-1]
UpperCAmelCase_ : Optional[Any] = orig_key.replace(F"transformer_{layer_num}" , F"encoder.layer.{layer_num}" )
if "mha.attn" in orig_key:
UpperCAmelCase_ : List[str] = orig_key.replace('mha.attn' , 'attention.self' )
if "mha" in orig_key:
UpperCAmelCase_ : Union[str, Any] = orig_key.replace('mha' , 'attention' )
if "W_q" in orig_key:
UpperCAmelCase_ : Any = orig_key.replace('W_q' , 'self.query' )
if "W_k" in orig_key:
UpperCAmelCase_ : Tuple = orig_key.replace('W_k' , 'self.key' )
if "W_v" in orig_key:
UpperCAmelCase_ : List[str] = orig_key.replace('W_v' , 'self.value' )
if "ff1" in orig_key:
UpperCAmelCase_ : str = orig_key.replace('ff1' , 'intermediate.dense' )
if "ff2" in orig_key:
UpperCAmelCase_ : Dict = orig_key.replace('ff2' , 'output.dense' )
if "ff" in orig_key:
UpperCAmelCase_ : Optional[int] = orig_key.replace('ff' , 'output.dense' )
if "mlm_class" in orig_key:
UpperCAmelCase_ : Optional[Any] = orig_key.replace('mlm.mlm_class' , 'cls.predictions.decoder' )
if "mlm" in orig_key:
UpperCAmelCase_ : Union[str, Any] = orig_key.replace('mlm' , 'cls.predictions.transform' )
if "cls" not in orig_key:
UpperCAmelCase_ : List[Any] = 'yoso.' + orig_key
return orig_key
def lowercase__ ( __snake_case : str , __snake_case : int ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
UpperCAmelCase_ : Any = orig_state_dict.pop(__snake_case )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
UpperCAmelCase_ : Union[str, Any] = val
UpperCAmelCase_ : List[Any] = orig_state_dict['cls.predictions.decoder.bias']
UpperCAmelCase_ : Tuple = torch.arange(__snake_case ).expand((1, -1) ) + 2
return orig_state_dict
def lowercase__ ( __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ : Any = torch.load(__snake_case , map_location='cpu' )['model_state_dict']
UpperCAmelCase_ : Dict = YosoConfig.from_json_file(__snake_case )
UpperCAmelCase_ : str = YosoForMaskedLM(__snake_case )
UpperCAmelCase_ : Dict = convert_checkpoint_helper(config.max_position_embeddings , __snake_case )
print(model.load_state_dict(__snake_case ) )
model.eval()
model.save_pretrained(__snake_case )
print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The json file for YOSO model config.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
__UpperCAmelCase = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 406
| 0
|
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class snake_case__ :
'''simple docstring'''
__A = field(
metadata={'''help''': '''The output directory where the model will be written.'''} , )
__A = field(
metadata={
'''help''': (
'''The encoder model checkpoint for weights initialization.'''
'''Don\'t set if you want to train an encoder model from scratch.'''
)
} , )
__A = field(
metadata={
'''help''': (
'''The decoder model checkpoint for weights initialization.'''
'''Don\'t set if you want to train a decoder model from scratch.'''
)
} , )
__A = field(
default=__snake_case , metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''} )
__A = field(
default=__snake_case , metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''} )
def _lowerCAmelCase ( ):
UpperCAmelCase_ = HfArgumentParser((ModelArguments,) )
((UpperCAmelCase_ ), ) = parser.parse_args_into_dataclasses()
# Load pretrained model and tokenizer
# Use explicit specified encoder config
if model_args.encoder_config_name:
UpperCAmelCase_ = AutoConfig.from_pretrained(model_args.encoder_config_name )
# Use pretrained encoder model's config
else:
UpperCAmelCase_ = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )
# Use explicit specified decoder config
if model_args.decoder_config_name:
UpperCAmelCase_ = AutoConfig.from_pretrained(model_args.decoder_config_name )
# Use pretrained decoder model's config
else:
UpperCAmelCase_ = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )
# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
UpperCAmelCase_ = True
UpperCAmelCase_ = True
UpperCAmelCase_ = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path , decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path , encoder_config=__magic_name__ , decoder_config=__magic_name__ , )
# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
UpperCAmelCase_ = decoder_config.decoder_start_token_id
UpperCAmelCase_ = decoder_config.pad_token_id
if decoder_start_token_id is None:
UpperCAmelCase_ = decoder_config.bos_token_id
if pad_token_id is None:
UpperCAmelCase_ = decoder_config.eos_token_id
# This is necessary to make Flax's generate() work
UpperCAmelCase_ = decoder_config.eos_token_id
UpperCAmelCase_ = decoder_start_token_id
UpperCAmelCase_ = pad_token_id
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )
UpperCAmelCase_ = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
UpperCAmelCase_ = tokenizer.convert_ids_to_tokens(model.config.pad_token_id )
model.save_pretrained(model_args.output_dir )
image_processor.save_pretrained(model_args.output_dir )
tokenizer.save_pretrained(model_args.output_dir )
if __name__ == "__main__":
main()
| 700
|
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _lowerCAmelCase ( __magic_name__ :Optional[int] , __magic_name__ :str , __magic_name__ :str , __magic_name__ :Path , __magic_name__ :str = None , __magic_name__ :str = None , __magic_name__ :str = None , ):
if config_name_or_path is None:
UpperCAmelCase_ = '''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''
if generator_tokenizer_name_or_path is None:
UpperCAmelCase_ = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
UpperCAmelCase_ = question_encoder_name_or_path
UpperCAmelCase_ = RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration
# Save model.
UpperCAmelCase_ = RagConfig.from_pretrained(__magic_name__ )
UpperCAmelCase_ = AutoConfig.from_pretrained(__magic_name__ )
UpperCAmelCase_ = AutoConfig.from_pretrained(__magic_name__ )
UpperCAmelCase_ = gen_config
UpperCAmelCase_ = question_encoder_config
UpperCAmelCase_ = model_class.from_pretrained_question_encoder_generator(
__magic_name__ , __magic_name__ , config=__magic_name__ )
rag_model.save_pretrained(__magic_name__ )
# Sanity check.
model_class.from_pretrained(__magic_name__ )
# Save tokenizers.
UpperCAmelCase_ = AutoTokenizer.from_pretrained(__magic_name__ )
gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/''' )
UpperCAmelCase_ = AutoTokenizer.from_pretrained(__magic_name__ )
question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/''' )
if __name__ == "__main__":
_lowerCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'--model_type',
choices=['rag_sequence', 'rag_token'],
required=True,
type=str,
help='RAG model type: rag_sequence, rag_token',
)
parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
parser.add_argument(
'--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
)
parser.add_argument(
'--generator_tokenizer_name_or_path',
type=str,
help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
)
parser.add_argument(
'--question_encoder_tokenizer_name_or_path',
type=str,
help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
)
parser.add_argument(
'--config_name_or_path',
type=str,
help=(
'Identifier of the model config to use, if not provided, resolves to a base config for a given'
' ``model_type``'
),
)
_lowerCamelCase : List[str] = parser.parse_args()
_lowerCamelCase : int = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 407
| 0
|
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_a = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
" Distillation"
)
)
parser.add_argument("--model_type", default="bert", choices=["bert"])
parser.add_argument("--model_name", default="bert-base-uncased", type=str)
parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
parser.add_argument("--vocab_transform", action="store_true")
_a = parser.parse_args()
if args.model_type == "bert":
_a = BertForMaskedLM.from_pretrained(args.model_name)
_a = "bert"
else:
raise ValueError("args.model_type should be \"bert\".")
_a = model.state_dict()
_a = {}
for w in ["word_embeddings", "position_embeddings"]:
_a = state_dict[f"""{prefix}.embeddings.{w}.weight"""]
for w in ["weight", "bias"]:
_a = state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]
_a = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
]
std_idx += 1
_a = state_dict["cls.predictions.decoder.weight"]
_a = state_dict["cls.predictions.bias"]
if args.vocab_transform:
for w in ["weight", "bias"]:
_a = state_dict[f"""cls.predictions.transform.dense.{w}"""]
_a = state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]
print(f"""N layers selected for distillation: {std_idx}""")
print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 481
|
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_a = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n journal = {Transactions of the Association for Computational Linguistics},\n volume = {4},\n year={2016},\n url = {https://www.aclweb.org/anthology/Q16-1029},\n pages = {401--415\n},\n@inproceedings{post-2018-call,\n title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n author = \"Post, Matt\",\n booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n month = oct,\n year = \"2018\",\n address = \"Belgium, Brussels\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W18-6319\",\n pages = \"186--191\",\n}\n"
_a = "\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"
_a = "\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n sources: list of source sentences where each sentence should be a string.\n predictions: list of predicted sentences where each sentence should be a string.\n references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n sari: sari score\n sacrebleu: sacrebleu score\n exact: exact score\n\nExamples:\n >>> sources=[\"About 95 species are currently accepted .\"]\n >>> predictions=[\"About 95 you now get in .\"]\n >>> references=[[\"About 95 species are currently known .\"]]\n >>> wiki_split = datasets.load_metric(\"wiki_split\")\n >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n >>> print(results)\n {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
def remove_articles(__snake_case ):
lowerCamelCase__ = re.compile(R'''\b(a|an|the)\b''' ,re.UNICODE )
return re.sub(__snake_case ,''' ''' ,__snake_case )
def white_space_fix(__snake_case ):
return " ".join(text.split() )
def remove_punc(__snake_case ):
lowerCamelCase__ = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__snake_case ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__snake_case ) ) ) )
def lowerCAmelCase__(__snake_case ,__snake_case ) -> Optional[int]:
'''simple docstring'''
return int(normalize_answer(__snake_case ) == normalize_answer(__snake_case ) )
def lowerCAmelCase__(__snake_case ,__snake_case ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase__ = [any(compute_exact(__snake_case ,__snake_case ) for ref in refs ) for pred, refs in zip(__snake_case ,__snake_case )]
return (sum(__snake_case ) / len(__snake_case )) * 100
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> int:
'''simple docstring'''
lowerCamelCase__ = [rgram for rgrams in rgramslist for rgram in rgrams]
lowerCamelCase__ = Counter(__snake_case )
lowerCamelCase__ = Counter(__snake_case )
lowerCamelCase__ = Counter()
for sgram, scount in sgramcounter.items():
lowerCamelCase__ = scount * numref
lowerCamelCase__ = Counter(__snake_case )
lowerCamelCase__ = Counter()
for cgram, ccount in cgramcounter.items():
lowerCamelCase__ = ccount * numref
# KEEP
lowerCamelCase__ = sgramcounter_rep & cgramcounter_rep
lowerCamelCase__ = keepgramcounter_rep & rgramcounter
lowerCamelCase__ = sgramcounter_rep & rgramcounter
lowerCamelCase__ = 0
lowerCamelCase__ = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
lowerCamelCase__ = 1
lowerCamelCase__ = 1
if len(__snake_case ) > 0:
lowerCamelCase__ = keeptmpscorea / len(__snake_case )
if len(__snake_case ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
lowerCamelCase__ = keeptmpscorea / sum(keepgramcounterall_rep.values() )
lowerCamelCase__ = 0
if keepscore_precision > 0 or keepscore_recall > 0:
lowerCamelCase__ = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
lowerCamelCase__ = sgramcounter_rep - cgramcounter_rep
lowerCamelCase__ = delgramcounter_rep - rgramcounter
lowerCamelCase__ = sgramcounter_rep - rgramcounter
lowerCamelCase__ = 0
lowerCamelCase__ = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
lowerCamelCase__ = 1
if len(__snake_case ) > 0:
lowerCamelCase__ = deltmpscorea / len(__snake_case )
# ADDITION
lowerCamelCase__ = set(__snake_case ) - set(__snake_case )
lowerCamelCase__ = set(__snake_case ) & set(__snake_case )
lowerCamelCase__ = set(__snake_case ) - set(__snake_case )
lowerCamelCase__ = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
lowerCamelCase__ = 1
lowerCamelCase__ = 1
if len(__snake_case ) > 0:
lowerCamelCase__ = addtmpscore / len(__snake_case )
if len(__snake_case ) > 0:
lowerCamelCase__ = addtmpscore / len(__snake_case )
lowerCamelCase__ = 0
if addscore_precision > 0 or addscore_recall > 0:
lowerCamelCase__ = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> List[str]:
'''simple docstring'''
lowerCamelCase__ = len(__snake_case )
lowerCamelCase__ = ssent.split(''' ''' )
lowerCamelCase__ = csent.split(''' ''' )
lowerCamelCase__ = []
lowerCamelCase__ = []
lowerCamelCase__ = []
lowerCamelCase__ = []
lowerCamelCase__ = []
lowerCamelCase__ = []
lowerCamelCase__ = []
lowerCamelCase__ = []
lowerCamelCase__ = []
lowerCamelCase__ = []
for rsent in rsents:
lowerCamelCase__ = rsent.split(''' ''' )
lowerCamelCase__ = []
lowerCamelCase__ = []
lowerCamelCase__ = []
ragramslist.append(__snake_case )
for i in range(0 ,len(__snake_case ) - 1 ):
if i < len(__snake_case ) - 1:
lowerCamelCase__ = ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(__snake_case )
if i < len(__snake_case ) - 2:
lowerCamelCase__ = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(__snake_case )
if i < len(__snake_case ) - 3:
lowerCamelCase__ = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(__snake_case )
ragramslist.append(__snake_case )
ragramslist.append(__snake_case )
ragramslist.append(__snake_case )
for i in range(0 ,len(__snake_case ) - 1 ):
if i < len(__snake_case ) - 1:
lowerCamelCase__ = sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(__snake_case )
if i < len(__snake_case ) - 2:
lowerCamelCase__ = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(__snake_case )
if i < len(__snake_case ) - 3:
lowerCamelCase__ = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(__snake_case )
for i in range(0 ,len(__snake_case ) - 1 ):
if i < len(__snake_case ) - 1:
lowerCamelCase__ = cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(__snake_case )
if i < len(__snake_case ) - 2:
lowerCamelCase__ = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(__snake_case )
if i < len(__snake_case ) - 3:
lowerCamelCase__ = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(__snake_case )
((lowerCamelCase__) , (lowerCamelCase__) , (lowerCamelCase__)) = SARIngram(__snake_case ,__snake_case ,__snake_case ,__snake_case )
((lowerCamelCase__) , (lowerCamelCase__) , (lowerCamelCase__)) = SARIngram(__snake_case ,__snake_case ,__snake_case ,__snake_case )
((lowerCamelCase__) , (lowerCamelCase__) , (lowerCamelCase__)) = SARIngram(__snake_case ,__snake_case ,__snake_case ,__snake_case )
((lowerCamelCase__) , (lowerCamelCase__) , (lowerCamelCase__)) = SARIngram(__snake_case ,__snake_case ,__snake_case ,__snake_case )
lowerCamelCase__ = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
lowerCamelCase__ = sum([delascore, delascore, delascore, delascore] ) / 4
lowerCamelCase__ = sum([addascore, addascore, addascore, addascore] ) / 4
lowerCamelCase__ = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def lowerCAmelCase__(__snake_case ,__snake_case = True ,__snake_case = "13a" ,__snake_case = True ) -> Tuple:
'''simple docstring'''
if lowercase:
lowerCamelCase__ = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
lowerCamelCase__ = sacrebleu.metrics.bleu._get_tokenizer(__snake_case )()(__snake_case )
else:
lowerCamelCase__ = sacrebleu.TOKENIZERS[tokenizer]()(__snake_case )
elif tokenizer == "moses":
lowerCamelCase__ = sacremoses.MosesTokenizer().tokenize(__snake_case ,return_str=__snake_case ,escape=__snake_case )
elif tokenizer == "penn":
lowerCamelCase__ = sacremoses.MosesTokenizer().penn_tokenize(__snake_case ,return_str=__snake_case )
else:
lowerCamelCase__ = sentence
if not return_str:
lowerCamelCase__ = normalized_sent.split()
return normalized_sent
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> List[Any]:
'''simple docstring'''
if not (len(__snake_case ) == len(__snake_case ) == len(__snake_case )):
raise ValueError('''Sources length must match predictions and references lengths.''' )
lowerCamelCase__ = 0
for src, pred, refs in zip(__snake_case ,__snake_case ,__snake_case ):
sari_score += SARIsent(normalize(__snake_case ) ,normalize(__snake_case ) ,[normalize(__snake_case ) for sent in refs] )
lowerCamelCase__ = sari_score / len(__snake_case )
return 100 * sari_score
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case="exp" ,__snake_case=None ,__snake_case=False ,__snake_case=False ,__snake_case=False ,) -> int:
'''simple docstring'''
lowerCamelCase__ = len(references[0] )
if any(len(__snake_case ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
lowerCamelCase__ = [[refs[i] for refs in references] for i in range(__snake_case )]
lowerCamelCase__ = sacrebleu.corpus_bleu(
__snake_case ,__snake_case ,smooth_method=__snake_case ,smooth_value=__snake_case ,force=__snake_case ,lowercase=__snake_case ,use_effective_order=__snake_case ,)
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
'''simple docstring'''
def __lowerCamelCase ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=[
'''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
'''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
'''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
'''https://github.com/mjpost/sacreBLEU''',
] , reference_urls=[
'''https://www.aclweb.org/anthology/Q16-1029.pdf''',
'''https://github.com/mjpost/sacreBLEU''',
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = {}
result.update({'''sari''': compute_sari(sources=__lowerCAmelCase , predictions=__lowerCAmelCase , references=__lowerCAmelCase )} )
result.update({'''sacrebleu''': compute_sacrebleu(predictions=__lowerCAmelCase , references=__lowerCAmelCase )} )
result.update({'''exact''': compute_em(predictions=__lowerCAmelCase , references=__lowerCAmelCase )} )
return result
| 481
| 1
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
_lowerCamelCase : Union[str, Any] = None
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : Tuple = '▁'
_lowerCamelCase : Optional[int] = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
_lowerCamelCase : Optional[int] = {
'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
'tokenizer_file': {
'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
},
}
_lowerCamelCase : Any = {
'google/pegasus-xsum': 512,
}
class snake_case__ ( __snake_case ):
'''simple docstring'''
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = PegasusTokenizer
__A = ['''input_ids''', '''attention_mask''']
def __init__( self : Dict , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Any=None , lowerCAmelCase_ : Any="<pad>" , lowerCAmelCase_ : Union[str, Any]="</s>" , lowerCAmelCase_ : Optional[int]="<unk>" , lowerCAmelCase_ : Tuple="<mask_2>" , lowerCAmelCase_ : int="<mask_1>" , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : int=1_03 , **lowerCAmelCase_ : Dict , ) -> Any:
UpperCAmelCase_ = offset
if additional_special_tokens is not None:
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError(
F'''additional_special_tokens should be of type {type(lowerCAmelCase_ )}, but is'''
F''' {type(lowerCAmelCase_ )}''' )
UpperCAmelCase_ = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F'''<unk_{i}>''' for i in range(len(lowerCAmelCase_ ) , self.offset - 1 )
]
if len(set(lowerCAmelCase_ ) ) != len(lowerCAmelCase_ ):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
UpperCAmelCase_ = additional_special_tokens_extended
else:
UpperCAmelCase_ = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F'''<unk_{i}>''' for i in range(2 , self.offset )]
super().__init__(
lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , eos_token=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , mask_token=lowerCAmelCase_ , mask_token_sent=lowerCAmelCase_ , offset=lowerCAmelCase_ , additional_special_tokens=lowerCAmelCase_ , **lowerCAmelCase_ , )
UpperCAmelCase_ = vocab_file
UpperCAmelCase_ = False if not self.vocab_file else True
def UpperCamelCase ( self : Optional[int] , lowerCAmelCase_ : Any ) -> Tuple:
UpperCAmelCase_ = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
'''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
F''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )
return [1 if x in all_special_ids else 0 for x in seq]
def UpperCamelCase ( self : int , lowerCAmelCase_ : List , lowerCAmelCase_ : Optional[List] = None , lowerCAmelCase_ : bool = False ) -> List[int]:
if already_has_special_tokens:
return self._special_token_mask(lowerCAmelCase_ )
elif token_ids_a is None:
return self._special_token_mask(lowerCAmelCase_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def UpperCamelCase ( self : Dict , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any]=None ) -> List[int]:
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def UpperCamelCase ( self : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(lowerCAmelCase_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase_ = os.path.join(
lowerCAmelCase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase_ ):
copyfile(self.vocab_file , lowerCAmelCase_ )
return (out_vocab_file,)
| 407
|
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : str = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
_lowerCamelCase : Dict = {
'vocab_file': {
'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
},
'merges_file': {
'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
},
'tokenizer_file': {
'gpt2': 'https://huggingface.co/gpt2/resolve/main/tokenizer.json',
'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json',
'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/tokenizer.json',
'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json',
'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/tokenizer.json',
},
}
_lowerCamelCase : Union[str, Any] = {
'gpt2': 1024,
'gpt2-medium': 1024,
'gpt2-large': 1024,
'gpt2-xl': 1024,
'distilgpt2': 1024,
}
class snake_case__ ( __snake_case ):
'''simple docstring'''
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = ['''input_ids''', '''attention_mask''']
__A = GPTaTokenizer
def __init__( self : Union[str, Any] , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Dict="<|endoftext|>" , lowerCAmelCase_ : List[str]="<|endoftext|>" , lowerCAmelCase_ : str="<|endoftext|>" , lowerCAmelCase_ : List[str]=False , **lowerCAmelCase_ : str , ) -> int:
super().__init__(
lowerCAmelCase_ , lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , bos_token=lowerCAmelCase_ , eos_token=lowerCAmelCase_ , add_prefix_space=lowerCAmelCase_ , **lowerCAmelCase_ , )
UpperCAmelCase_ = kwargs.pop('''add_bos_token''' , lowerCAmelCase_ )
UpperCAmelCase_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , lowerCAmelCase_ ) != add_prefix_space:
UpperCAmelCase_ = getattr(lowerCAmelCase_ , pre_tok_state.pop('''type''' ) )
UpperCAmelCase_ = add_prefix_space
UpperCAmelCase_ = pre_tok_class(**lowerCAmelCase_ )
UpperCAmelCase_ = add_prefix_space
def UpperCamelCase ( self : Tuple , *lowerCAmelCase_ : Optional[Any] , **lowerCAmelCase_ : Tuple ) -> BatchEncoding:
UpperCAmelCase_ = kwargs.get('''is_split_into_words''' , lowerCAmelCase_ )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*lowerCAmelCase_ , **lowerCAmelCase_ )
def UpperCamelCase ( self : Dict , *lowerCAmelCase_ : Tuple , **lowerCAmelCase_ : Optional[Any] ) -> BatchEncoding:
UpperCAmelCase_ = kwargs.get('''is_split_into_words''' , lowerCAmelCase_ )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*lowerCAmelCase_ , **lowerCAmelCase_ )
def UpperCamelCase ( self : Tuple , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None ) -> Tuple[str]:
UpperCAmelCase_ = self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_ )
return tuple(lowerCAmelCase_ )
def UpperCamelCase ( self : Optional[Any] , lowerCAmelCase_ : "Conversation" ) -> List[int]:
UpperCAmelCase_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) + [self.eos_token_id] )
if len(lowerCAmelCase_ ) > self.model_max_length:
UpperCAmelCase_ = input_ids[-self.model_max_length :]
return input_ids
| 407
| 1
|
'''simple docstring'''
from __future__ import annotations
def _A ( A ,A ) -> int:
# Checks if the entire collection has been sorted
if len(A ) <= 1 or n <= 1:
return
insert_next(A ,n - 1 )
rec_insertion_sort(A ,n - 1 )
def _A ( A ,A ) -> List[Any]:
# Checks order between adjacent elements
if index >= len(A ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
lowercase , lowercase : List[Any] = (
collection[index],
collection[index - 1],
)
insert_next(A ,index + 1 )
if __name__ == "__main__":
lowerCAmelCase : str = input("""Enter integers separated by spaces: """)
lowerCAmelCase : list[int] = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| 372
|
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
lowerCAmelCase : Dict = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _UpperCamelCase ( SCREAMING_SNAKE_CASE):
'''simple docstring'''
def __init__( self , *a_ , a_=None , a_=None , a_=None , **a_ ) -> Any:
super().__init__(*a_ , **a_ )
lowercase : Optional[Any] = eval_examples
lowercase : Any = post_process_function
lowercase : Dict = quant_trainer_args
lowercase : List[Any] = 1_2_8 # default number of calibration samples
def a__ ( self , a_=None ) -> List[str]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError("Trainer: calibration requires an calib_dataset." )
lowercase : Dict = calib_dataset if calib_dataset is not None else self.calib_dataset
lowercase : Optional[int] = self._remove_unused_columns(a_ , description="Calibration" )
return DataLoader(
a_ , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=a_ , )
def a__ ( self , a_=None ) -> str:
lowercase : Optional[Any] = self.train_dataset if calib_dataset is None else calib_dataset
lowercase : Optional[Any] = self.get_calib_dataloader(a_ )
lowercase : Union[str, Any] = self.model
quant_trainer.configure_model(a_ , self.quant_trainer_args , calib=a_ )
model.eval()
quant_trainer.enable_calibration(a_ )
logger.info("***** Running calibration *****" )
logger.info(F''' Num examples = {self.calib_num}''' )
logger.info(F''' Batch size = {calib_dataloader.batch_size}''' )
for step, inputs in enumerate(a_ ):
# Prediction step
lowercase , lowercase , lowercase : Union[str, Any] = self.prediction_step(a_ , a_ , prediction_loss_only=a_ )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(a_ , self.quant_trainer_args )
lowercase : List[Any] = model
def a__ ( self , a_=None , a_=None , a_=None , a_ = "eval" ) -> int:
lowercase : List[str] = self.eval_dataset if eval_dataset is None else eval_dataset
lowercase : Union[str, Any] = self.get_eval_dataloader(a_ )
lowercase : Tuple = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
lowercase : List[str] = self.compute_metrics
lowercase : Dict = None
lowercase : int = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowercase : Tuple = eval_loop(
a_ , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=a_ , )
finally:
lowercase : List[Any] = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
lowercase : Union[str, Any] = self.post_process_function(a_ , a_ , output.predictions )
lowercase : Union[str, Any] = self.compute_metrics(a_ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
lowercase : Dict = metrics.pop(a_ )
self.log(a_ )
else:
lowercase : Tuple = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
lowercase : int = self.callback_handler.on_evaluate(self.args , self.state , self.control , a_ )
return metrics
def a__ ( self , a_ , a_ , a_=None , a_ = "test" ) -> Optional[int]:
lowercase : Dict = self.get_test_dataloader(a_ )
# Temporarily disable metric computation, we will do it in the loop here.
lowercase : List[str] = self.compute_metrics
lowercase : List[str] = None
lowercase : int = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowercase : Optional[Any] = eval_loop(
a_ , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=a_ , )
finally:
lowercase : int = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
lowercase : List[str] = self.post_process_function(a_ , a_ , output.predictions , "predict" )
lowercase : Dict = self.compute_metrics(a_ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
lowercase : Union[str, Any] = metrics.pop(a_ )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=a_ )
def a__ ( self , a_="./" ) -> Optional[Any]:
lowercase : str = self.eval_dataset
lowercase : Dict = self.get_eval_dataloader(a_ )
lowercase : Any = next(iter(a_ ) )
# saving device - to make it consistent
lowercase : List[Any] = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
# convert to tuple
lowercase : Tuple = tuple(v.to(a_ ) for k, v in batch.items() )
logger.info("Converting model to be onnx compatible" )
from pytorch_quantization.nn import TensorQuantizer
lowercase : Optional[Any] = True
lowercase : int = self.model.to(a_ )
model.eval()
model.float()
lowercase : Union[str, Any] = model.module if hasattr(a_ , "module" ) else model
quant_trainer.configure_model(a_ , self.quant_trainer_args )
lowercase : List[str] = os.path.join(a_ , "model.onnx" )
logger.info(F'''exporting model to {output_model_file}''' )
lowercase : Any = {0: "batch_size", 1: "seq_len"}
torch.onnx.export(
a_ , a_ , a_ , export_params=a_ , opset_version=1_3 , do_constant_folding=a_ , input_names=["input_ids", "attention_mask", "token_type_ids"] , output_names=["output_start_logits", "output_end_logits"] , dynamic_axes={
"input_ids": axes,
"attention_mask": axes,
"token_type_ids": axes,
"output_start_logits": axes,
"output_end_logits": axes,
} , verbose=a_ , )
logger.info("onnx export finished" )
| 372
| 1
|
'''simple docstring'''
from __future__ import annotations
class SCREAMING_SNAKE_CASE :
def __init__( self : Any , A__ : list[list[int]] ):
"""simple docstring"""
__lowerCamelCase : List[str] = TypeError(
"""Matrices must be formed from a list of zero or more lists containing at """
"""least one and the same number of values, each of which must be of type """
"""int or float.""" )
if len(A__ ) != 0:
__lowerCamelCase : Optional[int] = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(A__ ) != cols:
raise error
for value in row:
if not isinstance(A__ , (int, float) ):
raise error
__lowerCamelCase : Tuple = rows
else:
__lowerCamelCase : Optional[int] = []
def a_ ( self : Optional[Any] ):
"""simple docstring"""
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def a_ ( self : List[Any] ):
"""simple docstring"""
return len(self.rows )
@property
def a_ ( self : Tuple ):
"""simple docstring"""
return len(self.rows[0] )
@property
def a_ ( self : Optional[int] ):
"""simple docstring"""
return (self.num_rows, self.num_columns)
@property
def a_ ( self : int ):
"""simple docstring"""
return self.order[0] == self.order[1]
def a_ ( self : Tuple ):
"""simple docstring"""
__lowerCamelCase : List[Any] = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(A__ )
def a_ ( self : List[str] ):
"""simple docstring"""
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def a_ ( self : Dict ):
"""simple docstring"""
return bool(self.determinant() )
def a_ ( self : List[Any] , A__ : int , A__ : int ):
"""simple docstring"""
__lowerCamelCase : Dict = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(A__ ).determinant()
def a_ ( self : Any , A__ : int , A__ : int ):
"""simple docstring"""
if (row + column) % 2 == 0:
return self.get_minor(A__ , A__ )
return -1 * self.get_minor(A__ , A__ )
def a_ ( self : Tuple ):
"""simple docstring"""
return Matrix(
[
[self.get_minor(A__ , A__ ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def a_ ( self : Optional[Any] ):
"""simple docstring"""
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def a_ ( self : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase : Optional[int] = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(A__ )
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
__lowerCamelCase : Dict = self.determinant()
if not determinant:
raise TypeError("""Only matrices with a non-zero determinant have an inverse""" )
return self.adjugate() * (1 / determinant)
def __repr__( self : List[str] ):
"""simple docstring"""
return str(self.rows )
def __str__( self : str ):
"""simple docstring"""
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
"""[""" + """. """.join([str(A__ ) for value in row] ) + """.]"""
for row in self.rows
] )
+ "]"
)
def a_ ( self : List[Any] , A__ : list[int] , A__ : int | None = None ):
"""simple docstring"""
__lowerCamelCase : List[str] = TypeError("""Row must be a list containing all ints and/or floats""" )
if not isinstance(A__ , A__ ):
raise type_error
for value in row:
if not isinstance(A__ , (int, float) ):
raise type_error
if len(A__ ) != self.num_columns:
raise ValueError(
"""Row must be equal in length to the other rows in the matrix""" )
if position is None:
self.rows.append(A__ )
else:
__lowerCamelCase : Tuple = self.rows[0:position] + [row] + self.rows[position:]
def a_ ( self : Optional[Any] , A__ : list[int] , A__ : int | None = None ):
"""simple docstring"""
__lowerCamelCase : str = TypeError(
"""Column must be a list containing all ints and/or floats""" )
if not isinstance(A__ , A__ ):
raise type_error
for value in column:
if not isinstance(A__ , (int, float) ):
raise type_error
if len(A__ ) != self.num_rows:
raise ValueError(
"""Column must be equal in length to the other columns in the matrix""" )
if position is None:
__lowerCamelCase : Optional[Any] = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
__lowerCamelCase : Union[str, Any] = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self : Any , A__ : object ):
"""simple docstring"""
if not isinstance(A__ , A__ ):
return NotImplemented
return self.rows == other.rows
def __ne__( self : List[str] , A__ : object ):
"""simple docstring"""
return not self == other
def __neg__( self : Union[str, Any] ):
"""simple docstring"""
return self * -1
def __add__( self : List[Any] , A__ : Matrix ):
"""simple docstring"""
if self.order != other.order:
raise ValueError("""Addition requires matrices of the same order""" )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self : int , A__ : Matrix ):
"""simple docstring"""
if self.order != other.order:
raise ValueError("""Subtraction requires matrices of the same order""" )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self : Tuple , A__ : Matrix | int | float ):
"""simple docstring"""
if isinstance(A__ , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(A__ , A__ ):
if self.num_columns != other.num_rows:
raise ValueError(
"""The number of columns in the first matrix must """
"""be equal to the number of rows in the second""" )
return Matrix(
[
[Matrix.dot_product(A__ , A__ ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
"""A Matrix can only be multiplied by an int, float, or another matrix""" )
def __pow__( self : List[str] , A__ : int ):
"""simple docstring"""
if not isinstance(A__ , A__ ):
raise TypeError("""A Matrix can only be raised to the power of an int""" )
if not self.is_square:
raise ValueError("""Only square matrices can be raised to a power""" )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
"""Only invertable matrices can be raised to a negative power""" )
__lowerCamelCase : Union[str, Any] = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def a_ ( cls : Union[str, Any] , A__ : list[int] , A__ : list[int] ):
"""simple docstring"""
return sum(row[i] * column[i] for i in range(len(A__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 483
|
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCAmelCase__ :List[str] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCAmelCase__ :Optional[int] = typing.Union[np.floataa, int, float] # noqa: UP007
def __lowercase (_lowercase, _lowercase ) -> VectorOut:
"""simple docstring"""
return np.sqrt(np.sum((np.asarray(_lowercase ) - np.asarray(_lowercase )) ** 2 ) )
def __lowercase (_lowercase, _lowercase ) -> VectorOut:
"""simple docstring"""
return sum((va - va) ** 2 for va, va in zip(_lowercase, _lowercase ) ) ** (1 / 2)
if __name__ == "__main__":
def __lowercase () -> None:
"""simple docstring"""
from timeit import timeit
print("""Without Numpy""" )
print(
timeit(
"""euclidean_distance_no_np([1, 2, 3], [4, 5, 6])""", number=10_000, globals=globals(), ) )
print("""With Numpy""" )
print(
timeit(
"""euclidean_distance([1, 2, 3], [4, 5, 6])""", number=10_000, globals=globals(), ) )
benchmark()
| 483
| 1
|
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : str = logging.get_logger(__name__)
def lowerCAmelCase ( UpperCamelCase__ : Tuple , UpperCamelCase__ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: int = []
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F"""encoder.deit.blocks.{i}.norm1.weight""", F"""encoder.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""encoder.deit.blocks.{i}.norm1.bias""", F"""encoder.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(F"""encoder.deit.blocks.{i}.attn.proj.weight""", F"""encoder.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append(
(F"""encoder.deit.blocks.{i}.attn.proj.bias""", F"""encoder.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append(
(F"""encoder.deit.blocks.{i}.norm2.weight""", F"""encoder.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""encoder.deit.blocks.{i}.norm2.bias""", F"""encoder.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append(
(F"""encoder.deit.blocks.{i}.mlp.fc1.weight""", F"""encoder.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append(
(F"""encoder.deit.blocks.{i}.mlp.fc1.bias""", F"""encoder.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append(
(F"""encoder.deit.blocks.{i}.mlp.fc2.weight""", F"""encoder.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""encoder.deit.blocks.{i}.mlp.fc2.bias""", F"""encoder.encoder.layer.{i}.output.dense.bias""") )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
] )
return rename_keys
def lowerCAmelCase ( UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] ) -> Dict:
"""simple docstring"""
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
__SCREAMING_SNAKE_CASE: Union[str, Any] = state_dict.pop(F"""encoder.deit.blocks.{i}.attn.qkv.weight""" )
__SCREAMING_SNAKE_CASE: List[str] = in_proj_weight[
: encoder_config.hidden_size, :
]
__SCREAMING_SNAKE_CASE: str = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
__SCREAMING_SNAKE_CASE: Union[str, Any] = in_proj_weight[
-encoder_config.hidden_size :, :
]
def lowerCAmelCase ( UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Tuple = dct.pop(UpperCamelCase__ )
__SCREAMING_SNAKE_CASE: int = val
def lowerCAmelCase ( UpperCamelCase__ : str ) -> Union[str, Any]:
"""simple docstring"""
if "handwritten" in checkpoint_url:
__SCREAMING_SNAKE_CASE: List[Any] = '''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg''' # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
__SCREAMING_SNAKE_CASE: str = '''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
__SCREAMING_SNAKE_CASE: List[str] = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw ).convert('''RGB''' )
return im
@torch.no_grad()
def lowerCAmelCase ( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: List[Any] = ViTConfig(image_size=384 , qkv_bias=UpperCamelCase__ )
__SCREAMING_SNAKE_CASE: Optional[Any] = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
__SCREAMING_SNAKE_CASE: Optional[Any] = 768
elif "large" in checkpoint_url:
# use ViT-large encoder
__SCREAMING_SNAKE_CASE: Optional[Any] = 1_024
__SCREAMING_SNAKE_CASE: Any = 4_096
__SCREAMING_SNAKE_CASE: str = 24
__SCREAMING_SNAKE_CASE: List[Any] = 16
__SCREAMING_SNAKE_CASE: Optional[int] = 1_024
else:
raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL''' )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
__SCREAMING_SNAKE_CASE: Optional[int] = False
__SCREAMING_SNAKE_CASE: Union[str, Any] = '''relu'''
__SCREAMING_SNAKE_CASE: Dict = 1_024
__SCREAMING_SNAKE_CASE: str = True
__SCREAMING_SNAKE_CASE: Optional[int] = False
__SCREAMING_SNAKE_CASE: int = False
# load HuggingFace model
__SCREAMING_SNAKE_CASE: int = ViTModel(UpperCamelCase__ , add_pooling_layer=UpperCamelCase__ )
__SCREAMING_SNAKE_CASE: int = TrOCRForCausalLM(UpperCamelCase__ )
__SCREAMING_SNAKE_CASE: Optional[Any] = VisionEncoderDecoderModel(encoder=UpperCamelCase__ , decoder=UpperCamelCase__ )
model.eval()
# load state_dict of original model, rename some keys
__SCREAMING_SNAKE_CASE: List[str] = torch.hub.load_state_dict_from_url(UpperCamelCase__ , map_location='''cpu''' , check_hash=UpperCamelCase__ )['''model''']
__SCREAMING_SNAKE_CASE: Optional[int] = create_rename_keys(UpperCamelCase__ , UpperCamelCase__ )
for src, dest in rename_keys:
rename_key(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
read_in_q_k_v(UpperCamelCase__ , UpperCamelCase__ )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
__SCREAMING_SNAKE_CASE: Optional[Any] = state_dict.pop(UpperCamelCase__ )
if key.startswith('''decoder''' ) and "output_projection" not in key:
__SCREAMING_SNAKE_CASE: Optional[Any] = val
else:
__SCREAMING_SNAKE_CASE: Dict = val
# load state dict
model.load_state_dict(UpperCamelCase__ )
# Check outputs on an image
__SCREAMING_SNAKE_CASE: Union[str, Any] = ViTImageProcessor(size=encoder_config.image_size )
__SCREAMING_SNAKE_CASE: Union[str, Any] = RobertaTokenizer.from_pretrained('''roberta-large''' )
__SCREAMING_SNAKE_CASE: List[Any] = TrOCRProcessor(UpperCamelCase__ , UpperCamelCase__ )
__SCREAMING_SNAKE_CASE: int = processor(images=prepare_img(UpperCamelCase__ ) , return_tensors='''pt''' ).pixel_values
# verify logits
__SCREAMING_SNAKE_CASE: int = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
__SCREAMING_SNAKE_CASE: List[str] = model(pixel_values=UpperCamelCase__ , decoder_input_ids=UpperCamelCase__ )
__SCREAMING_SNAKE_CASE: Any = outputs.logits
__SCREAMING_SNAKE_CASE: Optional[Any] = torch.Size([1, 1, 50_265] )
if "trocr-base-handwritten" in checkpoint_url:
__SCREAMING_SNAKE_CASE: Tuple = torch.tensor(
[-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11] )
elif "trocr-large-handwritten" in checkpoint_url:
__SCREAMING_SNAKE_CASE: List[str] = torch.tensor(
[-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70] )
elif "trocr-base-printed" in checkpoint_url:
__SCREAMING_SNAKE_CASE: Any = torch.tensor(
[-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10] )
elif "trocr-large-printed" in checkpoint_url:
__SCREAMING_SNAKE_CASE: Optional[int] = torch.tensor(
[-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :10] , UpperCamelCase__ , atol=1E-3 ), "First elements of logits not as expected"
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCamelCase__ )
print(F"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
type=str,
help="""URL to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
lowerCAmelCase : List[Any] = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 202
|
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase ( UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] ) -> str:
"""simple docstring"""
assert isinstance(UpperCamelCase__ , UpperCamelCase__ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase ( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Dict = tmp_path / '''cache'''
__SCREAMING_SNAKE_CASE: Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
__SCREAMING_SNAKE_CASE: Tuple = JsonDatasetReader(UpperCamelCase__ , cache_dir=UpperCamelCase__ , keep_in_memory=UpperCamelCase__ ).read()
_check_json_dataset(UpperCamelCase__ , UpperCamelCase__ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase ( UpperCamelCase__ : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: List[str] = tmp_path / '''cache'''
__SCREAMING_SNAKE_CASE: Tuple = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__SCREAMING_SNAKE_CASE: Any = features.copy() if features else default_expected_features
__SCREAMING_SNAKE_CASE: List[Any] = (
Features({feature: Value(UpperCamelCase__ ) for feature, dtype in features.items()} ) if features is not None else None
)
__SCREAMING_SNAKE_CASE: Tuple = JsonDatasetReader(UpperCamelCase__ , features=UpperCamelCase__ , cache_dir=UpperCamelCase__ ).read()
_check_json_dataset(UpperCamelCase__ , UpperCamelCase__ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def lowerCAmelCase ( UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[int] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Optional[int] = tmp_path / '''cache'''
__SCREAMING_SNAKE_CASE: Dict = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
__SCREAMING_SNAKE_CASE: int = features.copy() if features else default_expected_features
__SCREAMING_SNAKE_CASE: List[Any] = (
Features({feature: Value(UpperCamelCase__ ) for feature, dtype in features.items()} ) if features is not None else None
)
__SCREAMING_SNAKE_CASE: Union[str, Any] = JsonDatasetReader(UpperCamelCase__ , features=UpperCamelCase__ , cache_dir=UpperCamelCase__ ).read()
assert isinstance(UpperCamelCase__ , UpperCamelCase__ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def lowerCAmelCase ( UpperCamelCase__ : str , UpperCamelCase__ : Optional[int] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: str = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
__SCREAMING_SNAKE_CASE: Dict = features.copy()
__SCREAMING_SNAKE_CASE: Optional[int] = (
Features({feature: Value(UpperCamelCase__ ) for feature, dtype in features.items()} ) if features is not None else None
)
__SCREAMING_SNAKE_CASE: Union[str, Any] = tmp_path / '''cache'''
__SCREAMING_SNAKE_CASE: Tuple = JsonDatasetReader(UpperCamelCase__ , features=UpperCamelCase__ , cache_dir=UpperCamelCase__ ).read()
assert isinstance(UpperCamelCase__ , UpperCamelCase__ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase ( UpperCamelCase__ : Any , UpperCamelCase__ : str , UpperCamelCase__ : int ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: List[Any] = tmp_path / '''cache'''
__SCREAMING_SNAKE_CASE: Optional[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__SCREAMING_SNAKE_CASE: Dict = JsonDatasetReader(UpperCamelCase__ , cache_dir=UpperCamelCase__ , split=UpperCamelCase__ ).read()
_check_json_dataset(UpperCamelCase__ , UpperCamelCase__ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def lowerCAmelCase ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str ) -> Any:
"""simple docstring"""
if issubclass(UpperCamelCase__ , UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE: Optional[Any] = jsonl_path
elif issubclass(UpperCamelCase__ , UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE: List[str] = [jsonl_path]
__SCREAMING_SNAKE_CASE: List[Any] = tmp_path / '''cache'''
__SCREAMING_SNAKE_CASE: Union[str, Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__SCREAMING_SNAKE_CASE: Tuple = JsonDatasetReader(UpperCamelCase__ , cache_dir=UpperCamelCase__ ).read()
_check_json_dataset(UpperCamelCase__ , UpperCamelCase__ )
def lowerCAmelCase ( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any]=("train",) ) -> List[str]:
"""simple docstring"""
assert isinstance(UpperCamelCase__ , UpperCamelCase__ )
for split in splits:
__SCREAMING_SNAKE_CASE: Tuple = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Tuple ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Optional[Any] = tmp_path / '''cache'''
__SCREAMING_SNAKE_CASE: List[str] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
__SCREAMING_SNAKE_CASE: Dict = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=UpperCamelCase__ , keep_in_memory=UpperCamelCase__ ).read()
_check_json_datasetdict(UpperCamelCase__ , UpperCamelCase__ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase ( UpperCamelCase__ : Dict , UpperCamelCase__ : Tuple , UpperCamelCase__ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Optional[Any] = tmp_path / '''cache'''
__SCREAMING_SNAKE_CASE: Union[str, Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__SCREAMING_SNAKE_CASE: str = features.copy() if features else default_expected_features
__SCREAMING_SNAKE_CASE: Optional[int] = (
Features({feature: Value(UpperCamelCase__ ) for feature, dtype in features.items()} ) if features is not None else None
)
__SCREAMING_SNAKE_CASE: Dict = JsonDatasetReader({'''train''': jsonl_path} , features=UpperCamelCase__ , cache_dir=UpperCamelCase__ ).read()
_check_json_datasetdict(UpperCamelCase__ , UpperCamelCase__ )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : int ) -> Optional[Any]:
"""simple docstring"""
if split:
__SCREAMING_SNAKE_CASE: str = {split: jsonl_path}
else:
__SCREAMING_SNAKE_CASE: Dict = '''train'''
__SCREAMING_SNAKE_CASE: Tuple = {'''train''': jsonl_path, '''test''': jsonl_path}
__SCREAMING_SNAKE_CASE: Tuple = tmp_path / '''cache'''
__SCREAMING_SNAKE_CASE: str = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__SCREAMING_SNAKE_CASE: int = JsonDatasetReader(UpperCamelCase__ , cache_dir=UpperCamelCase__ ).read()
_check_json_datasetdict(UpperCamelCase__ , UpperCamelCase__ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase ( UpperCamelCase__ : Dict ) -> Optional[int]:
"""simple docstring"""
return json.load(UpperCamelCase__ )
def lowerCAmelCase ( UpperCamelCase__ : Any ) -> Any:
"""simple docstring"""
return [json.loads(UpperCamelCase__ ) for line in buffer]
class a :
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def snake_case_ ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
with io.BytesIO() as buffer:
JsonDatasetWriter(_lowerCAmelCase , _lowerCAmelCase , lines=_lowerCAmelCase ).write()
buffer.seek(0 )
__SCREAMING_SNAKE_CASE: str = load_json_function(_lowerCAmelCase )
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert isinstance(exported_content[0] , _lowerCAmelCase )
assert len(_lowerCAmelCase ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def snake_case_ ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
with io.BytesIO() as buffer:
JsonDatasetWriter(_lowerCAmelCase , _lowerCAmelCase , lines=_lowerCAmelCase , orient=_lowerCAmelCase ).write()
buffer.seek(0 )
__SCREAMING_SNAKE_CASE: Optional[int] = load_json(_lowerCAmelCase )
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(_lowerCAmelCase , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(_lowerCAmelCase ) == 10
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def snake_case_ ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
with io.BytesIO() as buffer:
JsonDatasetWriter(_lowerCAmelCase , _lowerCAmelCase , lines=_lowerCAmelCase , num_proc=2 ).write()
buffer.seek(0 )
__SCREAMING_SNAKE_CASE: Optional[Any] = load_json_function(_lowerCAmelCase )
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert isinstance(exported_content[0] , _lowerCAmelCase )
assert len(_lowerCAmelCase ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def snake_case_ ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
with io.BytesIO() as buffer:
JsonDatasetWriter(_lowerCAmelCase , _lowerCAmelCase , lines=_lowerCAmelCase , orient=_lowerCAmelCase , num_proc=2 ).write()
buffer.seek(0 )
__SCREAMING_SNAKE_CASE: Dict = load_json(_lowerCAmelCase )
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(_lowerCAmelCase , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(_lowerCAmelCase ) == 10
def snake_case_ ( self , _lowerCAmelCase ):
"""simple docstring"""
with pytest.raises(_lowerCAmelCase ):
with io.BytesIO() as buffer:
JsonDatasetWriter(_lowerCAmelCase , _lowerCAmelCase , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def snake_case_ ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Any = tmp_path_factory.mktemp('''data''' ) / f"""test.json.{extension}"""
__SCREAMING_SNAKE_CASE: Any = str(shared_datadir / f"""test_file.json.{extension}""" )
JsonDatasetWriter(_lowerCAmelCase , _lowerCAmelCase , compression=_lowerCAmelCase ).write()
with fsspec.open(_lowerCAmelCase , '''rb''' , compression='''infer''' ) as f:
__SCREAMING_SNAKE_CASE: Optional[Any] = f.read()
with fsspec.open(_lowerCAmelCase , '''rb''' , compression='''infer''' ) as f:
__SCREAMING_SNAKE_CASE: Any = f.read()
assert exported_content == original_content
| 202
| 1
|
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class lowerCAmelCase__ ( unittest.TestCase ):
def __UpperCamelCase ( self : Optional[int] ) -> Union[str, Any]:
A = 'ylacombe/bark-small'
A = tempfile.mkdtemp()
A = 'en_speaker_1'
A = 'This is a test string'
A = 'speaker_embeddings_path.json'
A = 'speaker_embeddings'
def __UpperCamelCase ( self : Optional[Any] , **__UpperCamelCase : Any ) -> Dict:
return AutoTokenizer.from_pretrained(self.checkpoint , **__UpperCamelCase )
def __UpperCamelCase ( self : Optional[Any] ) -> int:
shutil.rmtree(self.tmpdirname )
def __UpperCamelCase ( self : int ) -> List[Any]:
A = self.get_tokenizer()
A = BarkProcessor(tokenizer=__UpperCamelCase )
processor.save_pretrained(self.tmpdirname )
A = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def __UpperCamelCase ( self : Optional[Any] ) -> List[str]:
A = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
A = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
A = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='(BOS)' , eos_token='(EOS)' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def __UpperCamelCase ( self : Any ) -> List[str]:
A = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
A = 35
A = 2
A = 8
A = {
'semantic_prompt': np.ones(__UpperCamelCase ),
'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len) ),
'fine_prompt': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
A = processor(text=self.input_string , voice_preset=__UpperCamelCase )
A = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(__UpperCamelCase , np.array([] ) ).tolist() )
# test loading voice preset from npz file
A = os.path.join(self.tmpdirname , 'file.npz' )
np.savez(__UpperCamelCase , **__UpperCamelCase )
A = processor(text=self.input_string , voice_preset=__UpperCamelCase )
A = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(__UpperCamelCase , np.array([] ) ).tolist() )
# test loading voice preset from the hub
A = processor(text=self.input_string , voice_preset=self.voice_preset )
def __UpperCamelCase ( self : List[str] ) -> Dict:
A = self.get_tokenizer()
A = BarkProcessor(tokenizer=__UpperCamelCase )
A = processor(text=self.input_string )
A = tokenizer(
self.input_string , padding='max_length' , max_length=256 , add_special_tokens=__UpperCamelCase , return_attention_mask=__UpperCamelCase , return_token_type_ids=__UpperCamelCase , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 716
|
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowerCamelCase_ ( lowerCAmelCase__ : int ) -> Optional[Any]:
'''simple docstring'''
A = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'decoder.output_projection.weight',
'_float_tensor',
'encoder.embed_positions._float_tensor',
'decoder.embed_positions._float_tensor',
]
for k in ignore_keys:
state_dict.pop(lowerCAmelCase__ , lowerCAmelCase__ )
def lowerCamelCase_ ( lowerCAmelCase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
A , A = emb.weight.shape
A = nn.Linear(lowerCAmelCase__ , lowerCAmelCase__ , bias=lowerCAmelCase__ )
A = emb.weight.data
return lin_layer
def lowerCamelCase_ ( lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[int]=None ) -> Optional[int]:
'''simple docstring'''
A = {}
for old_key in state_dict.keys():
A = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
A = key.replace('moe_layer.experts.0' , F'''ffn.experts.expert_{expert_idx}''' )
else:
A = key.replace('moe_layer.experts.' , 'ffn.experts.expert_' )
if "gate" in key:
A = key.replace('.moe_layer.gate.wg' , '.ffn.router.classifier' )
if "fc2" and "experts" not in key:
A = key.replace('.fc2.' , '.ffn.fc2.' )
if "fc1" and "experts" not in key:
A = key.replace('.fc1.' , '.ffn.fc1.' )
if ".encoder_attn." in key:
A = key.replace('.encoder_attn.' , '.cross_attention.' )
if "encoder_attn_layer_norm" in key:
A = key.replace('encoder_attn_layer_norm' , 'cross_attention_layer_norm' )
if "final_layer_norm" in key:
A = key.replace('final_layer_norm' , 'ff_layer_norm' )
A = state_dict[old_key]
return new_dict
def lowerCamelCase_ ( lowerCAmelCase__ : Tuple , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : str = WEIGHTS_NAME ) -> List[str]:
'''simple docstring'''
A = []
A = 0
os.makedirs(lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
for expert in range(lowerCAmelCase__ ):
A = switch_checkpoint_path + F'''-rank-{expert}.pt'''
if os.path.isfile(lowerCAmelCase__ ):
A = torch.load(lowerCAmelCase__ )['model']
remove_ignore_keys_(lowerCAmelCase__ )
A = rename_fairseq_keys(lowerCAmelCase__ , lowerCAmelCase__ )
A = os.path.join(
lowerCAmelCase__ , weights_name.replace('.bin' , F'''-{len(lowerCAmelCase__ )+1:05d}-of-???.bin''' ) )
torch.save(lowerCAmelCase__ , lowerCAmelCase__ )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(lowerCAmelCase__ )[0]].dtype )
# Add the last block
A = os.path.join(lowerCAmelCase__ , weights_name.replace('.bin' , F'''-{len(lowerCAmelCase__ )+1:05d}-of-???.bin''' ) )
A = torch.load(switch_checkpoint_path + '-shared.pt' )['model']
remove_ignore_keys_(lowerCAmelCase__ )
A = rename_fairseq_keys(lowerCAmelCase__ , lowerCAmelCase__ )
A = shared_weights['decoder.embed_tokens.weight']
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(lowerCAmelCase__ ) == 1:
A = os.path.join(lowerCAmelCase__ , lowerCAmelCase__ )
torch.save(lowerCAmelCase__ , lowerCAmelCase__ )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(lowerCAmelCase__ , lowerCAmelCase__ )
# Otherwise, let's build the index
A = {}
for idx, shard in enumerate(lowerCAmelCase__ ):
A = weights_name.replace('.bin' , F'''-{idx+1:05d}-of-{len(lowerCAmelCase__ ):05d}.bin''' )
A = os.path.join(lowerCAmelCase__ , weights_name.replace('.bin' , F'''-{idx+1:05d}-of-???.bin''' ) )
os.rename(lowerCAmelCase__ , os.path.join(lowerCAmelCase__ , lowerCAmelCase__ ) )
for key in shard:
A = shard_file
# Add the metadata
A = {'total_size': total_size}
A = {'metadata': metadata, 'weight_map': weight_map}
with open(os.path.join(lowerCAmelCase__ , lowerCAmelCase__ ) , 'w' , encoding='utf-8' ) as f:
A = json.dumps(lowerCAmelCase__ , indent=2 , sort_keys=lowerCAmelCase__ ) + '\n'
f.write(lowerCAmelCase__ )
return metadata, index
if __name__ == "__main__":
__snake_case :Optional[Any] =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--nllb_moe_checkpoint_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
type=str,
required=False,
help='Path to a directory containing a folder per layer. Follows the original Google format.',
)
parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
parser.add_argument(
'--pytorch_dump_folder_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
type=str,
required=False,
help='Path to the output pytorch model.',
)
__snake_case :int =parser.parse_args()
__snake_case , __snake_case :List[Any] =shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
128,
args.dtype,
)
__snake_case :Dict =NllbMoeConfig.from_pretrained(
'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
)
config.save_pretrained(args.pytorch_dump_folder_path)
__snake_case :Optional[int] =NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print('Done')
model.save_pretrained(args.pytorch_dump_folder_path)
| 224
| 0
|
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:Optional[Any] = []
if isinstance(snake_case , snake_case ):
for v in tree.values():
shapes.extend(_fetch_dims(snake_case ) )
elif isinstance(snake_case , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(snake_case ) )
elif isinstance(snake_case , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError("Not supported" )
return shapes
@torch.jit.ignore
def A_ ( snake_case , snake_case ):
SCREAMING_SNAKE_CASE:List[str] = []
for d in reversed(snake_case ):
idx.append(flat_idx % d )
SCREAMING_SNAKE_CASE:Dict = flat_idx // d
return tuple(reversed(snake_case ) )
@torch.jit.ignore
def A_ ( snake_case , snake_case , snake_case , snake_case = None , snake_case = None , ):
# start_edges and end_edges both indicate whether, starting from any given
# dimension, the start/end index is at the top/bottom edge of the
# corresponding tensor, modeled as a tree
def reduce_edge_list(snake_case ) -> None:
SCREAMING_SNAKE_CASE:List[Any] = True
for i in range(len(snake_case ) ):
SCREAMING_SNAKE_CASE:List[Any] = -1 * (i + 1)
l[reversed_idx] &= tally
SCREAMING_SNAKE_CASE:Union[str, Any] = l[reversed_idx]
if start_edges is None:
SCREAMING_SNAKE_CASE:Any = [s == 0 for s in start]
reduce_edge_list(snake_case )
if end_edges is None:
SCREAMING_SNAKE_CASE:str = [e == (d - 1) for e, d in zip(snake_case , snake_case )]
reduce_edge_list(snake_case )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(snake_case ) == 0:
return [()]
elif len(snake_case ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
SCREAMING_SNAKE_CASE:List[Tuple[slice, ...]] = []
SCREAMING_SNAKE_CASE:List[slice] = []
# Dimensions common to start and end can be selected directly
for s, e in zip(snake_case , snake_case ):
if s == e:
path_list.append(slice(snake_case , s + 1 ) )
else:
break
SCREAMING_SNAKE_CASE:Tuple[slice, ...] = tuple(snake_case )
SCREAMING_SNAKE_CASE:Any = len(snake_case )
# start == end, and we're done
if divergence_idx == len(snake_case ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
SCREAMING_SNAKE_CASE:List[Any] = start[divergence_idx]
return tuple(
path + (slice(snake_case , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
SCREAMING_SNAKE_CASE:str = end[divergence_idx]
return tuple(
path + (slice(snake_case , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
SCREAMING_SNAKE_CASE:Any = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def A_ ( snake_case , snake_case , snake_case , snake_case ):
SCREAMING_SNAKE_CASE:str = t.shape[:no_batch_dims]
SCREAMING_SNAKE_CASE:List[Any] = list(_flat_idx_to_idx(snake_case , snake_case ) )
# _get_minimal_slice_set is inclusive
SCREAMING_SNAKE_CASE:Any = list(_flat_idx_to_idx(flat_end - 1 , snake_case ) )
# Get an ordered list of slices to perform
SCREAMING_SNAKE_CASE:Tuple = _get_minimal_slice_set(
snake_case , snake_case , snake_case , )
SCREAMING_SNAKE_CASE:List[Any] = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def A_ ( snake_case , snake_case , snake_case , snake_case , snake_case = False , snake_case = None , snake_case = False , ):
if not (len(snake_case ) > 0):
raise ValueError("Must provide at least one input" )
SCREAMING_SNAKE_CASE:Dict = [shape[:no_batch_dims] for shape in _fetch_dims(snake_case )]
SCREAMING_SNAKE_CASE:int = tuple([max(snake_case ) for s in zip(*snake_case )] )
def _prep_inputs(snake_case ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
SCREAMING_SNAKE_CASE:str = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
SCREAMING_SNAKE_CASE:Any = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
SCREAMING_SNAKE_CASE:int = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
SCREAMING_SNAKE_CASE:Dict[str, Any] = tensor_tree_map(_prep_inputs , snake_case )
SCREAMING_SNAKE_CASE:Optional[int] = None
if _out is not None:
SCREAMING_SNAKE_CASE:Tuple = tensor_tree_map(lambda snake_case : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
SCREAMING_SNAKE_CASE:Any = 1
for d in orig_batch_dims:
flat_batch_dim *= d
SCREAMING_SNAKE_CASE:Optional[int] = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(snake_case ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
SCREAMING_SNAKE_CASE:Dict = 0
SCREAMING_SNAKE_CASE:str = prepped_outputs
for _ in range(snake_case ):
# Chunk the input
if not low_mem:
SCREAMING_SNAKE_CASE:List[Any] = _select_chunk
else:
SCREAMING_SNAKE_CASE:Optional[int] = partial(
_chunk_slice , flat_start=snake_case , flat_end=min(snake_case , i + chunk_size ) , no_batch_dims=len(snake_case ) , )
SCREAMING_SNAKE_CASE:Dict[str, Any] = tensor_tree_map(snake_case , snake_case )
# Run the layer on the chunk
SCREAMING_SNAKE_CASE:List[str] = layer(**snake_case )
# Allocate space for the output
if out is None:
SCREAMING_SNAKE_CASE:Optional[int] = tensor_tree_map(lambda snake_case : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , snake_case )
# Put the chunk in its pre-allocated space
if isinstance(snake_case , snake_case ):
def assign(snake_case , snake_case ) -> None:
for k, v in da.items():
if isinstance(snake_case , snake_case ):
assign(snake_case , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
SCREAMING_SNAKE_CASE:List[Any] = da[k]
assign(snake_case , snake_case )
elif isinstance(snake_case , snake_case ):
for xa, xa in zip(snake_case , snake_case ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
SCREAMING_SNAKE_CASE:Union[str, Any] = xa
elif isinstance(snake_case , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
SCREAMING_SNAKE_CASE:Dict = output_chunk
else:
raise ValueError("Not supported" )
i += chunk_size
SCREAMING_SNAKE_CASE:Union[str, Any] = tensor_tree_map(lambda snake_case : t.view(orig_batch_dims + t.shape[1:] ) , snake_case )
return out
class _snake_case :
def __init__( self : Dict ,SCREAMING_SNAKE_CASE__ : int = 512 ,):
SCREAMING_SNAKE_CASE:Dict = max_chunk_size
SCREAMING_SNAKE_CASE:Optional[int] = None
SCREAMING_SNAKE_CASE:Optional[tuple] = None
def __UpperCamelCase ( self : Tuple ,SCREAMING_SNAKE_CASE__ : Callable ,SCREAMING_SNAKE_CASE__ : tuple ,SCREAMING_SNAKE_CASE__ : int ):
logging.info("Tuning chunk size..." )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
SCREAMING_SNAKE_CASE:List[int] = [2**l for l in range(int(math.log(self.max_chunk_size ,2 ) ) + 1 )]
SCREAMING_SNAKE_CASE:Tuple = [c for c in candidates if c > min_chunk_size]
SCREAMING_SNAKE_CASE:str = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(SCREAMING_SNAKE_CASE__ : int ) -> bool:
try:
with torch.no_grad():
fn(*SCREAMING_SNAKE_CASE__ ,chunk_size=SCREAMING_SNAKE_CASE__ )
return True
except RuntimeError:
return False
SCREAMING_SNAKE_CASE:List[str] = 0
SCREAMING_SNAKE_CASE:int = len(SCREAMING_SNAKE_CASE__ ) - 1
while i > min_viable_chunk_size_index:
SCREAMING_SNAKE_CASE:List[Any] = test_chunk_size(candidates[i] )
if not viable:
SCREAMING_SNAKE_CASE:Optional[Any] = (min_viable_chunk_size_index + i) // 2
else:
SCREAMING_SNAKE_CASE:Union[str, Any] = i
SCREAMING_SNAKE_CASE:Optional[int] = (i + len(SCREAMING_SNAKE_CASE__ ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def __UpperCamelCase ( self : int ,SCREAMING_SNAKE_CASE__ : Iterable ,SCREAMING_SNAKE_CASE__ : Iterable ):
SCREAMING_SNAKE_CASE:Optional[int] = True
for aa, aa in zip(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
assert type(SCREAMING_SNAKE_CASE__ ) == type(SCREAMING_SNAKE_CASE__ )
if isinstance(SCREAMING_SNAKE_CASE__ ,(list, tuple) ):
consistent &= self._compare_arg_caches(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
elif isinstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE:Any = [v for _, v in sorted(aa.items() ,key=lambda SCREAMING_SNAKE_CASE__ : x[0] )]
SCREAMING_SNAKE_CASE:str = [v for _, v in sorted(aa.items() ,key=lambda SCREAMING_SNAKE_CASE__ : x[0] )]
consistent &= self._compare_arg_caches(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
else:
consistent &= aa == aa
return consistent
def __UpperCamelCase ( self : Tuple ,SCREAMING_SNAKE_CASE__ : Callable ,SCREAMING_SNAKE_CASE__ : tuple ,SCREAMING_SNAKE_CASE__ : int ,):
SCREAMING_SNAKE_CASE:List[str] = True
SCREAMING_SNAKE_CASE:tuple = tree_map(lambda SCREAMING_SNAKE_CASE__ : a.shape if isinstance(SCREAMING_SNAKE_CASE__ ,torch.Tensor ) else a ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:int = self._compare_arg_caches(self.cached_arg_data ,SCREAMING_SNAKE_CASE__ )
else:
# Otherwise, we can reuse the precomputed value
SCREAMING_SNAKE_CASE:Dict = False
if not consistent:
SCREAMING_SNAKE_CASE:int = self._determine_favorable_chunk_size(
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,)
SCREAMING_SNAKE_CASE:Optional[Any] = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 143
|
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def A_ ( snake_case , snake_case , snake_case , snake_case , snake_case=True , snake_case="pt" ):
SCREAMING_SNAKE_CASE:Optional[int] = {"add_prefix_space": True} if isinstance(snake_case , snake_case ) and not line.startswith(" " ) else {}
SCREAMING_SNAKE_CASE:Any = padding_side
return tokenizer(
[line] , max_length=snake_case , padding="max_length" if pad_to_max_length else None , truncation=snake_case , return_tensors=snake_case , add_special_tokens=snake_case , **snake_case , )
def A_ ( snake_case , snake_case , snake_case=None , ):
SCREAMING_SNAKE_CASE:List[str] = input_ids.ne(snake_case ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class _snake_case ( _a ):
def __init__( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Tuple="train" ,SCREAMING_SNAKE_CASE__ : List[Any]=None ,SCREAMING_SNAKE_CASE__ : Optional[int]=None ,SCREAMING_SNAKE_CASE__ : List[str]=None ,SCREAMING_SNAKE_CASE__ : Any="" ,):
super().__init__()
SCREAMING_SNAKE_CASE:int = Path(SCREAMING_SNAKE_CASE__ ).joinpath(type_path + ".source" )
SCREAMING_SNAKE_CASE:Optional[int] = Path(SCREAMING_SNAKE_CASE__ ).joinpath(type_path + ".target" )
SCREAMING_SNAKE_CASE:List[str] = self.get_char_lens(self.src_file )
SCREAMING_SNAKE_CASE:Tuple = max_source_length
SCREAMING_SNAKE_CASE:Any = max_target_length
assert min(self.src_lens ) > 0, F'''found empty line in {self.src_file}'''
SCREAMING_SNAKE_CASE:List[Any] = tokenizer
SCREAMING_SNAKE_CASE:str = prefix
if n_obs is not None:
SCREAMING_SNAKE_CASE:Union[str, Any] = self.src_lens[:n_obs]
SCREAMING_SNAKE_CASE:Dict = src_lang
SCREAMING_SNAKE_CASE:Optional[int] = tgt_lang
def __len__( self : Union[str, Any] ):
return len(self.src_lens )
def __getitem__( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : str ):
SCREAMING_SNAKE_CASE:List[str] = index + 1 # linecache starts at 1
SCREAMING_SNAKE_CASE:Union[str, Any] = self.prefix + linecache.getline(str(self.src_file ) ,SCREAMING_SNAKE_CASE__ ).rstrip("\n" )
SCREAMING_SNAKE_CASE:Union[str, Any] = linecache.getline(str(self.tgt_file ) ,SCREAMING_SNAKE_CASE__ ).rstrip("\n" )
assert source_line, F'''empty source line for index {index}'''
assert tgt_line, F'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,SCREAMING_SNAKE_CASE__ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
SCREAMING_SNAKE_CASE:str = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,SCREAMING_SNAKE_CASE__ ) else self.tokenizer
)
SCREAMING_SNAKE_CASE:Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer ,SCREAMING_SNAKE_CASE__ ) else self.tokenizer
SCREAMING_SNAKE_CASE:int = encode_line(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,self.max_source_length ,"right" )
SCREAMING_SNAKE_CASE:List[Any] = encode_line(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,self.max_target_length ,"right" )
SCREAMING_SNAKE_CASE:Dict = source_inputs["input_ids"].squeeze()
SCREAMING_SNAKE_CASE:List[str] = target_inputs["input_ids"].squeeze()
SCREAMING_SNAKE_CASE:List[str] = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def __UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Dict ):
return [len(SCREAMING_SNAKE_CASE__ ) for x in Path(SCREAMING_SNAKE_CASE__ ).open().readlines()]
def __UpperCamelCase ( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ):
SCREAMING_SNAKE_CASE:Dict = torch.stack([x["input_ids"] for x in batch] )
SCREAMING_SNAKE_CASE:Union[str, Any] = torch.stack([x["attention_mask"] for x in batch] )
SCREAMING_SNAKE_CASE:int = torch.stack([x["decoder_input_ids"] for x in batch] )
SCREAMING_SNAKE_CASE:Union[str, Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,SCREAMING_SNAKE_CASE__ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE:Dict = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,SCREAMING_SNAKE_CASE__ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE:Dict = trim_batch(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE:List[Any] = trim_batch(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Tuple = {
"input_ids": source_ids,
"attention_mask": source_mask,
"decoder_input_ids": y,
}
return batch
A_ = getLogger(__name__)
def A_ ( snake_case ):
return list(itertools.chain.from_iterable(snake_case ) )
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:Tuple = get_git_info()
save_json(snake_case , os.path.join(snake_case , "git_log.json" ) )
def A_ ( snake_case , snake_case , snake_case=4 , **snake_case ):
with open(snake_case , "w" ) as f:
json.dump(snake_case , snake_case , indent=snake_case , **snake_case )
def A_ ( snake_case ):
with open(snake_case ) as f:
return json.load(snake_case )
def A_ ( ):
SCREAMING_SNAKE_CASE:int = git.Repo(search_parent_directories=snake_case )
SCREAMING_SNAKE_CASE:Any = {
"repo_id": str(snake_case ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
"hostname": str(socket.gethostname() ),
}
return repo_infos
def A_ ( snake_case , snake_case ):
return list(map(snake_case , snake_case ) )
def A_ ( snake_case , snake_case ):
with open(snake_case , "wb" ) as f:
return pickle.dump(snake_case , snake_case )
def A_ ( snake_case ):
def remove_articles(snake_case ):
return re.sub(r"\b(a|an|the)\b" , " " , snake_case )
def white_space_fix(snake_case ):
return " ".join(text.split() )
def remove_punc(snake_case ):
SCREAMING_SNAKE_CASE:Optional[int] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(snake_case ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(snake_case ) ) ) )
def A_ ( snake_case , snake_case ):
SCREAMING_SNAKE_CASE:Optional[Any] = normalize_answer(snake_case ).split()
SCREAMING_SNAKE_CASE:Optional[int] = normalize_answer(snake_case ).split()
SCREAMING_SNAKE_CASE:Optional[int] = Counter(snake_case ) & Counter(snake_case )
SCREAMING_SNAKE_CASE:List[str] = sum(common.values() )
if num_same == 0:
return 0
SCREAMING_SNAKE_CASE:Union[str, Any] = 1.0 * num_same / len(snake_case )
SCREAMING_SNAKE_CASE:List[Any] = 1.0 * num_same / len(snake_case )
SCREAMING_SNAKE_CASE:str = (2 * precision * recall) / (precision + recall)
return fa
def A_ ( snake_case , snake_case ):
return normalize_answer(snake_case ) == normalize_answer(snake_case )
def A_ ( snake_case , snake_case ):
assert len(snake_case ) == len(snake_case )
SCREAMING_SNAKE_CASE:Optional[Any] = 0
for hypo, pred in zip(snake_case , snake_case ):
em += exact_match_score(snake_case , snake_case )
if len(snake_case ) > 0:
em /= len(snake_case )
return {"em": em}
def A_ ( snake_case ):
return model_prefix.startswith("rag" )
def A_ ( snake_case , snake_case , snake_case ):
SCREAMING_SNAKE_CASE:List[str] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
SCREAMING_SNAKE_CASE:Dict = "dropout_rate"
for p in extra_params:
if getattr(snake_case , snake_case , snake_case ):
if not hasattr(snake_case , snake_case ) and not hasattr(snake_case , equivalent_param[p] ):
logger.info("config doesn't have a `{}` attribute".format(snake_case ) )
delattr(snake_case , snake_case )
continue
SCREAMING_SNAKE_CASE:Optional[int] = p if hasattr(snake_case , snake_case ) else equivalent_param[p]
setattr(snake_case , snake_case , getattr(snake_case , snake_case ) )
delattr(snake_case , snake_case )
return hparams, config
| 143
| 1
|
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ , A_=12 , A_=7 , A_=True , A_=True , A_=True , A_=99 , A_=32 , A_=32 , A_=2 , A_=4 , A_=37 , A_=0.1 , A_=0.1 , A_=512 , A_=0.02 , A_=0 , A_=None , ) -> List[Any]:
__UpperCamelCase =parent
__UpperCamelCase =batch_size
__UpperCamelCase =seq_length
__UpperCamelCase =is_training
__UpperCamelCase =use_input_mask
__UpperCamelCase =use_labels
__UpperCamelCase =vocab_size
__UpperCamelCase =hidden_size
__UpperCamelCase =projection_dim
__UpperCamelCase =num_hidden_layers
__UpperCamelCase =num_attention_heads
__UpperCamelCase =intermediate_size
__UpperCamelCase =dropout
__UpperCamelCase =attention_dropout
__UpperCamelCase =max_position_embeddings
__UpperCamelCase =initializer_range
__UpperCamelCase =scope
__UpperCamelCase =bos_token_id
def _a ( self ) -> Dict:
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase =None
if self.use_input_mask:
__UpperCamelCase =random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
__UpperCamelCase =input_mask.numpy()
__UpperCamelCase =input_mask.shape
__UpperCamelCase =np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase__ ):
__UpperCamelCase =1
__UpperCamelCase =0
__UpperCamelCase =self.get_config()
return config, input_ids, tf.convert_to_tensor(lowerCamelCase__ )
def _a ( self ) -> List[str]:
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def _a ( self , A_ , A_ , A_ ) -> Dict:
__UpperCamelCase =TFBlipTextModel(config=lowerCamelCase__ )
__UpperCamelCase =model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , training=lowerCamelCase__ )
__UpperCamelCase =model(lowerCamelCase__ , training=lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def _a ( self ) -> Tuple:
__UpperCamelCase =self.prepare_config_and_inputs()
__UpperCamelCase =config_and_inputs
__UpperCamelCase ={"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class UpperCAmelCase__ ( UpperCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = (TFBlipTextModel,) if is_tf_available() else ()
UpperCAmelCase__ : List[str] = False
UpperCAmelCase__ : List[Any] = False
UpperCAmelCase__ : Optional[Any] = False
def _a ( self ) -> List[str]:
__UpperCamelCase =BlipTextModelTester(self )
__UpperCamelCase =ConfigTester(self , config_class=lowerCamelCase__ , hidden_size=37 )
def _a ( self ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def _a ( self ) -> Tuple:
__UpperCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def _a ( self ) -> Optional[int]:
pass
def _a ( self ) -> Dict:
pass
@unittest.skip(reason='Blip does not use inputs_embeds' )
def _a ( self ) -> List[str]:
pass
@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING' )
def _a ( self ) -> Any:
pass
@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING' )
def _a ( self ) -> Optional[int]:
pass
@slow
def _a ( self ) -> Optional[Any]:
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCamelCase =TFBlipTextModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def _a ( self , A_=True ) -> Dict:
super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCamelCase__ )
| 718
|
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_A = logging.getLogger(__name__)
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self ) -> int:
__UpperCamelCase =False
def _a ( self , A_ , A_ , A_ , A_ ) -> List[Any]:
if not self.initialized:
__UpperCamelCase =RagRetriever(
A_ , question_encoder_tokenizer=A_ , generator_tokenizer=A_ , index=A_ , init_retrieval=A_ , )
__UpperCamelCase =True
def _a ( self ) -> Optional[Any]:
self.retriever.index.init_index()
def _a ( self , A_ , A_ ) -> Dict:
__UpperCamelCase , __UpperCamelCase =self.retriever._main_retrieve(A_ , A_ )
return doc_ids, retrieved_doc_embeds
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
def __init__( self , A_ , A_ , A_ , A_ , A_=None ) -> Dict:
if index is not None and index.is_initialized() and len(A_ ) > 0:
raise ValueError(
'When using Ray for distributed fine-tuning, '
'you\'ll need to provide the paths instead, '
'as the dataset and the index are loaded '
'separately. More info in examples/rag/use_own_knowledge_dataset.py ' )
super().__init__(
A_ , question_encoder_tokenizer=A_ , generator_tokenizer=A_ , index=A_ , init_retrieval=A_ , )
__UpperCamelCase =retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(A_ , A_ , A_ , A_ )
for worker in self.retrieval_workers
] )
def _a ( self ) -> Union[str, Any]:
logger.info('initializing retrieval' )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def _a ( self , A_ , A_ ) -> Optional[int]:
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
__UpperCamelCase =self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
__UpperCamelCase , __UpperCamelCase =ray.get(random_worker.retrieve.remote(A_ , A_ ) )
else:
__UpperCamelCase , __UpperCamelCase =self._main_retrieve(A_ , A_ )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A_ )
@classmethod
def _a ( cls , A_ , A_=None , **A_ ) -> List[str]:
return super(A_ , cls ).get_tokenizers(A_ , A_ , **A_ )
@classmethod
def _a ( cls , A_ , A_ , A_=None , **A_ ) -> str:
__UpperCamelCase =kwargs.pop('config' , A_ ) or RagConfig.from_pretrained(A_ , **A_ )
__UpperCamelCase =RagTokenizer.from_pretrained(A_ , config=A_ )
__UpperCamelCase =rag_tokenizer.question_encoder
__UpperCamelCase =rag_tokenizer.generator
if indexed_dataset is not None:
__UpperCamelCase ='custom'
__UpperCamelCase =CustomHFIndex(config.retrieval_vector_size , A_ )
else:
__UpperCamelCase =cls._build_index(A_ )
return cls(
A_ , question_encoder_tokenizer=A_ , generator_tokenizer=A_ , retrieval_workers=A_ , index=A_ , )
| 682
| 0
|
'''simple docstring'''
from __future__ import annotations
import math
def lowerCAmelCase (__A):
"""simple docstring"""
if num <= 0:
_a = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(__A)
_a = [True] * (num + 1)
_a = []
_a = 2
_a = int(math.sqrt(__A))
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(__A)
# Set multiples of start be False
for i in range(start * start , num + 1 , __A):
if sieve[i] is True:
_a = False
start += 1
for j in range(end + 1 , num + 1):
if sieve[j] is True:
prime.append(__A)
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("Enter a positive integer: ").strip())))
| 11
|
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class A__ ( _snake_case ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__=13 , UpperCamelCase__=7 , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=False , UpperCamelCase__=True , UpperCamelCase__=99 , UpperCamelCase__=32 , UpperCamelCase__=5 , UpperCamelCase__=4 , UpperCamelCase__=37 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=512 , UpperCamelCase__=16 , UpperCamelCase__=2 , UpperCamelCase__=0.02 , UpperCamelCase__=3 , UpperCamelCase__=4 , UpperCamelCase__=None , ) -> Dict:
'''simple docstring'''
A_ = parent
A_ = batch_size
A_ = seq_length
A_ = is_training
A_ = use_input_mask
A_ = use_token_type_ids
A_ = use_labels
A_ = vocab_size
A_ = hidden_size
A_ = num_hidden_layers
A_ = num_attention_heads
A_ = intermediate_size
A_ = hidden_act
A_ = hidden_dropout_prob
A_ = attention_probs_dropout_prob
A_ = max_position_embeddings
A_ = type_vocab_size
A_ = type_sequence_label_size
A_ = initializer_range
A_ = num_labels
A_ = num_choices
A_ = scope
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
A_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ = None
if self.use_input_mask:
A_ = random_attention_mask([self.batch_size, self.seq_length] )
A_ = None
A_ = None
A_ = None
if self.use_labels:
A_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A_ = ids_tensor([self.batch_size] , self.num_choices )
A_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case_ ( self ) -> List[str]:
'''simple docstring'''
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
A_ = DistilBertModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
A_ = model(UpperCamelCase__ , UpperCamelCase__ )
A_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
A_ = DistilBertForMaskedLM(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
A_ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
A_ = DistilBertForQuestionAnswering(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
A_ = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , start_positions=UpperCamelCase__ , end_positions=UpperCamelCase__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
A_ = self.num_labels
A_ = DistilBertForSequenceClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
A_ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
'''simple docstring'''
A_ = self.num_labels
A_ = DistilBertForTokenClassification(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
A_ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
A_ = self.num_choices
A_ = DistilBertForMultipleChoice(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
A_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A_ = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
A_ = self.prepare_config_and_inputs()
((A_) , (A_) , (A_) , (A_) , (A_) , (A_)) = config_and_inputs
A_ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class A__ ( _snake_case , _snake_case , unittest.TestCase ):
lowercase = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowercase = (
{
"feature-extraction": DistilBertModel,
"fill-mask": DistilBertForMaskedLM,
"question-answering": DistilBertForQuestionAnswering,
"text-classification": DistilBertForSequenceClassification,
"token-classification": DistilBertForTokenClassification,
"zero-shot": DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase = True
lowercase = True
lowercase = True
lowercase = True
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
A_ = DistilBertModelTester(self )
A_ = ConfigTester(self , config_class=UpperCamelCase__ , dim=37 )
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*UpperCamelCase__ )
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCamelCase__ )
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCamelCase__ )
def snake_case_ ( self ) -> Any:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCamelCase__ )
def snake_case_ ( self ) -> Dict:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCamelCase__ )
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCamelCase__ )
@slow
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ = DistilBertModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
@slow
@require_torch_gpu
def snake_case_ ( self ) -> str:
'''simple docstring'''
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
A_ = True
A_ = model_class(config=UpperCamelCase__ )
A_ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
A_ = torch.jit.trace(
UpperCamelCase__ , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(UpperCamelCase__ , os.path.join(UpperCamelCase__ , """traced_model.pt""" ) )
A_ = torch.jit.load(os.path.join(UpperCamelCase__ , """traced_model.pt""" ) , map_location=UpperCamelCase__ )
loaded(inputs_dict["""input_ids"""].to(UpperCamelCase__ ) , inputs_dict["""attention_mask"""].to(UpperCamelCase__ ) )
@require_torch
class A__ ( unittest.TestCase ):
@slow
def snake_case_ ( self ) -> int:
'''simple docstring'''
A_ = DistilBertModel.from_pretrained("""distilbert-base-uncased""" )
A_ = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
A_ = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
A_ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ )[0]
A_ = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , UpperCamelCase__ )
A_ = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , UpperCamelCase__ , atol=1e-4 ) )
| 288
| 0
|
"""simple docstring"""
from __future__ import annotations
import math
class __snake_case:
def __init__( self , __lowerCamelCase ):
'''simple docstring'''
__A : Optional[Any] = size
# approximate the overall size of segment tree with given value
__A : Tuple = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
__A : Optional[int] = [0 for i in range(0 , 4 * size )]
__A : Dict = [0 for i in range(0 , 4 * size )] # flag for lazy update
def _a ( self , __lowerCamelCase ):
'''simple docstring'''
return idx * 2
def _a ( self , __lowerCamelCase ):
'''simple docstring'''
return idx * 2 + 1
def _a ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
if left_element == right_element:
__A : Optional[int] = a[left_element - 1]
else:
__A : Optional[int] = (left_element + right_element) // 2
self.build(self.left(UpperCamelCase_ ) , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
self.build(self.right(UpperCamelCase_ ) , mid + 1 , UpperCamelCase_ , UpperCamelCase_ )
__A : List[str] = max(
self.segment_tree[self.left(UpperCamelCase_ )] , self.segment_tree[self.right(UpperCamelCase_ )] )
def _a ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
if self.flag[idx] is True:
__A : Tuple = self.lazy[idx]
__A : Dict = False
if left_element != right_element:
__A : Tuple = self.lazy[idx]
__A : Optional[int] = self.lazy[idx]
__A : Union[str, Any] = True
__A : Dict = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
__A : int = val
if left_element != right_element:
__A : Tuple = val
__A : str = val
__A : List[Any] = True
__A : Any = True
return True
__A : Dict = (left_element + right_element) // 2
self.update(self.left(UpperCamelCase_ ) , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
self.update(self.right(UpperCamelCase_ ) , mid + 1 , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
__A : str = max(
self.segment_tree[self.left(UpperCamelCase_ )] , self.segment_tree[self.right(UpperCamelCase_ )] )
return True
def _a ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
if self.flag[idx] is True:
__A : Dict = self.lazy[idx]
__A : Union[str, Any] = False
if left_element != right_element:
__A : Dict = self.lazy[idx]
__A : Tuple = self.lazy[idx]
__A : List[Any] = True
__A : List[str] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
__A : List[str] = (left_element + right_element) // 2
__A : Optional[int] = self.query(self.left(UpperCamelCase_ ) , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
__A : Optional[Any] = self.query(self.right(UpperCamelCase_ ) , mid + 1 , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
return max(UpperCamelCase_ , UpperCamelCase_ )
def __str__( self ):
'''simple docstring'''
return str([self.query(1 , 1 , self.size , UpperCamelCase_ , UpperCamelCase_ ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
lowerCamelCase : Union[str, Any] =[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
lowerCamelCase : Optional[Any] =15
lowerCamelCase : Tuple =SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 1_11)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 2_35)
print(segt)
| 714
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __snake_case( A_ , unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = DiTPipeline
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
"latents",
"num_images_per_prompt",
"callback",
"callback_steps",
}
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCAmelCase = False
def _a ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__A : str = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=__lowerCamelCase , activation_fn='gelu-approximate' , num_embeds_ada_norm=1000 , norm_type='ada_norm_zero' , norm_elementwise_affine=__lowerCamelCase , )
__A : int = AutoencoderKL()
__A : Optional[int] = DDIMScheduler()
__A : str = {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
return components
def _a ( self , __lowerCamelCase , __lowerCamelCase=0 ):
'''simple docstring'''
if str(__lowerCamelCase ).startswith('mps' ):
__A : Optional[Any] = torch.manual_seed(__lowerCamelCase )
else:
__A : Tuple = torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
__A : Tuple = {
'class_labels': [1],
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def _a ( self ):
'''simple docstring'''
__A : List[Any] = 'cpu'
__A : List[Any] = self.get_dummy_components()
__A : Dict = self.pipeline_class(**__lowerCamelCase )
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
__A : Tuple = self.get_dummy_inputs(__lowerCamelCase )
__A : Dict = pipe(**__lowerCamelCase ).images
__A : int = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
__A : Union[str, Any] = np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57] )
__A : Dict = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__lowerCamelCase , 1e-3 )
def _a ( self ):
'''simple docstring'''
self._test_inference_batch_single_identical(relax_max_difference=__lowerCamelCase , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def _a ( self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class __snake_case( unittest.TestCase ):
'''simple docstring'''
def _a ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self ):
'''simple docstring'''
__A : List[Any] = torch.manual_seed(0 )
__A : Tuple = DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
pipe.to('cuda' )
__A : Union[str, Any] = ['vase', 'umbrella', 'white shark', 'white wolf']
__A : Dict = pipe.get_label_ids(__lowerCamelCase )
__A : Optional[int] = pipe(__lowerCamelCase , generator=__lowerCamelCase , num_inference_steps=40 , output_type='np' ).images
for word, image in zip(__lowerCamelCase , __lowerCamelCase ):
__A : Optional[int] = load_numpy(
F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1e-2
def _a ( self ):
'''simple docstring'''
__A : Optional[Any] = DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
__A : Tuple = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to('cuda' )
__A : Optional[int] = ['vase', 'umbrella']
__A : Dict = pipe.get_label_ids(__lowerCamelCase )
__A : Optional[int] = torch.manual_seed(0 )
__A : List[Any] = pipe(__lowerCamelCase , generator=__lowerCamelCase , num_inference_steps=25 , output_type='np' ).images
for word, image in zip(__lowerCamelCase , __lowerCamelCase ):
__A : Tuple = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
F'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1e-1
| 237
| 0
|
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(_lowerCamelCase ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(_lowerCamelCase ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
SCREAMING_SNAKE_CASE = {
'vocab_file': {'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'},
'tokenizer_file': {
'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'
},
}
SCREAMING_SNAKE_CASE = {'mobilebert-uncased': 5_1_2}
SCREAMING_SNAKE_CASE = {}
class __UpperCAmelCase ( __A ):
"""simple docstring"""
_lowerCamelCase = VOCAB_FILES_NAMES
_lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase = PRETRAINED_INIT_CONFIGURATION
_lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase = MobileBertTokenizer
def __init__( self , __A=None , __A=None , __A=True , __A="[UNK]" , __A="[SEP]" , __A="[PAD]" , __A="[CLS]" , __A="[MASK]" , __A=True , __A=None , **__A , ):
super().__init__(
__A , tokenizer_file=__A , do_lower_case=__A , unk_token=__A , sep_token=__A , pad_token=__A , cls_token=__A , mask_token=__A , tokenize_chinese_chars=__A , strip_accents=__A , **__A , )
__a = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , __A ) != do_lower_case
or normalizer_state.get("""strip_accents""" , __A ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , __A ) != tokenize_chinese_chars
):
__a = getattr(__A , normalizer_state.pop("""type""" ) )
__a = do_lower_case
__a = strip_accents
__a = tokenize_chinese_chars
__a = normalizer_class(**__A )
__a = do_lower_case
def snake_case_ ( self , __A , __A=None ):
__a = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def snake_case_ ( self , __A , __A = None ):
__a = [self.sep_token_id]
__a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def snake_case_ ( self , __A , __A = None ):
__a = self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
| 99
| 0
|
"""simple docstring"""
def lowerCAmelCase (__UpperCamelCase : str , __UpperCamelCase : Any ):
"""simple docstring"""
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
if dist[i][j] != float('''inf''' ):
print(int(dist[i][j] ) , end='''\t''' )
else:
print('''INF''' , end='''\t''' )
print()
def lowerCAmelCase (__UpperCamelCase : Optional[Any] , __UpperCamelCase : List[str] ):
"""simple docstring"""
__UpperCamelCase =[[float('''inf''' ) for _ in range(__UpperCamelCase )] for _ in range(__UpperCamelCase )]
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
__UpperCamelCase =graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(__UpperCamelCase ):
# looping through rows of graph array
for i in range(__UpperCamelCase ):
# looping through columns of graph array
for j in range(__UpperCamelCase ):
if (
dist[i][k] != float('''inf''' )
and dist[k][j] != float('''inf''' )
and dist[i][k] + dist[k][j] < dist[i][j]
):
__UpperCamelCase =dist[i][k] + dist[k][j]
_print_dist(__UpperCamelCase , __UpperCamelCase )
return dist, v
if __name__ == "__main__":
__lowercase = int(input('''Enter number of vertices: '''))
__lowercase = int(input('''Enter number of edges: '''))
__lowercase = [[float('''inf''') for i in range(v)] for j in range(v)]
for i in range(v):
__lowercase = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print('''\nEdge ''', i + 1)
__lowercase = int(input('''Enter source:'''))
__lowercase = int(input('''Enter destination:'''))
__lowercase = float(input('''Enter weight:'''))
__lowercase = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| 705
|
"""simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase (__UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] ):
"""simple docstring"""
assert isinstance(__UpperCamelCase , __UpperCamelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase (__UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : List[Any] ):
"""simple docstring"""
__UpperCamelCase =tmp_path / '''cache'''
__UpperCamelCase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
__UpperCamelCase =JsonDatasetReader(__UpperCamelCase , cache_dir=__UpperCamelCase , keep_in_memory=__UpperCamelCase ).read()
_check_json_dataset(__UpperCamelCase , __UpperCamelCase )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase (__UpperCamelCase : Dict , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] ):
"""simple docstring"""
__UpperCamelCase =tmp_path / '''cache'''
__UpperCamelCase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__UpperCamelCase =features.copy() if features else default_expected_features
__UpperCamelCase =(
Features({feature: Value(__UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
__UpperCamelCase =JsonDatasetReader(__UpperCamelCase , features=__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
_check_json_dataset(__UpperCamelCase , __UpperCamelCase )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def lowerCAmelCase (__UpperCamelCase : int , __UpperCamelCase : Any , __UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
__UpperCamelCase =tmp_path / '''cache'''
__UpperCamelCase ={'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
__UpperCamelCase =features.copy() if features else default_expected_features
__UpperCamelCase =(
Features({feature: Value(__UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
__UpperCamelCase =JsonDatasetReader(__UpperCamelCase , features=__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
assert isinstance(__UpperCamelCase , __UpperCamelCase )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def lowerCAmelCase (__UpperCamelCase : Tuple , __UpperCamelCase : List[str] ):
"""simple docstring"""
__UpperCamelCase ={'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
__UpperCamelCase =features.copy()
__UpperCamelCase =(
Features({feature: Value(__UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
__UpperCamelCase =tmp_path / '''cache'''
__UpperCamelCase =JsonDatasetReader(__UpperCamelCase , features=__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
assert isinstance(__UpperCamelCase , __UpperCamelCase )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase (__UpperCamelCase : Any , __UpperCamelCase : Dict , __UpperCamelCase : str ):
"""simple docstring"""
__UpperCamelCase =tmp_path / '''cache'''
__UpperCamelCase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__UpperCamelCase =JsonDatasetReader(__UpperCamelCase , cache_dir=__UpperCamelCase , split=__UpperCamelCase ).read()
_check_json_dataset(__UpperCamelCase , __UpperCamelCase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def lowerCAmelCase (__UpperCamelCase : Optional[int] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Tuple ):
"""simple docstring"""
if issubclass(__UpperCamelCase , __UpperCamelCase ):
__UpperCamelCase =jsonl_path
elif issubclass(__UpperCamelCase , __UpperCamelCase ):
__UpperCamelCase =[jsonl_path]
__UpperCamelCase =tmp_path / '''cache'''
__UpperCamelCase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__UpperCamelCase =JsonDatasetReader(__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
_check_json_dataset(__UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase (__UpperCamelCase : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict=("train",) ):
"""simple docstring"""
assert isinstance(__UpperCamelCase , __UpperCamelCase )
for split in splits:
__UpperCamelCase =dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase (__UpperCamelCase : Any , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[int] ):
"""simple docstring"""
__UpperCamelCase =tmp_path / '''cache'''
__UpperCamelCase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
__UpperCamelCase =JsonDatasetReader({'''train''': jsonl_path} , cache_dir=__UpperCamelCase , keep_in_memory=__UpperCamelCase ).read()
_check_json_datasetdict(__UpperCamelCase , __UpperCamelCase )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase (__UpperCamelCase : Any , __UpperCamelCase : List[str] , __UpperCamelCase : List[Any] ):
"""simple docstring"""
__UpperCamelCase =tmp_path / '''cache'''
__UpperCamelCase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__UpperCamelCase =features.copy() if features else default_expected_features
__UpperCamelCase =(
Features({feature: Value(__UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
__UpperCamelCase =JsonDatasetReader({'''train''': jsonl_path} , features=__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
_check_json_datasetdict(__UpperCamelCase , __UpperCamelCase )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase (__UpperCamelCase : Optional[int] , __UpperCamelCase : Dict , __UpperCamelCase : Optional[Any] ):
"""simple docstring"""
if split:
__UpperCamelCase ={split: jsonl_path}
else:
__UpperCamelCase ='''train'''
__UpperCamelCase ={'''train''': jsonl_path, '''test''': jsonl_path}
__UpperCamelCase =tmp_path / '''cache'''
__UpperCamelCase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__UpperCamelCase =JsonDatasetReader(__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
_check_json_datasetdict(__UpperCamelCase , __UpperCamelCase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase (__UpperCamelCase : Dict ):
"""simple docstring"""
return json.load(__UpperCamelCase )
def lowerCAmelCase (__UpperCamelCase : Optional[Any] ):
"""simple docstring"""
return [json.loads(__UpperCamelCase ) for line in buffer]
class _lowercase :
"""simple docstring"""
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def UpperCAmelCase_ ( self : Dict , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase__ , UpperCamelCase__ , lines=UpperCamelCase__ ).write()
buffer.seek(0 )
__UpperCamelCase =load_json_function(UpperCamelCase__ )
assert isinstance(UpperCamelCase__ , UpperCamelCase__ )
assert isinstance(exported_content[0] , UpperCamelCase__ )
assert len(UpperCamelCase__ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def UpperCAmelCase_ ( self : int , UpperCamelCase__ : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : List[str] ) -> str:
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase__ , UpperCamelCase__ , lines=UpperCamelCase__ , orient=UpperCamelCase__ ).write()
buffer.seek(0 )
__UpperCamelCase =load_json(UpperCamelCase__ )
assert isinstance(UpperCamelCase__ , UpperCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase__ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase__ ) == 10
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def UpperCAmelCase_ ( self : Any , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str , UpperCamelCase__ : List[Any] ) -> Any:
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase__ , UpperCamelCase__ , lines=UpperCamelCase__ , num_proc=2 ).write()
buffer.seek(0 )
__UpperCamelCase =load_json_function(UpperCamelCase__ )
assert isinstance(UpperCamelCase__ , UpperCamelCase__ )
assert isinstance(exported_content[0] , UpperCamelCase__ )
assert len(UpperCamelCase__ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def UpperCAmelCase_ ( self : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : str ) -> Any:
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase__ , UpperCamelCase__ , lines=UpperCamelCase__ , orient=UpperCamelCase__ , num_proc=2 ).write()
buffer.seek(0 )
__UpperCamelCase =load_json(UpperCamelCase__ )
assert isinstance(UpperCamelCase__ , UpperCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase__ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase__ ) == 10
def UpperCAmelCase_ ( self : List[Any] , UpperCamelCase__ : List[Any] ) -> Dict:
'''simple docstring'''
with pytest.raises(UpperCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase__ , UpperCamelCase__ , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def UpperCAmelCase_ ( self : List[str] , UpperCamelCase__ : Dict , UpperCamelCase__ : List[str] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Any ) -> Tuple:
'''simple docstring'''
__UpperCamelCase =tmp_path_factory.mktemp('''data''' ) / f"""test.json.{extension}"""
__UpperCamelCase =str(shared_datadir / f"""test_file.json.{extension}""" )
JsonDatasetWriter(UpperCamelCase__ , UpperCamelCase__ , compression=UpperCamelCase__ ).write()
with fsspec.open(UpperCamelCase__ , '''rb''' , compression='''infer''' ) as f:
__UpperCamelCase =f.read()
with fsspec.open(UpperCamelCase__ , '''rb''' , compression='''infer''' ) as f:
__UpperCamelCase =f.read()
assert exported_content == original_content
| 296
| 0
|
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
snake_case_ : Any = logging.get_logger('transformers.models.speecht5')
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
hf_model.apply_weight_norm()
_UpperCamelCase : Any = checkpoint['input_conv.weight_g']
_UpperCamelCase : str = checkpoint['input_conv.weight_v']
_UpperCamelCase : Union[str, Any] = checkpoint['input_conv.bias']
for i in range(len(config.upsample_rates ) ):
_UpperCamelCase : int = checkpoint[f'upsamples.{i}.1.weight_g']
_UpperCamelCase : List[Any] = checkpoint[f'upsamples.{i}.1.weight_v']
_UpperCamelCase : List[str] = checkpoint[f'upsamples.{i}.1.bias']
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
_UpperCamelCase : int = checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
_UpperCamelCase : Dict = checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
_UpperCamelCase : Union[str, Any] = checkpoint[f'blocks.{i}.convs1.{j}.1.bias']
_UpperCamelCase : Tuple = checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
_UpperCamelCase : Tuple = checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
_UpperCamelCase : Tuple = checkpoint[f'blocks.{i}.convs2.{j}.1.bias']
_UpperCamelCase : Any = checkpoint['output_conv.1.weight_g']
_UpperCamelCase : str = checkpoint['output_conv.1.weight_v']
_UpperCamelCase : int = checkpoint['output_conv.1.bias']
hf_model.remove_weight_norm()
@torch.no_grad()
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=None , UpperCAmelCase_=None , ):
if config_path is not None:
_UpperCamelCase : List[Any] = SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_ )
else:
_UpperCamelCase : List[Any] = SpeechTaHifiGanConfig()
_UpperCamelCase : Optional[int] = SpeechTaHifiGan(UpperCAmelCase_ )
_UpperCamelCase : Union[str, Any] = torch.load(UpperCAmelCase_ )
load_weights(orig_checkpoint['model']['generator'] , UpperCAmelCase_ , UpperCAmelCase_ )
_UpperCamelCase : str = np.load(UpperCAmelCase_ )
_UpperCamelCase : Tuple = stats[0].reshape(-1 )
_UpperCamelCase : Union[str, Any] = stats[1].reshape(-1 )
_UpperCamelCase : Optional[int] = torch.from_numpy(UpperCAmelCase_ ).float()
_UpperCamelCase : List[str] = torch.from_numpy(UpperCAmelCase_ ).float()
model.save_pretrained(UpperCAmelCase_ )
if repo_id:
print('Pushing to the hub...' )
model.push_to_hub(UpperCAmelCase_ )
if __name__ == "__main__":
snake_case_ : Dict = argparse.ArgumentParser()
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
snake_case_ : str = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 195
|
'''simple docstring'''
from __future__ import annotations
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ ):
_UpperCamelCase , _UpperCamelCase : Dict = position
_UpperCamelCase : Any = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
_UpperCamelCase : Optional[Any] = []
for position in positions:
_UpperCamelCase , _UpperCamelCase : Any = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(UpperCAmelCase_ )
return permissible_positions
def A__ ( UpperCAmelCase_ ):
return not any(elem == 0 for row in board for elem in row )
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if is_complete(UpperCAmelCase_ ):
return True
for position in get_valid_pos(UpperCAmelCase_ , len(UpperCAmelCase_ ) ):
_UpperCamelCase , _UpperCamelCase : Any = position
if board[y][x] == 0:
_UpperCamelCase : int = curr + 1
if open_knight_tour_helper(UpperCAmelCase_ , UpperCAmelCase_ , curr + 1 ):
return True
_UpperCamelCase : Union[str, Any] = 0
return False
def A__ ( UpperCAmelCase_ ):
_UpperCamelCase : Dict = [[0 for i in range(UpperCAmelCase_ )] for j in range(UpperCAmelCase_ )]
for i in range(UpperCAmelCase_ ):
for j in range(UpperCAmelCase_ ):
_UpperCamelCase : Tuple = 1
if open_knight_tour_helper(UpperCAmelCase_ , (i, j) , 1 ):
return board
_UpperCamelCase : Union[str, Any] = 0
_UpperCamelCase : int = f'Open Kight Tour cannot be performed on a board of size {n}'
raise ValueError(UpperCAmelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 195
| 1
|
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
_UpperCAmelCase : List[Any] = random.Random()
if is_torch_available():
import torch
def lowerCAmelCase_ (lowercase__ : int , lowercase__ : List[str]=1.0 , lowercase__ : str=None , lowercase__ : List[Any]=None ) -> Dict:
'''simple docstring'''
if rng is None:
lowerCAmelCase__ = global_rng
lowerCAmelCase__ = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class lowerCAmelCase_ ( unittest.TestCase ):
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Dict=7 , SCREAMING_SNAKE_CASE_ : str=400 , SCREAMING_SNAKE_CASE_ : int=2_000 , SCREAMING_SNAKE_CASE_ : Optional[int]=1 , SCREAMING_SNAKE_CASE_ : Any=0.0 , SCREAMING_SNAKE_CASE_ : Dict=16_000 , SCREAMING_SNAKE_CASE_ : List[Any]=True , SCREAMING_SNAKE_CASE_ : List[Any]=True , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = min_seq_length
lowerCAmelCase__ = max_seq_length
lowerCAmelCase__ = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowerCAmelCase__ = feature_size
lowerCAmelCase__ = padding_value
lowerCAmelCase__ = sampling_rate
lowerCAmelCase__ = return_attention_mask
lowerCAmelCase__ = do_normalize
def __snake_case ( self : Tuple ):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def __snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[Any]=False , SCREAMING_SNAKE_CASE_ : int=False ):
def _flatten(SCREAMING_SNAKE_CASE_ : str ):
return list(itertools.chain(*SCREAMING_SNAKE_CASE_ ) )
if equal_length:
lowerCAmelCase__ = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
lowerCAmelCase__ = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
lowerCAmelCase__ = [np.asarray(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class lowerCAmelCase_ ( snake_case__ , unittest.TestCase ):
UpperCamelCase_ :Tuple = ASTFeatureExtractor
def __snake_case ( self : List[str] ):
lowerCAmelCase__ = ASTFeatureExtractionTester(self )
def __snake_case ( self : str ):
# Tests that all call wrap to encode_plus and batch_encode_plus
lowerCAmelCase__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowerCAmelCase__ = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
lowerCAmelCase__ = [np.asarray(SCREAMING_SNAKE_CASE_ ) for speech_input in speech_inputs]
# Test not batched input
lowerCAmelCase__ = feat_extract(speech_inputs[0] , return_tensors='''np''' ).input_values
lowerCAmelCase__ = feat_extract(np_speech_inputs[0] , return_tensors='''np''' ).input_values
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 ) )
# Test batched
lowerCAmelCase__ = feat_extract(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , return_tensors='''np''' ).input_values
lowerCAmelCase__ = feat_extract(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , return_tensors='''np''' ).input_values
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
lowerCAmelCase__ = [floats_list((1, x) )[0] for x in (800, 800, 800)]
lowerCAmelCase__ = np.asarray(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = feat_extract(SCREAMING_SNAKE_CASE_ , return_tensors='''np''' ).input_values
lowerCAmelCase__ = feat_extract(SCREAMING_SNAKE_CASE_ , return_tensors='''np''' ).input_values
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 ) )
@require_torch
def __snake_case ( self : List[Any] ):
import torch
lowerCAmelCase__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCAmelCase__ = np.random.rand(100 ).astype(np.floataa )
lowerCAmelCase__ = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowerCAmelCase__ = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''np''' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
lowerCAmelCase__ = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''pt''' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def __snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
from datasets import load_dataset
lowerCAmelCase__ = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
lowerCAmelCase__ = ds.sort('''id''' ).select(range(SCREAMING_SNAKE_CASE_ ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
@require_torch
def __snake_case ( self : int ):
# fmt: off
lowerCAmelCase__ = torch.tensor(
[-0.9_894, -1.2_776, -0.9_066, -1.2_776, -0.9_349, -1.2_609, -1.0_386, -1.2_776,
-1.1_561, -1.2_776, -1.2_052, -1.2_723, -1.2_190, -1.2_132, -1.2_776, -1.1_133,
-1.1_953, -1.1_343, -1.1_584, -1.2_203, -1.1_770, -1.2_474, -1.2_381, -1.1_936,
-0.9_270, -0.8_317, -0.8_049, -0.7_706, -0.7_565, -0.7_869] )
# fmt: on
lowerCAmelCase__ = self._load_datasamples(1 )
lowerCAmelCase__ = ASTFeatureExtractor()
lowerCAmelCase__ = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' ).input_values
self.assertEquals(input_values.shape , (1, 1_024, 128) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 288
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : str = {
"configuration_blenderbot_small": [
"BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlenderbotSmallConfig",
"BlenderbotSmallOnnxConfig",
],
"tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : List[str] = ["BlenderbotSmallTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : List[str] = [
"BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotSmallForCausalLM",
"BlenderbotSmallForConditionalGeneration",
"BlenderbotSmallModel",
"BlenderbotSmallPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Dict = [
"TFBlenderbotSmallForConditionalGeneration",
"TFBlenderbotSmallModel",
"TFBlenderbotSmallPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : List[str] = [
"FlaxBlenderbotSmallForConditionalGeneration",
"FlaxBlenderbotSmallModel",
"FlaxBlenderbotSmallPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
_UpperCAmelCase : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 288
| 1
|
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class _lowercase :
"""simple docstring"""
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = None # sigma(t_i)
@classmethod
def _UpperCAmelCase ( cls ):
'''simple docstring'''
return cls()
@dataclass
class _lowercase ( A_ ):
"""simple docstring"""
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
class _lowercase ( A_ , A_ ):
"""simple docstring"""
@property
def _UpperCAmelCase ( self ):
'''simple docstring'''
return True
@register_to_config
def __init__( self , UpperCAmelCase = 0.02 , UpperCAmelCase = 100 , UpperCAmelCase = 1.007 , UpperCAmelCase = 80 , UpperCAmelCase = 0.05 , UpperCAmelCase = 50 , ):
'''simple docstring'''
pass
def _UpperCAmelCase ( self ):
'''simple docstring'''
return KarrasVeSchedulerState.create()
def _UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = () ):
'''simple docstring'''
_lowercase = jnp.arange(0 , UpperCAmelCase )[::-1].copy()
_lowercase = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=UpperCAmelCase , schedule=jnp.array(UpperCAmelCase , dtype=jnp.floataa ) , timesteps=UpperCAmelCase , )
def _UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ):
'''simple docstring'''
if self.config.s_min <= sigma <= self.config.s_max:
_lowercase = min(self.config.s_churn / state.num_inference_steps , 2**0.5 - 1 )
else:
_lowercase = 0
# sample eps ~ N(0, S_noise^2 * I)
_lowercase = random.split(UpperCAmelCase , num=1 )
_lowercase = self.config.s_noise * random.normal(key=UpperCAmelCase , shape=sample.shape )
_lowercase = sigma + gamma * sigma
_lowercase = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def _UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = True , ):
'''simple docstring'''
_lowercase = sample_hat + sigma_hat * model_output
_lowercase = (sample_hat - pred_original_sample) / sigma_hat
_lowercase = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=UpperCAmelCase , derivative=UpperCAmelCase , state=UpperCAmelCase )
def _UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = True , ):
'''simple docstring'''
_lowercase = sample_prev + sigma_prev * model_output
_lowercase = (sample_prev - pred_original_sample) / sigma_prev
_lowercase = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=UpperCAmelCase , derivative=UpperCAmelCase , state=UpperCAmelCase )
def _UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
'''simple docstring'''
raise NotImplementedError()
| 398
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase = {
'''configuration_swiftformer''': [
'''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SwiftFormerConfig''',
'''SwiftFormerOnnxConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SwiftFormerForImageClassification''',
'''SwiftFormerModel''',
'''SwiftFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 157
| 0
|
"""simple docstring"""
def a ( __UpperCAmelCase : List[str] ) -> Union[str, Any]:
__magic_name__: Optional[Any] = len(__UpperCAmelCase )
while cur > 1:
# Find the maximum number in arr
__magic_name__: str = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
__magic_name__: Optional[int] = arr[mi::-1] + arr[mi + 1 : len(__UpperCAmelCase )]
# Reverse whole list
__magic_name__: int = arr[cur - 1 :: -1] + arr[cur : len(__UpperCAmelCase )]
cur -= 1
return arr
if __name__ == "__main__":
__lowerCamelCase = input('Enter numbers separated by a comma:\n').strip()
__lowerCamelCase = [int(item) for item in user_input.split(',')]
print(pancake_sort(unsorted))
| 213
|
"""simple docstring"""
from math import factorial
__lowerCamelCase = {str(digit): factorial(digit) for digit in range(10)}
def a ( __UpperCAmelCase : int ) -> int:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""Parameter number must be int""" )
if number < 0:
raise ValueError("""Parameter number must be greater than or equal to 0""" )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(__UpperCAmelCase ) )
def a ( __UpperCAmelCase : int = 6_0 , __UpperCAmelCase : int = 1_0_0_0_0_0_0 ) -> int:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ) or not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""Parameters chain_length and number_limit must be int""" )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
"""Parameters chain_length and number_limit must be greater than 0""" )
# the counter for the chains with the exact desired length
__magic_name__: Optional[Any] = 0
# the cached sizes of the previous chains
__magic_name__: dict[int, int] = {}
for start_chain_element in range(1 , __UpperCAmelCase ):
# The temporary set will contain the elements of the chain
__magic_name__: Tuple = set()
__magic_name__: Optional[Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__magic_name__: Dict = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(__UpperCAmelCase )
chain_set_length += 1
__magic_name__: Union[str, Any] = digit_factorial_sum(__UpperCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__magic_name__: int = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'''{solution()}''')
| 213
| 1
|
"""simple docstring"""
from __future__ import annotations
lowerCamelCase__ : Union[str, Any] = tuple[int, int, int]
lowerCamelCase__ : List[Any] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
lowerCamelCase__ : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# -------------------------- default selection --------------------------
# rotors --------------------------
lowerCamelCase__ : Optional[int] = "EGZWVONAHDCLFQMSIPJBYUKXTR"
lowerCamelCase__ : Tuple = "FOBHMDKEXQNRAULPGSJVTYICZW"
lowerCamelCase__ : Optional[int] = "ZJXESIUQLHAVRMDOYGTNFWPBKC"
# reflector --------------------------
lowerCamelCase__ : Tuple = {
"A": "N",
"N": "A",
"B": "O",
"O": "B",
"C": "P",
"P": "C",
"D": "Q",
"Q": "D",
"E": "R",
"R": "E",
"F": "S",
"S": "F",
"G": "T",
"T": "G",
"H": "U",
"U": "H",
"I": "V",
"V": "I",
"J": "W",
"W": "J",
"K": "X",
"X": "K",
"L": "Y",
"Y": "L",
"M": "Z",
"Z": "M",
}
# -------------------------- extra rotors --------------------------
lowerCamelCase__ : Any = "RMDJXFUWGISLHVTCQNKYPBEZOA"
lowerCamelCase__ : Any = "SGLCPQWZHKXAREONTFBVIYJUDM"
lowerCamelCase__ : Tuple = "HVSICLTYKQUBXDWAJZOMFGPREN"
lowerCamelCase__ : List[Any] = "RZWQHFMVDBKICJLNTUXAGYPSOE"
lowerCamelCase__ : Dict = "LFKIJODBEGAMQPXVUHYSTCZRWN"
lowerCamelCase__ : str = "KOAEGVDHXPQZMLFTYWJNBRCIUS"
def UpperCamelCase ( _lowerCAmelCase : RotorPositionT, _lowerCAmelCase : RotorSelectionT, _lowerCAmelCase : str ) -> Dict:
if (unique_rotsel := len(set(_UpperCamelCase ) )) < 3:
_UpperCAmelCase : List[Any] = f'''Please use 3 unique rotors (not {unique_rotsel})'''
raise Exception(_UpperCamelCase )
# Checks if rotor positions are valid
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : List[str] = rotpos
if not 0 < rotorposa <= len(_UpperCamelCase ):
_UpperCAmelCase : Union[str, Any] = f'''First rotor position is not within range of 1..26 ({rotorposa}'''
raise ValueError(_UpperCamelCase )
if not 0 < rotorposa <= len(_UpperCamelCase ):
_UpperCAmelCase : Optional[int] = f'''Second rotor position is not within range of 1..26 ({rotorposa})'''
raise ValueError(_UpperCamelCase )
if not 0 < rotorposa <= len(_UpperCamelCase ):
_UpperCAmelCase : Dict = f'''Third rotor position is not within range of 1..26 ({rotorposa})'''
raise ValueError(_UpperCamelCase )
# Validates string and returns dict
_UpperCAmelCase : Dict = _plugboard(_UpperCamelCase )
return rotpos, rotsel, pbdict
def UpperCamelCase ( _lowerCAmelCase : str ) -> Tuple:
if not isinstance(_UpperCamelCase, _UpperCamelCase ):
_UpperCAmelCase : Any = f'''Plugboard setting isn\'t type string ({type(_UpperCamelCase )})'''
raise TypeError(_UpperCamelCase )
elif len(_UpperCamelCase ) % 2 != 0:
_UpperCAmelCase : Union[str, Any] = f'''Odd number of symbols ({len(_UpperCamelCase )})'''
raise Exception(_UpperCamelCase )
elif pbstring == "":
return {}
pbstring.replace(""" """, """""" )
# Checks if all characters are unique
_UpperCAmelCase : Union[str, Any] = set()
for i in pbstring:
if i not in abc:
_UpperCAmelCase : Any = f'''\'{i}\' not in list of symbols'''
raise Exception(_UpperCamelCase )
elif i in tmppbl:
_UpperCAmelCase : List[str] = f'''Duplicate symbol ({i})'''
raise Exception(_UpperCamelCase )
else:
tmppbl.add(_UpperCamelCase )
del tmppbl
# Created the dictionary
_UpperCAmelCase : Optional[int] = {}
for j in range(0, len(_UpperCamelCase ) - 1, 2 ):
_UpperCAmelCase : Tuple = pbstring[j + 1]
_UpperCAmelCase : str = pbstring[j]
return pb
def UpperCamelCase ( _lowerCAmelCase : str, _lowerCAmelCase : RotorPositionT, _lowerCAmelCase : RotorSelectionT = (rotora, rotora, rotora), _lowerCAmelCase : str = "", ) -> int:
_UpperCAmelCase : List[Any] = text.upper()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Tuple = _validator(
_UpperCamelCase, _UpperCamelCase, plugb.upper() )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = rotor_position
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Optional[int] = rotor_selection
rotorposa -= 1
rotorposa -= 1
rotorposa -= 1
_UpperCAmelCase : Union[str, Any] = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
_UpperCAmelCase : Dict = plugboard[symbol]
# rotor ra --------------------------
_UpperCAmelCase : List[str] = abc.index(_UpperCamelCase ) + rotorposa
_UpperCAmelCase : Tuple = rotora[index % len(_UpperCamelCase )]
# rotor rb --------------------------
_UpperCAmelCase : str = abc.index(_UpperCamelCase ) + rotorposa
_UpperCAmelCase : Dict = rotora[index % len(_UpperCamelCase )]
# rotor rc --------------------------
_UpperCAmelCase : Tuple = abc.index(_UpperCamelCase ) + rotorposa
_UpperCAmelCase : Tuple = rotora[index % len(_UpperCamelCase )]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
_UpperCAmelCase : List[Any] = reflector[symbol]
# 2nd rotors
_UpperCAmelCase : int = abc[rotora.index(_UpperCamelCase ) - rotorposa]
_UpperCAmelCase : List[str] = abc[rotora.index(_UpperCamelCase ) - rotorposa]
_UpperCAmelCase : int = abc[rotora.index(_UpperCamelCase ) - rotorposa]
# 2nd plugboard
if symbol in plugboard:
_UpperCAmelCase : str = plugboard[symbol]
# moves/resets rotor positions
rotorposa += 1
if rotorposa >= len(_UpperCamelCase ):
_UpperCAmelCase : Dict = 0
rotorposa += 1
if rotorposa >= len(_UpperCamelCase ):
_UpperCAmelCase : Optional[int] = 0
rotorposa += 1
if rotorposa >= len(_UpperCamelCase ):
_UpperCAmelCase : List[str] = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(_UpperCamelCase )
return "".join(_UpperCamelCase )
if __name__ == "__main__":
lowerCamelCase__ : Optional[int] = "This is my Python script that emulates the Enigma machine from WWII."
lowerCamelCase__ : Optional[int] = (1, 1, 1)
lowerCamelCase__ : int = "pictures"
lowerCamelCase__ : List[str] = (rotora, rotora, rotora)
lowerCamelCase__ : str = enigma(message, rotor_pos, rotor_sel, pb)
print('''Encrypted message:''', en)
print('''Decrypted message:''', enigma(en, rotor_pos, rotor_sel, pb))
| 238
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowercase__ : str = logging.get_logger(__name__)
class lowerCamelCase ( lowerCamelCase ):
'''simple docstring'''
lowerCAmelCase__ = ['''pixel_values''']
def __init__( self : Dict , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Optional[Dict[str, int]] = None , UpperCAmelCase__ : PILImageResampling = PILImageResampling.BILINEAR , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Dict[str, int] = None , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Union[int, float] = 1 / 255 , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Optional[Union[float, List[float]]] = None , UpperCAmelCase__ : Optional[Union[float, List[float]]] = None , **UpperCAmelCase__ : Any , ) ->None:
super().__init__(**UpperCAmelCase__ )
UpperCAmelCase_ = size if size is not None else {'''shortest_edge''': 256}
UpperCAmelCase_ = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
UpperCAmelCase_ = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
UpperCAmelCase_ = get_size_dict(UpperCAmelCase__ )
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
UpperCAmelCase_ = resample
UpperCAmelCase_ = do_center_crop
UpperCAmelCase_ = crop_size
UpperCAmelCase_ = do_rescale
UpperCAmelCase_ = rescale_factor
UpperCAmelCase_ = do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase__ ( self : List[Any] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : Dict[str, int] , UpperCAmelCase__ : PILImageResampling = PILImageResampling.BICUBIC , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : Optional[Any] , ) ->np.ndarray:
UpperCAmelCase_ = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
if "shortest_edge" not in size:
raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
UpperCAmelCase_ = get_resize_output_image_size(UpperCAmelCase__ , size=size['''shortest_edge'''] , default_to_square=UpperCAmelCase__ )
return resize(UpperCAmelCase__ , size=UpperCAmelCase__ , resample=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCAmelCase__ ( self : List[Any] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : Dict[str, int] , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : List[Any] , ) ->np.ndarray:
UpperCAmelCase_ = get_size_dict(UpperCAmelCase__ )
return center_crop(UpperCAmelCase__ , size=(size['''height'''], size['''width''']) , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCAmelCase__ ( self : str , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : float , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : Tuple ) ->np.ndarray:
return rescale(UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCAmelCase__ ( self : Dict , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : Union[float, List[float]] , UpperCAmelCase__ : Union[float, List[float]] , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : Optional[int] , ) ->np.ndarray:
return normalize(UpperCAmelCase__ , mean=UpperCAmelCase__ , std=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCAmelCase__ ( self : Optional[Any] , UpperCAmelCase__ : ImageInput , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Dict[str, int] = None , UpperCAmelCase__ : PILImageResampling = None , UpperCAmelCase__ : bool = None , UpperCAmelCase__ : Dict[str, int] = None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[float] = None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[Union[float, List[float]]] = None , UpperCAmelCase__ : Optional[Union[float, List[float]]] = None , UpperCAmelCase__ : Optional[Union[str, TensorType]] = None , UpperCAmelCase__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **UpperCAmelCase__ : List[Any] , ) ->int:
UpperCAmelCase_ = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase_ = size if size is not None else self.size
UpperCAmelCase_ = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
UpperCAmelCase_ = resample if resample is not None else self.resample
UpperCAmelCase_ = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase_ = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase_ = get_size_dict(UpperCAmelCase__ )
UpperCAmelCase_ = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase_ = image_std if image_std is not None else self.image_std
UpperCAmelCase_ = make_list_of_images(UpperCAmelCase__ )
if not valid_images(UpperCAmelCase__ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
UpperCAmelCase_ = [to_numpy_array(UpperCAmelCase__ ) for image in images]
if do_resize:
UpperCAmelCase_ = [self.resize(image=UpperCAmelCase__ , size=UpperCAmelCase__ , resample=UpperCAmelCase__ ) for image in images]
if do_center_crop:
UpperCAmelCase_ = [self.center_crop(image=UpperCAmelCase__ , size=UpperCAmelCase__ ) for image in images]
if do_rescale:
UpperCAmelCase_ = [self.rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ ) for image in images]
if do_normalize:
UpperCAmelCase_ = [self.normalize(image=UpperCAmelCase__ , mean=UpperCAmelCase__ , std=UpperCAmelCase__ ) for image in images]
UpperCAmelCase_ = [to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ ) for image in images]
UpperCAmelCase_ = {'''pixel_values''': images}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
| 390
| 0
|
"""simple docstring"""
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 711
|
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCamelCase )
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :str = field(default="summarization" , metadata={"include_in_asdict_even_if_is_default": True} )
SCREAMING_SNAKE_CASE__ :ClassVar[Features] = Features({"text": Value("string" )} )
SCREAMING_SNAKE_CASE__ :ClassVar[Features] = Features({"summary": Value("string" )} )
SCREAMING_SNAKE_CASE__ :str = "text"
SCREAMING_SNAKE_CASE__ :str = "summary"
@property
def __SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Dict[str, str]:
return {self.text_column: "text", self.summary_column: "summary"}
| 51
| 0
|
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a_ ( __a ):
return "".join(sorted(__a ) )
def a_ ( __a ):
return word_by_signature[signature(__a )]
__snake_case : str = Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
__snake_case : Union[str, Any] = sorted({word.strip().lower() for word in data.splitlines()})
__snake_case : Dict = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
__snake_case : Tuple = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('anagrams.txt', 'w') as file:
file.write('all_anagrams = \n ')
file.write(pprint.pformat(all_anagrams))
| 571
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[str] , _lowerCamelCase : Optional[int] , _lowerCamelCase : List[Any]=1_3 , _lowerCamelCase : Optional[Any]=7 , _lowerCamelCase : Tuple=True , _lowerCamelCase : List[Any]=True , _lowerCamelCase : Dict=True , _lowerCamelCase : Optional[int]=True , _lowerCamelCase : List[str]=9_9 , _lowerCamelCase : Any=3_2 , _lowerCamelCase : List[Any]=5 , _lowerCamelCase : int=4 , _lowerCamelCase : List[Any]=3_7 , _lowerCamelCase : Optional[Any]="gelu" , _lowerCamelCase : Optional[int]=0.1 , _lowerCamelCase : Optional[Any]=0.1 , _lowerCamelCase : List[Any]=5_1_2 , _lowerCamelCase : List[str]=1_6 , _lowerCamelCase : Dict=2 , _lowerCamelCase : int=0.02 , _lowerCamelCase : str=4 , ):
A__ = parent
A__ = batch_size
A__ = seq_length
A__ = is_training
A__ = use_attention_mask
A__ = use_token_type_ids
A__ = use_labels
A__ = vocab_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = type_vocab_size
A__ = type_sequence_label_size
A__ = initializer_range
A__ = num_choices
def A__ ( self : List[str] ):
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ = None
if self.use_attention_mask:
A__ = random_attention_mask([self.batch_size, self.seq_length] )
A__ = None
if self.use_token_type_ids:
A__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A__ = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_lowerCamelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def A__ ( self : Union[str, Any] ):
A__ = self.prepare_config_and_inputs()
A__ , A__ , A__ , A__ = config_and_inputs
A__ = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class UpperCamelCase ( a , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase : List[str] =(
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def A__ ( self : Tuple ):
A__ = FlaxAlbertModelTester(self )
@slow
def A__ ( self : List[str] ):
for model_class_name in self.all_model_classes:
A__ = model_class_name.from_pretrained('''albert-base-v2''' )
A__ = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowerCamelCase )
@require_flax
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def A__ ( self : List[Any] ):
A__ = FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
A__ = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
A__ = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
A__ = model(_lowerCamelCase , attention_mask=_lowerCamelCase )[0]
A__ = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , _lowerCamelCase )
A__ = np.array(
[[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , _lowerCamelCase , atol=1E-4 ) )
| 571
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : str = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : Any = ["""MMBTForClassification""", """MMBTModel""", """ModalEmbeddings"""]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
_A : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 518
|
"""simple docstring"""
from graphs.minimum_spanning_tree_kruskal import kruskal
def __magic_name__ ( ) -> Optional[Any]:
lowercase : Optional[Any] = 9
lowercase : str = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
lowercase : List[str] = kruskal(__snake_case , __snake_case )
lowercase : Tuple = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(__snake_case ) == sorted(__snake_case )
| 518
| 1
|
"""simple docstring"""
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
snake_case_ : Tuple = 2
snake_case_ : Any = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(SCREAMING_SNAKE_CASE__ )
if n > 1:
factors.append(SCREAMING_SNAKE_CASE__ )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 480
|
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
a_ = {
'''cola''': 2,
'''mnli''': 3,
'''mrpc''': 2,
'''sst-2''': 2,
'''sts-b''': 1,
'''qqp''': 2,
'''qnli''': 2,
'''rte''': 2,
'''wnli''': 2,
}
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[str]=None ):
"""simple docstring"""
snake_case_ : Optional[int] = XLNetConfig.from_json_file(SCREAMING_SNAKE_CASE__ )
snake_case_ : int = finetuning_task.lower() if finetuning_task is not None else """"""
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(f'Building PyTorch XLNetForSequenceClassification model from configuration: {config}' )
snake_case_ : List[Any] = finetuning_task
snake_case_ : Optional[Any] = GLUE_TASKS_NUM_LABELS[finetuning_task]
snake_case_ : str = XLNetForSequenceClassification(SCREAMING_SNAKE_CASE__ )
elif "squad" in finetuning_task:
snake_case_ : Tuple = finetuning_task
snake_case_ : List[Any] = XLNetForQuestionAnswering(SCREAMING_SNAKE_CASE__ )
else:
snake_case_ : int = XLNetLMHeadModel(SCREAMING_SNAKE_CASE__ )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Save pytorch-model
snake_case_ : List[str] = os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case_ : Optional[Any] = os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
print(f'Save PyTorch model to {os.path.abspath(SCREAMING_SNAKE_CASE__ )}' )
torch.save(model.state_dict() , SCREAMING_SNAKE_CASE__ )
print(f'Save configuration file to {os.path.abspath(SCREAMING_SNAKE_CASE__ )}' )
with open(SCREAMING_SNAKE_CASE__ , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--xlnet_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained XLNet model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
)
parser.add_argument(
'''--finetuning_task''',
default=None,
type=str,
help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
)
a_ = parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
)
| 480
| 1
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
_lowercase: List[str] = logging.get_logger(__name__)
_lowercase: Union[str, Any] = {'vocab_file': 'vocab.txt'}
_lowercase: str = {
'vocab_file': {
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
}
}
_lowercase: List[str] = {
'YituTech/conv-bert-base': 5_1_2,
'YituTech/conv-bert-medium-small': 5_1_2,
'YituTech/conv-bert-small': 5_1_2,
}
_lowercase: Tuple = {
'YituTech/conv-bert-base': {'do_lower_case': True},
'YituTech/conv-bert-medium-small': {'do_lower_case': True},
'YituTech/conv-bert-small': {'do_lower_case': True},
}
class lowerCamelCase__ ( _A ):
UpperCamelCase__ =VOCAB_FILES_NAMES
UpperCamelCase__ =PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ =PRETRAINED_INIT_CONFIGURATION
UpperCamelCase__ =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ =ConvBertTokenizer
def __init__( self : str , lowercase__ : Optional[int]=None , lowercase__ : List[Any]=None , lowercase__ : Dict=True , lowercase__ : Optional[Any]="[UNK]" , lowercase__ : Optional[Any]="[SEP]" , lowercase__ : Tuple="[PAD]" , lowercase__ : Any="[CLS]" , lowercase__ : int="[MASK]" , lowercase__ : Any=True , lowercase__ : Optional[Any]=None , **lowercase__ : Dict , ):
super().__init__(
UpperCamelCase__ , tokenizer_file=UpperCamelCase__ , do_lower_case=UpperCamelCase__ , unk_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , mask_token=UpperCamelCase__ , tokenize_chinese_chars=UpperCamelCase__ , strip_accents=UpperCamelCase__ , **UpperCamelCase__ , )
_lowerCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , UpperCamelCase__ ) != do_lower_case
or normalizer_state.get('strip_accents' , UpperCamelCase__ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , UpperCamelCase__ ) != tokenize_chinese_chars
):
_lowerCAmelCase = getattr(UpperCamelCase__ , normalizer_state.pop('type' ) )
_lowerCAmelCase = do_lower_case
_lowerCAmelCase = strip_accents
_lowerCAmelCase = tokenize_chinese_chars
_lowerCAmelCase = normalizer_class(**UpperCamelCase__ )
_lowerCAmelCase = do_lower_case
def SCREAMING_SNAKE_CASE__ ( self : int , lowercase__ : int , lowercase__ : str=None ):
_lowerCAmelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , lowercase__ : List[int] , lowercase__ : Optional[List[int]] = None ):
_lowerCAmelCase = [self.sep_token_id]
_lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE__ ( self : Tuple , lowercase__ : str , lowercase__ : Optional[str] = None ):
_lowerCAmelCase = self._tokenizer.model.save(UpperCamelCase__ , name=UpperCamelCase__ )
return tuple(UpperCamelCase__ )
| 710
|
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class lowerCamelCase__ ( UpperCAmelCase ,unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase ):
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def SCREAMING_SNAKE_CASE__ ( self : str ):
_lowerCAmelCase = ort.SessionOptions()
_lowerCAmelCase = False
return options
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
_lowerCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
_lowerCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
_lowerCAmelCase = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'runwayml/stable-diffusion-inpainting' , revision='onnx' , safety_checker=lowercase__ , feature_extractor=lowercase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowercase__ )
_lowerCAmelCase = 'A red cat sitting on a park bench'
_lowerCAmelCase = np.random.RandomState(0 )
_lowerCAmelCase = pipe(
prompt=lowercase__ , image=lowercase__ , mask_image=lowercase__ , guidance_scale=7.5 , num_inference_steps=10 , generator=lowercase__ , output_type='np' , )
_lowerCAmelCase = output.images
_lowerCAmelCase = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
_lowerCAmelCase = np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
_lowerCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
_lowerCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
_lowerCAmelCase = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-inpainting' , subfolder='scheduler' , revision='onnx' )
_lowerCAmelCase = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'runwayml/stable-diffusion-inpainting' , revision='onnx' , scheduler=lowercase__ , safety_checker=lowercase__ , feature_extractor=lowercase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowercase__ )
_lowerCAmelCase = 'A red cat sitting on a park bench'
_lowerCAmelCase = np.random.RandomState(0 )
_lowerCAmelCase = pipe(
prompt=lowercase__ , image=lowercase__ , mask_image=lowercase__ , guidance_scale=7.5 , num_inference_steps=20 , generator=lowercase__ , output_type='np' , )
_lowerCAmelCase = output.images
_lowerCAmelCase = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
_lowerCAmelCase = np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 225
| 0
|
'''simple docstring'''
from __future__ import annotations
import numpy as np
def snake_case ( snake_case : list[float] ) -> Tuple:
"""simple docstring"""
return np.maximum(0 , snake_case )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 284
|
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_UpperCamelCase : Optional[Any] = logging.getLogger(__name__)
_UpperCamelCase : List[Any] = "Hello world! cécé herlolip"
_UpperCamelCase : int = namedtuple(
"BertAbsConfig",
[
"temp_dir",
"large",
"use_bert_emb",
"finetune_bert",
"encoder",
"share_emb",
"max_pos",
"enc_layers",
"enc_hidden_size",
"enc_heads",
"enc_ff_size",
"enc_dropout",
"dec_layers",
"dec_hidden_size",
"dec_heads",
"dec_ff_size",
"dec_dropout",
],
)
def snake_case ( snake_case : int , snake_case : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase = BertAbsConfig(
temp_dir='.' , finetune_bert=snake_case , large=snake_case , share_emb=snake_case , use_bert_emb=snake_case , encoder='bert' , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
lowerCAmelCase = torch.load(snake_case , lambda snake_case , snake_case : storage )
lowerCAmelCase = AbsSummarizer(snake_case , torch.device('cpu' ) , snake_case )
original.eval()
lowerCAmelCase = BertAbsSummarizer(snake_case , torch.device('cpu' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('convert the model' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('Make sure that the models\' outputs are identical' )
lowerCAmelCase = BertTokenizer.from_pretrained('bert-base-uncased' )
# prepare the model inputs
lowerCAmelCase = tokenizer.encode('This is sample éàalj\'-.' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(snake_case )) )
lowerCAmelCase = torch.tensor(snake_case ).unsqueeze(0 )
lowerCAmelCase = tokenizer.encode('This is sample 3 éàalj\'-.' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(snake_case )) )
lowerCAmelCase = torch.tensor(snake_case ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
lowerCAmelCase = encoder_input_ids
lowerCAmelCase = decoder_input_ids
lowerCAmelCase = lowerCAmelCase = None
lowerCAmelCase = None
lowerCAmelCase = lowerCAmelCase = None
lowerCAmelCase = lowerCAmelCase = None
lowerCAmelCase = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
lowerCAmelCase = original(snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case )[0]
lowerCAmelCase = original.generator(snake_case )
lowerCAmelCase = new_model(
snake_case , snake_case , snake_case , snake_case , snake_case )[0]
lowerCAmelCase = new_model.generator(snake_case )
lowerCAmelCase = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(snake_case ) )
lowerCAmelCase = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(snake_case ) )
lowerCAmelCase = torch.allclose(snake_case , snake_case , atol=1e-3 )
if are_identical:
logging.info('all weights are equal up to 1e-3' )
else:
raise ValueError('the weights are different. The new model is likely different from the original one.' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('saving the model\'s state dictionary' )
torch.save(
new_model.state_dict() , './bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin' )
if __name__ == "__main__":
_UpperCamelCase : List[str] = argparse.ArgumentParser()
parser.add_argument(
"--bertabs_checkpoint_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch dump.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the output PyTorch model.",
)
_UpperCamelCase : Optional[int] = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 284
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimesformerModel',
'TimesformerForVideoClassification',
'TimesformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 711
|
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 44
| 0
|
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
lowercase__ : Tuple = 'true'
def lowerCamelCase__ ( _A , _A=82 , _A=16 ):
'''simple docstring'''
set_seed(42 )
snake_case_ = RegressionModel()
snake_case_ = deepcopy(__lowerCAmelCase )
snake_case_ = RegressionDataset(length=__lowerCAmelCase )
snake_case_ = DataLoader(__lowerCAmelCase , batch_size=__lowerCAmelCase )
model.to(accelerator.device )
snake_case_ , snake_case_ = accelerator.prepare(__lowerCAmelCase , __lowerCAmelCase )
return model, ddp_model, dataloader
def lowerCamelCase__ ( _A , _A=False ):
'''simple docstring'''
snake_case_ = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
snake_case_ = load_dataset("glue" , "mrpc" , split="validation" )
def tokenize_function(_A ):
snake_case_ = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=__lowerCAmelCase , max_length=__lowerCAmelCase )
return outputs
with accelerator.main_process_first():
snake_case_ = dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , remove_columns=["idx", "sentence1", "sentence2"] , )
snake_case_ = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(_A ):
if use_longest:
return tokenizer.pad(__lowerCAmelCase , padding="longest" , return_tensors="pt" )
return tokenizer.pad(__lowerCAmelCase , padding="max_length" , max_length=128 , return_tensors="pt" )
return DataLoader(__lowerCAmelCase , shuffle=__lowerCAmelCase , collate_fn=__lowerCAmelCase , batch_size=16 )
def lowerCamelCase__ ( _A , _A ):
'''simple docstring'''
snake_case_ = Accelerator(dispatch_batches=__lowerCAmelCase , split_batches=__lowerCAmelCase )
snake_case_ = get_dataloader(__lowerCAmelCase , not dispatch_batches )
snake_case_ = AutoModelForSequenceClassification.from_pretrained(
"hf-internal-testing/mrpc-bert-base-cased" , return_dict=__lowerCAmelCase )
snake_case_ , snake_case_ = accelerator.prepare(__lowerCAmelCase , __lowerCAmelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def lowerCamelCase__ ( _A , _A , _A ):
'''simple docstring'''
snake_case_ = []
for batch in dataloader:
snake_case_ , snake_case_ = batch.values()
with torch.no_grad():
snake_case_ = model(__lowerCAmelCase )
snake_case_ , snake_case_ = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
snake_case_ , snake_case_ = [], []
for logit, targ in logits_and_targets:
logits.append(__lowerCAmelCase )
targs.append(__lowerCAmelCase )
snake_case_ , snake_case_ = torch.cat(__lowerCAmelCase ), torch.cat(__lowerCAmelCase )
return logits, targs
def lowerCamelCase__ ( _A , _A=82 , _A=False , _A=False , _A=16 ):
'''simple docstring'''
snake_case_ , snake_case_ , snake_case_ = get_basic_setup(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
snake_case_ , snake_case_ = generate_predictions(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
assert (
len(__lowerCAmelCase ) == num_samples
), f"Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(__lowerCAmelCase )}"
def lowerCamelCase__ ( _A = False , _A = False ):
'''simple docstring'''
snake_case_ = evaluate.load("glue" , "mrpc" )
snake_case_ , snake_case_ = get_mrpc_setup(__lowerCAmelCase , __lowerCAmelCase )
# First do baseline
snake_case_ , snake_case_ , snake_case_ = setup["no"]
model.to(__lowerCAmelCase )
model.eval()
for batch in dataloader:
batch.to(__lowerCAmelCase )
with torch.inference_mode():
snake_case_ = model(**__lowerCAmelCase )
snake_case_ = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=__lowerCAmelCase , references=batch["labels"] )
snake_case_ = metric.compute()
# Then do distributed
snake_case_ , snake_case_ , snake_case_ = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
snake_case_ = model(**__lowerCAmelCase )
snake_case_ = outputs.logits.argmax(dim=-1 )
snake_case_ = batch["labels"]
snake_case_ , snake_case_ = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=__lowerCAmelCase , references=__lowerCAmelCase )
snake_case_ = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"
def lowerCamelCase__ ( ):
'''simple docstring'''
snake_case_ = Accelerator(split_batches=__lowerCAmelCase , dispatch_batches=__lowerCAmelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("**Testing gather_for_metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`" )
test_mrpc(__lowerCAmelCase , __lowerCAmelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test torch metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
snake_case_ = Accelerator(split_batches=__lowerCAmelCase , dispatch_batches=__lowerCAmelCase )
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99" )
test_torch_metrics(__lowerCAmelCase , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test last batch is not dropped when perfectly divisible**" )
snake_case_ = Accelerator()
test_torch_metrics(__lowerCAmelCase , 512 )
accelerator.state._reset_state()
def lowerCamelCase__ ( _A ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 376
|
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : str = "cpu" , __lowerCAmelCase : Union[str, None] = None ):
lowerCamelCase__ = torch.load(__lowerCAmelCase , map_location=__lowerCAmelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__lowerCAmelCase , torch.Tensor ):
raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin""" )
lowerCamelCase__ = v.half()
if save_path is None: # overwrite src_path
lowerCamelCase__ = src_path
torch.save(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 50
| 0
|
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_MAPPING,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerModel,
)
from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class lowercase ( SCREAMING_SNAKE_CASE__ ):
def A__ ( self):
lowercase = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(A__ ,'''hidden_sizes'''))
self.parent.assertTrue(hasattr(A__ ,'''num_attention_heads'''))
self.parent.assertTrue(hasattr(A__ ,'''num_encoder_blocks'''))
class lowercase :
def __init__( self ,A__ ,A__=1_3 ,A__=6_4 ,A__=3 ,A__=4 ,A__=[2, 2, 2, 2] ,A__=[8, 4, 2, 1] ,A__=[1_6, 3_2, 6_4, 1_2_8] ,A__=[1, 4, 8, 1_6] ,A__=[1, 2, 4, 8] ,A__=True ,A__=True ,A__="gelu" ,A__=0.1 ,A__=0.1 ,A__=0.02 ,A__=3 ,A__=None ,):
lowercase = parent
lowercase = batch_size
lowercase = image_size
lowercase = num_channels
lowercase = num_encoder_blocks
lowercase = sr_ratios
lowercase = depths
lowercase = hidden_sizes
lowercase = downsampling_rates
lowercase = num_attention_heads
lowercase = is_training
lowercase = use_labels
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = initializer_range
lowercase = num_labels
lowercase = scope
def A__ ( self):
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels)
lowercase = self.get_config()
return config, pixel_values, labels
def A__ ( self):
return SegformerConfig(
image_size=self.image_size ,num_channels=self.num_channels ,num_encoder_blocks=self.num_encoder_blocks ,depths=self.depths ,hidden_sizes=self.hidden_sizes ,num_attention_heads=self.num_attention_heads ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,initializer_range=self.initializer_range ,)
def A__ ( self ,A__ ,A__ ,A__):
lowercase = SegformerModel(config=A__)
model.to(A__)
model.eval()
lowercase = model(A__)
lowercase = lowercase = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width))
def A__ ( self ,A__ ,A__ ,A__):
lowercase = self.num_labels
lowercase = SegformerForSemanticSegmentation(A__)
model.to(A__)
model.eval()
lowercase = model(A__)
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
lowercase = model(A__ ,labels=A__)
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
self.parent.assertGreater(result.loss ,0.0)
def A__ ( self ,A__ ,A__ ,A__):
lowercase = 1
lowercase = SegformerForSemanticSegmentation(config=A__)
model.to(A__)
model.eval()
lowercase = torch.randint(0 ,1 ,(self.batch_size, self.image_size, self.image_size)).to(A__)
lowercase = model(A__ ,labels=A__)
self.parent.assertGreater(result.loss ,0.0)
def A__ ( self):
lowercase = self.prepare_config_and_inputs()
lowercase , lowercase , lowercase = config_and_inputs
lowercase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
lowercase_ : Optional[int] =(
(
SegformerModel,
SegformerForSemanticSegmentation,
SegformerForImageClassification,
)
if is_torch_available()
else ()
)
lowercase_ : Optional[int] =(
{
'''feature-extraction''': SegformerModel,
'''image-classification''': SegformerForImageClassification,
'''image-segmentation''': SegformerForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowercase_ : Optional[int] =True
lowercase_ : Any =False
lowercase_ : Any =False
lowercase_ : Union[str, Any] =False
def A__ ( self):
lowercase = SegformerModelTester(self)
lowercase = SegformerConfigTester(self ,config_class=A__)
def A__ ( self):
self.config_tester.run_common_tests()
def A__ ( self):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__)
def A__ ( self):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_binary_image_segmentation(*A__)
def A__ ( self):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*A__)
@unittest.skip('''SegFormer does not use inputs_embeds''')
def A__ ( self):
pass
@unittest.skip('''SegFormer does not have get_input_embeddings method and get_output_embeddings methods''')
def A__ ( self):
pass
def A__ ( self):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(A__)
lowercase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase = [*signature.parameters.keys()]
lowercase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,A__)
def A__ ( self):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = True
for model_class in self.all_model_classes:
lowercase = True
lowercase = False
lowercase = True
lowercase = model_class(A__)
model.to(A__)
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(A__ ,A__))
lowercase = outputs.attentions
lowercase = sum(self.model_tester.depths)
self.assertEqual(len(A__) ,A__)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase = True
lowercase = model_class(A__)
model.to(A__)
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(A__ ,A__))
lowercase = outputs.attentions
self.assertEqual(len(A__) ,A__)
# verify the first attentions (first block, first layer)
lowercase = (self.model_tester.image_size // 4) ** 2
lowercase = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:]) ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,)
# verify the last attentions (last block, last layer)
lowercase = (self.model_tester.image_size // 3_2) ** 2
lowercase = (self.model_tester.image_size // (3_2 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:]) ,[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] ,)
lowercase = len(A__)
# Check attention is always last and order is fine
lowercase = True
lowercase = True
lowercase = model_class(A__)
model.to(A__)
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(A__ ,A__))
self.assertEqual(out_len + 1 ,len(A__))
lowercase = outputs.attentions
self.assertEqual(len(A__) ,A__)
# verify the first attentions (first block, first layer)
lowercase = (self.model_tester.image_size // 4) ** 2
lowercase = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:]) ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,)
def A__ ( self):
def check_hidden_states_output(A__ ,A__ ,A__):
lowercase = model_class(A__)
model.to(A__)
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(A__ ,A__))
lowercase = outputs.hidden_states
lowercase = self.model_tester.num_encoder_blocks
self.assertEqual(len(A__) ,A__)
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:]) ,[
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] ,)
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = True
check_hidden_states_output(A__ ,A__ ,A__)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase = True
check_hidden_states_output(A__ ,A__ ,A__)
def A__ ( self):
if not self.model_tester.is_training:
return
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = True
for model_class in self.all_model_classes:
if model_class in get_values(A__):
continue
lowercase = model_class(A__)
model.to(A__)
model.train()
lowercase = self._prepare_for_class(A__ ,A__ ,return_labels=A__)
lowercase = model(**A__).loss
loss.backward()
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
def A__ ( self):
pass
@slow
def A__ ( self):
for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = SegformerModel.from_pretrained(A__)
self.assertIsNotNone(A__)
def UpperCamelCase ( ):
'''simple docstring'''
lowercase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
class lowercase ( unittest.TestCase ):
@slow
def A__ ( self):
# only resize + normalize
lowercase = SegformerImageProcessor(
image_scale=(5_1_2, 5_1_2) ,keep_ratio=A__ ,align=A__ ,do_random_crop=A__)
lowercase = SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512''').to(
A__)
lowercase = prepare_img()
lowercase = image_processor(images=A__ ,return_tensors='''pt''')
lowercase = encoded_inputs.pixel_values.to(A__)
with torch.no_grad():
lowercase = model(A__)
lowercase = torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8))
self.assertEqual(outputs.logits.shape ,A__)
lowercase = torch.tensor(
[
[[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
[[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
[[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
]).to(A__)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] ,A__ ,atol=1E-4))
@slow
def A__ ( self):
# only resize + normalize
lowercase = SegformerImageProcessor(
image_scale=(5_1_2, 5_1_2) ,keep_ratio=A__ ,align=A__ ,do_random_crop=A__)
lowercase = SegformerForSemanticSegmentation.from_pretrained(
'''nvidia/segformer-b1-finetuned-cityscapes-1024-1024''').to(A__)
lowercase = prepare_img()
lowercase = image_processor(images=A__ ,return_tensors='''pt''')
lowercase = encoded_inputs.pixel_values.to(A__)
with torch.no_grad():
lowercase = model(A__)
lowercase = torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8))
self.assertEqual(outputs.logits.shape ,A__)
lowercase = torch.tensor(
[
[[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
[[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
[[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
]).to(A__)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] ,A__ ,atol=1E-1))
@slow
def A__ ( self):
# only resize + normalize
lowercase = SegformerImageProcessor(
image_scale=(5_1_2, 5_1_2) ,keep_ratio=A__ ,align=A__ ,do_random_crop=A__)
lowercase = SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512''').to(
A__)
lowercase = prepare_img()
lowercase = image_processor(images=A__ ,return_tensors='''pt''')
lowercase = encoded_inputs.pixel_values.to(A__)
with torch.no_grad():
lowercase = model(A__)
lowercase = outputs.logits.detach().cpu()
lowercase = image_processor.post_process_semantic_segmentation(outputs=A__ ,target_sizes=[(5_0_0, 3_0_0)])
lowercase = torch.Size((5_0_0, 3_0_0))
self.assertEqual(segmentation[0].shape ,A__)
lowercase = image_processor.post_process_semantic_segmentation(outputs=A__)
lowercase = torch.Size((1_2_8, 1_2_8))
self.assertEqual(segmentation[0].shape ,A__)
| 633
|
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def UpperCamelCase ( ):
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
assert and_gate(1 , 1 ) == 1
if __name__ == "__main__":
test_and_gate()
print(and_gate(1, 0))
print(and_gate(0, 0))
print(and_gate(0, 1))
print(and_gate(1, 1))
| 633
| 1
|
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class __snake_case ( SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ = field(default='language-modeling' , metadata={'include_in_asdict_even_if_is_default': True} )
SCREAMING_SNAKE_CASE__ = Features({'text': Value('string' )} )
SCREAMING_SNAKE_CASE__ = Features({} )
SCREAMING_SNAKE_CASE__ = "text"
@property
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return {self.text_column: "text"}
| 193
|
from typing import Dict
from .base import GenericTensor, Pipeline
class __snake_case ( SCREAMING_SNAKE_CASE ):
def SCREAMING_SNAKE_CASE_ ( self ,a_=None ,a_=None ,a_=None ,**a_ ):
"""simple docstring"""
if tokenize_kwargs is None:
lowerCAmelCase__ = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)' )
lowerCAmelCase__ = truncation
lowerCAmelCase__ = tokenize_kwargs
lowerCAmelCase__ = {}
if return_tensors is not None:
lowerCAmelCase__ = return_tensors
return preprocess_params, {}, postprocess_params
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,**a_ ):
"""simple docstring"""
lowerCAmelCase__ = self.framework
lowerCAmelCase__ = self.tokenizer(a_ ,return_tensors=a_ ,**a_ )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
lowerCAmelCase__ = self.model(**a_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_=False ):
"""simple docstring"""
# [0] is the first available tensor, logits or last_hidden_state.
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self ,*a_ ,**a_ ):
"""simple docstring"""
return super().__call__(*a_ ,**a_ )
| 193
| 1
|
def lowerCAmelCase_ ( snake_case_ : str = 1_00_00_00 ) -> int:
'''simple docstring'''
UpperCAmelCase_ = set(range(3 , __lowerCAmelCase , 2 ) )
primes.add(2 )
for p in range(3 , __lowerCAmelCase , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , __lowerCAmelCase , __lowerCAmelCase ) ) )
UpperCAmelCase_ = [float(__lowerCAmelCase ) for n in range(limit + 1 )]
for p in primes:
for n in range(__lowerCAmelCase , limit + 1 , __lowerCAmelCase ):
phi[n] *= 1 - 1 / p
return int(sum(phi[2:] ) )
if __name__ == "__main__":
print(f"{solution() = }")
| 712
|
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_: Dict ={
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AutoformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: int =[
'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'AutoformerForPrediction',
'AutoformerModel',
'AutoformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_: Tuple =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 415
| 0
|
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__a: Any = TypeVar('''T''')
class SCREAMING_SNAKE_CASE__ ( Generic[T] ):
'''simple docstring'''
_lowerCamelCase = 42 # Cache store of keys
_lowerCamelCase = 42 # References of the keys in cache
_lowerCamelCase = 10 # Maximum capacity of cache
def __init__( self : Optional[int] , lowerCamelCase : int ) -> None:
"""simple docstring"""
_UpperCAmelCase = deque()
_UpperCAmelCase = set()
if not n:
_UpperCAmelCase = sys.maxsize
elif n < 0:
raise ValueError("""n should be an integer greater than 0.""" )
else:
_UpperCAmelCase = n
def lowerCamelCase ( self : Optional[Any] , lowerCamelCase : T ) -> None:
"""simple docstring"""
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
_UpperCAmelCase = self.dq_store.pop()
self.key_reference.remove(lowerCamelCase )
else:
self.dq_store.remove(lowerCamelCase )
self.dq_store.appendleft(lowerCamelCase )
self.key_reference.add(lowerCamelCase )
def lowerCamelCase ( self : str ) -> None:
"""simple docstring"""
for k in self.dq_store:
print(lowerCamelCase )
def __repr__( self : Optional[Any] ) -> str:
"""simple docstring"""
return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
__a: LRUCache[str | int] = LRUCache(4)
lru_cache.refer('''A''')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('''A''')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 108
|
'''simple docstring'''
import doctest
from collections import deque
import numpy as np
class lowercase_ :
"""simple docstring"""
def __init__( self : Optional[Any] ):
__lowercase = [2, 1, 2, -1]
__lowercase = [1, 2, 3, 4]
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = len(self.first_signal )
__lowercase = len(self.second_signal )
__lowercase = max(lowercase__ ,lowercase__ )
# create a zero matrix of max_length x max_length
__lowercase = [[0] * max_length for i in range(lowercase__ )]
# fills the smaller signal with zeros to make both signals of same length
if length_first_signal < length_second_signal:
self.first_signal += [0] * (max_length - length_first_signal)
elif length_first_signal > length_second_signal:
self.second_signal += [0] * (max_length - length_second_signal)
for i in range(lowercase__ ):
__lowercase = deque(self.second_signal )
rotated_signal.rotate(lowercase__ )
for j, item in enumerate(lowercase__ ):
matrix[i][j] += item
# multiply the matrix with the first signal
__lowercase = np.matmul(np.transpose(lowercase__ ) ,np.transpose(self.first_signal ) )
# rounding-off to two decimal places
return [round(lowercase__ ,2 ) for i in final_signal]
if __name__ == "__main__":
doctest.testmod()
| 41
| 0
|
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 712
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert import RemBertTokenizer
else:
A_ : str = None
A_ : int = logging.get_logger(__name__)
A_ : int = {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}
A_ : Union[str, Any] = {
"vocab_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
},
"tokenizer_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
},
}
A_ : Dict = {
"google/rembert": 256,
}
A_ : Union[str, Any] = "▁"
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = RemBertTokenizer
def __init__( self , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE="[CLS]" , __SCREAMING_SNAKE_CASE="[SEP]" , __SCREAMING_SNAKE_CASE="<unk>" , __SCREAMING_SNAKE_CASE="[SEP]" , __SCREAMING_SNAKE_CASE="<pad>" , __SCREAMING_SNAKE_CASE="[CLS]" , __SCREAMING_SNAKE_CASE="[MASK]" , **__SCREAMING_SNAKE_CASE , ):
# Mask token behave like a normal word, i.e. include the space before it
snake_case__ : Any = AddedToken(__SCREAMING_SNAKE_CASE , lstrip=__SCREAMING_SNAKE_CASE , rstrip=__SCREAMING_SNAKE_CASE ) if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) else mask_token
super().__init__(
__SCREAMING_SNAKE_CASE , tokenizer_file=__SCREAMING_SNAKE_CASE , do_lower_case=__SCREAMING_SNAKE_CASE , remove_space=__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE , bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , sep_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , cls_token=__SCREAMING_SNAKE_CASE , mask_token=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
snake_case__ : List[Any] = do_lower_case
snake_case__ : Union[str, Any] = remove_space
snake_case__ : Tuple = keep_accents
snake_case__ : Optional[int] = vocab_file
snake_case__ : Union[str, Any] = False if not self.vocab_file else True
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None ):
snake_case__ : str = [self.sep_token_id]
snake_case__ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"""You should not supply a second sequence if the provided sequence of """
"""ids is already formatted with special tokens for the model.""" )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(__SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(__SCREAMING_SNAKE_CASE )) + [1]
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None ):
snake_case__ : int = [self.sep_token_id]
snake_case__ : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None ):
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error("""Vocabulary path ({}) should be a directory""".format(__SCREAMING_SNAKE_CASE ) )
return
snake_case__ : Optional[Any] = os.path.join(
__SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ):
copyfile(self.vocab_file , __SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 419
| 0
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase :int = logging.get_logger(__name__)
lowerCAmelCase :Any = '''▁'''
lowerCAmelCase :str = {'''vocab_file''': '''sentencepiece.bpe.model'''}
lowerCAmelCase :str = {
'''vocab_file''': {
'''facebook/mbart-large-50-one-to-many-mmt''': (
'''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
),
}
}
lowerCAmelCase :Dict = {
'''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}
# fmt: off
lowerCAmelCase :Union[str, Any] = ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
A_ : Any = VOCAB_FILES_NAMES
A_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
A_ : Dict = ["""input_ids""", """attention_mask"""]
A_ : List[int] = []
A_ : List[int] = []
def __init__( self : int , _A : Tuple , _A : Any=None , _A : Optional[int]=None , _A : List[str]="</s>" , _A : Any="</s>" , _A : List[str]="<s>" , _A : Union[str, Any]="<unk>" , _A : str="<pad>" , _A : str="<mask>" , _A : Optional[Dict[str, Any]] = None , **_A : Optional[int] , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
__magic_name__ : Any = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else mask_token
__magic_name__ : str = {} if sp_model_kwargs is None else sp_model_kwargs
__magic_name__ : Optional[int] = kwargs.get('additional_special_tokens' , [] )
kwargs["additional_special_tokens"] += [
code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=_A , tgt_lang=_A , eos_token=_A , unk_token=_A , sep_token=_A , cls_token=_A , pad_token=_A , mask_token=_A , sp_model_kwargs=self.sp_model_kwargs , **_A , )
__magic_name__ : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_A ) )
__magic_name__ : Any = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
__magic_name__ : List[str] = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
__magic_name__ : Union[str, Any] = 1
__magic_name__ : Optional[Any] = len(self.sp_model )
__magic_name__ : Union[str, Any] = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_A )
}
__magic_name__ : str = {v: k for k, v in self.lang_code_to_id.items()}
__magic_name__ : Dict = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
__magic_name__ : int = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
__magic_name__ : Tuple = src_lang if src_lang is not None else 'en_XX'
__magic_name__ : List[Any] = self.lang_code_to_id[self._src_lang]
__magic_name__ : str = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def __lowerCAmelCase ( self : Tuple ) -> int:
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def __lowerCAmelCase ( self : Optional[int] ) -> str:
return self._src_lang
@src_lang.setter
def __lowerCAmelCase ( self : Tuple , _A : str ) -> None:
__magic_name__ : int = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self : Optional[Any] ) -> Dict:
__magic_name__ : Optional[int] = self.__dict__.copy()
__magic_name__ : int = None
return state
def __setstate__( self : Union[str, Any] , _A : Dict ) -> None:
__magic_name__ : Optional[int] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
__magic_name__ : int = {}
__magic_name__ : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCAmelCase ( self : Optional[Any] ) -> Dict:
__magic_name__ : str = {self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __lowerCAmelCase ( self : int , _A : str ) -> List[str]:
return self.sp_model.encode(_A , out_type=_A )
def __lowerCAmelCase ( self : Union[str, Any] , _A : str ) -> int:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
__magic_name__ : Optional[int] = self.sp_model.PieceToId(_A )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def __lowerCAmelCase ( self : Dict , _A : int ) -> str:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def __lowerCAmelCase ( self : int , _A : Any ) -> Dict:
__magic_name__ : List[str] = []
__magic_name__ : Optional[int] = ''
__magic_name__ : List[str] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_A ) + token
__magic_name__ : List[str] = True
__magic_name__ : List[Any] = []
else:
current_sub_tokens.append(_A )
__magic_name__ : List[Any] = False
out_string += self.sp_model.decode(_A )
return out_string.strip()
def __lowerCAmelCase ( self : str , _A : str , _A : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(_A ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
__magic_name__ : Optional[int] = os.path.join(
_A , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _A )
elif not os.path.isfile(self.vocab_file ):
with open(_A , 'wb' ) as fi:
__magic_name__ : Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(_A )
return (out_vocab_file,)
def __lowerCAmelCase ( self : Dict , _A : List[int] , _A : Optional[List[int]] = None , _A : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_A , token_ids_a=_A , already_has_special_tokens=_A )
__magic_name__ : Any = [1] * len(self.prefix_tokens )
__magic_name__ : Optional[int] = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(_A )) + suffix_ones
return prefix_ones + ([0] * len(_A )) + ([0] * len(_A )) + suffix_ones
def __lowerCAmelCase ( self : int , _A : List[int] , _A : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __lowerCAmelCase ( self : int , _A : List[str] , _A : str , _A : Optional[str] , _A : Optional[str] , **_A : int ) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
__magic_name__ : Optional[Any] = src_lang
__magic_name__ : Tuple = self(_A , add_special_tokens=_A , return_tensors=_A , **_A )
__magic_name__ : Optional[int] = self.convert_tokens_to_ids(_A )
__magic_name__ : Tuple = tgt_lang_id
return inputs
def __lowerCAmelCase ( self : Tuple , _A : List[str] , _A : str = "en_XX" , _A : Optional[List[str]] = None , _A : str = "ro_RO" , **_A : List[Any] , ) -> BatchEncoding:
__magic_name__ : List[str] = src_lang
__magic_name__ : List[Any] = tgt_lang
return super().prepare_seqaseq_batch(_A , _A , **_A )
def __lowerCAmelCase ( self : Union[str, Any] ) -> int:
return self.set_src_lang_special_tokens(self.src_lang )
def __lowerCAmelCase ( self : Any ) -> Dict:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __lowerCAmelCase ( self : Optional[Any] , _A : str ) -> None:
__magic_name__ : Tuple = self.lang_code_to_id[src_lang]
__magic_name__ : Optional[int] = [self.cur_lang_code_id]
__magic_name__ : Tuple = [self.eos_token_id]
def __lowerCAmelCase ( self : str , _A : str ) -> None:
__magic_name__ : List[Any] = self.lang_code_to_id[tgt_lang]
__magic_name__ : Dict = [self.cur_lang_code_id]
__magic_name__ : List[str] = [self.eos_token_id]
| 561
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase :Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase :Optional[Any] = {
'''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
A_ : Optional[int] = """instructblip_vision_model"""
def __init__( self : List[Any] , _A : Dict=1408 , _A : Union[str, Any]=6144 , _A : Optional[int]=39 , _A : Optional[int]=16 , _A : Optional[int]=224 , _A : Any=14 , _A : Optional[int]="gelu" , _A : str=1E-6 , _A : str=0.0 , _A : str=1E-10 , _A : Optional[Any]=True , **_A : List[Any] , ) -> Dict:
super().__init__(**_A )
__magic_name__ : Optional[int] = hidden_size
__magic_name__ : int = intermediate_size
__magic_name__ : List[Any] = num_hidden_layers
__magic_name__ : int = num_attention_heads
__magic_name__ : Any = patch_size
__magic_name__ : Tuple = image_size
__magic_name__ : int = initializer_range
__magic_name__ : str = attention_dropout
__magic_name__ : int = layer_norm_eps
__magic_name__ : Optional[int] = hidden_act
__magic_name__ : Tuple = qkv_bias
@classmethod
def __lowerCAmelCase ( cls : List[Any] , _A : Union[str, os.PathLike] , **_A : Union[str, Any] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(_A )
__magic_name__ , __magic_name__ : Union[str, Any] = cls.get_config_dict(_A , **_A )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type' ) == "instructblip":
__magic_name__ : Dict = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_A , **_A )
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
A_ : Tuple = """instructblip_qformer"""
def __init__( self : Dict , _A : Dict=30522 , _A : List[str]=768 , _A : Tuple=12 , _A : List[Any]=12 , _A : Optional[int]=3072 , _A : Optional[Any]="gelu" , _A : Tuple=0.1 , _A : Any=0.1 , _A : int=512 , _A : Tuple=0.02 , _A : Optional[Any]=1E-12 , _A : List[Any]=0 , _A : Tuple="absolute" , _A : Dict=2 , _A : Tuple=1408 , **_A : int , ) -> Optional[int]:
super().__init__(pad_token_id=_A , **_A )
__magic_name__ : Any = vocab_size
__magic_name__ : str = hidden_size
__magic_name__ : Optional[int] = num_hidden_layers
__magic_name__ : str = num_attention_heads
__magic_name__ : str = hidden_act
__magic_name__ : List[str] = intermediate_size
__magic_name__ : List[str] = hidden_dropout_prob
__magic_name__ : Tuple = attention_probs_dropout_prob
__magic_name__ : List[Any] = max_position_embeddings
__magic_name__ : Union[str, Any] = initializer_range
__magic_name__ : List[str] = layer_norm_eps
__magic_name__ : Union[str, Any] = position_embedding_type
__magic_name__ : Any = cross_attention_frequency
__magic_name__ : int = encoder_hidden_size
@classmethod
def __lowerCAmelCase ( cls : int , _A : Union[str, os.PathLike] , **_A : List[str] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(_A )
__magic_name__ , __magic_name__ : str = cls.get_config_dict(_A , **_A )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type' ) == "instructblip":
__magic_name__ : Union[str, Any] = config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_A , **_A )
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
A_ : int = """instructblip"""
A_ : Any = True
def __init__( self : int , _A : Optional[int]=None , _A : List[str]=None , _A : Union[str, Any]=None , _A : Any=32 , **_A : int ) -> Any:
super().__init__(**_A )
if vision_config is None:
__magic_name__ : Any = {}
logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.' )
if qformer_config is None:
__magic_name__ : Union[str, Any] = {}
logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.' )
if text_config is None:
__magic_name__ : List[str] = {}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
__magic_name__ : Union[str, Any] = InstructBlipVisionConfig(**_A )
__magic_name__ : str = InstructBlipQFormerConfig(**_A )
__magic_name__ : int = text_config['model_type'] if 'model_type' in text_config else 'opt'
__magic_name__ : Tuple = CONFIG_MAPPING[text_model_type](**_A )
__magic_name__ : Optional[Any] = self.text_config.tie_word_embeddings
__magic_name__ : int = self.text_config.is_encoder_decoder
__magic_name__ : List[Any] = num_query_tokens
__magic_name__ : Tuple = self.vision_config.hidden_size
__magic_name__ : int = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__magic_name__ : int = 1.0
__magic_name__ : List[Any] = 0.02
@classmethod
def __lowerCAmelCase ( cls : str , _A : InstructBlipVisionConfig , _A : InstructBlipQFormerConfig , _A : PretrainedConfig , **_A : int , ) -> Union[str, Any]:
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **_A , )
def __lowerCAmelCase ( self : List[Any] ) -> int:
__magic_name__ : int = copy.deepcopy(self.__dict__ )
__magic_name__ : str = self.vision_config.to_dict()
__magic_name__ : List[str] = self.qformer_config.to_dict()
__magic_name__ : Tuple = self.text_config.to_dict()
__magic_name__ : str = self.__class__.model_type
return output
| 561
| 1
|
'''simple docstring'''
from __future__ import annotations
def a_ ( _UpperCAmelCase : list[float] ) -> float:
__snake_case : str = 0.0_0
__snake_case : Union[str, Any] = 0
for resistor in resistors:
if resistor <= 0:
__snake_case : Union[str, Any] = f'''Resistor at index {index} has a negative or zero value!'''
raise ValueError(_UpperCAmelCase )
first_sum += 1 / float(_UpperCAmelCase )
index += 1
return 1 / first_sum
def a_ ( _UpperCAmelCase : list[float] ) -> float:
__snake_case : Optional[Any] = 0.0_0
__snake_case : Any = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
__snake_case : Optional[Any] = f'''Resistor at index {index} has a negative value!'''
raise ValueError(_UpperCAmelCase )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 703
|
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
A__ : Tuple = random.Random()
def a_ ( _UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : Optional[Any]=1.0 ,_UpperCAmelCase : Optional[Any]=None ,_UpperCAmelCase : List[str]=None ) -> Optional[Any]:
if rng is None:
__snake_case : Any = global_rng
__snake_case : Union[str, Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class snake_case__ ( unittest.TestCase ):
def __init__( self : Tuple , __a : Optional[Any] , __a : Optional[int]=7 , __a : Any=400 , __a : str=2000 , __a : Union[str, Any]=1 , __a : Union[str, Any]=0.0 , __a : Tuple=16000 , __a : str=True , __a : int=True , ) -> Any:
'''simple docstring'''
__snake_case : List[str] = parent
__snake_case : List[str] = batch_size
__snake_case : List[str] = min_seq_length
__snake_case : Tuple = max_seq_length
__snake_case : Optional[Any] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
__snake_case : List[Any] = feature_size
__snake_case : List[Any] = padding_value
__snake_case : Tuple = sampling_rate
__snake_case : Tuple = return_attention_mask
__snake_case : Dict = do_normalize
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def A_ ( self : List[Any] , __a : str=False , __a : Optional[int]=False ) -> str:
'''simple docstring'''
def _flatten(__a : Dict ):
return list(itertools.chain(*__a ) )
if equal_length:
__snake_case : List[Any] = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
__snake_case : str = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
__snake_case : Optional[Any] = [np.asarray(__a ) for x in speech_inputs]
return speech_inputs
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = WavaVecaFeatureExtractor
def A_ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = WavaVecaFeatureExtractionTester(self )
def A_ ( self : Dict , __a : Tuple ) -> Any:
'''simple docstring'''
self.assertTrue(np.all(np.mean(__a , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(__a , axis=0 ) - 1 ) < 1e-3 ) )
def A_ ( self : Dict ) -> int:
'''simple docstring'''
# Tests that all call wrap to encode_plus and batch_encode_plus
__snake_case : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
__snake_case : List[str] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
__snake_case : Optional[Any] = [np.asarray(__a ) for speech_input in speech_inputs]
# Test not batched input
__snake_case : Optional[Any] = feat_extract(speech_inputs[0] , return_tensors='np' ).input_values
__snake_case : Tuple = feat_extract(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(__a , __a , atol=1e-3 ) )
# Test batched
__snake_case : Union[str, Any] = feat_extract(__a , return_tensors='np' ).input_values
__snake_case : Tuple = feat_extract(__a , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(__a , __a ):
self.assertTrue(np.allclose(__a , __a , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
__snake_case : Optional[int] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
__snake_case : List[str] = np.asarray(__a )
__snake_case : Union[str, Any] = feat_extract(__a , return_tensors='np' ).input_values
__snake_case : List[Any] = feat_extract(__a , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(__a , __a ):
self.assertTrue(np.allclose(__a , __a , atol=1e-3 ) )
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case : Optional[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
__snake_case : Dict = ['longest', 'max_length', 'do_not_pad']
__snake_case : Any = [None, 1600, None]
for max_length, padding in zip(__a , __a ):
__snake_case : Any = feat_extract(__a , padding=__a , max_length=__a , return_tensors='np' )
__snake_case : Optional[Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def A_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
__snake_case : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case : List[Any] = range(800 , 1400 , 200 )
__snake_case : Any = [floats_list((1, x) )[0] for x in lengths]
__snake_case : Tuple = ['longest', 'max_length', 'do_not_pad']
__snake_case : Dict = [None, 1600, None]
for max_length, padding in zip(__a , __a ):
__snake_case : str = feat_extract(__a , max_length=__a , padding=__a )
__snake_case : str = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def A_ ( self : List[str] ) -> str:
'''simple docstring'''
__snake_case : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case : Tuple = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
__snake_case : Any = feat_extract(
__a , truncation=__a , max_length=1000 , padding='max_length' , return_tensors='np' )
__snake_case : List[str] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def A_ ( self : List[Any] ) -> Any:
'''simple docstring'''
__snake_case : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
__snake_case : Union[str, Any] = feat_extract(
__a , truncation=__a , max_length=1000 , padding='longest' , return_tensors='np' )
__snake_case : Tuple = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
__snake_case : int = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
__snake_case : Any = feat_extract(
__a , truncation=__a , max_length=2000 , padding='longest' , return_tensors='np' )
__snake_case : Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
@require_torch
def A_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
import torch
__snake_case : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case : Dict = np.random.rand(100 ).astype(np.floataa )
__snake_case : Any = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
__snake_case : Dict = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
__snake_case : List[Any] = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
@slow
@require_torch
def A_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
# this test makes sure that models that are using
# group norm don't have their feature extractor return the
# attention_mask
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
__snake_case : List[str] = WavaVecaConfig.from_pretrained(__a )
__snake_case : str = WavaVecaFeatureExtractor.from_pretrained(__a )
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask , config.feat_extract_norm == 'layer' )
| 124
| 0
|
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def a ( __UpperCAmelCase : Iterable[str] , __UpperCAmelCase : int ) -> str:
__magic_name__: Optional[int] = iter(__UpperCAmelCase )
while True:
__magic_name__: List[Any] = tuple(itertools.islice(__UpperCAmelCase , __UpperCAmelCase ) )
if not chunk:
return
yield chunk
def a ( __UpperCAmelCase : str ) -> Tuple:
__magic_name__: Any = "".join([c.upper() for c in dirty if c in string.ascii_letters] )
__magic_name__: Optional[int] = ""
if len(__UpperCAmelCase ) < 2:
return dirty
for i in range(len(__UpperCAmelCase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(__UpperCAmelCase ) & 1:
clean += "X"
return clean
def a ( __UpperCAmelCase : str ) -> List[str]:
__magic_name__: Optional[Any] = "ABCDEFGHIKLMNOPQRSTUVWXYZ"
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
__magic_name__: Optional[Any] = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(__UpperCAmelCase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(__UpperCAmelCase )
return table
def a ( __UpperCAmelCase : str , __UpperCAmelCase : str ) -> List[str]:
__magic_name__: Any = generate_table(__UpperCAmelCase )
__magic_name__: Optional[Any] = prepare_input(__UpperCAmelCase )
__magic_name__: Optional[int] = ""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(__UpperCAmelCase , 2 ):
__magic_name__: int = divmod(table.index(__UpperCAmelCase ) , 5 )
__magic_name__: int = divmod(table.index(__UpperCAmelCase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def a ( __UpperCAmelCase : str , __UpperCAmelCase : str ) -> List[Any]:
__magic_name__: int = generate_table(__UpperCAmelCase )
__magic_name__: int = ""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(__UpperCAmelCase , 2 ):
__magic_name__: Optional[int] = divmod(table.index(__UpperCAmelCase ) , 5 )
__magic_name__: List[Any] = divmod(table.index(__UpperCAmelCase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 96
|
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCAmelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : List[str] = ["""input_values""", """attention_mask"""]
def __init__( self , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 1_6_0_0_0 , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = False , lowerCAmelCase__ = 8_0 , lowerCAmelCase__ = 1_6 , lowerCAmelCase__ = 6_4 , lowerCAmelCase__ = "hann_window" , lowerCAmelCase__ = 1.0 , lowerCAmelCase__ = 8_0 , lowerCAmelCase__ = 7_6_0_0 , lowerCAmelCase__ = 1E-10 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = True , **lowerCAmelCase__ , ) -> str:
'''simple docstring'''
super().__init__(feature_size=lowerCAmelCase__ , sampling_rate=lowerCAmelCase__ , padding_value=lowerCAmelCase__ , **lowerCAmelCase__ )
a__ : Tuple =do_normalize
a__ : Tuple =return_attention_mask
a__ : str =num_mel_bins
a__ : Any =hop_length
a__ : Optional[Any] =win_length
a__ : int =win_function
a__ : List[str] =frame_signal_scale
a__ : List[str] =fmin
a__ : str =fmax
a__ : Dict =mel_floor
a__ : Any =reduction_factor
a__ : str =win_length * sampling_rate // 1_0_0_0
a__ : List[str] =hop_length * sampling_rate // 1_0_0_0
a__ : Optional[Any] =optimal_fft_length(self.sample_size )
a__ : Any =(self.n_fft // 2) + 1
a__ : List[Any] =window_function(window_length=self.sample_size , name=self.win_function , periodic=lowerCAmelCase__ )
a__ : Optional[int] =mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm="slaney" , mel_scale="slaney" , )
if frame_signal_scale != 1.0:
warnings.warn(
"The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers" , lowerCAmelCase__ , )
if reduction_factor != 2.0:
warnings.warn(
"The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers" , lowerCAmelCase__ , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def _lowercase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 0.0 ) -> List[np.ndarray]:
'''simple docstring'''
if attention_mask is not None:
a__ : List[Any] =np.array(lowerCAmelCase__ , np.intaa )
a__ : Optional[Any] =[]
for vector, length in zip(lowerCAmelCase__ , attention_mask.sum(-1 ) ):
a__ : Tuple =(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
a__ : Any =padding_value
normed_input_values.append(lowerCAmelCase__ )
else:
a__ : Optional[int] =[(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def _lowercase ( self , lowerCAmelCase__ , ) -> np.ndarray:
'''simple docstring'''
a__ : Dict =spectrogram(
lowerCAmelCase__ , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel="log10" , )
return log_mel_spec.T
def __call__( self , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> BatchFeature:
'''simple docstring'''
if audio is None and audio_target is None:
raise ValueError("You must provide either `audio` or `audio_target` values." )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
"It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
if audio is not None:
a__ : Dict =self._process_audio(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ , )
else:
a__ : str =None
if audio_target is not None:
a__ : int =self._process_audio(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ , )
if inputs is None:
return inputs_target
else:
a__ : Any =inputs_target["input_values"]
a__ : List[Any] =inputs_target.get("attention_mask" )
if decoder_attention_mask is not None:
a__ : Optional[int] =decoder_attention_mask
return inputs
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = False , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> BatchFeature:
'''simple docstring'''
a__ : List[Any] =isinstance(lowerCAmelCase__ , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
a__ : Optional[int] =is_batched_numpy or (
isinstance(lowerCAmelCase__ , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a__ : int =[np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(lowerCAmelCase__ , np.ndarray ):
a__ : List[Any] =np.asarray(lowerCAmelCase__ , dtype=np.floataa )
elif isinstance(lowerCAmelCase__ , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
a__ : Optional[Any] =speech.astype(np.floataa )
# always return batch
if not is_batched:
a__ : Union[str, Any] =[speech]
# needed to make pad() work on spectrogram inputs
a__ : Union[str, Any] =self.feature_size
# convert into correct format for padding
if is_target:
a__ : Dict =[self._extract_mel_features(lowerCAmelCase__ ) for waveform in speech]
a__ : str =BatchFeature({"input_values": features} )
a__ : List[str] =self.num_mel_bins
else:
a__ : List[str] =BatchFeature({"input_values": speech} )
a__ : Optional[int] =self.pad(
lowerCAmelCase__ , padding=lowerCAmelCase__ , max_length=lowerCAmelCase__ , truncation=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , **lowerCAmelCase__ , )
a__ : Any =feature_size_hack
# convert input values to correct format
a__ : List[Any] =padded_inputs["input_values"]
if not isinstance(input_values[0] , np.ndarray ):
a__ : Union[str, Any] =[np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(lowerCAmelCase__ , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
a__ : str =[array.astype(np.floataa ) for array in input_values]
elif isinstance(lowerCAmelCase__ , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
a__ : Optional[int] =input_values.astype(np.floataa )
# convert attention_mask to correct format
a__ : str =padded_inputs.get("attention_mask" )
if attention_mask is not None:
a__ : str =[np.asarray(lowerCAmelCase__ , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
a__ : Union[str, Any] =(
attention_mask
if self._get_padding_strategies(lowerCAmelCase__ , max_length=lowerCAmelCase__ ) is not PaddingStrategy.DO_NOT_PAD
else None
)
a__ : List[Any] =self.zero_mean_unit_var_norm(
padded_inputs["input_values"] , attention_mask=lowerCAmelCase__ , padding_value=self.padding_value )
if return_tensors is not None:
a__ : int =padded_inputs.convert_to_tensors(lowerCAmelCase__ )
return padded_inputs
def _lowercase ( self ) -> Dict[str, Any]:
'''simple docstring'''
a__ : Optional[int] =super().to_dict()
# Don't serialize these as they are derived from the other properties.
a__ : Optional[Any] =["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
for name in names:
if name in output:
del output[name]
return output
| 563
| 0
|
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
A__ : str= (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A__ : list[int]= [ord(letter) for letter in string.ascii_lowercase]
A__ : set[int]= {ord(char) for char in VALID_CHARS}
A__ : list[str]= ["the", "be", "to", "of", "and", "in", "that", "have"]
def lowerCAmelCase_( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str | None:
"""simple docstring"""
UpperCamelCase__ = ''
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
for keychar, cipherchar in zip(cycle(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(SCREAMING_SNAKE_CASE )
return decoded
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> list[str]:
"""simple docstring"""
UpperCamelCase__ = []
for key in product(SCREAMING_SNAKE_CASE , repeat=3 ):
UpperCamelCase__ = try_key(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if encoded is not None:
possibles.append(SCREAMING_SNAKE_CASE )
return possibles
def lowerCAmelCase_( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> list[str]:
"""simple docstring"""
return [possible for possible in possibles if common_word in possible.lower()]
def lowerCAmelCase_( SCREAMING_SNAKE_CASE = "p059_cipher.txt" ) -> int:
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = Path(SCREAMING_SNAKE_CASE ).parent.joinpath(SCREAMING_SNAKE_CASE ).read_text(encoding='utf-8' )
UpperCamelCase__ = [int(SCREAMING_SNAKE_CASE ) for number in data.strip().split(',' )]
UpperCamelCase__ = filter_valid_chars(SCREAMING_SNAKE_CASE )
for common_word in COMMON_WORDS:
UpperCamelCase__ = filter_common_word(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) == 1:
break
UpperCamelCase__ = possibles[0]
return sum(ord(SCREAMING_SNAKE_CASE ) for char in decoded_text )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 707
|
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A__ : Any= """src/diffusers"""
# Matches is_xxx_available()
A__ : Tuple= re.compile(r"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
A__ : Any= re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
A__ : Optional[Any]= """
{0} = None
"""
A__ : List[Any]= """
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
"""
A__ : Dict= """
def {0}(*args, **kwargs):
requires_backends({0}, {1})
"""
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase__ = _re_backend.findall(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) == 0:
return None
return "_and_".join(SCREAMING_SNAKE_CASE )
def lowerCAmelCase_( ) -> str:
"""simple docstring"""
with open(os.path.join(SCREAMING_SNAKE_CASE , '__init__.py' ) , 'r' , encoding='utf-8' , newline='\n' ) as f:
UpperCamelCase__ = f.readlines()
# Get to the point we do the actual imports for type checking
UpperCamelCase__ = 0
UpperCamelCase__ = {}
# Go through the end of the file
while line_index < len(SCREAMING_SNAKE_CASE ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
UpperCamelCase__ = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith('else:' ):
line_index += 1
line_index += 1
UpperCamelCase__ = []
# Until we unindent, add backend objects to the list
while line_index < len(SCREAMING_SNAKE_CASE ) and len(lines[line_index] ) > 1:
UpperCamelCase__ = lines[line_index]
UpperCamelCase__ = _re_single_line_import.search(SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(SCREAMING_SNAKE_CASE ) > 0:
UpperCamelCase__ = objects
else:
line_index += 1
return backend_specific_objects
def lowerCAmelCase_( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
if name.isupper():
return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE )
elif name.islower():
return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
else:
return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def lowerCAmelCase_( SCREAMING_SNAKE_CASE=None ) -> int:
"""simple docstring"""
if backend_specific_objects is None:
UpperCamelCase__ = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
UpperCamelCase__ = {}
for backend, objects in backend_specific_objects.items():
UpperCamelCase__ = '[' + ', '.join(F'"{b}"' for b in backend.split('_and_' ) ) + ']'
UpperCamelCase__ = '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for o in objects] )
UpperCamelCase__ = dummy_file
return dummy_files
def lowerCAmelCase_( SCREAMING_SNAKE_CASE=False ) -> int:
"""simple docstring"""
UpperCamelCase__ = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
UpperCamelCase__ = {'torch': 'pt'}
# Locate actual dummy modules and read their content.
UpperCamelCase__ = os.path.join(SCREAMING_SNAKE_CASE , 'utils' )
UpperCamelCase__ = {
backend: os.path.join(SCREAMING_SNAKE_CASE , F'dummy_{short_names.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )}_objects.py' )
for backend in dummy_files.keys()
}
UpperCamelCase__ = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(SCREAMING_SNAKE_CASE ):
with open(SCREAMING_SNAKE_CASE , 'r' , encoding='utf-8' , newline='\n' ) as f:
UpperCamelCase__ = f.read()
else:
UpperCamelCase__ = ''
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
F'Updating diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )}_objects.py as the main '
'__init__ has new objects.' )
with open(dummy_file_paths[backend] , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
'The main __init__ has objects that are not present in '
F'diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )}_objects.py. Run `make fix-copies` '
'to fix this.' )
if __name__ == "__main__":
A__ : Any= argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
A__ : Optional[int]= parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 20
| 0
|
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
__A = logging.getLogger(__name__)
class _A ( UpperCamelCase ):
"""simple docstring"""
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Tuple=None , __SCREAMING_SNAKE_CASE : Optional[int]=None ) -> List[str]:
__UpperCAmelCase =self.layer[current_layer](__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , head_mask[current_layer] )
__UpperCAmelCase =layer_outputs[0]
return hidden_states
@add_start_docstrings(
'The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.' , UpperCamelCase , )
class _A ( UpperCamelCase ):
"""simple docstring"""
def __init__( self : int , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
super().__init__(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =BertEncoderWithPabee(__SCREAMING_SNAKE_CASE )
self.init_weights()
__UpperCAmelCase =0
__UpperCAmelCase =0
__UpperCAmelCase =0
__UpperCAmelCase =0
def _a ( self : Any , __SCREAMING_SNAKE_CASE : List[str] ) -> int:
__UpperCAmelCase =threshold
def _a ( self : Any , __SCREAMING_SNAKE_CASE : int ) -> Union[str, Any]:
__UpperCAmelCase =patience
def _a ( self : Optional[Any] ) -> Tuple:
__UpperCAmelCase =0
__UpperCAmelCase =0
def _a ( self : List[str] ) -> str:
__UpperCAmelCase =self.inference_layers_num / self.inference_instances_num
__UpperCAmelCase =(
f'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
f''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
)
print(__SCREAMING_SNAKE_CASE )
@add_start_docstrings_to_model_forward(__SCREAMING_SNAKE_CASE )
def _a ( self : int , __SCREAMING_SNAKE_CASE : int=None , __SCREAMING_SNAKE_CASE : Tuple=None , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING_SNAKE_CASE : Optional[Any]=None , __SCREAMING_SNAKE_CASE : Tuple=None , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : int=None , __SCREAMING_SNAKE_CASE : Dict=False , ) -> str:
if input_ids is not None and inputs_embeds is not None:
raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""" )
elif input_ids is not None:
__UpperCAmelCase =input_ids.size()
elif inputs_embeds is not None:
__UpperCAmelCase =inputs_embeds.size()[:-1]
else:
raise ValueError("""You have to specify either input_ids or inputs_embeds""" )
__UpperCAmelCase =input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
__UpperCAmelCase =torch.ones(__SCREAMING_SNAKE_CASE , device=__SCREAMING_SNAKE_CASE )
if token_type_ids is None:
__UpperCAmelCase =torch.zeros(__SCREAMING_SNAKE_CASE , dtype=torch.long , device=__SCREAMING_SNAKE_CASE )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
__UpperCAmelCase =self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase =encoder_hidden_states.size()
__UpperCAmelCase =(encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
__UpperCAmelCase =torch.ones(__SCREAMING_SNAKE_CASE , device=__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =self.invert_attention_mask(__SCREAMING_SNAKE_CASE )
else:
__UpperCAmelCase =None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
__UpperCAmelCase =self.get_head_mask(__SCREAMING_SNAKE_CASE , self.config.num_hidden_layers )
__UpperCAmelCase =self.embeddings(
input_ids=__SCREAMING_SNAKE_CASE , position_ids=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , inputs_embeds=__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =embedding_output
if self.training:
__UpperCAmelCase =[]
for i in range(self.config.num_hidden_layers ):
__UpperCAmelCase =self.encoder.adaptive_forward(
__SCREAMING_SNAKE_CASE , current_layer=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =self.pooler(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =output_layers[i](output_dropout(__SCREAMING_SNAKE_CASE ) )
res.append(__SCREAMING_SNAKE_CASE )
elif self.patience == 0: # Use all layers for inference
__UpperCAmelCase =self.encoder(
__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE , encoder_hidden_states=__SCREAMING_SNAKE_CASE , encoder_attention_mask=__SCREAMING_SNAKE_CASE , )
__UpperCAmelCase =self.pooler(encoder_outputs[0] )
__UpperCAmelCase =[output_layers[self.config.num_hidden_layers - 1](__SCREAMING_SNAKE_CASE )]
else:
__UpperCAmelCase =0
__UpperCAmelCase =None
__UpperCAmelCase =0
for i in range(self.config.num_hidden_layers ):
calculated_layer_num += 1
__UpperCAmelCase =self.encoder.adaptive_forward(
__SCREAMING_SNAKE_CASE , current_layer=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =self.pooler(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =output_layers[i](__SCREAMING_SNAKE_CASE )
if regression:
__UpperCAmelCase =logits.detach()
if patient_result is not None:
__UpperCAmelCase =patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
patient_counter += 1
else:
__UpperCAmelCase =0
else:
__UpperCAmelCase =logits.detach().argmax(dim=1 )
if patient_result is not None:
__UpperCAmelCase =patient_result.detach().argmax(dim=1 )
if (patient_result is not None) and torch.all(labels.eq(__SCREAMING_SNAKE_CASE ) ):
patient_counter += 1
else:
__UpperCAmelCase =0
__UpperCAmelCase =logits
if patient_counter == self.patience:
break
__UpperCAmelCase =[patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
'Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. ' , UpperCamelCase , )
class _A ( UpperCamelCase ):
"""simple docstring"""
def __init__( self : List[Any] , __SCREAMING_SNAKE_CASE : Any ) -> Dict:
super().__init__(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =config.num_labels
__UpperCAmelCase =BertModelWithPabee(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =nn.Dropout(config.hidden_dropout_prob )
__UpperCAmelCase =nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels ) for _ in range(config.num_hidden_layers )] )
self.init_weights()
@add_start_docstrings_to_model_forward(__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Optional[Any]=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : int=None , ) -> List[Any]:
__UpperCAmelCase =self.bert(
input_ids=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , position_ids=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE , inputs_embeds=__SCREAMING_SNAKE_CASE , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
__UpperCAmelCase =(logits[-1],)
if labels is not None:
__UpperCAmelCase =None
__UpperCAmelCase =0
for ix, logits_item in enumerate(__SCREAMING_SNAKE_CASE ):
if self.num_labels == 1:
# We are doing regression
__UpperCAmelCase =MSELoss()
__UpperCAmelCase =loss_fct(logits_item.view(-1 ) , labels.view(-1 ) )
else:
__UpperCAmelCase =CrossEntropyLoss()
__UpperCAmelCase =loss_fct(logits_item.view(-1 , self.num_labels ) , labels.view(-1 ) )
if total_loss is None:
__UpperCAmelCase =loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
__UpperCAmelCase =(total_loss / total_weights,) + outputs
return outputs
| 68
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class _A ( UpperCamelCase ):
"""simple docstring"""
lowerCamelCase : Tuple = 'ctrl'
lowerCamelCase : Any = ['past_key_values']
lowerCamelCase : Optional[int] = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any]=246534 , __SCREAMING_SNAKE_CASE : int=256 , __SCREAMING_SNAKE_CASE : Optional[Any]=1280 , __SCREAMING_SNAKE_CASE : Optional[Any]=8192 , __SCREAMING_SNAKE_CASE : int=48 , __SCREAMING_SNAKE_CASE : Union[str, Any]=16 , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : Dict=0.1 , __SCREAMING_SNAKE_CASE : List[Any]=1e-6 , __SCREAMING_SNAKE_CASE : List[str]=0.02 , __SCREAMING_SNAKE_CASE : Union[str, Any]=True , **__SCREAMING_SNAKE_CASE : int , ) -> Any:
__UpperCAmelCase =vocab_size
__UpperCAmelCase =n_positions
__UpperCAmelCase =n_embd
__UpperCAmelCase =n_layer
__UpperCAmelCase =n_head
__UpperCAmelCase =dff
__UpperCAmelCase =resid_pdrop
__UpperCAmelCase =embd_pdrop
__UpperCAmelCase =layer_norm_epsilon
__UpperCAmelCase =initializer_range
__UpperCAmelCase =use_cache
super().__init__(**__SCREAMING_SNAKE_CASE )
| 68
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {
"""configuration_mobilebert""": [
"""MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileBertConfig""",
"""MobileBertOnnxConfig""",
],
"""tokenization_mobilebert""": ["""MobileBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ["""MobileBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
"""MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MobileBertForMaskedLM""",
"""MobileBertForMultipleChoice""",
"""MobileBertForNextSentencePrediction""",
"""MobileBertForPreTraining""",
"""MobileBertForQuestionAnswering""",
"""MobileBertForSequenceClassification""",
"""MobileBertForTokenClassification""",
"""MobileBertLayer""",
"""MobileBertModel""",
"""MobileBertPreTrainedModel""",
"""load_tf_weights_in_mobilebert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
"""TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFMobileBertForMaskedLM""",
"""TFMobileBertForMultipleChoice""",
"""TFMobileBertForNextSentencePrediction""",
"""TFMobileBertForPreTraining""",
"""TFMobileBertForQuestionAnswering""",
"""TFMobileBertForSequenceClassification""",
"""TFMobileBertForTokenClassification""",
"""TFMobileBertMainLayer""",
"""TFMobileBertModel""",
"""TFMobileBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
UpperCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 562
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Tuple:
super().__init__()
# make sure scheduler can always be converted to DDIM
A__ = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE__ = 1 , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 0.0 , SCREAMING_SNAKE_CASE__ = 50 , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = "pil" , SCREAMING_SNAKE_CASE__ = True , ) -> Union[ImagePipelineOutput, Tuple]:
# Sample gaussian noise to begin loop
if isinstance(self.unet.config.sample_size , SCREAMING_SNAKE_CASE__ ):
A__ = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
A__ = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and len(SCREAMING_SNAKE_CASE__ ) != batch_size:
raise ValueError(
f"""You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__ )}, but requested an effective batch"""
f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
A__ = randn_tensor(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
A__ = self.unet(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
A__ = self.scheduler.step(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , eta=SCREAMING_SNAKE_CASE__ , use_clipped_model_output=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ ).prev_sample
A__ = (image / 2 + 0.5).clamp(0 , 1 )
A__ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
A__ = self.numpy_to_pil(SCREAMING_SNAKE_CASE__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__ )
| 562
| 1
|
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
__SCREAMING_SNAKE_CASE : str = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
__SCREAMING_SNAKE_CASE : Dict = '''
Args:
predictions (`list` of `int`): Predicted labels.
references (`list` of `int`): Ground truth labels.
labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.
- \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
- \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
- \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
- \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
sample_weight (`list` of `float`): Sample weights Defaults to None.
Returns:
f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.
Examples:
Example 1-A simple binary example
>>> f1_metric = datasets.load_metric("f1")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
>>> print(results)
{\'f1\': 0.5}
Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
>>> f1_metric = datasets.load_metric("f1")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
>>> print(round(results[\'f1\'], 2))
0.67
Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
>>> f1_metric = datasets.load_metric("f1")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
>>> print(round(results[\'f1\'], 2))
0.35
Example 4-A multiclass example, with different values for the `average` input.
>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
>>> print(round(results[\'f1\'], 2))
0.27
>>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
>>> print(round(results[\'f1\'], 2))
0.33
>>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
>>> print(round(results[\'f1\'], 2))
0.27
>>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{\'f1\': array([0.8, 0. , 0. ])}
'''
__SCREAMING_SNAKE_CASE : Tuple = '''
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def _UpperCAmelCase ( self : List[Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("int32" ) ),
"references": datasets.Sequence(datasets.Value("int32" ) ),
}
if self.config_name == "multilabel"
else {
"predictions": datasets.Value("int32" ),
"references": datasets.Value("int32" ),
} ) , reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"] , )
def _UpperCAmelCase ( self : Tuple , lowerCAmelCase : int , lowerCAmelCase : Dict , lowerCAmelCase : List[Any]=None , lowerCAmelCase : List[str]=1 , lowerCAmelCase : Tuple="binary" , lowerCAmelCase : Dict=None ):
A_ = fa_score(
lowerCAmelCase , lowerCAmelCase , labels=lowerCAmelCase , pos_label=lowerCAmelCase , average=lowerCAmelCase , sample_weight=lowerCAmelCase )
return {"f1": float(lowerCAmelCase ) if score.size == 1 else score}
| 452
|
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCamelCase( lowercase__ ) -> List[Any]:
'''simple docstring'''
if not is_accelerate_available():
return method
__lowercase= version.parse(accelerate.__version__ ).base_version
if version.parse(lowercase__ ) < version.parse('0.17.0' ):
return method
def wrapper(self , *lowercase__ , **lowercase__ ):
if hasattr(self , '_hf_hook' ) and hasattr(self._hf_hook , 'pre_forward' ):
self._hf_hook.pre_forward(self )
return method(self , *lowercase__ , **lowercase__ )
return wrapper
| 230
| 0
|
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 35
|
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=[1, 2, 1] , _SCREAMING_SNAKE_CASE=[2, 2, 4] , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=2.0 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=1e-5 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=10 , _SCREAMING_SNAKE_CASE=8 , _SCREAMING_SNAKE_CASE=["stage1", "stage2", "stage3"] , _SCREAMING_SNAKE_CASE=[1, 2, 3] , ) -> Any:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = image_size
UpperCamelCase = patch_size
UpperCamelCase = num_channels
UpperCamelCase = embed_dim
UpperCamelCase = depths
UpperCamelCase = num_heads
UpperCamelCase = window_size
UpperCamelCase = mlp_ratio
UpperCamelCase = qkv_bias
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = drop_path_rate
UpperCamelCase = hidden_act
UpperCamelCase = use_absolute_embeddings
UpperCamelCase = patch_norm
UpperCamelCase = layer_norm_eps
UpperCamelCase = initializer_range
UpperCamelCase = is_training
UpperCamelCase = scope
UpperCamelCase = use_labels
UpperCamelCase = type_sequence_label_size
UpperCamelCase = encoder_stride
UpperCamelCase = out_features
UpperCamelCase = out_indices
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = self.get_config()
return config, pixel_values, labels
def A__ ( self ) -> str:
"""simple docstring"""
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = MaskFormerSwinModel(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
UpperCamelCase = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
UpperCamelCase = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
UpperCamelCase = MaskFormerSwinBackbone(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = ["""stem"""]
UpperCamelCase = MaskFormerSwinBackbone(config=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = config_and_inputs
UpperCamelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
lowercase = {"""feature-extraction""": MaskFormerSwinModel} if is_torch_available() else {}
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = MaskFormerSwinModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
"""`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
""" `nn.DataParallel`"""
) )
def A__ ( self ) -> List[str]:
"""simple docstring"""
pass
def A__ ( self ) -> Dict:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A__ ( self ) -> int:
"""simple docstring"""
return
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_SCREAMING_SNAKE_CASE )
@unittest.skip("""Swin does not use inputs_embeds""" )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip("""Swin does not support feedforward chunking""" )
def A__ ( self ) -> Dict:
"""simple docstring"""
pass
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE , nn.Linear ) )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
UpperCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase = [*signature.parameters.keys()]
UpperCamelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _SCREAMING_SNAKE_CASE )
@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions""" )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
pass
@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone""" )
def A__ ( self ) -> List[str]:
"""simple docstring"""
pass
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
UpperCamelCase = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
UpperCamelCase = outputs.hidden_states
UpperCamelCase = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
# Swin has a different seq_length
UpperCamelCase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
UpperCamelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
UpperCamelCase = True
self.check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCamelCase = True
self.check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = 3
UpperCamelCase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
UpperCamelCase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
UpperCamelCase = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
UpperCamelCase = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
UpperCamelCase = True
self.check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCamelCase = True
self.check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , (padded_height, padded_width) )
@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints""" )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def A__ ( self ) -> str:
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
pass
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = 0
return t
def check_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE={} ):
with torch.no_grad():
UpperCamelCase = model(**_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase = model(**_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ).to_tuple()
def recursive_check(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if isinstance(_SCREAMING_SNAKE_CASE , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
recursive_check(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(_SCREAMING_SNAKE_CASE ) , set_nan_tensor_to_zero(_SCREAMING_SNAKE_CASE ) , atol=1e-5 ) , msg=(
"""Tuple and dict output are not equal. Difference:"""
F" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"
F" {torch.isnan(_SCREAMING_SNAKE_CASE ).any()} and `inf`: {torch.isinf(_SCREAMING_SNAKE_CASE )}. Dict has"
F" `nan`: {torch.isnan(_SCREAMING_SNAKE_CASE ).any()} and `inf`: {torch.isinf(_SCREAMING_SNAKE_CASE )}."
) , )
recursive_check(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
check_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , return_labels=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , return_labels=_SCREAMING_SNAKE_CASE )
check_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
check_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , {"""output_hidden_states""": True} )
UpperCamelCase = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , return_labels=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , return_labels=_SCREAMING_SNAKE_CASE )
check_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , {"""output_hidden_states""": True} )
@require_torch
class a_ ( unittest.TestCase , lowerCamelCase ):
lowercase = (MaskFormerSwinBackbone,) if is_torch_available() else ()
lowercase = MaskFormerSwinConfig
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = MaskFormerSwinModelTester(self )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = inputs_dict["""pixel_values"""].shape[0]
for backbone_class in self.all_model_classes:
UpperCamelCase = backbone_class(_SCREAMING_SNAKE_CASE )
backbone.to(_SCREAMING_SNAKE_CASE )
backbone.eval()
UpperCamelCase = backbone(**_SCREAMING_SNAKE_CASE )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , _SCREAMING_SNAKE_CASE )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
UpperCamelCase = backbone(**_SCREAMING_SNAKE_CASE , output_hidden_states=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
UpperCamelCase = backbone(**_SCREAMING_SNAKE_CASE , output_attentions=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(outputs.attentions )
| 35
| 1
|
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
"facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __a ( _snake_case ):
__UpperCamelCase : str = 'esm'
def __init__( self : Tuple ,lowerCamelCase : List[Any]=None ,lowerCamelCase : str=None ,lowerCamelCase : Any=None ,lowerCamelCase : Union[str, Any]=768 ,lowerCamelCase : Tuple=12 ,lowerCamelCase : int=12 ,lowerCamelCase : Optional[int]=3072 ,lowerCamelCase : List[Any]=0.1 ,lowerCamelCase : Optional[int]=0.1 ,lowerCamelCase : Any=1026 ,lowerCamelCase : str=0.02 ,lowerCamelCase : int=1E-1_2 ,lowerCamelCase : Union[str, Any]="absolute" ,lowerCamelCase : Optional[Any]=True ,lowerCamelCase : str=None ,lowerCamelCase : Optional[int]=False ,lowerCamelCase : int=False ,lowerCamelCase : Union[str, Any]=None ,lowerCamelCase : Any=None ,**lowerCamelCase : Any ,):
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase ,mask_token_id=lowerCamelCase ,**lowerCamelCase )
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = position_embedding_type
__SCREAMING_SNAKE_CASE = use_cache
__SCREAMING_SNAKE_CASE = emb_layer_norm_before
__SCREAMING_SNAKE_CASE = token_dropout
__SCREAMING_SNAKE_CASE = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("""No esmfold_config supplied for folding model, using default values.""" )
__SCREAMING_SNAKE_CASE = EsmFoldConfig()
elif isinstance(lowerCamelCase ,lowerCamelCase ):
__SCREAMING_SNAKE_CASE = EsmFoldConfig(**lowerCamelCase )
__SCREAMING_SNAKE_CASE = esmfold_config
if vocab_list is None:
logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!""" )
__SCREAMING_SNAKE_CASE = get_default_vocab_list()
else:
__SCREAMING_SNAKE_CASE = vocab_list
else:
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
if self.esmfold_config is not None and getattr(self.esmfold_config ,"""use_esm_attn_map""" ,lowerCamelCase ):
raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!""" )
def UpperCAmelCase__ ( self : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = super().to_dict()
if isinstance(self.esmfold_config ,lowerCamelCase ):
__SCREAMING_SNAKE_CASE = self.esmfold_config.to_dict()
return output
@dataclass
class __a :
__UpperCamelCase : str = None
__UpperCamelCase : bool = True
__UpperCamelCase : bool = False
__UpperCamelCase : bool = False
__UpperCamelCase : bool = False
__UpperCamelCase : float = 0
__UpperCamelCase : bool = True
__UpperCamelCase : bool = False
__UpperCamelCase : int = 128
__UpperCamelCase : "TrunkConfig" = None
def UpperCAmelCase__ ( self : List[str] ):
'''simple docstring'''
if self.trunk is None:
__SCREAMING_SNAKE_CASE = TrunkConfig()
elif isinstance(self.trunk ,lowerCamelCase ):
__SCREAMING_SNAKE_CASE = TrunkConfig(**self.trunk )
def UpperCAmelCase__ ( self : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = asdict(self )
__SCREAMING_SNAKE_CASE = self.trunk.to_dict()
return output
@dataclass
class __a :
__UpperCamelCase : int = 48
__UpperCamelCase : int = 1024
__UpperCamelCase : int = 128
__UpperCamelCase : int = 32
__UpperCamelCase : int = 32
__UpperCamelCase : int = 32
__UpperCamelCase : float = 0
__UpperCamelCase : float = 0
__UpperCamelCase : bool = False
__UpperCamelCase : int = 4
__UpperCamelCase : Optional[int] = 128
__UpperCamelCase : "StructureModuleConfig" = None
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
if self.structure_module is None:
__SCREAMING_SNAKE_CASE = StructureModuleConfig()
elif isinstance(self.structure_module ,lowerCamelCase ):
__SCREAMING_SNAKE_CASE = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f"""`max_recycles` should be positive, got {self.max_recycles}.""" )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"""`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
f""" {self.sequence_state_dim} and {self.sequence_state_dim}.""" )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"""`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
f""" {self.pairwise_state_dim} and {self.pairwise_state_dim}.""" )
__SCREAMING_SNAKE_CASE = self.sequence_state_dim // self.sequence_head_width
__SCREAMING_SNAKE_CASE = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"""`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
f""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.""" )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"""`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
f""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.""" )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.""" )
if self.dropout >= 0.4:
raise ValueError(f"""`dropout` should not be greater than 0.4, got {self.dropout}.""" )
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = asdict(self )
__SCREAMING_SNAKE_CASE = self.structure_module.to_dict()
return output
@dataclass
class __a :
__UpperCamelCase : int = 384
__UpperCamelCase : int = 128
__UpperCamelCase : int = 16
__UpperCamelCase : int = 128
__UpperCamelCase : int = 12
__UpperCamelCase : int = 4
__UpperCamelCase : int = 8
__UpperCamelCase : float = 0.1
__UpperCamelCase : int = 8
__UpperCamelCase : int = 1
__UpperCamelCase : int = 2
__UpperCamelCase : int = 7
__UpperCamelCase : int = 10
__UpperCamelCase : float = 1E-8
__UpperCamelCase : float = 1E5
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
return asdict(self )
def __magic_name__ ( ) -> Dict:
'''simple docstring'''
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 109
|
from collections import deque
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = len(_A )
lowerCAmelCase_ = deque()
lowerCAmelCase_ = [False for _ in range(_A )]
lowerCAmelCase_ = [-1 for _ in range(_A )]
lowerCAmelCase_ = index_of[:]
def strong_connect(_A , _A , _A ):
lowerCAmelCase_ = index # the number when this node is seen
lowerCAmelCase_ = index # lowest rank node reachable from here
index += 1
stack.append(_A )
lowerCAmelCase_ = True
for w in g[v]:
if index_of[w] == -1:
lowerCAmelCase_ = strong_connect(_A , _A , _A )
lowerCAmelCase_ = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
lowerCAmelCase_ = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
lowerCAmelCase_ = []
lowerCAmelCase_ = stack.pop()
lowerCAmelCase_ = False
component.append(_A )
while w != v:
lowerCAmelCase_ = stack.pop()
lowerCAmelCase_ = False
component.append(_A )
components.append(_A )
return index
lowerCAmelCase_ = []
for v in range(_A ):
if index_of[v] == -1:
strong_connect(_A , 0 , _A )
return components
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = [[] for _ in range(_A )]
for u, v in edges:
g[u].append(_A )
return g
if __name__ == "__main__":
# Test
_A = 7
_A = [0, 0, 1, 2, 3, 3, 4, 4, 6]
_A = [1, 3, 2, 0, 1, 4, 5, 6, 5]
_A = [(u, v) for u, v in zip(source, target)]
_A = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 431
| 0
|
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
class _lowercase ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = ['''input_features''', '''attention_mask''']
def __init__( self ,lowerCamelCase_=80 ,lowerCamelCase_=16000 ,lowerCamelCase_=80 ,lowerCamelCase_=0.0 ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=True ,**lowerCamelCase_ ,) -> Tuple:
'''simple docstring'''
super().__init__(feature_size=lowerCamelCase_ ,sampling_rate=lowerCamelCase_ ,padding_value=lowerCamelCase_ ,**lowerCamelCase_ )
UpperCAmelCase__ : List[Any] = num_mel_bins
UpperCAmelCase__ : Dict = do_ceptral_normalize
UpperCAmelCase__ : List[str] = normalize_means
UpperCAmelCase__ : List[str] = normalize_vars
UpperCAmelCase__ : Any = True
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,) -> np.ndarray:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = waveform * (2**15) # Kaldi compliance: 16-bit signed integers
UpperCAmelCase__ : List[Any] = torch.from_numpy(lowerCamelCase_ ).unsqueeze(0 )
UpperCAmelCase__ : int = ta_kaldi.fbank(lowerCamelCase_ ,num_mel_bins=self.num_mel_bins ,sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def lowerCAmelCase__ ( lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = True ,lowerCamelCase_ = True ,lowerCamelCase_ = 0.0 ,) -> np.ndarray:
'''simple docstring'''
if normalize_means:
UpperCAmelCase__ : List[str] = x[:input_length].mean(axis=0 )
UpperCAmelCase__ : Tuple = np.subtract(lowerCamelCase_ ,lowerCamelCase_ )
if normalize_vars:
UpperCAmelCase__ : Dict = x[:input_length].std(axis=0 )
UpperCAmelCase__ : List[str] = np.divide(lowerCamelCase_ ,lowerCamelCase_ )
if input_length < x.shape[0]:
UpperCAmelCase__ : Optional[Any] = padding_value
# make sure array is in float32
UpperCAmelCase__ : Optional[Any] = x.astype(np.floataa )
return x
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ) -> List[np.ndarray]:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(lowerCamelCase_ ,lowerCamelCase_ ,self.normalize_means ,self.normalize_vars ,self.padding_value )
for x, n in zip(lowerCamelCase_ ,lowerCamelCase_ )
]
def __call__( self ,lowerCamelCase_ ,lowerCamelCase_ = False ,lowerCamelCase_ = None ,lowerCamelCase_ = False ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,**lowerCamelCase_ ,) -> BatchFeature:
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
f''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
UpperCAmelCase__ : List[str] = isinstance(lowerCamelCase_ ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' )
UpperCAmelCase__ : Any = is_batched_numpy or (
isinstance(lowerCamelCase_ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ : Any = [np.asarray(lowerCamelCase_ ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCamelCase_ ,np.ndarray ):
UpperCAmelCase__ : Dict = np.asarray(lowerCamelCase_ ,dtype=np.floataa )
elif isinstance(lowerCamelCase_ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : List[Any] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ : Any = [raw_speech]
# extract fbank features
UpperCAmelCase__ : List[Any] = [self._extract_fbank_features(lowerCamelCase_ ) for waveform in raw_speech]
# convert into correct format for padding
UpperCAmelCase__ : Union[str, Any] = BatchFeature({'''input_features''': features} )
UpperCAmelCase__ : Any = self.pad(
lowerCamelCase_ ,padding=lowerCamelCase_ ,max_length=lowerCamelCase_ ,truncation=lowerCamelCase_ ,pad_to_multiple_of=lowerCamelCase_ ,return_attention_mask=lowerCamelCase_ ,**lowerCamelCase_ ,)
# make sure list is in array format
UpperCAmelCase__ : List[Any] = padded_inputs.get('''input_features''' )
if isinstance(input_features[0] ,lowerCamelCase_ ):
UpperCAmelCase__ : Union[str, Any] = [np.asarray(lowerCamelCase_ ,dtype=np.floataa ) for feature in input_features]
UpperCAmelCase__ : List[Any] = padded_inputs.get('''attention_mask''' )
if attention_mask is not None:
UpperCAmelCase__ : List[Any] = [np.asarray(lowerCamelCase_ ,dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
UpperCAmelCase__ : Optional[Any] = (
np.array(lowerCamelCase_ ,dtype=np.intaa )
if self._get_padding_strategies(lowerCamelCase_ ,max_length=lowerCamelCase_ ) is not PaddingStrategy.DO_NOT_PAD
else None
)
UpperCAmelCase__ : Any = self.normalize(
padded_inputs['''input_features'''] ,attention_mask=lowerCamelCase_ )
if return_tensors is not None:
UpperCAmelCase__ : List[str] = padded_inputs.convert_to_tensors(lowerCamelCase_ )
return padded_inputs
| 704
|
'''simple docstring'''
import unittest
import numpy as np
def __UpperCamelCase( _A : np.ndarray , _A : np.ndarray , _A : np.ndarray , _A : np.ndarray | None = None , ):
'''simple docstring'''
UpperCAmelCase__ : Any = np.shape(_A )
UpperCAmelCase__ : List[Any] = np.shape(_A )
UpperCAmelCase__ : Tuple = np.shape(_A )
if shape_a[0] != shape_b[0]:
UpperCAmelCase__ : Any = (
'''Expected the same number of rows for A and B. '''
F'''Instead found A of size {shape_a} and B of size {shape_b}'''
)
raise ValueError(_A )
if shape_b[1] != shape_c[1]:
UpperCAmelCase__ : List[str] = (
'''Expected the same number of columns for B and C. '''
F'''Instead found B of size {shape_b} and C of size {shape_c}'''
)
raise ValueError(_A )
UpperCAmelCase__ : Optional[Any] = pseudo_inv
if a_inv is None:
try:
UpperCAmelCase__ : str = np.linalg.inv(_A )
except np.linalg.LinAlgError:
raise ValueError(
'''Input matrix A is not invertible. Cannot compute Schur complement.''' )
return mat_c - mat_b.T @ a_inv @ mat_b
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self ) -> None:
'''simple docstring'''
UpperCAmelCase__ : int = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCAmelCase__ : List[Any] = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCAmelCase__ : Optional[Any] = np.array([[2, 1], [6, 3]] )
UpperCAmelCase__ : Tuple = schur_complement(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ )
UpperCAmelCase__ : Tuple = np.block([[a, b], [b.T, c]] )
UpperCAmelCase__ : int = np.linalg.det(lowerCamelCase_ )
UpperCAmelCase__ : Optional[Any] = np.linalg.det(lowerCamelCase_ )
UpperCAmelCase__ : Union[str, Any] = np.linalg.det(lowerCamelCase_ )
self.assertAlmostEqual(lowerCamelCase_ ,det_a * det_s )
def lowerCAmelCase__ ( self ) -> None:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCAmelCase__ : Tuple = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCAmelCase__ : Tuple = np.array([[2, 1], [6, 3]] )
with self.assertRaises(lowerCamelCase_ ):
schur_complement(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> None:
'''simple docstring'''
UpperCAmelCase__ : Dict = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCAmelCase__ : str = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCAmelCase__ : Any = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(lowerCamelCase_ ):
schur_complement(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 496
| 0
|
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