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stringlengths 82
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| code_codestyle
int64 0
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| style_context
stringlengths 91
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| style_context_codestyle
int64 0
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import math
def a__ ( snake_case , snake_case ):
"""simple docstring"""
if (
not isinstance(SCREAMING_SNAKE_CASE_ , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('''power_factor must be a valid float value between -1 and 1.''' )
return apparent_power * power_factor
def a__ ( snake_case , snake_case ):
"""simple docstring"""
if (
not isinstance(SCREAMING_SNAKE_CASE_ , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('''power_factor must be a valid float value between -1 and 1.''' )
return apparent_power * math.sqrt(1 - power_factor**2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 74
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__snake_case = {
"""configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
"""tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
"""GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GPTNeoXJapaneseForCausalLM""",
"""GPTNeoXJapaneseLayer""",
"""GPTNeoXJapaneseModel""",
"""GPTNeoXJapanesePreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 451
| 0
|
def snake_case (__lowercase ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
_snake_case : Tuple = 4
_snake_case : str = (1 << p) - 1
for _ in range(p - 2 ):
_snake_case : List[str] = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(1_1))
| 704
|
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
AccelerateTestCase,
TempDirTestCase,
execute_subprocess_async,
require_cuda,
require_fsdp,
require_multi_gpu,
slow,
)
from accelerate.utils.constants import (
FSDP_AUTO_WRAP_POLICY,
FSDP_BACKWARD_PREFETCH,
FSDP_SHARDING_STRATEGY,
FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment
set_seed(4_2)
__SCREAMING_SNAKE_CASE : int = 'bert-base-cased'
__SCREAMING_SNAKE_CASE : Union[str, Any] = 'fp16'
__SCREAMING_SNAKE_CASE : str = 'bf16'
__SCREAMING_SNAKE_CASE : Optional[int] = [FPaa, BFaa]
@require_fsdp
@require_cuda
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
super().setUp()
_snake_case : Optional[int] = dict(
ACCELERATE_USE_FSDP="true" , MASTER_ADDR="localhost" , MASTER_PORT="10999" , RANK="0" , LOCAL_RANK="0" , WORLD_SIZE="1" , )
def UpperCamelCase ( self ):
from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
for i, strategy in enumerate(lowercase_ ):
_snake_case : Optional[Any] = self.dist_env.copy()
_snake_case : List[str] = f"""{i + 1}"""
_snake_case : int = strategy
with mockenv_context(**lowercase_ ):
_snake_case : Any = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.sharding_strategy , ShardingStrategy(i + 1 ) )
def UpperCamelCase ( self ):
from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch
for i, prefetch_policy in enumerate(lowercase_ ):
_snake_case : List[str] = self.dist_env.copy()
_snake_case : List[Any] = prefetch_policy
with mockenv_context(**lowercase_ ):
_snake_case : List[str] = FullyShardedDataParallelPlugin()
if prefetch_policy == "NO_PREFETCH":
self.assertIsNone(fsdp_plugin.backward_prefetch )
else:
self.assertEqual(fsdp_plugin.backward_prefetch , BackwardPrefetch(i + 1 ) )
def UpperCamelCase ( self ):
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
for i, state_dict_type in enumerate(lowercase_ ):
_snake_case : str = self.dist_env.copy()
_snake_case : List[str] = state_dict_type
with mockenv_context(**lowercase_ ):
_snake_case : List[Any] = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.state_dict_type , StateDictType(i + 1 ) )
if state_dict_type == "FULL_STATE_DICT":
self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu )
self.assertTrue(fsdp_plugin.state_dict_config.ranka_only )
def UpperCamelCase ( self ):
_snake_case : Tuple = AutoModel.from_pretrained(lowercase_ )
for policy in FSDP_AUTO_WRAP_POLICY:
_snake_case : Optional[Any] = self.dist_env.copy()
_snake_case : List[str] = policy
if policy == "TRANSFORMER_BASED_WRAP":
_snake_case : List[str] = "BertLayer"
elif policy == "SIZE_BASED_WRAP":
_snake_case : str = "2000"
with mockenv_context(**lowercase_ ):
_snake_case : List[str] = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(lowercase_ )
if policy == "NO_WRAP":
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
else:
self.assertIsNotNone(fsdp_plugin.auto_wrap_policy )
_snake_case : str = self.dist_env.copy()
_snake_case : Tuple = "TRANSFORMER_BASED_WRAP"
_snake_case : Union[str, Any] = "T5Layer"
with mockenv_context(**lowercase_ ):
_snake_case : Optional[int] = FullyShardedDataParallelPlugin()
with self.assertRaises(lowercase_ ) as cm:
fsdp_plugin.set_auto_wrap_policy(lowercase_ )
self.assertTrue("Could not find the transformer layer class to wrap in the model." in str(cm.exception ) )
_snake_case : str = self.dist_env.copy()
_snake_case : Any = "SIZE_BASED_WRAP"
_snake_case : str = "0"
with mockenv_context(**lowercase_ ):
_snake_case : Optional[int] = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(lowercase_ )
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
def UpperCamelCase ( self ):
from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
for mp_dtype in dtypes:
_snake_case : Union[str, Any] = self.dist_env.copy()
_snake_case : int = mp_dtype
with mockenv_context(**lowercase_ ):
_snake_case : str = Accelerator()
if mp_dtype == "fp16":
_snake_case : List[str] = torch.floataa
elif mp_dtype == "bf16":
_snake_case : Any = torch.bfloataa
_snake_case : Dict = MixedPrecision(param_dtype=lowercase_ , reduce_dtype=lowercase_ , buffer_dtype=lowercase_ )
self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy , lowercase_ )
if mp_dtype == FPaa:
self.assertTrue(isinstance(accelerator.scaler , lowercase_ ) )
elif mp_dtype == BFaa:
self.assertIsNone(accelerator.scaler )
AcceleratorState._reset_state(lowercase_ )
def UpperCamelCase ( self ):
from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload
for flag in [True, False]:
_snake_case : Union[str, Any] = self.dist_env.copy()
_snake_case : Tuple = str(lowercase_ ).lower()
with mockenv_context(**lowercase_ ):
_snake_case : Dict = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.cpu_offload , CPUOffload(offload_params=lowercase_ ) )
@require_fsdp
@require_multi_gpu
@slow
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
super().setUp()
_snake_case : Dict = 0.82
_snake_case : str = [
"fsdp_shard_grad_op_transformer_based_wrap",
"fsdp_full_shard_transformer_based_wrap",
]
_snake_case : Tuple = {
"multi_gpu_fp16": 3_200,
"fsdp_shard_grad_op_transformer_based_wrap_fp16": 2_000,
"fsdp_full_shard_transformer_based_wrap_fp16": 1_900,
# Disabling below test as it overwhelms the RAM memory usage
# on CI self-hosted runner leading to tests getting killed.
# "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500, # fp16 was leading to indefinite hang
}
_snake_case : Tuple = 160
_snake_case : Optional[int] = 160
_snake_case : Optional[Any] = inspect.getfile(accelerate.test_utils )
_snake_case : Tuple = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "external_deps"] )
def UpperCamelCase ( self ):
_snake_case : Optional[int] = os.path.join(self.test_scripts_folder , "test_performance.py" )
_snake_case : int = ["accelerate", "launch", "--num_processes=2", "--num_machines=1", "--machine_rank=0", "--use_fsdp"]
for config in self.performance_configs:
_snake_case : str = cmd.copy()
for i, strategy in enumerate(lowercase_ ):
if strategy.lower() in config:
cmd_config.append(f"""--fsdp_sharding_strategy={i+1}""" )
break
if "fp32" in config:
cmd_config.append("--mixed_precision=no" )
else:
cmd_config.append("--mixed_precision=fp16" )
if "cpu_offload" in config:
cmd_config.append("--fsdp_offload_params=True" )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in config:
cmd_config.append(f"""--fsdp_auto_wrap_policy={policy}""" )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append("--fsdp_transformer_layer_cls_to_wrap=BertLayer" )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append("--fsdp_min_num_params=2000" )
cmd_config.extend(
[
self.test_file_path,
f"""--output_dir={self.tmpdir}""",
f"""--performance_lower_bound={self.performance_lower_bound}""",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowercase_ , env=os.environ.copy() )
def UpperCamelCase ( self ):
_snake_case : Tuple = os.path.join(self.test_scripts_folder , "test_checkpointing.py" )
_snake_case : str = [
"accelerate",
"launch",
"--num_processes=2",
"--num_machines=1",
"--machine_rank=0",
"--use_fsdp",
"--mixed_precision=fp16",
"--fsdp_transformer_layer_cls_to_wrap=BertLayer",
]
for i, strategy in enumerate(lowercase_ ):
_snake_case : str = cmd.copy()
cmd_config.append(f"""--fsdp_sharding_strategy={i+1}""" )
if strategy != "FULL_SHARD":
continue
_snake_case : int = len(lowercase_ )
for state_dict_type in FSDP_STATE_DICT_TYPE:
_snake_case : int = cmd_config[:state_dict_config_index]
cmd_config.append(f"""--fsdp_state_dict_type={state_dict_type}""" )
cmd_config.extend(
[
self.test_file_path,
f"""--output_dir={self.tmpdir}""",
"--partial_train_epoch=1",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowercase_ , env=os.environ.copy() )
_snake_case : Union[str, Any] = cmd_config[:-1]
_snake_case : Dict = os.path.join(self.tmpdir , "epoch_0" )
cmd_config.extend(
[
f"""--resume_from_checkpoint={resume_from_checkpoint}""",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowercase_ , env=os.environ.copy() )
def UpperCamelCase ( self ):
_snake_case : List[Any] = os.path.join(self.test_scripts_folder , "test_peak_memory_usage.py" )
_snake_case : Any = [
"accelerate",
"launch",
"--num_processes=2",
"--num_machines=1",
"--machine_rank=0",
]
for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
_snake_case : Tuple = cmd.copy()
if "fp16" in spec:
cmd_config.extend(["--mixed_precision=fp16"] )
else:
cmd_config.extend(["--mixed_precision=no"] )
if "multi_gpu" in spec:
continue
else:
cmd_config.extend(["--use_fsdp"] )
for i, strategy in enumerate(lowercase_ ):
if strategy.lower() in spec:
cmd_config.append(f"""--fsdp_sharding_strategy={i+1}""" )
break
if "cpu_offload" in spec:
cmd_config.append("--fsdp_offload_params=True" )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in spec:
cmd_config.append(f"""--fsdp_auto_wrap_policy={policy}""" )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append("--fsdp_transformer_layer_cls_to_wrap=BertLayer" )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append("--fsdp_min_num_params=2000" )
cmd_config.extend(
[
self.test_file_path,
f"""--output_dir={self.tmpdir}""",
f"""--peak_memory_upper_bound={peak_mem_upper_bound}""",
f"""--n_train={self.n_train}""",
f"""--n_val={self.n_val}""",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowercase_ , env=os.environ.copy() )
| 580
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ : List[str] = {"processing_layoutxlm": ["LayoutXLMProcessor"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[Any] = ["LayoutXLMTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : str = ["LayoutXLMTokenizerFast"]
if TYPE_CHECKING:
from .processing_layoutxlm import LayoutXLMProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm import LayoutXLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
else:
import sys
UpperCAmelCase_ : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21
|
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 : Dict = random.Random()
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__=1.0 , UpperCamelCase__=None , UpperCamelCase__=None ) -> Any:
'''simple docstring'''
if rng is None:
UpperCAmelCase = global_rng
UpperCAmelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class A_ (unittest.TestCase ):
def __init__( self , _A , _A=7 , _A=4_0_0 , _A=2_0_0_0 , _A=1 , _A=0.0 , _A=1_6_0_0_0 , _A=True , _A=True , ):
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = min_seq_length
UpperCAmelCase = max_seq_length
UpperCAmelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCAmelCase = feature_size
UpperCAmelCase = padding_value
UpperCAmelCase = sampling_rate
UpperCAmelCase = return_attention_mask
UpperCAmelCase = do_normalize
def _lowercase ( self ):
'''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 _lowercase ( self , _A=False , _A=False ):
'''simple docstring'''
def _flatten(_A ):
return list(itertools.chain(*_A ) )
if equal_length:
UpperCAmelCase = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
UpperCAmelCase = [
_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:
UpperCAmelCase = [np.asarray(_A ) for x in speech_inputs]
return speech_inputs
class A_ (a_ , unittest.TestCase ):
UpperCAmelCase__ = WavaVecaFeatureExtractor
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = WavaVecaFeatureExtractionTester(self )
def _lowercase ( self , _A ):
'''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 _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCAmelCase = [np.asarray(_A ) for speech_input in speech_inputs]
# Test not batched input
UpperCAmelCase = feat_extract(speech_inputs[0] , return_tensors='''np''' ).input_values
UpperCAmelCase = feat_extract(np_speech_inputs[0] , return_tensors='''np''' ).input_values
self.assertTrue(np.allclose(_A , _A , atol=1E-3 ) )
# Test batched
UpperCAmelCase = feat_extract(_A , return_tensors='''np''' ).input_values
UpperCAmelCase = 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.
UpperCAmelCase = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
UpperCAmelCase = np.asarray(_A )
UpperCAmelCase = feat_extract(_A , return_tensors='''np''' ).input_values
UpperCAmelCase = 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 _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCAmelCase = ['''longest''', '''max_length''', '''do_not_pad''']
UpperCAmelCase = [None, 1_6_0_0, None]
for max_length, padding in zip(_A , _A ):
UpperCAmelCase = feat_extract(_A , padding=_A , max_length=_A , return_tensors='''np''' )
UpperCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCAmelCase = range(8_0_0 , 1_4_0_0 , 2_0_0 )
UpperCAmelCase = [floats_list((1, x) )[0] for x in lengths]
UpperCAmelCase = ['''longest''', '''max_length''', '''do_not_pad''']
UpperCAmelCase = [None, 1_6_0_0, None]
for max_length, padding in zip(_A , _A ):
UpperCAmelCase = feat_extract(_A , max_length=_A , padding=_A )
UpperCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCAmelCase = feat_extract(
_A , truncation=_A , max_length=1_0_0_0 , padding='''max_length''' , return_tensors='''np''' )
UpperCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCAmelCase = feat_extract(
_A , truncation=_A , max_length=1_0_0_0 , padding='''longest''' , return_tensors='''np''' )
UpperCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
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, 1_0_0_0) )
UpperCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCAmelCase = feat_extract(
_A , truncation=_A , max_length=2_0_0_0 , padding='''longest''' , return_tensors='''np''' )
UpperCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
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, 1_2_0_0) )
@require_torch
def _lowercase ( self ):
'''simple docstring'''
import torch
UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCAmelCase = np.random.rand(1_0_0 ).astype(np.floataa )
UpperCAmelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
UpperCAmelCase = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''np''' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
UpperCAmelCase = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''pt''' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
@slow
@require_torch
def _lowercase ( self ):
'''simple docstring'''
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
UpperCAmelCase = WavaVecaConfig.from_pretrained(_A )
UpperCAmelCase = 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''' )
| 130
| 0
|
"""simple docstring"""
import os
def __lowerCAmelCase ( ) -> List[Any]:
"""simple docstring"""
with open(os.path.dirname(lowercase ) + "/grid.txt" ) as f:
snake_case : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(lowercase ) for x in f.readline().split()] )
snake_case : Any = 0
# right
for i in range(20 ):
for j in range(17 ):
snake_case : Optional[Any] = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
snake_case : List[Any] = temp
# down
for i in range(17 ):
for j in range(20 ):
snake_case : Optional[Any] = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
snake_case : Any = temp
# diagonal 1
for i in range(17 ):
for j in range(17 ):
snake_case : Optional[Any] = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
snake_case : Tuple = temp
# diagonal 2
for i in range(17 ):
for j in range(3 , 20 ):
snake_case : str = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
snake_case : int = temp
return maximum
if __name__ == "__main__":
print(solution())
| 117
|
"""simple docstring"""
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
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.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class _lowerCAmelCase ( snake_case_ ):
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
snake_case : List[Any] = tempfile.mkdtemp()
snake_case : Union[str, Any] = 8
# DPR tok
snake_case : str = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
snake_case : Union[str, Any] = os.path.join(self.tmpdirname , "dpr_tokenizer" )
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
snake_case : Union[str, Any] = os.path.join(UpperCamelCase__ , 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
snake_case : Union[str, Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
snake_case : Tuple = dict(zip(UpperCamelCase__ , range(len(UpperCamelCase__ ) ) ) )
snake_case : List[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
snake_case : Optional[Any] = {"unk_token": "<unk>"}
snake_case : Tuple = os.path.join(self.tmpdirname , "bart_tokenizer" )
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
snake_case : Optional[Any] = os.path.join(UpperCamelCase__ , BART_VOCAB_FILES_NAMES["vocab_file"] )
snake_case : int = os.path.join(UpperCamelCase__ , BART_VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(UpperCamelCase__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(UpperCamelCase__ ) )
def lowerCamelCase ( self ) -> DPRQuestionEncoderTokenizer:
'''simple docstring'''
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , "dpr_tokenizer" ) )
def lowerCamelCase ( self ) -> DPRContextEncoderTokenizer:
'''simple docstring'''
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , "dpr_tokenizer" ) )
def lowerCamelCase ( self ) -> BartTokenizer:
'''simple docstring'''
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , "bart_tokenizer" ) )
def lowerCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCamelCase ( self ) -> Dict:
'''simple docstring'''
snake_case : Dict = Dataset.from_dict(
{
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index("embeddings" , string_factory="Flat" , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def lowerCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case : List[str] = self.get_dummy_dataset()
snake_case : int = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch("transformers.models.rag.retrieval_rag.load_dataset" ) as mock_load_dataset:
snake_case : int = dataset
snake_case : int = RagRetriever(
UpperCamelCase__ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def lowerCamelCase ( self , UpperCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
snake_case : List[str] = self.get_dummy_dataset()
snake_case : Any = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="custom" , )
if from_disk:
snake_case : str = os.path.join(self.tmpdirname , "dataset" )
snake_case : Any = os.path.join(self.tmpdirname , "index.faiss" )
dataset.get_index("embeddings" ).save(os.path.join(self.tmpdirname , "index.faiss" ) )
dataset.drop_index("embeddings" )
dataset.save_to_disk(os.path.join(self.tmpdirname , "dataset" ) )
del dataset
snake_case : Any = RagRetriever(
UpperCamelCase__ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
snake_case : str = RagRetriever(
UpperCamelCase__ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , UpperCamelCase__ ) , )
return retriever
def lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case : Optional[int] = Dataset.from_dict(
{
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index("embeddings" , string_factory="Flat" , metric_type=faiss.METRIC_INNER_PRODUCT )
snake_case : Dict = os.path.join(self.tmpdirname , "hf_bert_base.hnswSQ8_correct_phi_128.c_index" )
dataset.save_faiss_index("embeddings" , index_file_name + ".index.dpr" )
pickle.dump(dataset["id"] , open(index_file_name + ".index_meta.dpr" , "wb" ) )
snake_case : int = os.path.join(self.tmpdirname , "psgs_w100.tsv.pkl" )
snake_case : Any = {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
pickle.dump(UpperCamelCase__ , open(UpperCamelCase__ , "wb" ) )
snake_case : List[Any] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="legacy" , index_path=self.tmpdirname , )
snake_case : Dict = RagRetriever(
UpperCamelCase__ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
snake_case : str = 1
snake_case : Any = self.get_dummy_canonical_hf_index_retriever()
snake_case : Dict = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case ,snake_case ,snake_case : Any = retriever.retrieve(UpperCamelCase__ , n_docs=UpperCamelCase__ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(UpperCamelCase__ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) , UpperCamelCase__ )
self.assertEqual(doc_dicts[0]["id"][0] , "1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] , "0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
snake_case : List[str] = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch("transformers.models.rag.retrieval_rag.load_dataset" ) as mock_load_dataset:
snake_case : List[str] = self.get_dummy_dataset()
retriever.save_pretrained(UpperCamelCase__ )
snake_case : Union[str, Any] = RagRetriever.from_pretrained(UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
snake_case : Union[str, Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case : List[Any] = retriever.retrieve(UpperCamelCase__ , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Dict = 1
snake_case : Union[str, Any] = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__ )
snake_case : int = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case ,snake_case ,snake_case : Any = retriever.retrieve(UpperCamelCase__ , n_docs=UpperCamelCase__ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(UpperCamelCase__ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) , UpperCamelCase__ )
self.assertEqual(doc_dicts[0]["id"][0] , "1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] , "0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Dict = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(UpperCamelCase__ )
snake_case : int = RagRetriever.from_pretrained(UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
snake_case : Optional[int] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case : int = retriever.retrieve(UpperCamelCase__ , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case : List[str] = 1
snake_case : int = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__ )
snake_case : int = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case ,snake_case ,snake_case : List[str] = retriever.retrieve(UpperCamelCase__ , n_docs=UpperCamelCase__ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(UpperCamelCase__ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) , UpperCamelCase__ )
self.assertEqual(doc_dicts[0]["id"][0] , "1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] , "0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ) -> Tuple:
'''simple docstring'''
snake_case : Tuple = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(UpperCamelCase__ )
snake_case : Any = RagRetriever.from_pretrained(UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
snake_case : Optional[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case : Any = retriever.retrieve(UpperCamelCase__ , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Union[str, Any] = 1
snake_case : Tuple = self.get_dummy_legacy_index_retriever()
snake_case : str = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case ,snake_case ,snake_case : Any = retriever.retrieve(UpperCamelCase__ , n_docs=UpperCamelCase__ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(UpperCamelCase__ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["text", "title"] )
self.assertEqual(len(doc_dicts[0]["text"] ) , UpperCamelCase__ )
self.assertEqual(doc_dicts[0]["text"][0] , "bar" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["text"][0] , "foo" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
snake_case : int = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(UpperCamelCase__ )
snake_case : Tuple = RagRetriever.from_pretrained(UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
snake_case : int = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case : Optional[Any] = retriever.retrieve(UpperCamelCase__ , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
import torch
snake_case : str = 1
snake_case : Dict = self.get_dummy_canonical_hf_index_retriever()
snake_case : str = [[5, 7], [10, 11]]
snake_case : Any = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case : int = retriever(UpperCamelCase__ , UpperCamelCase__ , prefix=retriever.config.generator.prefix , n_docs=UpperCamelCase__ )
snake_case ,snake_case ,snake_case : Dict = (
out["context_input_ids"],
out["context_attention_mask"],
out["retrieved_doc_embeds"],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , np.ndarray )
snake_case : Tuple = retriever(
UpperCamelCase__ , UpperCamelCase__ , prefix=retriever.config.generator.prefix , n_docs=UpperCamelCase__ , return_tensors="pt" , )
snake_case ,snake_case ,snake_case ,snake_case : str = ( # noqa: F841
out["context_input_ids"],
out["context_attention_mask"],
out["retrieved_doc_embeds"],
out["doc_ids"],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
snake_case : Tuple = self.get_dpr_ctx_encoder_tokenizer()
snake_case : Union[str, Any] = 1
snake_case : List[str] = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__ )
retriever.set_ctx_encoder_tokenizer(UpperCamelCase__ )
snake_case : str = [[5, 7], [10, 11]]
snake_case : str = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case : Dict = retriever(UpperCamelCase__ , UpperCamelCase__ , prefix=retriever.config.generator.prefix , n_docs=UpperCamelCase__ )
self.assertEqual(
len(UpperCamelCase__ ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask") ) , UpperCamelCase__ ) # check for doc token related keys in dictionary.
| 117
| 1
|
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def a ( UpperCamelCase_ : str = "isbn/0140328726" ) -> dict:
snake_case__ =olid.strip().strip('/' ) # Remove leading/trailing whitespace & slashes
if new_olid.count('/' ) != 1:
snake_case__ =f"""{olid} is not a valid Open Library olid"""
raise ValueError(UpperCamelCase_ )
return requests.get(f"""https://openlibrary.org/{new_olid}.json""" ).json()
def a ( UpperCamelCase_ : dict ) -> dict:
snake_case__ ={
'title': 'Title',
'publish_date': 'Publish date',
'authors': 'Authors',
'number_of_pages': 'Number of pages:',
'first_sentence': 'First sentence',
'isbn_10': 'ISBN (10)',
'isbn_13': 'ISBN (13)',
}
snake_case__ ={better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
snake_case__ =[
get_openlibrary_data(author['key'] )['name'] for author in data['Authors']
]
snake_case__ =data['First sentence']['value']
for key, value in data.items():
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
snake_case__ =', '.join(UpperCamelCase_ )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
SCREAMING_SNAKE_CASE__ : Dict = input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (1_0, 1_3) or not isbn.isdigit():
print(f"""Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.""")
continue
print(f"""\nSearching Open Library for ISBN: {isbn}...\n""")
try:
SCREAMING_SNAKE_CASE__ : Dict = summarize_book(get_openlibrary_data(f"""isbn/{isbn}"""))
print('''\n'''.join(f"""{key}: {value}""" for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(f"""Sorry, there are no results for ISBN: {isbn}.""")
| 538
|
'''simple docstring'''
import os
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}
def a ( UpperCamelCase_ : str ) -> int:
snake_case__ =0
snake_case__ =0
while index < len(UpperCamelCase_ ) - 1:
snake_case__ =SYMBOLS[numerals[index]]
snake_case__ =SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def a ( UpperCamelCase_ : int ) -> str:
snake_case__ =''
snake_case__ =num // 1000
numerals += m_count * "M"
num %= 1000
snake_case__ =num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
snake_case__ =num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def a ( UpperCamelCase_ : str = "/p089_roman.txt" ) -> int:
snake_case__ =0
with open(os.path.dirname(UpperCamelCase_ ) + roman_numerals_filename ) as filea:
snake_case__ =filea.readlines()
for line in lines:
snake_case__ =line.strip()
snake_case__ =parse_roman_numerals(UpperCamelCase_ )
snake_case__ =generate_roman_numerals(UpperCamelCase_ )
savings += len(UpperCamelCase_ ) - len(UpperCamelCase_ )
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 538
| 1
|
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
AlbertConfig,
BartConfig,
BertConfig,
CamembertConfig,
CTRLConfig,
DistilBertConfig,
DPRConfig,
ElectraConfig,
FlaubertConfig,
GPTaConfig,
LayoutLMConfig,
LxmertConfig,
OpenAIGPTConfig,
RobertaConfig,
TaConfig,
TFAlbertForPreTraining,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFCamembertForMaskedLM,
TFCTRLLMHeadModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
TFElectraForPreTraining,
TFFlaubertWithLMHeadModel,
TFGPTaLMHeadModel,
TFLayoutLMForMaskedLM,
TFLxmertForPreTraining,
TFLxmertVisualFeatureEncoder,
TFOpenAIGPTLMHeadModel,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFTaForConditionalGeneration,
TFTransfoXLLMHeadModel,
TFWavaVecaModel,
TFXLMRobertaForMaskedLM,
TFXLMWithLMHeadModel,
TFXLNetLMHeadModel,
TransfoXLConfig,
WavaVecaConfig,
WavaVecaModel,
XLMConfig,
XLMRobertaConfig,
XLNetConfig,
is_torch_available,
load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging
if is_torch_available():
import numpy as np
import torch
from . import (
AlbertForPreTraining,
BartForConditionalGeneration,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
CamembertForMaskedLM,
CTRLLMHeadModel,
DistilBertForMaskedLM,
DistilBertForQuestionAnswering,
DPRContextEncoder,
DPRQuestionEncoder,
DPRReader,
ElectraForPreTraining,
FlaubertWithLMHeadModel,
GPTaLMHeadModel,
LayoutLMForMaskedLM,
LxmertForPreTraining,
LxmertVisualFeatureEncoder,
OpenAIGPTLMHeadModel,
RobertaForMaskedLM,
RobertaForSequenceClassification,
TaForConditionalGeneration,
TransfoXLLMHeadModel,
XLMRobertaForMaskedLM,
XLMWithLMHeadModel,
XLNetLMHeadModel,
)
logging.set_verbosity_info()
__lowerCAmelCase = {
'''bart''': (
BartConfig,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
BartForConditionalGeneration,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''bert''': (
BertConfig,
TFBertForPreTraining,
BertForPreTraining,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-base-cased-finetuned-mrpc''': (
BertConfig,
TFBertForSequenceClassification,
BertForSequenceClassification,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''dpr''': (
DPRConfig,
TFDPRQuestionEncoder,
TFDPRContextEncoder,
TFDPRReader,
DPRQuestionEncoder,
DPRContextEncoder,
DPRReader,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''gpt2''': (
GPTaConfig,
TFGPTaLMHeadModel,
GPTaLMHeadModel,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlnet''': (
XLNetConfig,
TFXLNetLMHeadModel,
XLNetLMHeadModel,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlm''': (
XLMConfig,
TFXLMWithLMHeadModel,
XLMWithLMHeadModel,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlm-roberta''': (
XLMRobertaConfig,
TFXLMRobertaForMaskedLM,
XLMRobertaForMaskedLM,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''transfo-xl''': (
TransfoXLConfig,
TFTransfoXLLMHeadModel,
TransfoXLLMHeadModel,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''openai-gpt''': (
OpenAIGPTConfig,
TFOpenAIGPTLMHeadModel,
OpenAIGPTLMHeadModel,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''roberta''': (
RobertaConfig,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
RobertaForMaskedLM,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''layoutlm''': (
LayoutLMConfig,
TFLayoutLMForMaskedLM,
LayoutLMForMaskedLM,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''roberta-large-mnli''': (
RobertaConfig,
TFRobertaForSequenceClassification,
RobertaForSequenceClassification,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''camembert''': (
CamembertConfig,
TFCamembertForMaskedLM,
CamembertForMaskedLM,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''flaubert''': (
FlaubertConfig,
TFFlaubertWithLMHeadModel,
FlaubertWithLMHeadModel,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''distilbert''': (
DistilBertConfig,
TFDistilBertForMaskedLM,
DistilBertForMaskedLM,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''distilbert-base-distilled-squad''': (
DistilBertConfig,
TFDistilBertForQuestionAnswering,
DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''lxmert''': (
LxmertConfig,
TFLxmertForPreTraining,
LxmertForPreTraining,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''lxmert-visual-feature-encoder''': (
LxmertConfig,
TFLxmertVisualFeatureEncoder,
LxmertVisualFeatureEncoder,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''ctrl''': (
CTRLConfig,
TFCTRLLMHeadModel,
CTRLLMHeadModel,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''albert''': (
AlbertConfig,
TFAlbertForPreTraining,
AlbertForPreTraining,
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''t5''': (
TaConfig,
TFTaForConditionalGeneration,
TaForConditionalGeneration,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''electra''': (
ElectraConfig,
TFElectraForPreTraining,
ElectraForPreTraining,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''wav2vec2''': (
WavaVecaConfig,
TFWavaVecaModel,
WavaVecaModel,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
}
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case=False , snake_case=True ) -> Union[str, Any]:
if model_type not in MODEL_CLASSES:
raise ValueError(f'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.' )
lowercase__: str = MODEL_CLASSES[model_type]
# Initialise TF model
if config_file in aws_config_map:
lowercase__: Optional[Any] = cached_file(lowerCamelCase__ , lowerCamelCase__ , force_download=not use_cached_models )
lowercase__: Tuple = config_class.from_json_file(lowerCamelCase__ )
lowercase__: List[Any] = True
lowercase__: Dict = True
print(f'Building TensorFlow model from configuration: {config}' )
lowercase__: Tuple = model_class(lowerCamelCase__ )
# Load weights from tf checkpoint
if pytorch_checkpoint_path in aws_config_map.keys():
lowercase__: Union[str, Any] = cached_file(
lowerCamelCase__ , lowerCamelCase__ , force_download=not use_cached_models )
# Load PyTorch checkpoint in tf2 model:
lowercase__: str = load_pytorch_checkpoint_in_tfa_model(lowerCamelCase__ , lowerCamelCase__ )
if compare_with_pt_model:
lowercase__: int = tf_model(tf_model.dummy_inputs , training=lowerCamelCase__ ) # build the network
lowercase__: Tuple = torch.load(lowerCamelCase__ , map_location='cpu' )
lowercase__: Optional[Any] = pt_model_class.from_pretrained(
pretrained_model_name_or_path=lowerCamelCase__ , config=lowerCamelCase__ , state_dict=lowerCamelCase__ )
with torch.no_grad():
lowercase__: Any = pt_model(**pt_model.dummy_inputs )
lowercase__: int = pto[0].numpy()
lowercase__: List[Any] = tfo[0].numpy()
lowercase__: List[Any] = np.amax(np.abs(np_pt - np_tf ) )
print(f'Max absolute difference between models outputs {diff}' )
assert diff <= 2e-2, f'Error, model absolute difference is >2e-2: {diff}'
# Save pytorch-model
print(f'Save TensorFlow model to {tf_dump_path}' )
tf_model.save_weights(lowerCamelCase__ , save_format='h5' )
def snake_case_ ( snake_case , snake_case , snake_case=None , snake_case=None , snake_case=False , snake_case=False , snake_case=False , snake_case=False , ) -> Tuple:
if args_model_type is None:
lowercase__: Tuple = list(MODEL_CLASSES.keys() )
else:
lowercase__: int = [args_model_type]
for j, model_type in enumerate(lowerCamelCase__ , start=1 ):
print('=' * 1_00 )
print(f' Converting model type {j}/{len(lowerCamelCase__ )}: {model_type}' )
print('=' * 1_00 )
if model_type not in MODEL_CLASSES:
raise ValueError(f'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.' )
lowercase__: Dict = MODEL_CLASSES[model_type]
if model_shortcut_names_or_path is None:
lowercase__: int = list(aws_model_maps.keys() )
if config_shortcut_names_or_path is None:
lowercase__: Union[str, Any] = model_shortcut_names_or_path
for i, (model_shortcut_name, config_shortcut_name) in enumerate(
zip(lowerCamelCase__ , lowerCamelCase__ ) , start=1 ):
print('-' * 1_00 )
if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
if not only_convert_finetuned_models:
print(f' Skipping finetuned checkpoint {model_shortcut_name}' )
continue
lowercase__: Tuple = model_shortcut_name
elif only_convert_finetuned_models:
print(f' Skipping not finetuned checkpoint {model_shortcut_name}' )
continue
print(
f' Converting checkpoint {i}/{len(lowerCamelCase__ )}: {model_shortcut_name} - model_type {model_type}' )
print('-' * 1_00 )
if config_shortcut_name in aws_config_map:
lowercase__: Optional[Any] = cached_file(lowerCamelCase__ , lowerCamelCase__ , force_download=not use_cached_models )
else:
lowercase__: Union[str, Any] = config_shortcut_name
if model_shortcut_name in aws_model_maps:
lowercase__: Union[str, Any] = cached_file(lowerCamelCase__ , lowerCamelCase__ , force_download=not use_cached_models )
else:
lowercase__: Any = model_shortcut_name
if os.path.isfile(lowerCamelCase__ ):
lowercase__: Optional[Any] = "converted_model"
convert_pt_checkpoint_to_tf(
model_type=lowerCamelCase__ , pytorch_checkpoint_path=lowerCamelCase__ , config_file=lowerCamelCase__ , tf_dump_path=os.path.join(lowerCamelCase__ , model_shortcut_name + '-tf_model.h5' ) , compare_with_pt_model=lowerCamelCase__ , )
if remove_cached_files:
os.remove(lowerCamelCase__ )
os.remove(lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
)
parser.add_argument(
'''--model_type''',
default=None,
type=str,
help=(
F'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
'''convert all the models from AWS.'''
),
)
parser.add_argument(
'''--pytorch_checkpoint_path''',
default=None,
type=str,
help=(
'''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
'''If not given, will download and convert all the checkpoints from AWS.'''
),
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
help=(
'''The config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture. If not given and '''
'''--pytorch_checkpoint_path is not given or is a shortcut name '''
'''use the configuration associated to the shortcut name on the AWS'''
),
)
parser.add_argument(
'''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
)
parser.add_argument(
'''--use_cached_models''',
action='''store_true''',
help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
)
parser.add_argument(
'''--remove_cached_files''',
action='''store_true''',
help='''Remove pytorch models after conversion (save memory when converting in batches).''',
)
parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
__lowerCAmelCase = parser.parse_args()
# if args.pytorch_checkpoint_path is not None:
# convert_pt_checkpoint_to_tf(args.model_type.lower(),
# args.pytorch_checkpoint_path,
# args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
# args.tf_dump_path,
# compare_with_pt_model=args.compare_with_pt_model,
# use_cached_models=args.use_cached_models)
# else:
convert_all_pt_checkpoints_to_tf(
args.model_type.lower() if args.model_type is not None else None,
args.tf_dump_path,
model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
if args.pytorch_checkpoint_path is not None
else None,
config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
compare_with_pt_model=args.compare_with_pt_model,
use_cached_models=args.use_cached_models,
remove_cached_files=args.remove_cached_files,
only_convert_finetuned_models=args.only_convert_finetuned_models,
)
| 715
|
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 = logging.get_logger(__name__)
__lowerCAmelCase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__lowerCAmelCase = {
'''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 = {
'''gpt2''': 10_24,
'''gpt2-medium''': 10_24,
'''gpt2-large''': 10_24,
'''gpt2-xl''': 10_24,
'''distilgpt2''': 10_24,
}
class __a ( __UpperCamelCase ):
__lowercase : Optional[int] = VOCAB_FILES_NAMES
__lowercase : List[str] = PRETRAINED_VOCAB_FILES_MAP
__lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase : Dict = ['input_ids', 'attention_mask']
__lowercase : Any = GPTaTokenizer
def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__="<|endoftext|>" , lowerCAmelCase__="<|endoftext|>" , lowerCAmelCase__="<|endoftext|>" , lowerCAmelCase__=False , **lowerCAmelCase__ , ) -> Optional[int]:
'''simple docstring'''
super().__init__(
lowerCAmelCase__ , lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , **lowerCAmelCase__ , )
lowercase__: Optional[Any] = kwargs.pop('add_bos_token' , lowerCAmelCase__ )
lowercase__: Any = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , lowerCAmelCase__ ) != add_prefix_space:
lowercase__: Optional[int] = getattr(lowerCAmelCase__ , pre_tok_state.pop('type' ) )
lowercase__: Union[str, Any] = add_prefix_space
lowercase__: Tuple = pre_tok_class(**lowerCAmelCase__ )
lowercase__: Optional[int] = add_prefix_space
def SCREAMING_SNAKE_CASE__ ( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> BatchEncoding:
'''simple docstring'''
lowercase__: List[str] = 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 SCREAMING_SNAKE_CASE__ ( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> BatchEncoding:
'''simple docstring'''
lowercase__: Union[str, Any] = 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 SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Tuple[str]:
'''simple docstring'''
lowercase__: Dict = self._tokenizer.model.save(lowerCAmelCase__ , name=lowerCAmelCase__ )
return tuple(lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> List[int]:
'''simple docstring'''
lowercase__: List[str] = []
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:
lowercase__: Union[str, Any] = input_ids[-self.model_max_length :]
return input_ids
| 335
| 0
|
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A = logging.get_logger(__name__)
__A = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
__A = {
"tokenizer_file": {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
},
}
__A = {
"gpt-neox-20b": 2048,
}
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = ["input_ids", "attention_mask"]
def __init__(self : int , UpperCAmelCase_ : int=None , UpperCAmelCase_ : Any=None , UpperCAmelCase_ : Tuple=None , UpperCAmelCase_ : List[str]="<|endoftext|>" , UpperCAmelCase_ : Optional[int]="<|endoftext|>" , UpperCAmelCase_ : int="<|endoftext|>" , UpperCAmelCase_ : int=False , **UpperCAmelCase_ : Optional[int] , ) ->int:
'''simple docstring'''
super().__init__(
UpperCAmelCase_ , UpperCAmelCase_ , tokenizer_file=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , bos_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , add_prefix_space=UpperCAmelCase_ , **UpperCAmelCase_ , )
lowerCamelCase__: Union[str, Any] =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("add_prefix_space" , UpperCAmelCase_) != add_prefix_space:
lowerCamelCase__: Dict =getattr(UpperCAmelCase_ , pre_tok_state.pop("type"))
lowerCamelCase__: Optional[int] =add_prefix_space
lowerCamelCase__: Any =pre_tok_class(**UpperCAmelCase_)
lowerCamelCase__: Optional[Any] =add_prefix_space
def SCREAMING_SNAKE_CASE_ (self : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None) ->Tuple[str]:
'''simple docstring'''
lowerCamelCase__: str =self._tokenizer.model.save(UpperCAmelCase_ , name=UpperCAmelCase_)
return tuple(UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Any , UpperCAmelCase_ : "Conversation") ->List[int]:
'''simple docstring'''
lowerCamelCase__: int =[]
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_) + [self.eos_token_id])
if len(UpperCAmelCase_) > self.model_max_length:
lowerCamelCase__: str =input_ids[-self.model_max_length :]
return input_ids
| 59
|
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowercase__( A ):
# A local function to see if a dot lands in the circle.
def is_in_circle(A , A ) -> bool:
snake_case__ : Optional[Any] = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
snake_case__ : Optional[int] = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(A ) )
# The ratio of the area for circle to square is pi/4.
snake_case__ : Optional[Any] = proportion * 4
print(f'''The estimated value of pi is {pi_estimate}''' )
print(f'''The numpy value of pi is {pi}''' )
print(f'''The total error is {abs(pi - pi_estimate )}''' )
def lowercase__( A , A , A = 0.0 , A = 1.0 , ):
return mean(
function_to_integrate(uniform(A , A ) ) for _ in range(A ) ) * (max_value - min_value)
def lowercase__( A , A = 0.0 , A = 1.0 ):
def identity_function(A ) -> float:
return x
snake_case__ : List[Any] = area_under_curve_estimator(
A , A , A , A )
snake_case__ : List[str] = (max_value * max_value - min_value * min_value) / 2
print('******************' )
print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}''' )
print(f'''Estimated value is {estimated_value}''' )
print(f'''Expected value is {expected_value}''' )
print(f'''Total error is {abs(estimated_value - expected_value )}''' )
print('******************' )
def lowercase__( A ):
def function_to_integrate(A ) -> float:
return sqrt(4.0 - x * x )
snake_case__ : Tuple = area_under_curve_estimator(
A , A , 0.0 , 2.0 )
print('******************' )
print('Estimating pi using area_under_curve_estimator' )
print(f'''Estimated value is {estimated_value}''' )
print(f'''Expected value is {pi}''' )
print(f'''Total error is {abs(estimated_value - pi )}''' )
print('******************' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 170
| 0
|
"""simple docstring"""
def a__ ( __lowercase ) -> list:
_A = int(__lowercase )
if n_element < 1:
_A = ValueError("a should be a positive number" )
raise my_error
_A = [1]
_A = (0, 0, 0)
_A = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
a_ = input("Enter the last number (nth term) of the Hamming Number Series: ")
print("Formula of Hamming Number Series => 2^i * 3^j * 5^k")
a_ = hamming(int(n))
print("-----------------------------------------------------")
print(f'''The list with nth numbers is: {hamming_numbers}''')
print("-----------------------------------------------------")
| 719
|
"""simple docstring"""
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
a_ = logging.get_logger(__name__)
a_ = {"vocab_file": "spiece.model"}
a_ = {
"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
a_ = {
"t5-small": 5_12,
"t5-base": 5_12,
"t5-large": 5_12,
"t5-3b": 5_12,
"t5-11b": 5_12,
}
a_ = "▁"
class snake_case ( _UpperCamelCase):
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ['input_ids', 'attention_mask']
def __init__( self : List[str] , a__ : Optional[int] , a__ : Union[str, Any]="</s>" , a__ : Union[str, Any]="<unk>" , a__ : str="<pad>" , a__ : Optional[int]=1_00 , a__ : List[Any]=None , a__ : Optional[Dict[str, Any]] = None , a__ : Any=True , **a__ : Optional[int] , ) -> None:
'''simple docstring'''
if extra_ids > 0 and additional_special_tokens is None:
_A = [F"""<extra_id_{i}>""" for i in range(a__ )]
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 a__ : bool("extra_id" in str(a__ ) ) , a__ ) ) )
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=a__ , unk_token=a__ , pad_token=a__ , extra_ids=a__ , additional_special_tokens=a__ , sp_model_kwargs=self.sp_model_kwargs , legacy=a__ , **a__ , )
_A = vocab_file
_A = extra_ids
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(a__ )
@staticmethod
def a_ ( a__ : List[str] , a__ : Optional[int] , a__ : Tuple ) -> Tuple:
'''simple docstring'''
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." , a__ , )
return max_model_length
@property
def a_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
return self.sp_model.get_piece_size() + self._extra_ids
def a_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
_A = {self.convert_ids_to_tokens(a__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def a_ ( self : Optional[Any] , a__ : List[int] , a__ : Optional[List[int]] = None , a__ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=a__ , token_ids_a=a__ , already_has_special_tokens=a__ )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(a__ )) + [1]
return ([0] * len(a__ )) + [1] + ([0] * len(a__ )) + [1]
def a_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
return list(
set(filter(lambda a__ : bool(re.search(r"<extra_id_\d+>" , a__ ) ) is not None , self.additional_special_tokens ) ) )
def a_ ( self : str ) -> List[Any]:
'''simple docstring'''
return [self._convert_token_to_id(a__ ) for token in self.get_sentinel_tokens()]
def a_ ( self : List[Any] , a__ : List[int] ) -> List[int]:
'''simple docstring'''
if len(a__ ) > 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 : int , a__ : List[int] , a__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
_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 : Union[str, Any] , a__ : List[int] , a__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
_A = self._add_eos_if_not_present(a__ )
if token_ids_a is None:
return token_ids_a
else:
_A = self._add_eos_if_not_present(a__ )
return token_ids_a + token_ids_a
def __getstate__( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
_A = self.__dict__.copy()
_A = None
return state
def __setstate__( self : int , a__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_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 : int , a__ : "TextInput" , **a__ : List[str] ) -> List[str]:
'''simple docstring'''
if not self.legacy:
_A = SPIECE_UNDERLINE + text.replace(a__ , " " )
return super().tokenize(a__ , **a__ )
def a_ ( self : str , a__ : Dict , **a__ : Optional[int] ) -> Any:
'''simple docstring'''
if not self.legacy:
_A = text.startswith(a__ )
if is_first:
_A = text[1:]
_A = self.sp_model.encode(a__ , out_type=a__ )
if not self.legacy and not is_first and not text.startswith(" " ) and tokens[0].startswith(a__ ):
_A = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def a_ ( self : int , a__ : List[Any] ) -> List[str]:
'''simple docstring'''
if token.startswith("<extra_id_" ):
_A = re.match(r"<extra_id_(\d+)>" , a__ )
_A = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(a__ )
def a_ ( self : Dict , a__ : Union[str, Any] ) -> Any:
'''simple docstring'''
if index < self.sp_model.get_piece_size():
_A = self.sp_model.IdToPiece(a__ )
else:
_A = F"""<extra_id_{self.vocab_size - 1 - index}>"""
return token
def a_ ( self : Optional[int] , a__ : Tuple ) -> List[str]:
'''simple docstring'''
_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(a__ ) + token
_A = True
_A = []
else:
current_sub_tokens.append(a__ )
_A = False
out_string += self.sp_model.decode(a__ )
return out_string.strip()
def a_ ( self : Dict , a__ : str , a__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(a__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_A = 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:
_A = self.sp_model.serialized_model_proto()
fi.write(a__ )
return (out_vocab_file,)
| 621
| 0
|
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase__ = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase__ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
re.sub('<n>','',_SCREAMING_SNAKE_CASE ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE ) )
| 186
|
'''simple docstring'''
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__ = {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
'''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class a__ ( a_ ):
'''simple docstring'''
A : Union[str, Any] = '''roberta'''
def __init__( self : Any , lowerCAmelCase_ : int=50_265 , lowerCAmelCase_ : int=768 , lowerCAmelCase_ : Optional[Any]=12 , lowerCAmelCase_ : Tuple=12 , lowerCAmelCase_ : Optional[int]=3_072 , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : List[str]=0.1 , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : List[Any]=512 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : List[Any]=0.02 , lowerCAmelCase_ : Optional[int]=1E-12 , lowerCAmelCase_ : str=1 , lowerCAmelCase_ : Dict=0 , lowerCAmelCase_ : Any=2 , lowerCAmelCase_ : List[str]="absolute" , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : Dict=None , **lowerCAmelCase_ : Any , ) -> int:
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= hidden_act
__A= intermediate_size
__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= position_embedding_type
__A= use_cache
__A= classifier_dropout
class a__ ( a_ ):
'''simple docstring'''
@property
def lowerCAmelCase ( self : Any ) -> Mapping[str, Mapping[int, str]]:
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),
] )
| 186
| 1
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : int = logging.get_logger(__name__)
_a : str = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class a_ ( a ):
A__ : str = 'sew-d'
def __init__( self : Tuple , UpperCAmelCase__ : int=32 , UpperCAmelCase__ : Tuple=768 , UpperCAmelCase__ : Tuple=12 , UpperCAmelCase__ : Dict=12 , UpperCAmelCase__ : Any=3_072 , UpperCAmelCase__ : List[str]=2 , UpperCAmelCase__ : Optional[int]=512 , UpperCAmelCase__ : Tuple=256 , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : str=True , UpperCAmelCase__ : List[str]=("p2c", "c2p") , UpperCAmelCase__ : int="layer_norm" , UpperCAmelCase__ : List[str]="gelu_python" , UpperCAmelCase__ : Any=0.1 , UpperCAmelCase__ : str=0.1 , UpperCAmelCase__ : Optional[Any]=0.1 , UpperCAmelCase__ : Tuple=0.0 , UpperCAmelCase__ : int=0.1 , UpperCAmelCase__ : List[Any]=0.02 , UpperCAmelCase__ : Tuple=1e-7 , UpperCAmelCase__ : Union[str, Any]=1e-5 , UpperCAmelCase__ : List[Any]="group" , UpperCAmelCase__ : Union[str, Any]="gelu" , UpperCAmelCase__ : Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , UpperCAmelCase__ : Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , UpperCAmelCase__ : Any=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , UpperCAmelCase__ : Optional[Any]=False , UpperCAmelCase__ : List[Any]=128 , UpperCAmelCase__ : List[str]=16 , UpperCAmelCase__ : str=True , UpperCAmelCase__ : Tuple=0.05 , UpperCAmelCase__ : Tuple=10 , UpperCAmelCase__ : Union[str, Any]=2 , UpperCAmelCase__ : List[str]=0.0 , UpperCAmelCase__ : Dict=10 , UpperCAmelCase__ : List[str]=0 , UpperCAmelCase__ : List[Any]="mean" , UpperCAmelCase__ : Optional[Any]=False , UpperCAmelCase__ : Dict=False , UpperCAmelCase__ : Union[str, Any]=256 , UpperCAmelCase__ : List[Any]=0 , UpperCAmelCase__ : Tuple=1 , UpperCAmelCase__ : str=2 , **UpperCAmelCase__ : Optional[int] , ):
"""simple docstring"""
super().__init__(**UpperCAmelCase__ , pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ )
snake_case : Dict = hidden_size
snake_case : List[str] = feat_extract_norm
snake_case : List[str] = feat_extract_activation
snake_case : int = list(UpperCAmelCase__ )
snake_case : Optional[Any] = list(UpperCAmelCase__ )
snake_case : Any = list(UpperCAmelCase__ )
snake_case : Union[str, Any] = conv_bias
snake_case : Dict = num_conv_pos_embeddings
snake_case : List[str] = num_conv_pos_embedding_groups
snake_case : Any = len(self.conv_dim )
snake_case : Optional[int] = num_hidden_layers
snake_case : Dict = intermediate_size
snake_case : List[str] = squeeze_factor
snake_case : Optional[Any] = max_position_embeddings
snake_case : str = position_buckets
snake_case : str = share_att_key
snake_case : Optional[int] = relative_attention
snake_case : Dict = norm_rel_ebd
snake_case : str = list(UpperCAmelCase__ )
snake_case : Optional[int] = hidden_act
snake_case : Union[str, Any] = num_attention_heads
snake_case : str = hidden_dropout
snake_case : Optional[int] = attention_dropout
snake_case : Any = activation_dropout
snake_case : Optional[Any] = feat_proj_dropout
snake_case : List[Any] = final_dropout
snake_case : Any = layer_norm_eps
snake_case : Optional[int] = feature_layer_norm_eps
snake_case : str = initializer_range
snake_case : Tuple = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
F"but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"
F"= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`." )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
snake_case : List[str] = apply_spec_augment
snake_case : Optional[int] = mask_time_prob
snake_case : Union[str, Any] = mask_time_length
snake_case : List[Any] = mask_time_min_masks
snake_case : Dict = mask_feature_prob
snake_case : Optional[Any] = mask_feature_length
snake_case : int = mask_feature_min_masks
# ctc loss
snake_case : Optional[Any] = ctc_loss_reduction
snake_case : int = ctc_zero_infinity
# sequence classification
snake_case : str = use_weighted_layer_sum
snake_case : List[Any] = classifier_proj_size
@property
def lowerCAmelCase( self : Union[str, Any] ):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 701
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_a : str = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : int = ['EncoderDecoderModel']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Dict = ['TFEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : int = ['FlaxEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
_a : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 84
| 0
|
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
@dataclass
class snake_case_ ( _lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_: Dict = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self , **__a ):
"""simple docstring"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
A__ = deprecated_arg[3:]
A__ = not kwargs.pop(__a )
logger.warning(
f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
f''' {positive_arg}={kwargs[positive_arg]}''' )
A__ = kwargs.pop('tpu_name' , self.tpu_name )
A__ = kwargs.pop('device_idx' , self.device_idx )
A__ = kwargs.pop('eager_mode' , self.eager_mode )
A__ = kwargs.pop('use_xla' , self.use_xla )
super().__init__(**__a )
SCREAMING_SNAKE_CASE_: str = field(
default=_lowerCamelCase , metadata={"""help""": """Name of TPU"""} , )
SCREAMING_SNAKE_CASE_: int = field(
default=0 , metadata={"""help""": """CPU / GPU device index. Defaults to 0."""} , )
SCREAMING_SNAKE_CASE_: bool = field(default=_lowerCamelCase , metadata={"""help""": """Benchmark models in eager model."""} )
SCREAMING_SNAKE_CASE_: bool = field(
default=_lowerCamelCase , metadata={
"""help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
} , )
@cached_property
def _UpperCAmelCase ( self ):
"""simple docstring"""
requires_backends(self , ['tf'] )
A__ = None
if self.tpu:
try:
if self.tpu_name:
A__ = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
A__ = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
A__ = None
return tpu
@cached_property
def _UpperCAmelCase ( self ):
"""simple docstring"""
requires_backends(self , ['tf'] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
A__ = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , 'GPU' )
A__ = tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}''' )
else:
tf.config.set_visible_devices([] , 'GPU' ) # disable GPU
A__ = tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}''' )
return strategy
@property
def _UpperCAmelCase ( self ):
"""simple docstring"""
requires_backends(self , ['tf'] )
return self._setup_tpu is not None
@property
def _UpperCAmelCase ( self ):
"""simple docstring"""
requires_backends(self , ['tf'] )
return self._setup_strategy
@property
def _UpperCAmelCase ( self ):
"""simple docstring"""
requires_backends(self , ['tf'] )
return tf.config.list_physical_devices('GPU' )
@property
def _UpperCAmelCase ( self ):
"""simple docstring"""
requires_backends(self , ['tf'] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def _UpperCAmelCase ( self ):
"""simple docstring"""
return self.n_gpu > 0
| 260
|
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
class snake_case_ ( _lowerCamelCase ):
"""simple docstring"""
def _UpperCAmelCase ( self , __a ):
"""simple docstring"""
if isinstance(__a , __a ):
A__ = [label.strip() for label in labels.split(',' ) if label.strip()]
return labels
def __call__( self , __a , __a , __a ):
"""simple docstring"""
if len(__a ) == 0 or len(__a ) == 0:
raise ValueError('You must include at least one label and at least one sequence.' )
if hypothesis_template.format(labels[0] ) == hypothesis_template:
raise ValueError(
(
'The provided hypothesis_template "{}" was not able to be formatted with the target labels. '
'Make sure the passed template includes formatting syntax such as {{}} where the label should go.'
).format(__a ) )
if isinstance(__a , __a ):
A__ = [sequences]
A__ = []
for sequence in sequences:
sequence_pairs.extend([[sequence, hypothesis_template.format(__a )] for label in labels] )
return sequence_pairs, sequences
@add_end_docstrings(_lowerCamelCase )
class snake_case_ ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self , __a=ZeroShotClassificationArgumentHandler() , *__a , **__a ):
"""simple docstring"""
A__ = args_parser
super().__init__(*__a , **__a )
if self.entailment_id == -1:
logger.warning(
'Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '
'-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.' )
@property
def _UpperCAmelCase ( self ):
"""simple docstring"""
for label, ind in self.model.config.labelaid.items():
if label.lower().startswith('entail' ):
return ind
return -1
def _UpperCAmelCase ( self , __a , __a=True , __a=True , __a=TruncationStrategy.ONLY_FIRST , **__a ):
"""simple docstring"""
A__ = self.framework
if self.tokenizer.pad_token is None:
# Override for tokenizers not supporting padding
logger.error(
'Tokenizer was not supporting padding necessary for zero-shot, attempting to use '
' `pad_token=eos_token`' )
A__ = self.tokenizer.eos_token
try:
A__ = self.tokenizer(
__a , add_special_tokens=__a , return_tensors=__a , padding=__a , truncation=__a , )
except Exception as e:
if "too short" in str(__a ):
# tokenizers might yell that we want to truncate
# to a value that is not even reached by the input.
# In that case we don't want to truncate.
# It seems there's not a really better way to catch that
# exception.
A__ = self.tokenizer(
__a , add_special_tokens=__a , return_tensors=__a , padding=__a , truncation=TruncationStrategy.DO_NOT_TRUNCATE , )
else:
raise e
return inputs
def _UpperCAmelCase ( self , **__a ):
"""simple docstring"""
if kwargs.get('multi_class' , __a ) is not None:
A__ = kwargs['multi_class']
logger.warning(
'The `multi_class` argument has been deprecated and renamed to `multi_label`. '
'`multi_class` will be removed in a future version of Transformers.' )
A__ = {}
if "candidate_labels" in kwargs:
A__ = self._args_parser._parse_labels(kwargs['candidate_labels'] )
if "hypothesis_template" in kwargs:
A__ = kwargs['hypothesis_template']
A__ = {}
if "multi_label" in kwargs:
A__ = kwargs['multi_label']
return preprocess_params, {}, postprocess_params
def __call__( self , __a , *__a , **__a , ):
"""simple docstring"""
if len(__a ) == 0:
pass
elif len(__a ) == 1 and "candidate_labels" not in kwargs:
A__ = args[0]
else:
raise ValueError(f'''Unable to understand extra arguments {args}''' )
return super().__call__(__a , **__a )
def _UpperCAmelCase ( self , __a , __a=None , __a="This example is {}." ):
"""simple docstring"""
A__ , A__ = self._args_parser(__a , __a , __a )
for i, (candidate_label, sequence_pair) in enumerate(zip(__a , __a ) ):
A__ = self._parse_and_tokenize([sequence_pair] )
yield {
"candidate_label": candidate_label,
"sequence": sequences[0],
"is_last": i == len(__a ) - 1,
**model_input,
}
def _UpperCAmelCase ( self , __a ):
"""simple docstring"""
A__ = inputs['candidate_label']
A__ = inputs['sequence']
A__ = {k: inputs[k] for k in self.tokenizer.model_input_names}
A__ = self.model(**__a )
A__ = {
'candidate_label': candidate_label,
'sequence': sequence,
'is_last': inputs['is_last'],
**outputs,
}
return model_outputs
def _UpperCAmelCase ( self , __a , __a=False ):
"""simple docstring"""
A__ = [outputs['candidate_label'] for outputs in model_outputs]
A__ = [outputs['sequence'] for outputs in model_outputs]
A__ = np.concatenate([output['logits'].numpy() for output in model_outputs] )
A__ = logits.shape[0]
A__ = len(__a )
A__ = N // n
A__ = logits.reshape((num_sequences, n, -1) )
if multi_label or len(__a ) == 1:
# softmax over the entailment vs. contradiction dim for each label independently
A__ = self.entailment_id
A__ = -1 if entailment_id == 0 else 0
A__ = reshaped_outputs[..., [contradiction_id, entailment_id]]
A__ = np.exp(__a ) / np.exp(__a ).sum(-1 , keepdims=__a )
A__ = scores[..., 1]
else:
# softmax the "entailment" logits over all candidate labels
A__ = reshaped_outputs[..., self.entailment_id]
A__ = np.exp(__a ) / np.exp(__a ).sum(-1 , keepdims=__a )
A__ = list(reversed(scores[0].argsort() ) )
return {
"sequence": sequences[0],
"labels": [candidate_labels[i] for i in top_inds],
"scores": scores[0, top_inds].tolist(),
}
| 260
| 1
|
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
return int(input_a == input_a == 0 )
def a__ ( ) -> None:
print('''Truth Table of NOR Gate:''' )
print('''| Input 1 | Input 2 | Output |''' )
print(F"""| 0 | 0 | {nor_gate(0 , 0 )} |""" )
print(F"""| 0 | 1 | {nor_gate(0 , 1 )} |""" )
print(F"""| 1 | 0 | {nor_gate(1 , 0 )} |""" )
print(F"""| 1 | 1 | {nor_gate(1 , 1 )} |""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 312
|
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None ) -> None:
if start is None:
UpperCAmelCase__ : List[Any] = 0
if end is None:
UpperCAmelCase__ : Optional[int] = len(lowerCAmelCase__ ) - 1
if start >= end:
return
UpperCAmelCase__ : List[Any] = (start + end) // 2
slowsort(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
slowsort(lowerCAmelCase__ , mid + 1 , lowerCAmelCase__ )
if sequence[end] < sequence[mid]:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = sequence[mid], sequence[end]
slowsort(lowerCAmelCase__ , lowerCAmelCase__ , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 312
| 1
|
import heapq
def a_ ( SCREAMING_SNAKE_CASE__ : dict ):
'''simple docstring'''
_lowerCamelCase : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(A_ , [-1 * len(A_ ), (key, value)] )
# chosen_vertices = set of chosen vertices
_lowerCamelCase : str =set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
_lowerCamelCase : Dict =heapq.heappop(A_ )[1][0]
chosen_vertices.add(A_ )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
_lowerCamelCase : List[str] =elem[1][1].index(A_ )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(A_ )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
| 464
|
"""simple docstring"""
def snake_case_ ( A_ : int, A_ : int ):
'''simple docstring'''
return int(input_a == input_a == 0 )
def snake_case_ ( ):
'''simple docstring'''
print('''Truth Table of NOR Gate:''' )
print('''| Input 1 | Input 2 | Output |''' )
print(F'''| 0 | 0 | {nor_gate(0, 0 )} |''' )
print(F'''| 0 | 1 | {nor_gate(0, 1 )} |''' )
print(F'''| 1 | 0 | {nor_gate(1, 0 )} |''' )
print(F'''| 1 | 1 | {nor_gate(1, 1 )} |''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 83
| 0
|
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
UpperCAmelCase_ = logging.getLogger(__name__)
class lowerCamelCase__( __lowerCamelCase):
def __init__( self: Any , UpperCamelCase_: Dict , UpperCamelCase_: int , UpperCamelCase_: Tuple , UpperCamelCase_: Dict=None ):
super().__init__(
UpperCamelCase_ , question_encoder_tokenizer=UpperCamelCase_ , generator_tokenizer=UpperCamelCase_ , index=UpperCamelCase_ , init_retrieval=UpperCamelCase_ , )
__lowerCamelCase = None
def lowerCAmelCase__ ( self: Optional[int] , UpperCamelCase_: int ):
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
__lowerCamelCase = self._infer_socket_ifname()
# avoid clash with the NCCL port
__lowerCamelCase = str(distributed_port + 1 )
__lowerCamelCase = dist.new_group(ranks=UpperCamelCase_ , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def lowerCAmelCase__ ( self: Tuple ):
return dist.get_rank(group=self.process_group ) == 0
def lowerCAmelCase__ ( self: Union[str, Any] , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: Optional[int] , UpperCamelCase_: List[str]=torch.floataa ):
__lowerCamelCase = torch.empty(UpperCamelCase_ , dtype=UpperCamelCase_ )
dist.scatter(UpperCamelCase_ , src=0 , scatter_list=UpperCamelCase_ , group=self.process_group )
return target_tensor
def lowerCAmelCase__ ( self: Union[str, Any] ):
__lowerCamelCase = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
__lowerCamelCase = next((addr for addr in addrs if addr.startswith("""e""" )) , UpperCamelCase_ )
return ifname
def lowerCAmelCase__ ( self: Union[str, Any] , UpperCamelCase_: np.ndarray , UpperCamelCase_: int ):
# single GPU training
if not dist.is_initialized():
__lowerCamelCase, __lowerCamelCase = self._main_retrieve(UpperCamelCase_ , UpperCamelCase_ )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase_ )
# distributed training
__lowerCamelCase = dist.get_world_size(group=self.process_group )
# gather logic
__lowerCamelCase = None
if self._is_main():
__lowerCamelCase = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(UpperCamelCase_ )]
dist.gather(torch.tensor(UpperCamelCase_ ) , dst=0 , gather_list=UpperCamelCase_ , group=self.process_group )
# scatter logic
__lowerCamelCase = question_hidden_states.shape[0]
__lowerCamelCase = []
__lowerCamelCase = []
if self._is_main():
assert len(UpperCamelCase_ ) == world_size
__lowerCamelCase, __lowerCamelCase = self._main_retrieve(torch.cat(UpperCamelCase_ ).numpy() , UpperCamelCase_ )
__lowerCamelCase, __lowerCamelCase = torch.tensor(UpperCamelCase_ ), torch.tensor(UpperCamelCase_ )
__lowerCamelCase = self._chunk_tensor(UpperCamelCase_ , UpperCamelCase_ )
__lowerCamelCase = self._chunk_tensor(UpperCamelCase_ , UpperCamelCase_ )
__lowerCamelCase = self._scattered(UpperCamelCase_ , [n_queries, n_docs] , target_type=torch.intaa )
__lowerCamelCase = self._scattered(UpperCamelCase_ , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(UpperCamelCase_ )
| 719
|
from __future__ import annotations
def lowerCamelCase__ ( A__ : list ):
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(A__ ) / len(A__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80
| 0
|
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
snake_case : str = logging.get_logger(__name__)
snake_case : Dict = '''▁'''
snake_case : List[Any] = {'''vocab_file''': '''sentencepiece.bpe.model'''}
snake_case : str = {
'''vocab_file''': {
'''facebook/nllb-200-distilled-600M''': (
'''https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'''
),
}
}
snake_case : List[Any] = {
'''facebook/nllb-200-distilled-600M''': 10_24,
}
# fmt: off
snake_case : Optional[Any] = ['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']
class snake_case_ (lowerCamelCase_ ):
UpperCAmelCase__ : Union[str, Any] = VOCAB_FILES_NAMES
UpperCAmelCase__ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ : Any = ['''input_ids''', '''attention_mask''']
UpperCAmelCase__ : List[int] = []
UpperCAmelCase__ : List[int] = []
def __init__( self :Optional[Any] ,__snake_case :Tuple ,__snake_case :int="<s>" ,__snake_case :Union[str, Any]="</s>" ,__snake_case :Optional[Any]="</s>" ,__snake_case :str="<s>" ,__snake_case :Union[str, Any]="<unk>" ,__snake_case :str="<pad>" ,__snake_case :Optional[int]="<mask>" ,__snake_case :Any=None ,__snake_case :Dict=None ,__snake_case :int=None ,__snake_case :Optional[Dict[str, Any]] = None ,__snake_case :Optional[Any]=None ,__snake_case :List[Any]=False ,**__snake_case :Union[str, Any] ,) -> Optional[int]:
# Mask token behave like a normal word, i.e. include the space before it
a__ = AddedToken(__snake_case ,lstrip=__snake_case ,rstrip=__snake_case ) if isinstance(__snake_case ,__snake_case ) else mask_token
a__ = {} if sp_model_kwargs is None else sp_model_kwargs
a__ = legacy_behaviour
super().__init__(
bos_token=__snake_case ,eos_token=__snake_case ,unk_token=__snake_case ,sep_token=__snake_case ,cls_token=__snake_case ,pad_token=__snake_case ,mask_token=__snake_case ,tokenizer_file=__snake_case ,src_lang=__snake_case ,tgt_lang=__snake_case ,additional_special_tokens=__snake_case ,sp_model_kwargs=self.sp_model_kwargs ,legacy_behaviour=__snake_case ,**__snake_case ,)
a__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__snake_case ) )
a__ = 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>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
# spm | '<unk>' | '<s>' | '</s>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'
# Mimic fairseq token-to-id alignment for the first 4 token
a__ = {'<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
a__ = 1
a__ = len(self.sp_model )
a__ = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__snake_case )
}
a__ = {v: k for k, v in self.lang_code_to_id.items()}
a__ = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
a__ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
a__ = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
a__ = src_lang if src_lang is not None else 'eng_Latn'
a__ = self.lang_code_to_id[self._src_lang]
a__ = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self :List[str] ) -> List[Any]:
a__ = self.__dict__.copy()
a__ = None
a__ = self.sp_model.serialized_model_proto()
return state
def __setstate__( self :Union[str, Any] ,__snake_case :int ) -> Union[str, Any]:
a__ = d
# for backward compatibility
if not hasattr(self ,'sp_model_kwargs' ):
a__ = {}
a__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def lowerCamelCase__( self :List[Any] ) -> Dict:
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 :Any ) -> str:
return self._src_lang
@src_lang.setter
def lowerCamelCase__( self :Dict ,__snake_case :str ) -> None:
a__ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def lowerCamelCase__( self :List[Any] ,__snake_case :List[int] ,__snake_case :Optional[List[int]] = None ,__snake_case :bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__snake_case ,token_ids_a=__snake_case ,already_has_special_tokens=__snake_case )
a__ = [1] * len(self.prefix_tokens )
a__ = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(__snake_case )) + suffix_ones
return prefix_ones + ([0] * len(__snake_case )) + ([0] * len(__snake_case )) + suffix_ones
def lowerCamelCase__( self :Dict ,__snake_case :List[int] ,__snake_case :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 :List[Any] ,__snake_case :List[int] ,__snake_case :Optional[List[int]] = None ) -> List[int]:
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 + sep + token_ids_a + sep ) * [0]
def lowerCamelCase__( self :int ,__snake_case :Any ,__snake_case :str ,__snake_case :Optional[str] ,__snake_case :Optional[str] ,**__snake_case :str ) -> Optional[Any]:
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
a__ = src_lang
a__ = self(__snake_case ,add_special_tokens=__snake_case ,return_tensors=__snake_case ,**__snake_case )
a__ = self.convert_tokens_to_ids(__snake_case )
a__ = tgt_lang_id
return inputs
def lowerCamelCase__( self :Any ) -> Optional[int]:
a__ = {self.convert_ids_to_tokens(__snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase__( self :List[str] ,__snake_case :str ) -> List[str]:
return self.sp_model.encode(__snake_case ,out_type=__snake_case )
def lowerCamelCase__( self :List[Any] ,__snake_case :List[Any] ) -> Optional[int]:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
a__ = self.sp_model.PieceToId(__snake_case )
# 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 :Union[str, Any] ,__snake_case :Union[str, Any] ) -> Optional[Any]:
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 :List[str] ,__snake_case :List[str] ) -> Optional[int]:
a__ = ''.join(__snake_case ).replace(__snake_case ,' ' ).strip()
return out_string
def lowerCamelCase__( self :int ,__snake_case :str ,__snake_case :Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__snake_case ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
a__ = os.path.join(
__snake_case ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__snake_case ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,__snake_case )
elif not os.path.isfile(self.vocab_file ):
with open(__snake_case ,'wb' ) as fi:
a__ = self.sp_model.serialized_model_proto()
fi.write(__snake_case )
return (out_vocab_file,)
def lowerCamelCase__( self :List[str] ,__snake_case :List[str] ,__snake_case :str = "eng_Latn" ,__snake_case :Optional[List[str]] = None ,__snake_case :str = "fra_Latn" ,**__snake_case :Dict ,) -> BatchEncoding:
a__ = src_lang
a__ = tgt_lang
return super().prepare_seqaseq_batch(__snake_case ,__snake_case ,**__snake_case )
def lowerCamelCase__( self :List[Any] ) -> Union[str, Any]:
return self.set_src_lang_special_tokens(self.src_lang )
def lowerCamelCase__( self :Dict ) -> Union[str, Any]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def lowerCamelCase__( self :Union[str, Any] ,__snake_case :List[str] ) -> None:
a__ = self.lang_code_to_id[src_lang]
if self.legacy_behaviour:
a__ = []
a__ = [self.eos_token_id, self.cur_lang_code]
else:
a__ = [self.cur_lang_code]
a__ = [self.eos_token_id]
def lowerCamelCase__( self :Tuple ,__snake_case :str ) -> None:
a__ = self.lang_code_to_id[lang]
if self.legacy_behaviour:
a__ = []
a__ = [self.eos_token_id, self.cur_lang_code]
else:
a__ = [self.cur_lang_code]
a__ = [self.eos_token_id]
| 335
|
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
| 335
| 1
|
def UpperCAmelCase_ ( UpperCAmelCase__ ):
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
lowercase_ = sum(UpperCAmelCase__ ) / len(UpperCAmelCase__ ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(UpperCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 720
|
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( __magic_name__ , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : List[Any] = XGLMTokenizer
__SCREAMING_SNAKE_CASE : List[Any] = XGLMTokenizerFast
__SCREAMING_SNAKE_CASE : List[Any] = True
__SCREAMING_SNAKE_CASE : int = True
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowercase_ = XGLMTokenizer(UpperCamelCase__ , keep_accents=UpperCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
lowercase_ = """<pad>"""
lowercase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__ ) , UpperCamelCase__ )
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
lowercase_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(len(UpperCamelCase__ ) , 1_008 )
def UpperCAmelCase__ ( self : int ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_008 )
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
lowercase_ = XGLMTokenizer(UpperCamelCase__ , keep_accents=UpperCamelCase__ )
lowercase_ = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(UpperCamelCase__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowercase_ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCamelCase__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
lowercase_ = tokenizer.convert_tokens_to_ids(UpperCamelCase__ )
self.assertListEqual(
UpperCamelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
lowercase_ = tokenizer.convert_ids_to_tokens(UpperCamelCase__ )
self.assertListEqual(
UpperCamelCase__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
@cached_property
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
return XGLMTokenizer.from_pretrained("""facebook/xglm-564M""" )
def UpperCAmelCase__ ( self : Dict ):
'''simple docstring'''
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(UpperCamelCase__ , f.name )
lowercase_ = XGLMTokenizer(f.name , keep_accents=UpperCamelCase__ )
lowercase_ = pickle.dumps(UpperCamelCase__ )
pickle.loads(UpperCamelCase__ )
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowercase_ = self.get_tokenizer()
lowercase_ = self.get_rust_tokenizer()
lowercase_ = """I was born in 92000, and this is falsé."""
lowercase_ = tokenizer.tokenize(UpperCamelCase__ )
lowercase_ = rust_tokenizer.tokenize(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
lowercase_ = tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ )
lowercase_ = rust_tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
lowercase_ = self.get_rust_tokenizer()
lowercase_ = tokenizer.encode(UpperCamelCase__ )
lowercase_ = rust_tokenizer.encode(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
@slow
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase_ = """Hello World!"""
lowercase_ = [2, 31_227, 4_447, 35]
self.assertListEqual(UpperCamelCase__ , self.big_tokenizer.encode(UpperCamelCase__ ) )
@slow
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
lowercase_ = (
"""This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
""" add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"""
)
# fmt: off
lowercase_ = [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(UpperCamelCase__ , self.big_tokenizer.encode(UpperCamelCase__ ) )
@slow
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
lowercase_ = {
"""input_ids""": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
"""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, 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, 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, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCamelCase__ , model_name="""facebook/xglm-564M""" , padding=UpperCamelCase__ , )
| 650
| 0
|
import unittest
from transformers import MraConfig, is_torch_available
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, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase:
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_=2 , snake_case_=8 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=16 , snake_case_=5 , snake_case_=2 , snake_case_=36 , snake_case_="gelu" , snake_case_=0.0 , snake_case_=0.0 , snake_case_=512 , snake_case_=16 , snake_case_=2 , snake_case_=0.02 , snake_case_=3 , snake_case_=4 , snake_case_=None , ):
_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 lowerCAmelCase__ ( self ):
_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
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase__ ( self ):
return MraConfig(
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=snake_case_ , initializer_range=self.initializer_range , )
def lowerCAmelCase__ ( self ):
_A = self.get_config()
_A = 300
return config
def lowerCAmelCase__ ( self ):
(
(
_A
), (
_A
), (
_A
), (
_A
), (
_A
), (
_A
), (
_A
),
) = self.prepare_config_and_inputs()
_A = True
_A = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_A = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_A = MraModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_A = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ )
_A = model(snake_case_ , token_type_ids=snake_case_ )
_A = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ):
_A = True
_A = MraModel(snake_case_ )
model.to(snake_case_ )
model.eval()
_A = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , encoder_hidden_states=snake_case_ , encoder_attention_mask=snake_case_ , )
_A = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , encoder_hidden_states=snake_case_ , )
_A = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_A = MraForMaskedLM(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_A = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_A = MraForQuestionAnswering(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_A = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , start_positions=snake_case_ , end_positions=snake_case_ , )
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 lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_A = self.num_labels
_A = MraForSequenceClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
_A = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_A = self.num_labels
_A = MraForTokenClassification(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_A = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_A = self.num_choices
_A = MraForMultipleChoice(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_A = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCAmelCase__ ( self ):
_A = self.prepare_config_and_inputs()
(
(
_A
), (
_A
), (
_A
), (
_A
), (
_A
), (
_A
), (
_A
),
) = config_and_inputs
_A = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase( __snake_case , unittest.TestCase ):
'''simple docstring'''
__magic_name__ = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
__magic_name__ = ()
def lowerCAmelCase__ ( self ):
_A = MraModelTester(self )
_A = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def lowerCAmelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_A = type
self.model_tester.create_and_check_model(*snake_case_ )
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case_ )
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*snake_case_ )
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case_ )
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case_ )
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case_ )
@slow
def lowerCAmelCase__ ( self ):
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = MraModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
@unittest.skip(reason='MRA does not output attentions' )
def lowerCAmelCase__ ( self ):
return
@require_torch
class lowerCamelCase( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase__ ( self ):
_A = MraModel.from_pretrained('uw-madison/mra-base-512-4' )
_A = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
_A = model(snake_case_ )[0]
_A = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , snake_case_ )
_A = torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case_ , atol=1E-4 ) )
@slow
def lowerCAmelCase__ ( self ):
_A = MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
_A = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
_A = model(snake_case_ )[0]
_A = 5_0265
_A = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , snake_case_ )
_A = torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case_ , atol=1E-4 ) )
@slow
def lowerCAmelCase__ ( self ):
_A = MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
_A = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
_A = model(snake_case_ )[0]
_A = 5_0265
_A = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape , snake_case_ )
_A = torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case_ , atol=1E-4 ) )
| 27
|
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class UpperCamelCase_ ( UpperCamelCase__ ):
lowerCamelCase_ = 42
class UpperCamelCase_ ( UpperCamelCase__ , UpperCamelCase__ ):
@register_to_config
def __init__( self :Union[str, Any] , __A :int = 3 , __A :int = 3 , __A :Tuple[str] = ("DownEncoderBlock2D",) , __A :Tuple[str] = ("UpDecoderBlock2D",) , __A :Tuple[int] = (64,) , __A :int = 1 , __A :str = "silu" , __A :int = 3 , __A :int = 32 , __A :int = 256 , __A :int = 32 , __A :Optional[int] = None , __A :float = 0.1_8_2_1_5 , __A :str = "group" , ) -> Any:
"""simple docstring"""
super().__init__()
# pass init params to Encoder
SCREAMING_SNAKE_CASE__ = Encoder(
in_channels=__A , out_channels=__A , down_block_types=__A , block_out_channels=__A , layers_per_block=__A , act_fn=__A , norm_num_groups=__A , double_z=__A , )
SCREAMING_SNAKE_CASE__ = vq_embed_dim if vq_embed_dim is not None else latent_channels
SCREAMING_SNAKE_CASE__ = nn.Convad(__A , __A , 1 )
SCREAMING_SNAKE_CASE__ = VectorQuantizer(__A , __A , beta=0.2_5 , remap=__A , sane_index_shape=__A )
SCREAMING_SNAKE_CASE__ = nn.Convad(__A , __A , 1 )
# pass init params to Decoder
SCREAMING_SNAKE_CASE__ = Decoder(
in_channels=__A , out_channels=__A , up_block_types=__A , block_out_channels=__A , layers_per_block=__A , act_fn=__A , norm_num_groups=__A , norm_type=__A , )
@apply_forward_hook
def _snake_case ( self :Union[str, Any] , __A :torch.FloatTensor , __A :bool = True ) -> VQEncoderOutput:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.encoder(__A )
SCREAMING_SNAKE_CASE__ = self.quant_conv(__A )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=__A )
@apply_forward_hook
def _snake_case ( self :Tuple , __A :torch.FloatTensor , __A :bool = False , __A :bool = True ) -> Union[DecoderOutput, torch.FloatTensor]:
"""simple docstring"""
if not force_not_quantize:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.quantize(__A )
else:
SCREAMING_SNAKE_CASE__ = h
SCREAMING_SNAKE_CASE__ = self.post_quant_conv(__A )
SCREAMING_SNAKE_CASE__ = self.decoder(__A , quant if self.config.norm_type == """spatial""" else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__A )
def _snake_case ( self :int , __A :torch.FloatTensor , __A :bool = True ) -> Union[DecoderOutput, torch.FloatTensor]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = sample
SCREAMING_SNAKE_CASE__ = self.encode(__A ).latents
SCREAMING_SNAKE_CASE__ = self.decode(__A ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=__A )
| 6
| 0
|
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_UpperCamelCase )
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :str = field(default="image-classification" , metadata={"include_in_asdict_even_if_is_default": True} )
SCREAMING_SNAKE_CASE__ :ClassVar[Features] = Features({"image": Image()} )
SCREAMING_SNAKE_CASE__ :ClassVar[Features] = Features({"labels": ClassLabel} )
SCREAMING_SNAKE_CASE__ :str = "image"
SCREAMING_SNAKE_CASE__ :str = "labels"
def __SCREAMING_SNAKE_CASE ( self : List[str] , __a : Optional[Any] ) -> Optional[int]:
if self.label_column not in features:
raise ValueError(F'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , __a ):
raise ValueError(F'''Column {self.label_column} is not a ClassLabel.''' )
_UpperCamelCase : List[Any] = copy.deepcopy(self )
_UpperCamelCase : Any = self.label_schema.copy()
_UpperCamelCase : str = features[self.label_column]
_UpperCamelCase : str = label_schema
return task_template
@property
def __SCREAMING_SNAKE_CASE ( self : str ) -> Dict[str, str]:
return {
self.image_column: "image",
self.label_column: "labels",
}
| 51
|
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :torch.FloatTensor
class __SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , __a : Dict=3 , __a : Any=3 , __a : Union[str, Any]=("DownEncoderBlock2D",) , __a : Optional[int]=(64,) , __a : int=2 , __a : Tuple=32 , __a : int="silu" , __a : str=True , ) -> Dict:
super().__init__()
_UpperCamelCase : List[str] = layers_per_block
_UpperCamelCase : Dict = torch.nn.Convad(
__a , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
_UpperCamelCase : int = None
_UpperCamelCase : Any = nn.ModuleList([] )
# down
_UpperCamelCase : List[str] = block_out_channels[0]
for i, down_block_type in enumerate(__a ):
_UpperCamelCase : Tuple = output_channel
_UpperCamelCase : int = block_out_channels[i]
_UpperCamelCase : int = i == len(__a ) - 1
_UpperCamelCase : Dict = get_down_block(
__a , num_layers=self.layers_per_block , in_channels=__a , out_channels=__a , add_downsample=not is_final_block , resnet_eps=1e-6 , downsample_padding=0 , resnet_act_fn=__a , resnet_groups=__a , attention_head_dim=__a , temb_channels=__a , )
self.down_blocks.append(__a )
# mid
_UpperCamelCase : Union[str, Any] = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=__a , output_scale_factor=1 , resnet_time_scale_shift="default" , attention_head_dim=block_out_channels[-1] , resnet_groups=__a , temb_channels=__a , )
# out
_UpperCamelCase : Any = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=__a , eps=1e-6 )
_UpperCamelCase : Any = nn.SiLU()
_UpperCamelCase : Union[str, Any] = 2 * out_channels if double_z else out_channels
_UpperCamelCase : Tuple = nn.Convad(block_out_channels[-1] , __a , 3 , padding=1 )
_UpperCamelCase : Optional[int] = False
def __SCREAMING_SNAKE_CASE ( self : List[str] , __a : Dict ) -> List[str]:
_UpperCamelCase : int = x
_UpperCamelCase : Optional[int] = self.conv_in(__a )
if self.training and self.gradient_checkpointing:
def create_custom_forward(__a : Tuple ):
def custom_forward(*__a : Any ):
return module(*__a )
return custom_forward
# down
if is_torch_version(">=" , "1.11.0" ):
for down_block in self.down_blocks:
_UpperCamelCase : Optional[int] = torch.utils.checkpoint.checkpoint(
create_custom_forward(__a ) , __a , use_reentrant=__a )
# middle
_UpperCamelCase : Tuple = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , __a , use_reentrant=__a )
else:
for down_block in self.down_blocks:
_UpperCamelCase : Any = torch.utils.checkpoint.checkpoint(create_custom_forward(__a ) , __a )
# middle
_UpperCamelCase : int = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , __a )
else:
# down
for down_block in self.down_blocks:
_UpperCamelCase : int = down_block(__a )
# middle
_UpperCamelCase : int = self.mid_block(__a )
# post-process
_UpperCamelCase : Any = self.conv_norm_out(__a )
_UpperCamelCase : Any = self.conv_act(__a )
_UpperCamelCase : Optional[Any] = self.conv_out(__a )
return sample
class __SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , __a : int=3 , __a : Any=3 , __a : str=("UpDecoderBlock2D",) , __a : Optional[int]=(64,) , __a : int=2 , __a : Optional[int]=32 , __a : Tuple="silu" , __a : Union[str, Any]="group" , ) -> str:
super().__init__()
_UpperCamelCase : List[Any] = layers_per_block
_UpperCamelCase : Tuple = nn.Convad(
__a , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
_UpperCamelCase : List[str] = None
_UpperCamelCase : Dict = nn.ModuleList([] )
_UpperCamelCase : List[Any] = in_channels if norm_type == "spatial" else None
# mid
_UpperCamelCase : Optional[Any] = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=__a , output_scale_factor=1 , resnet_time_scale_shift="default" if norm_type == "group" else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=__a , temb_channels=__a , )
# up
_UpperCamelCase : List[str] = list(reversed(__a ) )
_UpperCamelCase : int = reversed_block_out_channels[0]
for i, up_block_type in enumerate(__a ):
_UpperCamelCase : int = output_channel
_UpperCamelCase : Union[str, Any] = reversed_block_out_channels[i]
_UpperCamelCase : Optional[Any] = i == len(__a ) - 1
_UpperCamelCase : Union[str, Any] = get_up_block(
__a , num_layers=self.layers_per_block + 1 , in_channels=__a , out_channels=__a , prev_output_channel=__a , add_upsample=not is_final_block , resnet_eps=1e-6 , resnet_act_fn=__a , resnet_groups=__a , attention_head_dim=__a , temb_channels=__a , resnet_time_scale_shift=__a , )
self.up_blocks.append(__a )
_UpperCamelCase : Optional[Any] = output_channel
# out
if norm_type == "spatial":
_UpperCamelCase : Optional[int] = SpatialNorm(block_out_channels[0] , __a )
else:
_UpperCamelCase : Optional[int] = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=__a , eps=1e-6 )
_UpperCamelCase : str = nn.SiLU()
_UpperCamelCase : str = nn.Convad(block_out_channels[0] , __a , 3 , padding=1 )
_UpperCamelCase : Dict = False
def __SCREAMING_SNAKE_CASE ( self : Optional[int] , __a : List[Any] , __a : Union[str, Any]=None ) -> Tuple:
_UpperCamelCase : List[str] = z
_UpperCamelCase : Dict = self.conv_in(__a )
_UpperCamelCase : Any = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(__a : Any ):
def custom_forward(*__a : Tuple ):
return module(*__a )
return custom_forward
if is_torch_version(">=" , "1.11.0" ):
# middle
_UpperCamelCase : str = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , __a , __a , use_reentrant=__a )
_UpperCamelCase : Optional[int] = sample.to(__a )
# up
for up_block in self.up_blocks:
_UpperCamelCase : List[Any] = torch.utils.checkpoint.checkpoint(
create_custom_forward(__a ) , __a , __a , use_reentrant=__a )
else:
# middle
_UpperCamelCase : Optional[int] = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , __a , __a )
_UpperCamelCase : Union[str, Any] = sample.to(__a )
# up
for up_block in self.up_blocks:
_UpperCamelCase : str = torch.utils.checkpoint.checkpoint(create_custom_forward(__a ) , __a , __a )
else:
# middle
_UpperCamelCase : str = self.mid_block(__a , __a )
_UpperCamelCase : int = sample.to(__a )
# up
for up_block in self.up_blocks:
_UpperCamelCase : Any = up_block(__a , __a )
# post-process
if latent_embeds is None:
_UpperCamelCase : List[str] = self.conv_norm_out(__a )
else:
_UpperCamelCase : Optional[int] = self.conv_norm_out(__a , __a )
_UpperCamelCase : Tuple = self.conv_act(__a )
_UpperCamelCase : List[Any] = self.conv_out(__a )
return sample
class __SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , __a : Tuple , __a : List[str] , __a : List[str] , __a : str=None , __a : Optional[int]="random" , __a : Any=False , __a : Optional[Any]=True ) -> List[Any]:
super().__init__()
_UpperCamelCase : Tuple = n_e
_UpperCamelCase : Tuple = vq_embed_dim
_UpperCamelCase : Union[str, Any] = beta
_UpperCamelCase : str = legacy
_UpperCamelCase : Dict = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
_UpperCamelCase : Any = remap
if self.remap is not None:
self.register_buffer("used" , torch.tensor(np.load(self.remap ) ) )
_UpperCamelCase : Dict = self.used.shape[0]
_UpperCamelCase : Optional[int] = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
_UpperCamelCase : Optional[int] = self.re_embed
_UpperCamelCase : Any = self.re_embed + 1
print(
F'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
F'''Using {self.unknown_index} for unknown indices.''' )
else:
_UpperCamelCase : Union[str, Any] = n_e
_UpperCamelCase : List[str] = sane_index_shape
def __SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __a : Optional[Any] ) -> Optional[int]:
_UpperCamelCase : str = inds.shape
assert len(__a ) > 1
_UpperCamelCase : Union[str, Any] = inds.reshape(ishape[0] , -1 )
_UpperCamelCase : Optional[Any] = self.used.to(__a )
_UpperCamelCase : List[str] = (inds[:, :, None] == used[None, None, ...]).long()
_UpperCamelCase : Optional[Any] = match.argmax(-1 )
_UpperCamelCase : Any = match.sum(2 ) < 1
if self.unknown_index == "random":
_UpperCamelCase : Optional[int] = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
_UpperCamelCase : Dict = self.unknown_index
return new.reshape(__a )
def __SCREAMING_SNAKE_CASE ( self : Dict , __a : Optional[int] ) -> Optional[int]:
_UpperCamelCase : int = inds.shape
assert len(__a ) > 1
_UpperCamelCase : List[Any] = inds.reshape(ishape[0] , -1 )
_UpperCamelCase : Optional[int] = self.used.to(__a )
if self.re_embed > self.used.shape[0]: # extra token
_UpperCamelCase : int = 0 # simply set to zero
_UpperCamelCase : Union[str, Any] = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , __a )
return back.reshape(__a )
def __SCREAMING_SNAKE_CASE ( self : Optional[int] , __a : str ) -> Optional[int]:
# reshape z -> (batch, height, width, channel) and flatten
_UpperCamelCase : List[str] = z.permute(0 , 2 , 3 , 1 ).contiguous()
_UpperCamelCase : int = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
_UpperCamelCase : Optional[int] = torch.argmin(torch.cdist(__a , self.embedding.weight ) , dim=1 )
_UpperCamelCase : int = self.embedding(__a ).view(z.shape )
_UpperCamelCase : str = None
_UpperCamelCase : Any = None
# compute loss for embedding
if not self.legacy:
_UpperCamelCase : List[str] = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
_UpperCamelCase : str = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
_UpperCamelCase : List[str] = z + (z_q - z).detach()
# reshape back to match original input shape
_UpperCamelCase : Optional[Any] = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
_UpperCamelCase : Tuple = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
_UpperCamelCase : Dict = self.remap_to_used(__a )
_UpperCamelCase : List[str] = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
_UpperCamelCase : str = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def __SCREAMING_SNAKE_CASE ( self : List[str] , __a : List[str] , __a : str ) -> Any:
# shape specifying (batch, height, width, channel)
if self.remap is not None:
_UpperCamelCase : str = indices.reshape(shape[0] , -1 ) # add batch axis
_UpperCamelCase : str = self.unmap_to_all(__a )
_UpperCamelCase : int = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
_UpperCamelCase : Optional[int] = self.embedding(__a )
if shape is not None:
_UpperCamelCase : Tuple = z_q.view(__a )
# reshape back to match original input shape
_UpperCamelCase : Tuple = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
'''simple docstring'''
def __init__( self : Optional[int] , __a : List[str] , __a : Optional[Any]=False ) -> int:
_UpperCamelCase : Dict = parameters
_UpperCamelCase, _UpperCamelCase : str = torch.chunk(__a , 2 , dim=1 )
_UpperCamelCase : Tuple = torch.clamp(self.logvar , -30.0 , 20.0 )
_UpperCamelCase : Union[str, Any] = deterministic
_UpperCamelCase : Dict = torch.exp(0.5 * self.logvar )
_UpperCamelCase : Any = torch.exp(self.logvar )
if self.deterministic:
_UpperCamelCase : List[Any] = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def __SCREAMING_SNAKE_CASE ( self : Optional[int] , __a : Optional[torch.Generator] = None ) -> torch.FloatTensor:
# make sure sample is on the same device as the parameters and has same dtype
_UpperCamelCase : List[Any] = randn_tensor(
self.mean.shape , generator=__a , device=self.parameters.device , dtype=self.parameters.dtype )
_UpperCamelCase : List[Any] = self.mean + self.std * sample
return x
def __SCREAMING_SNAKE_CASE ( self : Any , __a : List[str]=None ) -> List[Any]:
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def __SCREAMING_SNAKE_CASE ( self : str , __a : Tuple , __a : List[str]=[1, 2, 3] ) -> int:
if self.deterministic:
return torch.Tensor([0.0] )
_UpperCamelCase : List[str] = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=__a )
def __SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Optional[int]:
return self.mean
| 51
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_A : int = {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : Tuple = ["""GLPNFeatureExtractor"""]
_A : List[str] = ["""GLPNImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : List[Any] = [
"""GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GLPNForDepthEstimation""",
"""GLPNLayer""",
"""GLPNModel""",
"""GLPNPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_glpn import GLPNFeatureExtractor
from .image_processing_glpn import GLPNImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_glpn import (
GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
GLPNForDepthEstimation,
GLPNLayer,
GLPNModel,
GLPNPreTrainedModel,
)
else:
import sys
_A : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 100
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case : Dict = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : str = [
'''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''OPTForCausalLM''',
'''OPTModel''',
'''OPTPreTrainedModel''',
'''OPTForSequenceClassification''',
'''OPTForQuestionAnswering''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : Optional[int] = ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : Optional[Any] = [
'''FlaxOPTForCausalLM''',
'''FlaxOPTModel''',
'''FlaxOPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
snake_case : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 335
| 0
|
'''simple docstring'''
a = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_9344,
"knot": 1.852,
}
a = {
"km/h": 1.0,
"m/s": 0.2_7777_7778,
"mph": 0.6_2137_1192,
"knot": 0.5_3995_6803,
}
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> float:
'''simple docstring'''
if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
__SCREAMING_SNAKE_CASE = (
f"""Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"""
f"""Valid values are: {', '.join(__UpperCAmelCase )}"""
)
raise ValueError(__UpperCAmelCase )
return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to] , 3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 13
|
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __a ( _snake_case ):
__UpperCamelCase : Dict = 'deta'
__UpperCamelCase : List[str] = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Tuple ,lowerCamelCase : List[Any]=None ,lowerCamelCase : Any=900 ,lowerCamelCase : int=2048 ,lowerCamelCase : Any=6 ,lowerCamelCase : Optional[Any]=2048 ,lowerCamelCase : str=8 ,lowerCamelCase : Union[str, Any]=6 ,lowerCamelCase : List[str]=1024 ,lowerCamelCase : int=8 ,lowerCamelCase : Any=0.0 ,lowerCamelCase : Any=True ,lowerCamelCase : Optional[int]="relu" ,lowerCamelCase : int=256 ,lowerCamelCase : Tuple=0.1 ,lowerCamelCase : Optional[Any]=0.0 ,lowerCamelCase : Tuple=0.0 ,lowerCamelCase : List[str]=0.02 ,lowerCamelCase : Any=1.0 ,lowerCamelCase : Optional[int]=True ,lowerCamelCase : int=False ,lowerCamelCase : Optional[Any]="sine" ,lowerCamelCase : Dict=5 ,lowerCamelCase : List[Any]=4 ,lowerCamelCase : Optional[Any]=4 ,lowerCamelCase : Any=True ,lowerCamelCase : int=300 ,lowerCamelCase : Any=True ,lowerCamelCase : Tuple=True ,lowerCamelCase : int=1 ,lowerCamelCase : Tuple=5 ,lowerCamelCase : Union[str, Any]=2 ,lowerCamelCase : Tuple=1 ,lowerCamelCase : int=1 ,lowerCamelCase : str=5 ,lowerCamelCase : Optional[Any]=2 ,lowerCamelCase : List[Any]=0.1 ,lowerCamelCase : Union[str, Any]=0.25 ,**lowerCamelCase : int ,):
'''simple docstring'''
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
__SCREAMING_SNAKE_CASE = CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""] )
else:
if isinstance(lowerCamelCase ,lowerCamelCase ):
__SCREAMING_SNAKE_CASE = backbone_config.pop("""model_type""" )
__SCREAMING_SNAKE_CASE = CONFIG_MAPPING[backbone_model_type]
__SCREAMING_SNAKE_CASE = config_class.from_dict(lowerCamelCase )
__SCREAMING_SNAKE_CASE = backbone_config
__SCREAMING_SNAKE_CASE = num_queries
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = d_model
__SCREAMING_SNAKE_CASE = encoder_ffn_dim
__SCREAMING_SNAKE_CASE = encoder_layers
__SCREAMING_SNAKE_CASE = encoder_attention_heads
__SCREAMING_SNAKE_CASE = decoder_ffn_dim
__SCREAMING_SNAKE_CASE = decoder_layers
__SCREAMING_SNAKE_CASE = decoder_attention_heads
__SCREAMING_SNAKE_CASE = dropout
__SCREAMING_SNAKE_CASE = attention_dropout
__SCREAMING_SNAKE_CASE = activation_dropout
__SCREAMING_SNAKE_CASE = activation_function
__SCREAMING_SNAKE_CASE = init_std
__SCREAMING_SNAKE_CASE = init_xavier_std
__SCREAMING_SNAKE_CASE = encoder_layerdrop
__SCREAMING_SNAKE_CASE = auxiliary_loss
__SCREAMING_SNAKE_CASE = position_embedding_type
# deformable attributes
__SCREAMING_SNAKE_CASE = num_feature_levels
__SCREAMING_SNAKE_CASE = encoder_n_points
__SCREAMING_SNAKE_CASE = decoder_n_points
__SCREAMING_SNAKE_CASE = two_stage
__SCREAMING_SNAKE_CASE = two_stage_num_proposals
__SCREAMING_SNAKE_CASE = with_box_refine
__SCREAMING_SNAKE_CASE = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError("""If two_stage is True, with_box_refine must be True.""" )
# Hungarian matcher
__SCREAMING_SNAKE_CASE = class_cost
__SCREAMING_SNAKE_CASE = bbox_cost
__SCREAMING_SNAKE_CASE = giou_cost
# Loss coefficients
__SCREAMING_SNAKE_CASE = mask_loss_coefficient
__SCREAMING_SNAKE_CASE = dice_loss_coefficient
__SCREAMING_SNAKE_CASE = bbox_loss_coefficient
__SCREAMING_SNAKE_CASE = giou_loss_coefficient
__SCREAMING_SNAKE_CASE = eos_coefficient
__SCREAMING_SNAKE_CASE = focal_alpha
super().__init__(is_encoder_decoder=lowerCamelCase ,**lowerCamelCase )
@property
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
return self.encoder_attention_heads
@property
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
return self.d_model
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
__SCREAMING_SNAKE_CASE = self.backbone_config.to_dict()
__SCREAMING_SNAKE_CASE = self.__class__.model_type
return output
| 13
| 1
|
import unittest
from transformers import DonutProcessor
__lowerCAmelCase = """naver-clova-ix/donut-base"""
class lowerCamelCase_ ( unittest.TestCase ):
def lowercase ( self ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = DonutProcessor.from_pretrained(lowerCamelCase_ )
def lowercase ( self ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = {
"name": "John Doe",
"age": "99",
"city": "Atlanta",
"state": "GA",
"zip": "30301",
"phone": "123-4567",
"nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
}
_UpperCamelCase = (
"<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
"<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
"<s_nicknames><s_nickname>Johnny</s_nickname>"
"<sep/><s_nickname>JD</s_nickname></s_nicknames>"
)
_UpperCamelCase = self.processor.tokenajson(lowerCamelCase_ )
self.assertDictEqual(lowerCamelCase_ , lowerCamelCase_ )
| 147
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class lowerCamelCase_ ( unittest.TestCase ):
def lowercase ( self ) -> int:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def lowercase ( self ) -> Dict:
"""simple docstring"""
_UpperCamelCase = 1
_UpperCamelCase = 3
_UpperCamelCase = (32, 32)
_UpperCamelCase = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(lowerCamelCase_ )
return image
@property
def lowercase ( self ) -> str:
"""simple docstring"""
torch.manual_seed(0 )
_UpperCamelCase = UNetaDConditionModel(
block_out_channels=(32, 32, 64) , layers_per_block=2 , sample_size=32 , in_channels=7 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , attention_head_dim=8 , use_linear_projection=lowerCamelCase_ , only_cross_attention=(True, True, False) , num_class_embeds=1_00 , )
return model
@property
def lowercase ( self ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
_UpperCamelCase = AutoencoderKL(
block_out_channels=[32, 32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
return model
@property
def lowercase ( self ) -> List[Any]:
"""simple docstring"""
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act="gelu" , projection_dim=5_12 , )
return CLIPTextModel(lowerCamelCase_ )
def lowercase ( self ) -> Dict:
"""simple docstring"""
_UpperCamelCase = "cpu" # ensure determinism for the device-dependent torch.Generator
_UpperCamelCase = self.dummy_cond_unet_upscale
_UpperCamelCase = DDPMScheduler()
_UpperCamelCase = DDIMScheduler(prediction_type="v_prediction" )
_UpperCamelCase = self.dummy_vae
_UpperCamelCase = self.dummy_text_encoder
_UpperCamelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_UpperCamelCase = self.dummy_image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCamelCase = Image.fromarray(np.uinta(lowerCamelCase_ ) ).convert("RGB" ).resize((64, 64) )
# make sure here that pndm scheduler skips prk
_UpperCamelCase = StableDiffusionUpscalePipeline(
unet=lowerCamelCase_ , low_res_scheduler=lowerCamelCase_ , scheduler=lowerCamelCase_ , vae=lowerCamelCase_ , text_encoder=lowerCamelCase_ , tokenizer=lowerCamelCase_ , max_noise_level=3_50 , )
_UpperCamelCase = sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
_UpperCamelCase = "A painting of a squirrel eating a burger"
_UpperCamelCase = torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
_UpperCamelCase = sd_pipe(
[prompt] , image=lowerCamelCase_ , generator=lowerCamelCase_ , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type="np" , )
_UpperCamelCase = output.images
_UpperCamelCase = torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
_UpperCamelCase = sd_pipe(
[prompt] , image=lowerCamelCase_ , generator=lowerCamelCase_ , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type="np" , return_dict=lowerCamelCase_ , )[0]
_UpperCamelCase = image[0, -3:, -3:, -1]
_UpperCamelCase = image_from_tuple[0, -3:, -3:, -1]
_UpperCamelCase = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
_UpperCamelCase = np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def lowercase ( self ) -> Any:
"""simple docstring"""
_UpperCamelCase = "cpu" # ensure determinism for the device-dependent torch.Generator
_UpperCamelCase = self.dummy_cond_unet_upscale
_UpperCamelCase = DDPMScheduler()
_UpperCamelCase = DDIMScheduler(prediction_type="v_prediction" )
_UpperCamelCase = self.dummy_vae
_UpperCamelCase = self.dummy_text_encoder
_UpperCamelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_UpperCamelCase = self.dummy_image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCamelCase = Image.fromarray(np.uinta(lowerCamelCase_ ) ).convert("RGB" ).resize((64, 64) )
# make sure here that pndm scheduler skips prk
_UpperCamelCase = StableDiffusionUpscalePipeline(
unet=lowerCamelCase_ , low_res_scheduler=lowerCamelCase_ , scheduler=lowerCamelCase_ , vae=lowerCamelCase_ , text_encoder=lowerCamelCase_ , tokenizer=lowerCamelCase_ , max_noise_level=3_50 , )
_UpperCamelCase = sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
_UpperCamelCase = "A painting of a squirrel eating a burger"
_UpperCamelCase = sd_pipe(
2 * [prompt] , image=2 * [low_res_image] , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type="np" , )
_UpperCamelCase = output.images
assert image.shape[0] == 2
_UpperCamelCase = torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
_UpperCamelCase = sd_pipe(
[prompt] , image=lowerCamelCase_ , generator=lowerCamelCase_ , num_images_per_prompt=2 , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type="np" , )
_UpperCamelCase = output.images
assert image.shape[0] == 2
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU" )
def lowercase ( self ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = self.dummy_cond_unet_upscale
_UpperCamelCase = DDPMScheduler()
_UpperCamelCase = DDIMScheduler(prediction_type="v_prediction" )
_UpperCamelCase = self.dummy_vae
_UpperCamelCase = self.dummy_text_encoder
_UpperCamelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_UpperCamelCase = self.dummy_image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCamelCase = Image.fromarray(np.uinta(lowerCamelCase_ ) ).convert("RGB" ).resize((64, 64) )
# put models in fp16, except vae as it overflows in fp16
_UpperCamelCase = unet.half()
_UpperCamelCase = text_encoder.half()
# make sure here that pndm scheduler skips prk
_UpperCamelCase = StableDiffusionUpscalePipeline(
unet=lowerCamelCase_ , low_res_scheduler=lowerCamelCase_ , scheduler=lowerCamelCase_ , vae=lowerCamelCase_ , text_encoder=lowerCamelCase_ , tokenizer=lowerCamelCase_ , max_noise_level=3_50 , )
_UpperCamelCase = sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
_UpperCamelCase = "A painting of a squirrel eating a burger"
_UpperCamelCase = torch.manual_seed(0 )
_UpperCamelCase = sd_pipe(
[prompt] , image=lowerCamelCase_ , generator=lowerCamelCase_ , num_inference_steps=2 , output_type="np" , ).images
_UpperCamelCase = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
@slow
@require_torch_gpu
class lowerCamelCase_ ( unittest.TestCase ):
def lowercase ( self ) -> Optional[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self ) -> Any:
"""simple docstring"""
_UpperCamelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png" )
_UpperCamelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
"/upsampled_cat.npy" )
_UpperCamelCase = "stabilityai/stable-diffusion-x4-upscaler"
_UpperCamelCase = StableDiffusionUpscalePipeline.from_pretrained(lowerCamelCase_ )
pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
pipe.enable_attention_slicing()
_UpperCamelCase = "a cat sitting on a park bench"
_UpperCamelCase = torch.manual_seed(0 )
_UpperCamelCase = pipe(
prompt=lowerCamelCase_ , image=lowerCamelCase_ , generator=lowerCamelCase_ , output_type="np" , )
_UpperCamelCase = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert np.abs(expected_image - image ).max() < 1E-3
def lowercase ( self ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png" )
_UpperCamelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
"/upsampled_cat_fp16.npy" )
_UpperCamelCase = "stabilityai/stable-diffusion-x4-upscaler"
_UpperCamelCase = StableDiffusionUpscalePipeline.from_pretrained(
lowerCamelCase_ , torch_dtype=torch.floataa , )
pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
pipe.enable_attention_slicing()
_UpperCamelCase = "a cat sitting on a park bench"
_UpperCamelCase = torch.manual_seed(0 )
_UpperCamelCase = pipe(
prompt=lowerCamelCase_ , image=lowerCamelCase_ , generator=lowerCamelCase_ , output_type="np" , )
_UpperCamelCase = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert np.abs(expected_image - image ).max() < 5E-1
def lowercase ( self ) -> str:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_UpperCamelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png" )
_UpperCamelCase = "stabilityai/stable-diffusion-x4-upscaler"
_UpperCamelCase = StableDiffusionUpscalePipeline.from_pretrained(
lowerCamelCase_ , torch_dtype=torch.floataa , )
pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = "a cat sitting on a park bench"
_UpperCamelCase = torch.manual_seed(0 )
_UpperCamelCase = pipe(
prompt=lowerCamelCase_ , image=lowerCamelCase_ , generator=lowerCamelCase_ , num_inference_steps=5 , output_type="np" , )
_UpperCamelCase = torch.cuda.max_memory_allocated()
# make sure that less than 2.9 GB is allocated
assert mem_bytes < 2.9 * 10**9
| 147
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowerCAmelCase = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
"""processing_trocr""": ["""TrOCRProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase = [
"""TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TrOCRForCausalLM""",
"""TrOCRPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 713
|
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __A ( a_ : Any=None ,a_ : List[Any]=None ):
return field(default_factory=lambda: default ,metadata=a_ )
@dataclass
class lowerCamelCase :
snake_case_ = field(
metadata={"help": "The csv file to plot."} , )
snake_case_ = field(
default=_A , metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."} , )
snake_case_ = field(
default=_A , metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."} , )
snake_case_ = field(
default=_A , metadata={"help": "Disable logarithmic scale when plotting"} , )
snake_case_ = field(
default=_A , metadata={
"help": "Whether the csv file has training results or inference results. Defaults to inference results."
} , )
snake_case_ = field(
default=_A , metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."} , )
snake_case_ = list_field(
default=_A , metadata={"help": "List of model names that are used instead of the ones in the csv file."} )
def __A ( a_ : Tuple ):
try:
int(a_ )
return True
except ValueError:
return False
def __A ( a_ : int ):
try:
float(a_ )
return True
except ValueError:
return False
class lowerCamelCase :
def __init__( self , a_ ):
lowerCAmelCase : Optional[Any] = args
lowerCAmelCase : List[str] = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )
with open(self.args.csv_file , newline="" ) as csv_file:
lowerCAmelCase : str = csv.DictReader(a_ )
for row in reader:
lowerCAmelCase : Tuple = row["model"]
self.result_dict[model_name]["bsz"].append(int(row["batch_size"] ) )
self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"] ) )
if can_convert_to_int(row["result"] ):
# value is not None
lowerCAmelCase : Union[str, Any] = int(row["result"] )
elif can_convert_to_float(row["result"] ):
# value is not None
lowerCAmelCase : Optional[int] = float(row["result"] )
def _lowerCamelCase ( self ):
lowerCAmelCase , lowerCAmelCase : Any = plt.subplots()
lowerCAmelCase : int = "Time usage" if self.args.is_time else "Memory usage"
lowerCAmelCase : List[Any] = title_str + " for training" if self.args.is_train else title_str + " for inference"
if not self.args.no_log_scale:
# set logarithm scales
ax.set_xscale("log" )
ax.set_yscale("log" )
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter() )
for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
lowerCAmelCase : str = sorted(set(self.result_dict[model_name]["bsz"] ) )
lowerCAmelCase : List[str] = sorted(set(self.result_dict[model_name]["seq_len"] ) )
lowerCAmelCase : Union[str, Any] = self.result_dict[model_name]["result"]
((lowerCAmelCase) , (lowerCAmelCase)) : str = (
(batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
)
lowerCAmelCase : Union[str, Any] = (
model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
)
for inner_loop_value in inner_loop_array:
if self.args.plot_along_batch:
lowerCAmelCase : int = np.asarray(
[results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] , dtype=a_ , )
else:
lowerCAmelCase : Any = np.asarray(
[results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] , dtype=np.floataa , )
((lowerCAmelCase) , (lowerCAmelCase)) : Any = (
("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
)
lowerCAmelCase : Union[str, Any] = np.asarray(a_ , a_ )[: len(a_ )]
plt.scatter(
a_ , a_ , label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}''' )
plt.plot(a_ , a_ , "--" )
title_str += F''' {label_model_name} vs.'''
lowerCAmelCase : List[str] = title_str[:-4]
lowerCAmelCase : List[Any] = "Time in s" if self.args.is_time else "Memory in MB"
# plot
plt.title(a_ )
plt.xlabel(a_ )
plt.ylabel(a_ )
plt.legend()
if self.args.figure_png_file is not None:
plt.savefig(self.args.figure_png_file )
else:
plt.show()
def __A ( ):
lowerCAmelCase : Optional[Any] = HfArgumentParser(a_ )
lowerCAmelCase : List[Any] = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase : str = Plot(args=a_ )
plot.plot()
if __name__ == "__main__":
main()
| 551
| 0
|
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _lowerCAmelCase ( UpperCamelCase__: List[str] ) -> Optional[Any]:
"""simple docstring"""
A = int(number**0.5 )
return number == sq * sq
def _lowerCAmelCase ( UpperCamelCase__: Union[str, Any] , UpperCamelCase__: List[str] , UpperCamelCase__: int , UpperCamelCase__: Any , UpperCamelCase__: List[str] , UpperCamelCase__: List[str] ) -> List[str]:
"""simple docstring"""
A = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
A = x_den * y_den * z_den
A = gcd(_lowercase , _lowercase )
top //= hcf
bottom //= hcf
return top, bottom
def _lowerCAmelCase ( UpperCamelCase__: Dict = 35 ) -> List[Any]:
"""simple docstring"""
A = set()
A = 42
A = Fraction(0 )
A = 42
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
A = x_num * y_den + x_den * y_num
A = x_den * y_den
A = gcd(_lowercase , _lowercase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
A = add_three(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
unique_s.add(_lowercase )
# n=2
A = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
A = x_den * x_den * y_den * y_den
if is_sq(_lowercase ) and is_sq(_lowercase ):
A = int(sqrt(_lowercase ) )
A = int(sqrt(_lowercase ) )
A = gcd(_lowercase , _lowercase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
A = add_three(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
unique_s.add(_lowercase )
# n=-1
A = x_num * y_num
A = x_den * y_num + x_num * y_den
A = gcd(_lowercase , _lowercase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
A = add_three(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
unique_s.add(_lowercase )
# n=2
A = x_num * x_num * y_num * y_num
A = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(_lowercase ) and is_sq(_lowercase ):
A = int(sqrt(_lowercase ) )
A = int(sqrt(_lowercase ) )
A = gcd(_lowercase , _lowercase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
A = add_three(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
unique_s.add(_lowercase )
for num, den in unique_s:
total += Fraction(_lowercase , _lowercase )
return total.denominator + total.numerator
if __name__ == "__main__":
print(f'''{solution() = }''')
| 641
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileBertConfig',
'MobileBertOnnxConfig',
],
'tokenization_mobilebert': ['MobileBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ['MobileBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'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:
__A = [
'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
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 484
| 0
|
'''simple docstring'''
UpperCAmelCase_ : Any = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
UpperCAmelCase_ : Optional[Any] = [{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase_ : str = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 540
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def snake_case__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def snake_case__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
__A : Optional[int] = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return model
@property
def snake_case__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
__A : Dict = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') , cross_attention_dim=10 , )
return model
@property
def snake_case__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
__A : Union[str, Any] = AutoencoderKL(
sample_size=(128, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , )
__A : Union[str, Any] = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return vqvae, unet
@slow
def snake_case__ ( self ):
"""simple docstring"""
__A : str = 'cpu' # ensure determinism for the device-dependent torch.Generator
__A : int = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
__A : Tuple = DDPMScheduler()
__A : Optional[int] = AudioDiffusionPipeline(vqvae=__lowercase , unet=self.dummy_unet , mel=__lowercase , scheduler=__lowercase )
__A : Dict = pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
__A : Tuple = torch.Generator(device=__lowercase ).manual_seed(42 )
__A : Tuple = pipe(generator=__lowercase , steps=4 )
__A : Union[str, Any] = output.audios[0]
__A : Dict = output.images[0]
__A : Optional[Any] = torch.Generator(device=__lowercase ).manual_seed(42 )
__A : Optional[int] = pipe(generator=__lowercase , steps=4 , return_dict=__lowercase )
__A : int = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
__A : Optional[int] = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
__A : List[Any] = np.frombuffer(image_from_tuple.tobytes() , dtype='uint8' )[:10]
__A : Optional[Any] = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
__A : Optional[int] = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] , )
__A : Optional[Any] = DDIMScheduler()
__A : List[Any] = self.dummy_vqvae_and_unet
__A : int = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=__lowercase , scheduler=__lowercase )
__A : Dict = pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
np.random.seed(0 )
__A : Dict = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
__A : Dict = torch.Generator(device=__lowercase ).manual_seed(42 )
__A : List[Any] = pipe(raw_audio=__lowercase , generator=__lowercase , start_step=5 , steps=10 )
__A : Tuple = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
__A : int = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
__A : List[str] = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
__A : Any = self.dummy_unet_condition
__A : Optional[int] = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=__lowercase , mel=__lowercase , scheduler=__lowercase )
__A : Union[str, Any] = pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
np.random.seed(0 )
__A : Dict = torch.rand((1, 1, 10) )
__A : Any = pipe(generator=__lowercase , encoding=__lowercase )
__A : Union[str, Any] = output.images[0]
__A : List[Any] = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
__A : int = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def snake_case__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case__ ( self ):
"""simple docstring"""
__A : Union[str, Any] = torch_device
__A : Union[str, Any] = DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256' )
__A : Dict = pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
__A : Any = torch.Generator(device=__lowercase ).manual_seed(42 )
__A : Optional[Any] = pipe(generator=__lowercase )
__A : Union[str, Any] = output.audios[0]
__A : List[Any] = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
__A : Union[str, Any] = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
__A : Optional[Any] = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 540
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
_lowerCamelCase = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'ASTForAudioClassification',
'ASTModel',
'ASTPreTrainedModel',
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = ['ASTFeatureExtractor']
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 114
|
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __UpperCAmelCase( lowercase_ ):
# vision encoder
if "img_encoder.pos_embed" in name:
_lowerCamelCase : Tuple = name.replace('''img_encoder.pos_embed''' , '''vision_model.embeddings.position_embeddings''' )
if "img_encoder.patch_embed.proj" in name:
_lowerCamelCase : List[str] = name.replace('''img_encoder.patch_embed.proj''' , '''vision_model.embeddings.patch_embeddings.projection''' )
if "img_encoder.patch_embed.norm" in name:
_lowerCamelCase : Dict = name.replace('''img_encoder.patch_embed.norm''' , '''vision_model.embeddings.layernorm''' )
if "img_encoder.layers" in name:
_lowerCamelCase : Dict = name.replace('''img_encoder.layers''' , '''vision_model.encoder.stages''' )
if "blocks" in name and "res" not in name:
_lowerCamelCase : Optional[Any] = name.replace('''blocks''' , '''layers''' )
if "attn" in name and "pre_assign" not in name:
_lowerCamelCase : Tuple = name.replace('''attn''' , '''self_attn''' )
if "proj" in name and "self_attn" in name and "text" not in name:
_lowerCamelCase : Optional[int] = name.replace('''proj''' , '''out_proj''' )
if "pre_assign_attn.attn.proj" in name:
_lowerCamelCase : List[Any] = name.replace('''pre_assign_attn.attn.proj''' , '''pre_assign_attn.attn.out_proj''' )
if "norm1" in name:
_lowerCamelCase : int = name.replace('''norm1''' , '''layer_norm1''' )
if "norm2" in name and "pre_assign" not in name:
_lowerCamelCase : Optional[int] = name.replace('''norm2''' , '''layer_norm2''' )
if "img_encoder.norm" in name:
_lowerCamelCase : Dict = name.replace('''img_encoder.norm''' , '''vision_model.layernorm''' )
# text encoder
if "text_encoder.token_embedding" in name:
_lowerCamelCase : List[Any] = name.replace('''text_encoder.token_embedding''' , '''text_model.embeddings.token_embedding''' )
if "text_encoder.positional_embedding" in name:
_lowerCamelCase : Optional[Any] = name.replace('''text_encoder.positional_embedding''' , '''text_model.embeddings.position_embedding.weight''' )
if "text_encoder.transformer.resblocks." in name:
_lowerCamelCase : Dict = name.replace('''text_encoder.transformer.resblocks.''' , '''text_model.encoder.layers.''' )
if "ln_1" in name:
_lowerCamelCase : Tuple = name.replace('''ln_1''' , '''layer_norm1''' )
if "ln_2" in name:
_lowerCamelCase : List[str] = name.replace('''ln_2''' , '''layer_norm2''' )
if "c_fc" in name:
_lowerCamelCase : List[str] = name.replace('''c_fc''' , '''fc1''' )
if "c_proj" in name:
_lowerCamelCase : str = name.replace('''c_proj''' , '''fc2''' )
if "text_encoder" in name:
_lowerCamelCase : Tuple = name.replace('''text_encoder''' , '''text_model''' )
if "ln_final" in name:
_lowerCamelCase : str = name.replace('''ln_final''' , '''final_layer_norm''' )
# projection layers
if "img_projector.linear_hidden." in name:
_lowerCamelCase : str = name.replace('''img_projector.linear_hidden.''' , '''visual_projection.''' )
if "img_projector.linear_out." in name:
_lowerCamelCase : Union[str, Any] = name.replace('''img_projector.linear_out.''' , '''visual_projection.3.''' )
if "text_projector.linear_hidden" in name:
_lowerCamelCase : List[str] = name.replace('''text_projector.linear_hidden''' , '''text_projection''' )
if "text_projector.linear_out" in name:
_lowerCamelCase : Any = name.replace('''text_projector.linear_out''' , '''text_projection.3''' )
return name
def __UpperCAmelCase( lowercase_ , lowercase_ ):
for key in orig_state_dict.copy().keys():
_lowerCamelCase : Optional[int] = orig_state_dict.pop(lowercase_ )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
_lowerCamelCase : Tuple = key.split('''.''' )
_lowerCamelCase, _lowerCamelCase : Dict = int(key_split[2] ), int(key_split[4] )
_lowerCamelCase : Optional[int] = config.vision_config.hidden_size
if "weight" in key:
_lowerCamelCase : List[Any] = val[:dim, :]
_lowerCamelCase : List[Any] = val[dim : dim * 2, :]
_lowerCamelCase : Optional[Any] = val[-dim:, :]
else:
_lowerCamelCase : Union[str, Any] = val[:dim]
_lowerCamelCase : Optional[Any] = val[dim : dim * 2]
_lowerCamelCase : Any = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
_lowerCamelCase : Optional[int] = key.split('''.''' )
_lowerCamelCase : Optional[Any] = int(key_split[3] )
_lowerCamelCase : Union[str, Any] = config.text_config.hidden_size
if "weight" in key:
_lowerCamelCase : Any = val[:dim, :]
_lowerCamelCase : Optional[Any] = val[
dim : dim * 2, :
]
_lowerCamelCase : Dict = val[-dim:, :]
else:
_lowerCamelCase : List[Any] = val[:dim]
_lowerCamelCase : Tuple = val[dim : dim * 2]
_lowerCamelCase : str = val[-dim:]
else:
_lowerCamelCase : Optional[Any] = rename_key(lowercase_ )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
_lowerCamelCase : List[str] = val.squeeze_()
else:
_lowerCamelCase : Tuple = val
return orig_state_dict
def __UpperCAmelCase( ):
_lowerCamelCase : Union[str, Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_lowerCamelCase : Any = Image.open(requests.get(lowercase_ , stream=lowercase_ ).raw )
return im
@torch.no_grad()
def __UpperCAmelCase( lowercase_ , lowercase_ , lowercase_="groupvit-gcc-yfcc" , lowercase_=False ):
_lowerCamelCase : Optional[Any] = GroupViTConfig()
_lowerCamelCase : Any = GroupViTModel(lowercase_ ).eval()
_lowerCamelCase : Optional[Any] = torch.load(lowercase_ , map_location='''cpu''' )['''model''']
_lowerCamelCase : List[str] = convert_state_dict(lowercase_ , lowercase_ )
_lowerCamelCase, _lowerCamelCase : List[str] = model.load_state_dict(lowercase_ , strict=lowercase_ )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase_ ) == 0)
# verify result
_lowerCamelCase : Optional[Any] = CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
_lowerCamelCase : Dict = prepare_img()
_lowerCamelCase : str = processor(text=['''a photo of a cat''', '''a photo of a dog'''] , images=lowercase_ , padding=lowercase_ , return_tensors='''pt''' )
with torch.no_grad():
_lowerCamelCase : Optional[int] = model(**lowercase_ )
if model_name == "groupvit-gcc-yfcc":
_lowerCamelCase : Dict = torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]] )
elif model_name == "groupvit-gcc-redcaps":
_lowerCamelCase : Tuple = torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]] )
else:
raise ValueError(F"""Model name {model_name} not supported.""" )
assert torch.allclose(outputs.logits_per_image , lowercase_ , atol=1e-3 )
processor.save_pretrained(lowercase_ )
model.save_pretrained(lowercase_ )
print('''Successfully saved processor and model to''' , lowercase_ )
if push_to_hub:
print('''Pushing to the hub...''' )
processor.push_to_hub(lowercase_ , organization='''nielsr''' )
model.push_to_hub(lowercase_ , organization='''nielsr''' )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
)
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
parser.add_argument(
'--model_name',
default='groupvit-gccy-fcc',
type=str,
help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
)
_lowerCamelCase = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 114
| 1
|
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
UpperCamelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCamelCase__ = 256
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Optional[Any] = ["""melgan"""]
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ):
super().__init__()
# From MELGAN
UpperCamelCase__ = math.log(1E-5 ) # Matches MelGAN training.
UpperCamelCase__ = 4.0 # Largest value for most examples
UpperCamelCase__ = 128
self.register_modules(
notes_encoder=__lowerCAmelCase , continuous_encoder=__lowerCAmelCase , decoder=__lowerCAmelCase , scheduler=__lowerCAmelCase , melgan=__lowerCAmelCase , )
def _lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase=(-1.0, 1.0) , __lowerCAmelCase=False ):
UpperCamelCase__ , UpperCamelCase__ = output_range
if clip:
UpperCamelCase__ = torch.clip(__lowerCAmelCase , self.min_value , self.max_value )
# Scale to [0, 1].
UpperCamelCase__ = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def _lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase=(-1.0, 1.0) , __lowerCAmelCase=False ):
UpperCamelCase__ , UpperCamelCase__ = input_range
UpperCamelCase__ = torch.clip(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) if clip else outputs
# Scale to [0, 1].
UpperCamelCase__ = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def _lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
UpperCamelCase__ = input_tokens > 0
UpperCamelCase__ , UpperCamelCase__ = self.notes_encoder(
encoder_input_tokens=__lowerCAmelCase , encoder_inputs_mask=__lowerCAmelCase )
UpperCamelCase__ , UpperCamelCase__ = self.continuous_encoder(
encoder_inputs=__lowerCAmelCase , encoder_inputs_mask=__lowerCAmelCase )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def _lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
UpperCamelCase__ = noise_time
if not torch.is_tensor(__lowerCAmelCase ):
UpperCamelCase__ = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(__lowerCAmelCase ) and len(timesteps.shape ) == 0:
UpperCamelCase__ = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCamelCase__ = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
UpperCamelCase__ = self.decoder(
encodings_and_masks=__lowerCAmelCase , decoder_input_tokens=__lowerCAmelCase , decoder_noise_time=__lowerCAmelCase )
return logits
@torch.no_grad()
def __call__( self , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = 100 , __lowerCAmelCase = True , __lowerCAmelCase = "numpy" , __lowerCAmelCase = None , __lowerCAmelCase = 1 , ):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(__lowerCAmelCase )}.""" )
UpperCamelCase__ = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
UpperCamelCase__ = np.zeros([1, 0, self.n_dims] , np.floataa )
UpperCamelCase__ = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=__lowerCAmelCase , device=self.device )
for i, encoder_input_tokens in enumerate(__lowerCAmelCase ):
if i == 0:
UpperCamelCase__ = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
UpperCamelCase__ = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=__lowerCAmelCase , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
UpperCamelCase__ = ones
UpperCamelCase__ = self.scale_features(
__lowerCAmelCase , output_range=[-1.0, 1.0] , clip=__lowerCAmelCase )
UpperCamelCase__ = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=__lowerCAmelCase , continuous_mask=__lowerCAmelCase , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
UpperCamelCase__ = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=__lowerCAmelCase , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(__lowerCAmelCase )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCamelCase__ = self.decode(
encodings_and_masks=__lowerCAmelCase , input_tokens=__lowerCAmelCase , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
UpperCamelCase__ = self.scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase ).prev_sample
UpperCamelCase__ = self.scale_to_features(__lowerCAmelCase , input_range=[-1.0, 1.0] )
UpperCamelCase__ = mel[:1]
UpperCamelCase__ = mel.cpu().float().numpy()
UpperCamelCase__ = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__lowerCAmelCase , __lowerCAmelCase )
logger.info("""Generated segment""" , __lowerCAmelCase )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"""Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'.""" )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"""Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'.""" )
if output_type == "numpy":
UpperCamelCase__ = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
UpperCamelCase__ = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=__lowerCAmelCase )
| 548
|
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--txt2img_unclip",
default="kakaobrain/karlo-v1-alpha",
type=str,
required=False,
help="The pretrained txt2img unclip.",
)
UpperCamelCase__ = parser.parse_args()
UpperCamelCase__ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
UpperCamelCase__ = CLIPImageProcessor()
UpperCamelCase__ = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
UpperCamelCase__ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 548
| 1
|
'''simple docstring'''
import math
class lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase=0 ) -> str: # a graph with Node 0,1,...,N-1
_lowerCAmelCase = n
_lowerCAmelCase = [
[math.inf for j in range(0 , _lowerCAmelCase )] for i in range(0 , _lowerCAmelCase )
] # adjacency matrix for weight
_lowerCAmelCase = [
[math.inf for j in range(0 , _lowerCAmelCase )] for i in range(0 , _lowerCAmelCase )
] # dp[i][j] stores minimum distance from i to j
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> List[str]:
_lowerCAmelCase = w
def _snake_case ( self ) -> Optional[Any]:
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
_lowerCAmelCase = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase ) -> Any:
return self.dp[u][v]
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 18
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ : Tuple = logging.get_logger(__name__)
lowerCamelCase__ : Any = {
"bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
"bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
"bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
"bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
"bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
"bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
"bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
"bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
"bert-large-uncased-whole-word-masking": (
"https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"
),
"bert-large-cased-whole-word-masking": (
"https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"
),
"bert-large-uncased-whole-word-masking-finetuned-squad": (
"https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"
),
"bert-large-cased-whole-word-masking-finetuned-squad": (
"https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"
),
"bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
"bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
"bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
"cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
"cl-tohoku/bert-base-japanese-whole-word-masking": (
"https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"
),
"cl-tohoku/bert-base-japanese-char": (
"https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"
),
"cl-tohoku/bert-base-japanese-char-whole-word-masking": (
"https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"
),
"TurkuNLP/bert-base-finnish-cased-v1": (
"https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"
),
"TurkuNLP/bert-base-finnish-uncased-v1": (
"https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"
),
"wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
# See all BERT models at https://huggingface.co/models?filter=bert
}
class lowercase__( _UpperCAmelCase ):
'''simple docstring'''
UpperCamelCase = """bert"""
def __init__( self :Any , lowerCamelCase_ :List[Any]=3_05_22 , lowerCamelCase_ :List[str]=7_68 , lowerCamelCase_ :Tuple=12 , lowerCamelCase_ :List[Any]=12 , lowerCamelCase_ :int=30_72 , lowerCamelCase_ :Dict="gelu" , lowerCamelCase_ :List[Any]=0.1 , lowerCamelCase_ :int=0.1 , lowerCamelCase_ :int=5_12 , lowerCamelCase_ :Optional[int]=2 , lowerCamelCase_ :int=0.0_2 , lowerCamelCase_ :Optional[int]=1E-12 , lowerCamelCase_ :Optional[Any]=0 , lowerCamelCase_ :int="absolute" , lowerCamelCase_ :List[Any]=True , lowerCamelCase_ :Optional[Any]=None , **lowerCamelCase_ :List[Any] , ) -> List[str]:
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase_ , **lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE : int = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE : Dict = hidden_act
SCREAMING_SNAKE_CASE : Union[str, Any] = intermediate_size
SCREAMING_SNAKE_CASE : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : List[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE : int = type_vocab_size
SCREAMING_SNAKE_CASE : List[str] = initializer_range
SCREAMING_SNAKE_CASE : Union[str, Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : Optional[Any] = position_embedding_type
SCREAMING_SNAKE_CASE : str = use_cache
SCREAMING_SNAKE_CASE : Union[str, Any] = classifier_dropout
class lowercase__( _UpperCAmelCase ):
'''simple docstring'''
@property
def __lowerCAmelCase ( self :List[str] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[Any] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
SCREAMING_SNAKE_CASE : Optional[Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 698
| 0
|
'''simple docstring'''
from __future__ import annotations
import queue
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Union[str, Any] , __lowerCAmelCase : str) -> Optional[int]:
lowercase_ = data
lowercase_ = None
lowercase_ = None
def __a ( ) -> int:
'''simple docstring'''
print("\n********Press N to stop entering at any point of time********\n" )
lowercase_ = input("Enter the value of the root node: " ).strip().lower()
lowercase_ = queue.Queue()
lowercase_ = TreeNode(int(lowerCAmelCase__ ) )
q.put(lowerCAmelCase__ )
while not q.empty():
lowercase_ = q.get()
lowercase_ = f'Enter the left node of {node_found.data}: '
lowercase_ = input(lowerCAmelCase__ ).strip().lower() or "n"
if check == "n":
return tree_node
lowercase_ = TreeNode(int(lowerCAmelCase__ ) )
lowercase_ = left_node
q.put(lowerCAmelCase__ )
lowercase_ = f'Enter the right node of {node_found.data}: '
lowercase_ = input(lowerCAmelCase__ ).strip().lower() or "n"
if check == "n":
return tree_node
lowercase_ = TreeNode(int(lowerCAmelCase__ ) )
lowercase_ = right_node
q.put(lowerCAmelCase__ )
raise
def __a ( __lowerCamelCase : Dict ) -> Optional[int]:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or not node:
return
print(node.data , end="," )
pre_order(node.left )
pre_order(node.right )
def __a ( __lowerCamelCase : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or not node:
return
in_order(node.left )
print(node.data , end="," )
in_order(node.right )
def __a ( __lowerCamelCase : Any ) -> Optional[int]:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end="," )
def __a ( __lowerCamelCase : List[str] ) -> str:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or not node:
return
lowercase_ = queue.Queue()
q.put(lowerCAmelCase__ )
while not q.empty():
lowercase_ = q.get()
print(node_dequeued.data , end="," )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def __a ( __lowerCamelCase : str ) -> List[str]:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or not node:
return
lowercase_ = queue.Queue()
q.put(lowerCAmelCase__ )
while not q.empty():
lowercase_ = []
while not q.empty():
lowercase_ = q.get()
print(node_dequeued.data , end="," )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(lowerCAmelCase__ )
def __a ( __lowerCamelCase : Tuple ) -> Optional[int]:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or not node:
return
lowercase_ = []
lowercase_ = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end="," )
stack.append(lowerCAmelCase__ )
lowercase_ = n.left
# end of while means current node doesn't have left child
lowercase_ = stack.pop()
# start to traverse its right child
lowercase_ = n.right
def __a ( __lowerCamelCase : int ) -> int:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or not node:
return
lowercase_ = []
lowercase_ = node
while n or stack:
while n:
stack.append(lowerCAmelCase__ )
lowercase_ = n.left
lowercase_ = stack.pop()
print(n.data , end="," )
lowercase_ = n.right
def __a ( __lowerCamelCase : Dict ) -> Dict:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or not node:
return
lowercase_ , lowercase_ = [], []
lowercase_ = node
stacka.append(lowerCAmelCase__ )
while stacka: # to find the reversed order of post order, store it in stack2
lowercase_ = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(lowerCAmelCase__ )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end="," )
def __a ( __lowerCamelCase : Any = "" , __lowerCamelCase : str=50 , __lowerCamelCase : Any="*" ) -> Optional[int]:
'''simple docstring'''
if not s:
return "\n" + width * char
lowercase_ , lowercase_ = divmod(width - len(lowerCAmelCase__ ) - 2 , 2 )
return f'{left * char} {s} {(left + extra) * char}'
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt("Binary Tree Traversals"))
lowerCAmelCase_ : Optional[Any] = build_tree()
print(prompt("Pre Order Traversal"))
pre_order(node)
print(prompt() + "\n")
print(prompt("In Order Traversal"))
in_order(node)
print(prompt() + "\n")
print(prompt("Post Order Traversal"))
post_order(node)
print(prompt() + "\n")
print(prompt("Level Order Traversal"))
level_order(node)
print(prompt() + "\n")
print(prompt("Actual Level Order Traversal"))
level_order_actual(node)
print("*" * 50 + "\n")
print(prompt("Pre Order Traversal - Iteration Version"))
pre_order_iter(node)
print(prompt() + "\n")
print(prompt("In Order Traversal - Iteration Version"))
in_order_iter(node)
print(prompt() + "\n")
print(prompt("Post Order Traversal - Iteration Version"))
post_order_iter(node)
print(prompt())
| 714
|
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowerCAmelCase_ : Any = logging.get_logger(__name__)
class lowercase ( __lowerCamelCase ):
lowerCamelCase_ =['pixel_values']
def __init__( self : Optional[int] , __lowerCAmelCase : bool = True , __lowerCAmelCase : int = 32 , __lowerCAmelCase : List[str]=PILImageResampling.BILINEAR , __lowerCAmelCase : bool = True , **__lowerCAmelCase : Union[str, Any] , ) -> None:
lowercase_ = do_resize
lowercase_ = do_rescale
lowercase_ = size_divisor
lowercase_ = resample
super().__init__(**__lowerCAmelCase)
def __UpperCAmelCase ( self : List[str] , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : int , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[ChannelDimension] = None , **__lowerCAmelCase : Any) -> np.ndarray:
lowercase_ , lowercase_ = get_image_size(__lowerCAmelCase)
# Rounds the height and width down to the closest multiple of size_divisor
lowercase_ = height // size_divisor * size_divisor
lowercase_ = width // size_divisor * size_divisor
lowercase_ = resize(__lowerCAmelCase , (new_h, new_w) , resample=__lowerCAmelCase , data_format=__lowerCAmelCase , **__lowerCAmelCase)
return image
def __UpperCAmelCase ( self : str , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : float , __lowerCAmelCase : Optional[ChannelDimension] = None , **__lowerCAmelCase : int) -> np.ndarray:
return rescale(image=__lowerCAmelCase , scale=__lowerCAmelCase , data_format=__lowerCAmelCase , **__lowerCAmelCase)
def __UpperCAmelCase ( self : Optional[Any] , __lowerCAmelCase : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : str=None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Optional[Union[TensorType, str]] = None , __lowerCAmelCase : ChannelDimension = ChannelDimension.FIRST , **__lowerCAmelCase : List[str] , ) -> BatchFeature:
lowercase_ = do_resize if do_resize is not None else self.do_resize
lowercase_ = do_rescale if do_rescale is not None else self.do_rescale
lowercase_ = size_divisor if size_divisor is not None else self.size_divisor
lowercase_ = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError("size_divisor is required for resizing")
lowercase_ = make_list_of_images(__lowerCAmelCase)
if not valid_images(__lowerCAmelCase):
raise ValueError("Invalid image(s)")
# All transformations expect numpy arrays.
lowercase_ = [to_numpy_array(__lowerCAmelCase) for img in images]
if do_resize:
lowercase_ = [self.resize(__lowerCAmelCase , size_divisor=__lowerCAmelCase , resample=__lowerCAmelCase) for image in images]
if do_rescale:
lowercase_ = [self.rescale(__lowerCAmelCase , scale=1 / 255) for image in images]
lowercase_ = [to_channel_dimension_format(__lowerCAmelCase , __lowerCAmelCase) for image in images]
lowercase_ = {"pixel_values": images}
return BatchFeature(data=__lowerCAmelCase , tensor_type=__lowerCAmelCase)
| 461
| 0
|
def lowerCAmelCase_ (lowercase__ : List[str] , lowercase__ : Any ) -> str:
'''simple docstring'''
lowerCAmelCase__ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCAmelCase_ (lowercase__ : Optional[Any] , lowercase__ : str , lowercase__ : List[str] ) -> int:
'''simple docstring'''
lowerCAmelCase__ = 0
while b > 0:
if b & 1:
lowerCAmelCase__ = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res
| 668
|
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ = 5000_0000 ):
lowercase__ = set()
lowercase__ = int((limit - 24) ** (1 / 2) )
lowercase__ = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , prime_square_limit + 1 , SCREAMING_SNAKE_CASE_ ) ) )
for primea in primes:
lowercase__ = primea * primea
for primea in primes:
lowercase__ = primea * primea * primea
if square + cube >= limit - 16:
break
for primea in primes:
lowercase__ = primea * primea * primea * primea
lowercase__ = square + cube + tetr
if total >= limit:
break
ret.add(SCREAMING_SNAKE_CASE_ )
return len(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
print(F'{solution() = }')
| 413
| 0
|
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class a :
def __init__( self , __magic_name__ ) -> Tuple:
_a = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
_a = len(UpperCamelCase__ ) - 1
def __UpperCAmelCase ( self , __magic_name__ ) -> List[str]:
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_a = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , UpperCamelCase__ ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(UpperCamelCase__ ) , 5 ) == 1
return output_values
def __UpperCAmelCase ( self , __magic_name__ ) -> List[Any]:
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_a = self.basis_function(UpperCamelCase__ )
_a = 0.0
_a = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def __UpperCAmelCase ( self , __magic_name__ = 0.0_1 ) -> Tuple:
from matplotlib import pyplot as plt # type: ignore
_a = [] # x coordinates of points to plot
_a = [] # y coordinates of points to plot
_a = 0.0
while t <= 1:
_a = self.bezier_curve_function(UpperCamelCase__ )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
_a = [i[0] for i in self.list_of_points]
_a = [i[1] for i in self.list_of_points]
plt.plot(
UpperCamelCase__ , UpperCamelCase__ , color='blue' , label='Curve of Degree ' + str(self.degree ) , )
plt.scatter(UpperCamelCase__ , UpperCamelCase__ , color='red' , label='Control Points' )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 712
|
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a :
def __init__( self , __magic_name__ , __magic_name__=13 , __magic_name__=32 , __magic_name__=2 , __magic_name__=3 , __magic_name__=16 , __magic_name__=[1, 2, 1] , __magic_name__=[2, 2, 4] , __magic_name__=2 , __magic_name__=2.0 , __magic_name__=True , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=0.1 , __magic_name__="gelu" , __magic_name__=False , __magic_name__=True , __magic_name__=0.0_2 , __magic_name__=1e-5 , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=10 , __magic_name__=8 , ) -> Optional[int]:
_a = parent
_a = batch_size
_a = image_size
_a = patch_size
_a = num_channels
_a = embed_dim
_a = depths
_a = num_heads
_a = window_size
_a = mlp_ratio
_a = qkv_bias
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = drop_path_rate
_a = hidden_act
_a = use_absolute_embeddings
_a = patch_norm
_a = layer_norm_eps
_a = initializer_range
_a = is_training
_a = scope
_a = use_labels
_a = type_sequence_label_size
_a = encoder_stride
def __UpperCAmelCase ( self ) -> List[Any]:
_a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = self.get_config()
return config, pixel_values, labels
def __UpperCAmelCase ( self ) -> int:
return SwinvaConfig(
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 , )
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Union[str, Any]:
_a = SwinvaModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
_a = model(__magic_name__ )
_a = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
_a = 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 __UpperCAmelCase ( self , __magic_name__ , __magic_name__ , __magic_name__ ) -> str:
_a = SwinvaForMaskedImageModeling(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
_a = model(__magic_name__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
_a = 1
_a = SwinvaForMaskedImageModeling(__magic_name__ )
model.to(__magic_name__ )
model.eval()
_a = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_a = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Optional[Any]:
_a = self.type_sequence_label_size
_a = SwinvaForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
_a = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __UpperCAmelCase ( self ) -> List[str]:
_a = self.prepare_config_and_inputs()
_a , _a , _a = config_and_inputs
_a = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
_lowerCAmelCase = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
_lowerCAmelCase = (
{"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
if is_torch_available()
else {}
)
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def __UpperCAmelCase ( self ) -> List[str]:
_a = SwinvaModelTester(self )
_a = ConfigTester(self , config_class=__magic_name__ , embed_dim=37 )
def __UpperCAmelCase ( self ) -> int:
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 __UpperCAmelCase ( self ) -> Optional[Any]:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.' )
def __UpperCAmelCase ( self ) -> Optional[int]:
pass
@unittest.skip(reason='Swinv2 does not use inputs_embeds' )
def __UpperCAmelCase ( self ) -> Tuple:
pass
def __UpperCAmelCase ( self ) -> str:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def __UpperCAmelCase ( self ) -> Any:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(__magic_name__ )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = ['pixel_values']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def __UpperCAmelCase ( self ) -> Union[str, Any]:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = True
for model_class in self.all_model_classes:
_a = True
_a = False
_a = True
_a = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
_a = outputs.attentions
_a = len(self.model_tester.depths )
self.assertEqual(len(__magic_name__ ) , __magic_name__ )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_a = True
_a = config.window_size**2
_a = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
_a = outputs.attentions
self.assertEqual(len(__magic_name__ ) , __magic_name__ )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
_a = len(__magic_name__ )
# Check attention is always last and order is fine
_a = True
_a = True
_a = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
if hasattr(self.model_tester , 'num_hidden_states_types' ):
_a = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
_a = 2
self.assertEqual(out_len + added_hidden_states , len(__magic_name__ ) )
_a = outputs.attentions
self.assertEqual(len(__magic_name__ ) , __magic_name__ )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[Any]:
_a = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
_a = outputs.hidden_states
_a = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__magic_name__ ) , __magic_name__ )
# Swinv2 has a different seq_length
_a = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_a = (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] , )
_a = outputs.reshaped_hidden_states
self.assertEqual(len(__magic_name__ ) , __magic_name__ )
_a , _a , _a , _a = reshaped_hidden_states[0].shape
_a = (
reshaped_hidden_states[0].view(__magic_name__ , __magic_name__ , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __UpperCAmelCase ( self ) -> Union[str, Any]:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = (
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:
_a = True
self.check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a = True
self.check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
def __UpperCAmelCase ( self ) -> Optional[Any]:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = (
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)
)
_a = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_a = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
_a = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
_a = True
self.check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a = True
self.check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ , (padded_height, padded_width) )
def __UpperCAmelCase ( self ) -> Tuple:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__magic_name__ )
def __UpperCAmelCase ( self ) -> str:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
@slow
def __UpperCAmelCase ( self ) -> str:
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = SwinvaModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def __UpperCAmelCase ( self ) -> Optional[int]:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = _config_zero_init(__magic_name__ )
for model_class in self.all_model_classes:
_a = model_class(config=__magic_name__ )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
@require_vision
@require_torch
class a ( unittest.TestCase ):
@cached_property
def __UpperCAmelCase ( self ) -> Optional[int]:
return (
AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' )
if is_vision_available()
else None
)
@slow
def __UpperCAmelCase ( self ) -> Optional[int]:
_a = SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' ).to(
__magic_name__ )
_a = self.default_image_processor
_a = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
_a = image_processor(images=__magic_name__ , return_tensors='pt' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
_a = model(**__magic_name__ )
# verify the logits
_a = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
_a = torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 532
| 0
|
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def __lowerCamelCase ( A__ , A__=False ) -> Optional[Any]:
"""simple docstring"""
try:
UpperCamelCase = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
UpperCamelCase = default
else:
# KEY is set, convert it to True or False.
try:
UpperCamelCase = strtobool(A__ )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F"""If set, {key} must be yes or no.""" )
return _value
_lowerCamelCase : Dict = parse_flag_from_env("RUN_SLOW", default=False)
def __lowerCamelCase ( A__ ) -> List[str]:
"""simple docstring"""
return unittest.skip('Test was skipped' )(A__ )
def __lowerCamelCase ( A__ ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(_run_slow_tests , 'test is slow' )(A__ )
def __lowerCamelCase ( A__ ) -> str:
"""simple docstring"""
return unittest.skipUnless(not torch.cuda.is_available() , 'test requires only a CPU' )(A__ )
def __lowerCamelCase ( A__ ) -> Union[str, Any]:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.is_available() , 'test requires a GPU' )(A__ )
def __lowerCamelCase ( A__ ) -> Tuple:
"""simple docstring"""
return unittest.skipUnless(is_xpu_available() , 'test requires a XPU' )(A__ )
def __lowerCamelCase ( A__ ) -> Union[str, Any]:
"""simple docstring"""
return unittest.skipUnless(is_mps_available() , 'test requires a `mps` backend support in `torch`' )(A__ )
def __lowerCamelCase ( A__ ) -> Tuple:
"""simple docstring"""
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , 'test requires the Hugging Face suite' )(A__ )
def __lowerCamelCase ( A__ ) -> Optional[int]:
"""simple docstring"""
return unittest.skipUnless(is_bnb_available() , 'test requires the bitsandbytes library' )(A__ )
def __lowerCamelCase ( A__ ) -> Tuple:
"""simple docstring"""
return unittest.skipUnless(is_tpu_available() , 'test requires TPU' )(A__ )
def __lowerCamelCase ( A__ ) -> Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.device_count() == 1 , 'test requires a GPU' )(A__ )
def __lowerCamelCase ( A__ ) -> int:
"""simple docstring"""
return unittest.skipUnless(torch.xpu.device_count() == 1 , 'test requires a XPU' )(A__ )
def __lowerCamelCase ( A__ ) -> Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.device_count() > 1 , 'test requires multiple GPUs' )(A__ )
def __lowerCamelCase ( A__ ) -> Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(torch.xpu.device_count() > 1 , 'test requires multiple XPUs' )(A__ )
def __lowerCamelCase ( A__ ) -> Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(is_safetensors_available() , 'test requires safetensors' )(A__ )
def __lowerCamelCase ( A__ ) -> int:
"""simple docstring"""
return unittest.skipUnless(is_deepspeed_available() , 'test requires DeepSpeed' )(A__ )
def __lowerCamelCase ( A__ ) -> Union[str, Any]:
"""simple docstring"""
return unittest.skipUnless(is_torch_version('>=' , '1.12.0' ) , 'test requires torch version >= 1.12.0' )(A__ )
def __lowerCamelCase ( A__=None , A__=None ) -> Dict:
"""simple docstring"""
if test_case is None:
return partial(A__ , version=A__ )
return unittest.skipUnless(is_torch_version('>=' , A__ ) , F"""test requires torch version >= {version}""" )(A__ )
def __lowerCamelCase ( A__ ) -> Union[str, Any]:
"""simple docstring"""
return unittest.skipUnless(is_tensorboard_available() , 'test requires Tensorboard' )(A__ )
def __lowerCamelCase ( A__ ) -> Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(is_wandb_available() , 'test requires wandb' )(A__ )
def __lowerCamelCase ( A__ ) -> str:
"""simple docstring"""
return unittest.skipUnless(is_comet_ml_available() , 'test requires comet_ml' )(A__ )
_lowerCamelCase : Dict = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def __lowerCamelCase ( A__ ) -> Any:
"""simple docstring"""
return unittest.skipUnless(
_atleast_one_tracker_available , 'test requires at least one tracker to be available and for `comet_ml` to not be installed' , )(A__ )
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = True
@classmethod
def A ( cls : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = tempfile.mkdtemp()
@classmethod
def A ( cls : Tuple ):
"""simple docstring"""
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def A ( self : Optional[int] ):
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob('**/*' ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(UpperCamelCase__ )
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def A ( self : Optional[int] ):
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def A ( self : int , UpperCamelCase__ : Union[mock.Mock, List[mock.Mock]] ):
"""simple docstring"""
UpperCamelCase = mocks if isinstance(UpperCamelCase__ , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def __lowerCamelCase ( A__ ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = AcceleratorState()
UpperCamelCase = tensor[None].clone().to(state.device )
UpperCamelCase = gather(A__ ).cpu()
UpperCamelCase = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , A__ ):
return False
return True
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase__ : str , UpperCamelCase__ : Tuple , UpperCamelCase__ : List[str] ):
"""simple docstring"""
UpperCamelCase = returncode
UpperCamelCase = stdout
UpperCamelCase = stderr
async def __lowerCamelCase ( A__ , A__ ) -> List[str]:
"""simple docstring"""
while True:
UpperCamelCase = await stream.readline()
if line:
callback(A__ )
else:
break
async def __lowerCamelCase ( A__ , A__=None , A__=None , A__=None , A__=False , A__=False ) -> _RunOutput:
"""simple docstring"""
if echo:
print('\nRunning: ' , ' '.join(A__ ) )
UpperCamelCase = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=A__ , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=A__ , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
UpperCamelCase = []
UpperCamelCase = []
def tee(A__ , A__ , A__ , A__="" ):
UpperCamelCase = line.decode('utf-8' ).rstrip()
sink.append(A__ )
if not quiet:
print(A__ , A__ , file=A__ )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda A__ : tee(A__ , A__ , sys.stdout , label='stdout:' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda A__ : tee(A__ , A__ , sys.stderr , label='stderr:' ) ) ),
] , timeout=A__ , )
return _RunOutput(await p.wait() , A__ , A__ )
def __lowerCamelCase ( A__ , A__=None , A__=None , A__=180 , A__=False , A__=True ) -> _RunOutput:
"""simple docstring"""
UpperCamelCase = asyncio.get_event_loop()
UpperCamelCase = loop.run_until_complete(
_stream_subprocess(A__ , env=A__ , stdin=A__ , timeout=A__ , quiet=A__ , echo=A__ ) )
UpperCamelCase = ' '.join(A__ )
if result.returncode > 0:
UpperCamelCase = '\n'.join(result.stderr )
raise RuntimeError(
F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
F"""The combined stderr from workers follows:\n{stderr}""" )
return result
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
pass
def __lowerCamelCase ( A__ , A__=False ) -> Union[str, Any]:
"""simple docstring"""
try:
UpperCamelCase = subprocess.check_output(A__ , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(A__ , 'decode' ):
UpperCamelCase = output.decode('utf-8' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F"""Command `{' '.join(A__ )}` failed with the following error:\n\n{e.output.decode()}""" ) from e
| 430
|
'''simple docstring'''
from collections import deque
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : str , UpperCamelCase__ : int , UpperCamelCase__ : int ):
"""simple docstring"""
UpperCamelCase = process_name # process name
UpperCamelCase = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
UpperCamelCase = arrival_time
UpperCamelCase = burst_time # remaining burst time
UpperCamelCase = 0 # total time of the process wait in ready queue
UpperCamelCase = 0 # time from arrival time to completion time
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : str , UpperCamelCase__ : int , UpperCamelCase__ : list[int] , UpperCamelCase__ : deque[Process] , UpperCamelCase__ : int , ):
"""simple docstring"""
UpperCamelCase = number_of_queues
# time slice of queues that round robin algorithm applied
UpperCamelCase = time_slices
# unfinished process is in this ready_queue
UpperCamelCase = queue
# current time
UpperCamelCase = current_time
# finished process is in this sequence queue
UpperCamelCase = deque()
def A ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def A ( self : Optional[Any] , UpperCamelCase__ : list[Process] ):
"""simple docstring"""
UpperCamelCase = []
for i in range(len(UpperCamelCase__ ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def A ( self : Dict , UpperCamelCase__ : list[Process] ):
"""simple docstring"""
UpperCamelCase = []
for i in range(len(UpperCamelCase__ ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def A ( self : int , UpperCamelCase__ : list[Process] ):
"""simple docstring"""
UpperCamelCase = []
for i in range(len(UpperCamelCase__ ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def A ( self : Optional[int] , UpperCamelCase__ : deque[Process] ):
"""simple docstring"""
return [q.burst_time for q in queue]
def A ( self : Any , UpperCamelCase__ : Process ):
"""simple docstring"""
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def A ( self : Union[str, Any] , UpperCamelCase__ : deque[Process] ):
"""simple docstring"""
UpperCamelCase = deque() # sequence deque of finished process
while len(UpperCamelCase__ ) != 0:
UpperCamelCase = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(UpperCamelCase__ )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
UpperCamelCase = 0
# set the process's turnaround time because it is finished
UpperCamelCase = self.current_time - cp.arrival_time
# set the completion time
UpperCamelCase = self.current_time
# add the process to queue that has finished queue
finished.append(UpperCamelCase__ )
self.finish_queue.extend(UpperCamelCase__ ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def A ( self : Union[str, Any] , UpperCamelCase__ : deque[Process] , UpperCamelCase__ : int ):
"""simple docstring"""
UpperCamelCase = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(UpperCamelCase__ ) ):
UpperCamelCase = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(UpperCamelCase__ )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
UpperCamelCase = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(UpperCamelCase__ )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
UpperCamelCase = 0
# set the finish time
UpperCamelCase = self.current_time
# update the process' turnaround time because it is finished
UpperCamelCase = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(UpperCamelCase__ )
self.finish_queue.extend(UpperCamelCase__ ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def A ( self : Any ):
"""simple docstring"""
for i in range(self.number_of_queues - 1 ):
UpperCamelCase , UpperCamelCase = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
_lowerCamelCase : Optional[Any] = Process("P1", 0, 53)
_lowerCamelCase : List[str] = Process("P2", 0, 17)
_lowerCamelCase : Optional[int] = Process("P3", 0, 68)
_lowerCamelCase : Dict = Process("P4", 0, 24)
_lowerCamelCase : Union[str, Any] = 3
_lowerCamelCase : int = [17, 25]
_lowerCamelCase : List[Any] = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})
_lowerCamelCase : Dict = Process("P1", 0, 53)
_lowerCamelCase : int = Process("P2", 0, 17)
_lowerCamelCase : Union[str, Any] = Process("P3", 0, 68)
_lowerCamelCase : str = Process("P4", 0, 24)
_lowerCamelCase : Optional[Any] = 3
_lowerCamelCase : Any = [17, 25]
_lowerCamelCase : str = deque([Pa, Pa, Pa, Pa])
_lowerCamelCase : List[Any] = MLFQ(number_of_queues, time_slices, queue, 0)
_lowerCamelCase : int = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f'''waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print completion times of processes(P1, P2, P3, P4)
print(
f'''completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f'''turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print sequence of finished processes
print(
f'''sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}'''
)
| 430
| 1
|
from typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCamelCase (a_ ):
def __UpperCAmelCase ( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase )-> Union[str, Any]:
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 __UpperCAmelCase ( self , __UpperCamelCase , **__UpperCamelCase )-> Dict[str, GenericTensor]:
__lowerCAmelCase = self.framework
__lowerCAmelCase = self.tokenizer(__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase )
return model_inputs
def __UpperCAmelCase ( self , __UpperCamelCase )-> Tuple:
__lowerCAmelCase = self.model(**__UpperCamelCase )
return model_outputs
def __UpperCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=False )-> Optional[int]:
# [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 , *__UpperCamelCase , **__UpperCamelCase )-> List[Any]:
return super().__call__(*__UpperCamelCase , **__UpperCamelCase )
| 719
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : int = logging.get_logger(__name__)
lowerCamelCase : Tuple = {
'''caidas/swin2sr-classicalsr-x2-64''': (
'''https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'''
),
}
class _UpperCamelCase (a_ ):
snake_case_ = """swin2sr"""
snake_case_ = {
"""hidden_size""": """embed_dim""",
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self , __UpperCamelCase=6_4 , __UpperCamelCase=1 , __UpperCamelCase=3 , __UpperCamelCase=1_8_0 , __UpperCamelCase=[6, 6, 6, 6, 6, 6] , __UpperCamelCase=[6, 6, 6, 6, 6, 6] , __UpperCamelCase=8 , __UpperCamelCase=2.0 , __UpperCamelCase=True , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase="gelu" , __UpperCamelCase=False , __UpperCamelCase=0.0_2 , __UpperCamelCase=1e-5 , __UpperCamelCase=2 , __UpperCamelCase=1.0 , __UpperCamelCase="1conv" , __UpperCamelCase="pixelshuffle" , **__UpperCamelCase , )-> Tuple:
super().__init__(**__UpperCamelCase )
__lowerCAmelCase = image_size
__lowerCAmelCase = patch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = embed_dim
__lowerCAmelCase = depths
__lowerCAmelCase = len(__UpperCamelCase )
__lowerCAmelCase = num_heads
__lowerCAmelCase = window_size
__lowerCAmelCase = mlp_ratio
__lowerCAmelCase = qkv_bias
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = drop_path_rate
__lowerCAmelCase = hidden_act
__lowerCAmelCase = use_absolute_embeddings
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = initializer_range
__lowerCAmelCase = upscale
__lowerCAmelCase = img_range
__lowerCAmelCase = resi_connection
__lowerCAmelCase = upsampler
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from abc import ABC, abstractmethod
from argparse import ArgumentParser
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
@staticmethod
@abstractmethod
def UpperCamelCase_ ( __lowercase : ArgumentParser ):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
raise NotImplementedError()
| 225
|
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__lowerCamelCase : torch.FloatTensor
__lowerCamelCase : torch.FloatTensor
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase : Dict =1
@register_to_config
def __init__( self : List[Any] , __lowercase : int = 2000 , __lowercase : float = 0.15 , __lowercase : float = 0.01 , __lowercase : float = 1348.0 , __lowercase : float = 1E-5 , __lowercase : int = 1 , ):
'''simple docstring'''
# standard deviation of the initial noise distribution
__a = sigma_max
# setable values
__a = None
self.set_sigmas(__lowercase , __lowercase , __lowercase , __lowercase )
def UpperCamelCase_ ( self : List[str] , __lowercase : torch.FloatTensor , __lowercase : Optional[int] = None ):
'''simple docstring'''
return sample
def UpperCamelCase_ ( self : Union[str, Any] , __lowercase : int , __lowercase : float = None , __lowercase : Union[str, torch.device] = None ):
'''simple docstring'''
__a = sampling_eps if sampling_eps is not None else self.config.sampling_eps
__a = torch.linspace(1 , __lowercase , __lowercase , device=__lowercase )
def UpperCamelCase_ ( self : str , __lowercase : int , __lowercase : float = None , __lowercase : float = None , __lowercase : float = None ):
'''simple docstring'''
__a = sigma_min if sigma_min is not None else self.config.sigma_min
__a = sigma_max if sigma_max is not None else self.config.sigma_max
__a = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(__lowercase , __lowercase )
__a = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
__a = torch.exp(torch.linspace(math.log(__lowercase ) , math.log(__lowercase ) , __lowercase ) )
__a = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )
def UpperCamelCase_ ( self : Dict , __lowercase : Any , __lowercase : Optional[Any] ):
'''simple docstring'''
return torch.where(
timesteps == 0 , torch.zeros_like(t.to(timesteps.device ) ) , self.discrete_sigmas[timesteps - 1].to(timesteps.device ) , )
def UpperCamelCase_ ( self : Union[str, Any] , __lowercase : torch.FloatTensor , __lowercase : int , __lowercase : torch.FloatTensor , __lowercase : Optional[torch.Generator] = None , __lowercase : bool = True , ):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
__a = timestep * torch.ones(
sample.shape[0] , device=sample.device ) # torch.repeat_interleave(timestep, sample.shape[0])
__a = (timestep * (len(self.timesteps ) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
__a = timesteps.to(self.discrete_sigmas.device )
__a = self.discrete_sigmas[timesteps].to(sample.device )
__a = self.get_adjacent_sigma(__lowercase , __lowercase ).to(sample.device )
__a = torch.zeros_like(__lowercase )
__a = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
__a = diffusion.flatten()
while len(diffusion.shape ) < len(sample.shape ):
__a = diffusion.unsqueeze(-1 )
__a = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
__a = randn_tensor(
sample.shape , layout=sample.layout , generator=__lowercase , device=sample.device , dtype=sample.dtype )
__a = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
__a = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=__lowercase , prev_sample_mean=__lowercase )
def UpperCamelCase_ ( self : List[str] , __lowercase : torch.FloatTensor , __lowercase : torch.FloatTensor , __lowercase : Optional[torch.Generator] = None , __lowercase : bool = True , ):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
__a = randn_tensor(sample.shape , layout=sample.layout , generator=__lowercase ).to(sample.device )
# compute step size from the model_output, the noise, and the snr
__a = torch.norm(model_output.reshape(model_output.shape[0] , -1 ) , dim=-1 ).mean()
__a = torch.norm(noise.reshape(noise.shape[0] , -1 ) , dim=-1 ).mean()
__a = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
__a = step_size * torch.ones(sample.shape[0] ).to(sample.device )
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
__a = step_size.flatten()
while len(step_size.shape ) < len(sample.shape ):
__a = step_size.unsqueeze(-1 )
__a = sample + step_size * model_output
__a = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__lowercase )
def UpperCamelCase_ ( self : int , __lowercase : torch.FloatTensor , __lowercase : torch.FloatTensor , __lowercase : torch.FloatTensor , ):
'''simple docstring'''
# Make sure sigmas and timesteps have the same device and dtype as original_samples
__a = timesteps.to(original_samples.device )
__a = self.discrete_sigmas.to(original_samples.device )[timesteps]
__a = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(__lowercase ) * sigmas[:, None, None, None]
)
__a = noise + original_samples
return noisy_samples
def __len__( self : Union[str, Any] ):
'''simple docstring'''
return self.config.num_train_timesteps
| 225
| 1
|
'''simple docstring'''
import argparse
A : Dict = """docs/source/_static/js/custom.js"""
def _a ( lowerCamelCase_ ):
with open(lowerCamelCase_ , encoding='''utf-8''' , newline='''\n''' ) as f:
snake_case : Optional[Any] =f.readlines()
snake_case : List[str] =0
# First let's put the right version
while not lines[index].startswith('''const stableVersion =''' ):
index += 1
snake_case : Any =F'''const stableVersion = "v{version}"\n'''
# Then update the dictionary
while not lines[index].startswith('''const versionMapping = {''' ):
index += 1
# We go until the end
while not lines[index].startswith('''}''' ):
index += 1
# We add the new version at the end
lines[index - 1] += F''' "v{version}": "v{version}",\n'''
with open(lowerCamelCase_ , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(lowerCamelCase_ )
if __name__ == "__main__":
A : Tuple = argparse.ArgumentParser()
parser.add_argument("""--version""", help="""Release version.""")
A : Dict = parser.parse_args()
update_custom_js(args.version)
| 136
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Optional[Any] = logging.get_logger(__name__)
A : Any = {
"""distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/config.json""",
"""distilbert-base-uncased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"""
),
"""distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/config.json""",
"""distilbert-base-cased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"""
),
"""distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json""",
"""distilbert-base-multilingual-cased""": (
"""https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"""
),
"""distilbert-base-uncased-finetuned-sst-2-english""": (
"""https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"""
),
}
class lowerCAmelCase_ ( a_ ):
__UpperCAmelCase = 'distilbert'
__UpperCAmelCase = {
'hidden_size': 'dim',
'num_attention_heads': 'n_heads',
'num_hidden_layers': 'n_layers',
}
def __init__( self : Optional[Any], _snake_case : Optional[int]=30_522, _snake_case : str=512, _snake_case : Union[str, Any]=False, _snake_case : Any=6, _snake_case : List[Any]=12, _snake_case : Union[str, Any]=768, _snake_case : int=4 * 768, _snake_case : List[str]=0.1, _snake_case : str=0.1, _snake_case : Any="gelu", _snake_case : Dict=0.02, _snake_case : Any=0.1, _snake_case : int=0.2, _snake_case : List[Any]=0, **_snake_case : str, ):
'''simple docstring'''
snake_case : int =vocab_size
snake_case : Dict =max_position_embeddings
snake_case : Any =sinusoidal_pos_embds
snake_case : List[Any] =n_layers
snake_case : Optional[Any] =n_heads
snake_case : Optional[int] =dim
snake_case : int =hidden_dim
snake_case : Tuple =dropout
snake_case : Dict =attention_dropout
snake_case : List[str] =activation
snake_case : Tuple =initializer_range
snake_case : str =qa_dropout
snake_case : str =seq_classif_dropout
super().__init__(**_snake_case, pad_token_id=_snake_case )
class lowerCAmelCase_ ( a_ ):
@property
def __snake_case ( self : Union[str, Any] ):
'''simple docstring'''
if self.task == "multiple-choice":
snake_case : int ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
snake_case : List[Any] ={0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 136
| 1
|
from __future__ import annotations
import pandas as pd
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
A_ = [0] * no_of_processes
A_ = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(__snake_case ):
A_ = burst_time[i]
A_ = 0
A_ = 0
A_ = 9_99_99_99_99
A_ = 0
A_ = False
# Process until all processes are completed
while complete != no_of_processes:
for j in range(__snake_case ):
if arrival_time[j] <= increment_time and remaining_time[j] > 0:
if remaining_time[j] < minm:
A_ = remaining_time[j]
A_ = j
A_ = True
if not check:
increment_time += 1
continue
remaining_time[short] -= 1
A_ = remaining_time[short]
if minm == 0:
A_ = 9_99_99_99_99
if remaining_time[short] == 0:
complete += 1
A_ = False
# Find finish time of current process
A_ = increment_time + 1
# Calculate waiting time
A_ = finish_time - arrival_time[short]
A_ = finar - burst_time[short]
if waiting_time[short] < 0:
A_ = 0
# Increment time
increment_time += 1
return waiting_time
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
A_ = [0] * no_of_processes
for i in range(__snake_case ):
A_ = burst_time[i] + waiting_time[i]
return turn_around_time
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
A_ = 0
A_ = 0
for i in range(__snake_case ):
A_ = total_waiting_time + waiting_time[i]
A_ = total_turn_around_time + turn_around_time[i]
print(F"Average waiting time = {total_waiting_time / no_of_processes:.5f}" )
print('''Average turn around time =''' , total_turn_around_time / no_of_processes )
if __name__ == "__main__":
print("Enter how many process you want to analyze")
SCREAMING_SNAKE_CASE : Tuple = int(input())
SCREAMING_SNAKE_CASE : Any = [0] * no_of_processes
SCREAMING_SNAKE_CASE : Union[str, Any] = [0] * no_of_processes
SCREAMING_SNAKE_CASE : Optional[Any] = list(range(1, no_of_processes + 1))
for i in range(no_of_processes):
print("Enter the arrival time and burst time for process:--" + str(i + 1))
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = map(int, input().split())
SCREAMING_SNAKE_CASE : Union[str, Any] = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
SCREAMING_SNAKE_CASE : Optional[Any] = burst_time
SCREAMING_SNAKE_CASE : Dict = no_of_processes
SCREAMING_SNAKE_CASE : Union[str, Any] = waiting_time
SCREAMING_SNAKE_CASE : Union[str, Any] = calculate_turnaroundtime(bt, n, wt)
calculate_average_times(waiting_time, turn_around_time, no_of_processes)
SCREAMING_SNAKE_CASE : Union[str, Any] = pd.DataFrame(
list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
columns=[
"Process",
"BurstTime",
"ArrivalTime",
"WaitingTime",
"TurnAroundTime",
],
)
# Printing the dataFrame
pd.set_option("display.max_rows", fcfs.shape[0] + 1)
print(fcfs)
| 141
|
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__lowerCamelCase = logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class _snake_case ( lowerCamelCase ):
"""simple docstring"""
lowerCamelCase_ = field(default=lowerCamelCase ,metadata={'''help''': '''Whether to use SortishSampler or not.'''} )
lowerCamelCase_ = field(
default=lowerCamelCase ,metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''} )
lowerCamelCase_ = field(
default=lowerCamelCase ,metadata={
'''help''': (
'''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
'''to the `max_length` value of the model configuration.'''
)
} ,)
lowerCamelCase_ = field(
default=lowerCamelCase ,metadata={
'''help''': (
'''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
'''to the `num_beams` value of the model configuration.'''
)
} ,)
lowerCamelCase_ = field(
default=lowerCamelCase ,metadata={
'''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
} ,)
def lowercase_ ( self ) -> str:
"""simple docstring"""
_A = super().to_dict()
for k, v in d.items():
if isinstance(a , a ):
_A = v.to_dict()
return d
| 317
| 0
|
"""simple docstring"""
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE =DebertaTokenizer
_SCREAMING_SNAKE_CASE =True
_SCREAMING_SNAKE_CASE =DebertaTokenizerFast
def lowercase ( self: int ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
a__ = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''[UNK]''',
]
a__ = dict(zip(__A , range(len(__A ) ) ) )
a__ = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
a__ = {'''unk_token''': '''[UNK]'''}
a__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
a__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__A ) )
def lowercase ( self: List[str] , **__A: Dict ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__A )
def lowercase ( self: Any , __A: Any ):
'''simple docstring'''
a__ = '''lower newer'''
a__ = '''lower newer'''
return input_text, output_text
def lowercase ( self: Any ):
'''simple docstring'''
a__ = self.get_tokenizer()
a__ = '''lower newer'''
a__ = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
a__ = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
a__ = tokens + [tokenizer.unk_token]
a__ = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
def lowercase ( self: Optional[Any] ):
'''simple docstring'''
a__ = self.get_tokenizer()
a__ = tokenizer('''Hello''' , '''World''' )
a__ = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
self.assertListEqual(tokd['''token_type_ids'''] , __A )
@slow
def lowercase ( self: List[str] ):
'''simple docstring'''
a__ = self.tokenizer_class.from_pretrained('''microsoft/deberta-base''' )
a__ = tokenizer.encode('''sequence builders''' , add_special_tokens=__A )
a__ = tokenizer.encode('''multi-sequence build''' , add_special_tokens=__A )
a__ = tokenizer.encode(
'''sequence builders''' , add_special_tokens=__A , add_prefix_space=__A )
a__ = tokenizer.encode(
'''sequence builders''' , '''multi-sequence build''' , add_special_tokens=__A , add_prefix_space=__A )
a__ = tokenizer.build_inputs_with_special_tokens(__A )
a__ = tokenizer.build_inputs_with_special_tokens(__A , __A )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
@slow
def lowercase ( self: Tuple ):
'''simple docstring'''
a__ = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class )
for tokenizer_class in tokenizer_classes:
a__ = tokenizer_class.from_pretrained('''microsoft/deberta-base''' )
a__ = [
'''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
'''ALBERT incorporates two parameter reduction techniques''',
'''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
''' vocabulary embedding.''',
]
a__ = tokenizer(__A , padding=__A )
a__ = [tokenizer.decode(__A , skip_special_tokens=__A ) for seq in encoding['''input_ids''']]
# fmt: off
a__ = {
'''input_ids''': [
[1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
],
'''token_type_ids''': [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
],
'''attention_mask''': [
[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, 0, 0, 0],
[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, 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]
]
}
# fmt: on
a__ = [
'''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
'''ALBERT incorporates two parameter reduction techniques''',
'''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
''' vocabulary embedding.''',
]
self.assertDictEqual(encoding.data , __A )
for expected, decoded in zip(__A , __A ):
self.assertEqual(__A , __A )
| 706
|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__a : int = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_=False , lowerCamelCase_=False):
a__ = '''backbone.''' if is_semantic else ''''''
a__ = []
for i in range(config.num_hidden_layers):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'{prefix}blocks.{i}.norm1.weight', f'beit.encoder.layer.{i}.layernorm_before.weight'))
rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias'))
rename_keys.append(
(f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight'))
rename_keys.append(
(f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias'))
rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight'))
rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias'))
rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight'))
rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias'))
rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight'))
rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias'))
# projection layer + position embeddings
rename_keys.extend(
[
(f'{prefix}cls_token', '''beit.embeddings.cls_token'''),
(f'{prefix}patch_embed.proj.weight', '''beit.embeddings.patch_embeddings.projection.weight'''),
(f'{prefix}patch_embed.proj.bias', '''beit.embeddings.patch_embeddings.projection.bias'''),
(f'{prefix}pos_embed', '''beit.embeddings.position_embeddings'''),
])
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
('''mask_token''', '''beit.embeddings.mask_token'''),
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
])
else:
# layernorm + classification head
rename_keys.extend(
[
('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
])
return rename_keys
def SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=False , lowerCamelCase_=False):
for i in range(config.num_hidden_layers):
a__ = '''backbone.''' if is_semantic else ''''''
# queries, keys and values
a__ = state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight')
a__ = state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias')
a__ = state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias')
a__ = in_proj_weight[
: config.hidden_size, :
]
a__ = q_bias
a__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
a__ = in_proj_weight[
-config.hidden_size :, :
]
a__ = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
a__ = state_dict.pop(f'{prefix}blocks.{i}.gamma_1')
a__ = state_dict.pop(f'{prefix}blocks.{i}.gamma_2')
a__ = gamma_a
a__ = gamma_a
def SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_):
a__ = dct.pop(lowerCamelCase_)
a__ = val
def SCREAMING_SNAKE_CASE ( ):
a__ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
a__ = Image.open(requests.get(lowerCamelCase_ , stream=lowerCamelCase_).raw)
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=False):
a__ = False if '''rvlcdip''' in checkpoint_url else True
a__ = BeitConfig(use_absolute_position_embeddings=lowerCamelCase_ , use_mask_token=lowerCamelCase_)
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
a__ = 1024
a__ = 4096
a__ = 24
a__ = 16
# labels
if "rvlcdip" in checkpoint_url:
a__ = 16
a__ = '''huggingface/label-files'''
a__ = '''rvlcdip-id2label.json'''
a__ = json.load(open(hf_hub_download(lowerCamelCase_ , lowerCamelCase_ , repo_type='''dataset''') , '''r'''))
a__ = {int(lowerCamelCase_): v for k, v in idalabel.items()}
a__ = idalabel
a__ = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
a__ = torch.hub.load_state_dict_from_url(lowerCamelCase_ , map_location='''cpu''')['''model''']
a__ = create_rename_keys(lowerCamelCase_ , has_lm_head=lowerCamelCase_)
for src, dest in rename_keys:
rename_key(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_)
read_in_q_k_v(lowerCamelCase_ , lowerCamelCase_ , has_lm_head=lowerCamelCase_)
# load HuggingFace model
a__ = BeitForMaskedImageModeling(lowerCamelCase_) if has_lm_head else BeitForImageClassification(lowerCamelCase_)
model.eval()
model.load_state_dict(lowerCamelCase_)
# Check outputs on an image
a__ = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=lowerCamelCase_)
a__ = prepare_img()
a__ = image_processor(images=lowerCamelCase_ , return_tensors='''pt''')
a__ = encoding['''pixel_values''']
a__ = model(lowerCamelCase_)
a__ = outputs.logits
# verify logits
a__ = [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
assert logits.shape == torch.Size(lowerCamelCase_), "Shape of logits not as expected"
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:
if has_lm_head:
a__ = '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
else:
a__ = '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
image_processor.push_to_hub(
repo_path_or_name=Path(lowerCamelCase_ , lowerCamelCase_) , organization='''nielsr''' , commit_message='''Add image processor''' , use_temp_dir=lowerCamelCase_ , )
model.push_to_hub(
repo_path_or_name=Path(lowerCamelCase_ , lowerCamelCase_) , organization='''nielsr''' , commit_message='''Add model''' , use_temp_dir=lowerCamelCase_ , )
if __name__ == "__main__":
__a : Dict = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_url',
default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
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.'
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
__a : List[Any] = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 200
| 0
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Tuple = logging.get_logger(__name__)
a_ : Optional[int] = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class _snake_case ( A__ ):
_lowercase : Union[str, Any] = '''wav2vec2'''
def __init__( self , a=32 , a=768 , a=12 , a=12 , a=3072 , a="gelu" , a=0.1 , a=0.1 , a=0.1 , a=0.0 , a=0.0 , a=0.1 , a=0.1 , a=0.02 , a=1E-5 , a="group" , a="gelu" , a=(512, 512, 512, 512, 512, 512, 512) , a=(5, 2, 2, 2, 2, 2, 2) , a=(10, 3, 3, 3, 3, 2, 2) , a=False , a=128 , a=16 , a=False , a=True , a=0.05 , a=10 , a=2 , a=0.0 , a=10 , a=0 , a=320 , a=2 , a=0.1 , a=100 , a=256 , a=256 , a=0.1 , a="sum" , a=False , a=False , a=256 , a=(512, 512, 512, 512, 1500) , a=(5, 3, 3, 1, 1) , a=(1, 2, 3, 1, 1) , a=512 , a=0 , a=1 , a=2 , a=False , a=3 , a=2 , a=3 , a=None , a=None , **a , ) -> Tuple:
super().__init__(**a , pad_token_id=a , bos_token_id=a , eos_token_id=a)
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = feat_extract_norm
SCREAMING_SNAKE_CASE = feat_extract_activation
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = conv_bias
SCREAMING_SNAKE_CASE = num_conv_pos_embeddings
SCREAMING_SNAKE_CASE = num_conv_pos_embedding_groups
SCREAMING_SNAKE_CASE = len(self.conv_dim)
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = activation_dropout
SCREAMING_SNAKE_CASE = feat_proj_dropout
SCREAMING_SNAKE_CASE = final_dropout
SCREAMING_SNAKE_CASE = layerdrop
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = do_stable_layer_norm
SCREAMING_SNAKE_CASE = use_weighted_layer_sum
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
SCREAMING_SNAKE_CASE = apply_spec_augment
SCREAMING_SNAKE_CASE = mask_time_prob
SCREAMING_SNAKE_CASE = mask_time_length
SCREAMING_SNAKE_CASE = mask_time_min_masks
SCREAMING_SNAKE_CASE = mask_feature_prob
SCREAMING_SNAKE_CASE = mask_feature_length
SCREAMING_SNAKE_CASE = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
SCREAMING_SNAKE_CASE = num_codevectors_per_group
SCREAMING_SNAKE_CASE = num_codevector_groups
SCREAMING_SNAKE_CASE = contrastive_logits_temperature
SCREAMING_SNAKE_CASE = feat_quantizer_dropout
SCREAMING_SNAKE_CASE = num_negatives
SCREAMING_SNAKE_CASE = codevector_dim
SCREAMING_SNAKE_CASE = proj_codevector_dim
SCREAMING_SNAKE_CASE = diversity_loss_weight
# ctc loss
SCREAMING_SNAKE_CASE = ctc_loss_reduction
SCREAMING_SNAKE_CASE = ctc_zero_infinity
# adapter
SCREAMING_SNAKE_CASE = add_adapter
SCREAMING_SNAKE_CASE = adapter_kernel_size
SCREAMING_SNAKE_CASE = adapter_stride
SCREAMING_SNAKE_CASE = num_adapter_layers
SCREAMING_SNAKE_CASE = output_hidden_size or hidden_size
SCREAMING_SNAKE_CASE = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = xvector_output_dim
@property
def SCREAMING_SNAKE_CASE__ ( self) -> Union[str, Any]:
return functools.reduce(operator.mul , self.conv_stride , 1)
| 73
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__magic_name__ : Optional[int] = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""",
"""SwiftFormerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ : Optional[int] = [
"""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
__magic_name__ : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 672
| 0
|
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
UpperCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase_ = 256
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : str = ['melgan']
def __init__( self: Tuple , UpperCamelCase_: SpectrogramNotesEncoder , UpperCamelCase_: SpectrogramContEncoder , UpperCamelCase_: TaFilmDecoder , UpperCamelCase_: DDPMScheduler , UpperCamelCase_: OnnxRuntimeModel if is_onnx_available() else Any , ):
super().__init__()
# From MELGAN
__lowerCamelCase = math.log(1E-5 ) # Matches MelGAN training.
__lowerCamelCase = 4.0 # Largest value for most examples
__lowerCamelCase = 1_28
self.register_modules(
notes_encoder=UpperCamelCase_ , continuous_encoder=UpperCamelCase_ , decoder=UpperCamelCase_ , scheduler=UpperCamelCase_ , melgan=UpperCamelCase_ , )
def lowerCAmelCase__ ( self: List[str] , UpperCamelCase_: List[Any] , UpperCamelCase_: Any=(-1.0, 1.0) , UpperCamelCase_: Optional[int]=False ):
__lowerCamelCase, __lowerCamelCase = output_range
if clip:
__lowerCamelCase = torch.clip(UpperCamelCase_ , self.min_value , self.max_value )
# Scale to [0, 1].
__lowerCamelCase = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def lowerCAmelCase__ ( self: Tuple , UpperCamelCase_: Any , UpperCamelCase_: Any=(-1.0, 1.0) , UpperCamelCase_: Optional[Any]=False ):
__lowerCamelCase, __lowerCamelCase = input_range
__lowerCamelCase = torch.clip(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) if clip else outputs
# Scale to [0, 1].
__lowerCamelCase = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def lowerCAmelCase__ ( self: str , UpperCamelCase_: Dict , UpperCamelCase_: int , UpperCamelCase_: Dict ):
__lowerCamelCase = input_tokens > 0
__lowerCamelCase, __lowerCamelCase = self.notes_encoder(
encoder_input_tokens=UpperCamelCase_ , encoder_inputs_mask=UpperCamelCase_ )
__lowerCamelCase, __lowerCamelCase = self.continuous_encoder(
encoder_inputs=UpperCamelCase_ , encoder_inputs_mask=UpperCamelCase_ )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def lowerCAmelCase__ ( self: Optional[int] , UpperCamelCase_: int , UpperCamelCase_: str , UpperCamelCase_: Union[str, Any] ):
__lowerCamelCase = noise_time
if not torch.is_tensor(UpperCamelCase_ ):
__lowerCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(UpperCamelCase_ ) and len(timesteps.shape ) == 0:
__lowerCamelCase = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__lowerCamelCase = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
__lowerCamelCase = self.decoder(
encodings_and_masks=UpperCamelCase_ , decoder_input_tokens=UpperCamelCase_ , decoder_noise_time=UpperCamelCase_ )
return logits
@torch.no_grad()
def __call__( self: Optional[int] , UpperCamelCase_: List[List[int]] , UpperCamelCase_: Optional[torch.Generator] = None , UpperCamelCase_: int = 1_00 , UpperCamelCase_: bool = True , UpperCamelCase_: str = "numpy" , UpperCamelCase_: Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCamelCase_: int = 1 , ):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or callback_steps <= 0)
):
raise ValueError(
F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
F' {type(UpperCamelCase_ )}.' )
__lowerCamelCase = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
__lowerCamelCase = np.zeros([1, 0, self.n_dims] , np.floataa )
__lowerCamelCase = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=UpperCamelCase_ , device=self.device )
for i, encoder_input_tokens in enumerate(UpperCamelCase_ ):
if i == 0:
__lowerCamelCase = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
__lowerCamelCase = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=UpperCamelCase_ , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
__lowerCamelCase = ones
__lowerCamelCase = self.scale_features(
UpperCamelCase_ , output_range=[-1.0, 1.0] , clip=UpperCamelCase_ )
__lowerCamelCase = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=UpperCamelCase_ , continuous_mask=UpperCamelCase_ , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
__lowerCamelCase = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=UpperCamelCase_ , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(UpperCamelCase_ )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
__lowerCamelCase = self.decode(
encodings_and_masks=UpperCamelCase_ , input_tokens=UpperCamelCase_ , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
__lowerCamelCase = self.scheduler.step(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , generator=UpperCamelCase_ ).prev_sample
__lowerCamelCase = self.scale_to_features(UpperCamelCase_ , input_range=[-1.0, 1.0] )
__lowerCamelCase = mel[:1]
__lowerCamelCase = mel.cpu().float().numpy()
__lowerCamelCase = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(UpperCamelCase_ , UpperCamelCase_ )
logger.info("""Generated segment""" , UpperCamelCase_ )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"""Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'.""" )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"""Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'.""" )
if output_type == "numpy":
__lowerCamelCase = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
__lowerCamelCase = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=UpperCamelCase_ )
| 80
|
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()
UpperCAmelCase_ = logging.get_logger(__name__)
def lowerCamelCase__ ( A__ : str ):
'''simple docstring'''
__lowerCamelCase = DPTConfig()
if "large" in checkpoint_url:
__lowerCamelCase = 1024
__lowerCamelCase = 4096
__lowerCamelCase = 24
__lowerCamelCase = 16
__lowerCamelCase = [5, 11, 17, 23]
__lowerCamelCase = [256, 512, 1024, 1024]
__lowerCamelCase = (1, 384, 384)
if "ade" in checkpoint_url:
__lowerCamelCase = True
__lowerCamelCase = 150
__lowerCamelCase = """huggingface/label-files"""
__lowerCamelCase = """ade20k-id2label.json"""
__lowerCamelCase = json.load(open(cached_download(hf_hub_url(A__ , A__ , repo_type="""dataset""" ) ) , """r""" ) )
__lowerCamelCase = {int(A__ ): v for k, v in idalabel.items()}
__lowerCamelCase = idalabel
__lowerCamelCase = {v: k for k, v in idalabel.items()}
__lowerCamelCase = [1, 150, 480, 480]
return config, expected_shape
def lowerCamelCase__ ( A__ : Dict ):
'''simple docstring'''
__lowerCamelCase = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(A__ , A__ )
def lowerCamelCase__ ( A__ : Dict ):
'''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 lowerCamelCase__ ( A__ : Tuple , A__ : Any ):
'''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 lowerCamelCase__ ( ):
'''simple docstring'''
__lowerCamelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowerCamelCase = Image.open(requests.get(A__ , stream=A__ ).raw )
return im
@torch.no_grad()
def lowerCamelCase__ ( A__ : Optional[int] , A__ : Union[str, Any] , A__ : List[str] , A__ : Union[str, Any] ):
'''simple docstring'''
__lowerCamelCase, __lowerCamelCase = get_dpt_config(A__ )
# load original state_dict from URL
__lowerCamelCase = torch.hub.load_state_dict_from_url(A__ , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(A__ )
# rename keys
for key in state_dict.copy().keys():
__lowerCamelCase = state_dict.pop(A__ )
__lowerCamelCase = val
# read in qkv matrices
read_in_q_k_v(A__ , A__ )
# load HuggingFace model
__lowerCamelCase = DPTForSemanticSegmentation(A__ ) if """ade""" in checkpoint_url else DPTForDepthEstimation(A__ )
model.load_state_dict(A__ )
model.eval()
# Check outputs on an image
__lowerCamelCase = 480 if """ade""" in checkpoint_url else 384
__lowerCamelCase = DPTImageProcessor(size=A__ )
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(A__ , return_tensors="""pt""" )
# forward pass
__lowerCamelCase = model(**A__ ).logits if """ade""" in checkpoint_url else model(**A__ ).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(A__ )
assert (
torch.allclose(outputs[0, 0, :3, :3] , A__ , atol=1E-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] , A__ )
)
Path(A__ ).mkdir(exist_ok=A__ )
print(f'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(A__ )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(A__ )
if push_to_hub:
print("""Pushing model to hub...""" )
model.push_to_hub(
repo_path_or_name=Path(A__ , A__ ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=A__ , )
image_processor.push_to_hub(
repo_path_or_name=Path(A__ , A__ ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=A__ , )
if __name__ == "__main__":
UpperCAmelCase_ = 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.',
)
UpperCAmelCase_ = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 80
| 1
|
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a_ = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 339
|
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 lowercase__ ( _UpperCAmelCase, unittest.TestCase ):
a_ =FunnelTokenizer
a_ =FunnelTokenizerFast
a_ =True
a_ =True
def UpperCAmelCase ( self )-> str:
'''simple docstring'''
super().setUp()
lowerCAmelCase__ = [
"<unk>",
"<cls>",
"<sep>",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
lowerCAmelCase__ = 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 UpperCAmelCase ( self , **__UpperCAmelCase )-> Any:
'''simple docstring'''
return FunnelTokenizer.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def UpperCAmelCase ( self , **__UpperCAmelCase )-> Union[str, Any]:
'''simple docstring'''
return FunnelTokenizerFast.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def UpperCAmelCase ( self , __UpperCAmelCase )-> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase__ = "UNwant\u00E9d,running"
lowerCAmelCase__ = "unwanted, running"
return input_text, output_text
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
lowerCAmelCase__ = self.tokenizer_class(self.vocab_file )
lowerCAmelCase__ = 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 UpperCAmelCase ( self )-> str:
'''simple docstring'''
lowerCAmelCase__ = self.get_tokenizers(do_lower_case=__UpperCAmelCase )
for tokenizer in tokenizers:
lowerCAmelCase__ = tokenizer("UNwant\u00E9d,running" )
lowerCAmelCase__ = len(inputs["input_ids"] ) - 1
self.assertListEqual(inputs["token_type_ids"] , [2] + [0] * sentence_len )
lowerCAmelCase__ = tokenizer("UNwant\u00E9d,running" , "UNwant\u00E9d,running" )
self.assertListEqual(inputs["token_type_ids"] , [2] + [0] * sentence_len + [1] * sentence_len )
| 339
| 1
|
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
lowerCamelCase : int = 1_6
lowerCamelCase : Tuple = 3_2
def lowercase__( A , A = 1_6 , A = "bert-base-cased" ):
snake_case__ : List[Any] = AutoTokenizer.from_pretrained(A )
snake_case__ : Tuple = load_dataset('glue' , 'mrpc' )
def tokenize_function(A ):
# max_length=None => use the model max length (it's actually the default)
snake_case__ : Optional[Any] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=A , max_length=A )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
snake_case__ : Optional[Any] = datasets.map(
A , batched=A , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=A )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case__ : str = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(A ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(A , padding='max_length' , max_length=1_2_8 , return_tensors='pt' )
return tokenizer.pad(A , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
snake_case__ : int = DataLoader(
tokenized_datasets['train'] , shuffle=A , collate_fn=A , batch_size=A )
snake_case__ : List[Any] = DataLoader(
tokenized_datasets['validation'] , shuffle=A , collate_fn=A , batch_size=A )
return train_dataloader, eval_dataloader
def lowercase__( A , A , A , A ):
model.eval()
snake_case__ : List[str] = 0
for step, batch in enumerate(A ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case__ : List[str] = model(**A )
snake_case__ : int = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
snake_case__ : str = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(A ) - 1:
snake_case__ : str = predictions[: len(eval_dataloader.dataset ) - samples_seen]
snake_case__ : Union[str, Any] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=A , references=A , )
snake_case__ : Optional[Any] = metric.compute()
return eval_metric["accuracy"]
def lowercase__( A , A ):
# Initialize accelerator
snake_case__ : Union[str, Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case__ : Any = config['lr']
snake_case__ : Dict = int(config['num_epochs'] )
snake_case__ : Dict = int(config['seed'] )
snake_case__ : Any = int(config['batch_size'] )
snake_case__ : List[Any] = args.model_name_or_path
set_seed(A )
snake_case__ : Optional[int] = get_dataloaders(A , A , A )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case__ : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(A , return_dict=A )
# Instantiate optimizer
snake_case__ : Tuple = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
snake_case__ : List[Any] = optimizer_cls(params=model.parameters() , lr=A )
if accelerator.state.deepspeed_plugin is not None:
snake_case__ : List[Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
snake_case__ : Any = 1
snake_case__ : Tuple = (len(A ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
snake_case__ : Optional[Any] = get_linear_schedule_with_warmup(
optimizer=A , num_warmup_steps=0 , num_training_steps=A , )
else:
snake_case__ : int = DummyScheduler(A , total_num_steps=A , warmup_num_steps=0 )
# 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.
snake_case__ : Union[str, Any] = accelerator.prepare(
A , A , A , A , A )
# We need to keep track of how many total steps we have iterated over
snake_case__ : Dict = 0
# We also need to keep track of the stating epoch so files are named properly
snake_case__ : Tuple = 0
snake_case__ : List[str] = evaluate.load('glue' , 'mrpc' )
snake_case__ : List[Any] = num_epochs
if args.partial_train_epoch is not None:
snake_case__ : Dict = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
snake_case__ : Dict = args.resume_from_checkpoint.split('epoch_' )[1]
snake_case__ : List[str] = ''
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
snake_case__ : Optional[int] = int(A ) + 1
snake_case__ : Union[str, Any] = evaluation_loop(A , A , A , A )
accelerator.print('resumed checkpoint performance:' , A )
accelerator.print('resumed checkpoint\'s scheduler\'s lr:' , lr_scheduler.get_lr()[0] )
accelerator.print('resumed optimizers\'s lr:' , optimizer.param_groups[0]['lr'] )
with open(os.path.join(args.output_dir , f'''state_{starting_epoch-1}.json''' ) , 'r' ) as f:
snake_case__ : int = json.load(A )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
snake_case__ : Any = {}
for epoch in range(A , A ):
model.train()
for step, batch in enumerate(A ):
snake_case__ : str = model(**A )
snake_case__ : Optional[int] = outputs.loss
snake_case__ : str = loss / gradient_accumulation_steps
accelerator.backward(A )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
snake_case__ : Optional[Any] = f'''epoch_{epoch}'''
snake_case__ : Optional[int] = os.path.join(args.output_dir , A )
accelerator.save_state(A )
snake_case__ : int = evaluation_loop(A , A , A , A )
snake_case__ : Union[str, Any] = accuracy
snake_case__ : Dict = lr_scheduler.get_lr()[0]
snake_case__ : Dict = optimizer.param_groups[0]['lr']
snake_case__ : Optional[Any] = epoch
snake_case__ : Union[str, Any] = overall_step
accelerator.print(f'''epoch {epoch}:''' , A )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , f'''state_{epoch}.json''' ) , 'w' ) as f:
json.dump(A , A )
def lowercase__( ):
snake_case__ : str = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=A , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=A , )
parser.add_argument(
'--output_dir' , type=A , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--resume_from_checkpoint' , type=A , default=A , help='If the training should continue from a checkpoint folder.' , )
parser.add_argument(
'--partial_train_epoch' , type=A , default=A , help='If passed, the training will stop after this number of epochs.' , )
parser.add_argument(
'--num_epochs' , type=A , default=2 , help='Number of train epochs.' , )
snake_case__ : Optional[Any] = parser.parse_args()
snake_case__ : Tuple = {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}
training_function(A , A )
if __name__ == "__main__":
main()
| 714
|
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowerCamelCase : Optional[Any] = 1_0
def lowercase__( A , A , A , A ):
for i in range(A , A ):
if array[i] == target:
return i
return -1
def lowercase__( A , A ):
snake_case__ : int = 0
snake_case__ : List[Any] = len(A )
while left <= right:
if right - left < precision:
return lin_search(A , A , A , A )
snake_case__ : Tuple = (left + right) // 3 + 1
snake_case__ : str = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
snake_case__ : Any = one_third - 1
elif array[two_third] < target:
snake_case__ : Any = two_third + 1
else:
snake_case__ : Optional[int] = one_third + 1
snake_case__ : Any = two_third - 1
else:
return -1
def lowercase__( A , A , A , A ):
if left < right:
if right - left < precision:
return lin_search(A , A , A , A )
snake_case__ : Optional[int] = (left + right) // 3 + 1
snake_case__ : Union[str, Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(A , one_third - 1 , A , A )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , A , A , A )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , A , A )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase : Dict = input('Enter numbers separated by comma:\n').strip()
lowerCamelCase : Dict = [int(item.strip()) for item in user_input.split(',')]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
lowerCamelCase : List[str] = int(input('Enter the number to be found in the list:\n').strip())
lowerCamelCase : int = ite_ternary_search(collection, target)
lowerCamelCase : List[str] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F"""Iterative search: {target} found at positions: {resulta}""")
print(F"""Recursive search: {target} found at positions: {resulta}""")
else:
print('Not found')
| 303
| 0
|
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# 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.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
lowercase : Optional[Any] = logging.get_logger(__name__)
@dataclass
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Optional[int] , a :Tuple=False , a :Optional[Any]=False , a :Union[str, Any]=6.0 , a :Optional[Any]=None , a :int=False , a :str=False , a :Tuple=None , a :List[str]="fp4" , a :Tuple=False , **a :int , ) -> List[str]:
__UpperCamelCase : Optional[Any] = load_in_abit
__UpperCamelCase : Optional[int] = load_in_abit
__UpperCamelCase : List[str] = llm_inta_threshold
__UpperCamelCase : List[str] = llm_inta_skip_modules
__UpperCamelCase : Dict = llm_inta_enable_fpaa_cpu_offload
__UpperCamelCase : Union[str, Any] = llm_inta_has_fpaa_weight
__UpperCamelCase : Any = bnb_abit_quant_type
__UpperCamelCase : Tuple = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
__UpperCamelCase : int = torch.floataa
elif isinstance(a , a ):
__UpperCamelCase : Tuple = getattr(a , a )
elif isinstance(a , torch.dtype ):
__UpperCamelCase : List[str] = bnb_abit_compute_dtype
else:
raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype" )
self.post_init()
def _lowerCamelCase ( self :int ) -> List[str]:
if not isinstance(self.llm_inta_threshold , a ):
raise ValueError("llm_int8_threshold must be a float" )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , a ):
raise ValueError("llm_int8_skip_modules must be a list of strings" )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , a ):
raise ValueError("llm_int8_enable_fp32_cpu_offload must be a boolean" )
if not isinstance(self.llm_inta_has_fpaa_weight , a ):
raise ValueError("llm_int8_has_fp16_weight must be a boolean" )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError("bnb_4bit_compute_dtype must be torch.dtype" )
if not isinstance(self.bnb_abit_quant_type , a ):
raise ValueError("bnb_4bit_quant_type must be a string" )
if not isinstance(self.bnb_abit_use_double_quant , a ):
raise ValueError("bnb_4bit_use_double_quant must be a boolean" )
if self.load_in_abit and not version.parse(importlib.metadata.version("bitsandbytes" ) ) >= version.parse(
"0.39.0" ):
raise ValueError(
"4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version" )
def _lowerCamelCase ( self :Any ) -> Any:
return self.load_in_abit or self.load_in_abit
def _lowerCamelCase ( self :Optional[int] ) -> Optional[int]:
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def _lowerCamelCase ( cls :Dict , a :str , a :int , **a :List[Any] ) -> str:
__UpperCamelCase : List[str] = cls(**a )
__UpperCamelCase : List[str] = []
for key, value in kwargs.items():
if hasattr(a , a ):
setattr(a , a , a )
to_remove.append(a )
for key in to_remove:
kwargs.pop(a , a )
if return_unused_kwargs:
return config, kwargs
else:
return config
def _lowerCamelCase ( self :Optional[Any] , a :Union[str, os.PathLike] ) -> Union[str, Any]:
with open(a , "w" , encoding="utf-8" ) as writer:
__UpperCamelCase : Dict = self.to_dict()
__UpperCamelCase : Any = json.dumps(a , indent=2 , sort_keys=a ) + "\n"
writer.write(a )
def _lowerCamelCase ( self :List[Any] ) -> Dict[str, Any]:
__UpperCamelCase : Any = copy.deepcopy(self.__dict__ )
__UpperCamelCase : str = str(output["bnb_4bit_compute_dtype"] ).split("." )[1]
return output
def __repr__( self :int ) -> Dict:
return f'{self.__class__.__name__} {self.to_json_string()}'
def _lowerCamelCase ( self :Optional[Any] , a :bool = True ) -> str:
if use_diff is True:
__UpperCamelCase : Tuple = self.to_diff_dict()
else:
__UpperCamelCase : List[str] = self.to_dict()
return json.dumps(a , indent=2 , sort_keys=a ) + "\n"
def _lowerCamelCase ( self :Dict ) -> Dict[str, Any]:
__UpperCamelCase : Any = self.to_dict()
# get the default config dict
__UpperCamelCase : Any = BitsAndBytesConfig().to_dict()
__UpperCamelCase : Tuple = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
__UpperCamelCase : int = value
return serializable_config_dict
| 557
|
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 557
| 1
|
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if not len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError('Both a & b of two equations can\'t be zero.' )
# Extract the coefficients
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =equationa
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =equationa
# Calculate the determinants of the matrices
SCREAMING_SNAKE_CASE =aa * ba - aa * ba
SCREAMING_SNAKE_CASE =ca * ba - ca * ba
SCREAMING_SNAKE_CASE =aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError('Infinite solutions. (Consistent system)' )
else:
raise ValueError('No solution. (Inconsistent system)' )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
SCREAMING_SNAKE_CASE =determinant_x / determinant
SCREAMING_SNAKE_CASE =determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 705
|
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if not len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError('Both a & b of two equations can\'t be zero.' )
# Extract the coefficients
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =equationa
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =equationa
# Calculate the determinants of the matrices
SCREAMING_SNAKE_CASE =aa * ba - aa * ba
SCREAMING_SNAKE_CASE =ca * ba - ca * ba
SCREAMING_SNAKE_CASE =aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError('Infinite solutions. (Consistent system)' )
else:
raise ValueError('No solution. (Inconsistent system)' )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
SCREAMING_SNAKE_CASE =determinant_x / determinant
SCREAMING_SNAKE_CASE =determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 252
| 0
|
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 100 * 2**20, 900 * 2**20] )
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , '''IN_MEMORY_MAX_SIZE''' , _lowercase )
UpperCAmelCase_ : List[Any] = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
UpperCAmelCase_ : Union[str, Any] = dataset_size < in_memory_max_size
else:
UpperCAmelCase_ : Dict = False
UpperCAmelCase_ : str = is_small_dataset(_lowercase )
assert result == expected
| 30
|
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ ( __lowercase , unittest.TestCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : int = LxmertTokenizer
_SCREAMING_SNAKE_CASE : int = LxmertTokenizerFast
_SCREAMING_SNAKE_CASE : Tuple = True
_SCREAMING_SNAKE_CASE : Any = True
def snake_case__ ( self) -> Dict:
"""simple docstring"""
super().setUp()
_UpperCAmelCase : int = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_UpperCAmelCase : Union[str, Any] = 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 snake_case__ ( self , _A) -> Dict:
"""simple docstring"""
_UpperCAmelCase : int = '''UNwant\u00E9d,running'''
_UpperCAmelCase : Union[str, Any] = '''unwanted, running'''
return input_text, output_text
def snake_case__ ( self) -> str:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.tokenizer_class(self.vocab_file)
_UpperCAmelCase : Any = tokenizer.tokenize('''UNwant\u00E9d,running''')
self.assertListEqual(_A , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''])
self.assertListEqual(tokenizer.convert_tokens_to_ids(_A) , [7, 4, 5, 10, 8, 9])
def snake_case__ ( self) -> Any:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_UpperCAmelCase : List[Any] = self.get_tokenizer()
_UpperCAmelCase : Dict = self.get_rust_tokenizer()
_UpperCAmelCase : Dict = '''I was born in 92000, and this is falsé.'''
_UpperCAmelCase : List[str] = tokenizer.tokenize(_A)
_UpperCAmelCase : Union[str, Any] = rust_tokenizer.tokenize(_A)
self.assertListEqual(_A , _A)
_UpperCAmelCase : int = tokenizer.encode(_A , add_special_tokens=_A)
_UpperCAmelCase : Dict = rust_tokenizer.encode(_A , add_special_tokens=_A)
self.assertListEqual(_A , _A)
_UpperCAmelCase : Tuple = self.get_rust_tokenizer()
_UpperCAmelCase : List[Any] = tokenizer.encode(_A)
_UpperCAmelCase : Tuple = rust_tokenizer.encode(_A)
self.assertListEqual(_A , _A)
| 485
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__snake_case = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""",
"""SwiftFormerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
"""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
__snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 117
|
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
__snake_case = """"""
if version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):
class _lowerCAmelCase ( tr.AbstractTransform ):
def __init__( self , UpperCamelCase__ = " " ) -> Any:
'''simple docstring'''
snake_case : Optional[Any] = sentence_delimiter
def lowerCamelCase ( self , UpperCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
return list(UpperCamelCase__ )
def lowerCamelCase ( self , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
snake_case : Dict = []
for sent_idx, sentence in enumerate(UpperCamelCase__ ):
chars.extend(self.process_string(UpperCamelCase__ ) )
if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCamelCase__ ) - 1:
chars.append(self.sentence_delimiter )
return chars
__snake_case = tr.Compose(
[tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
)
else:
__snake_case = tr.Compose(
[
tr.RemoveMultipleSpaces(),
tr.Strip(),
tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
tr.ReduceToListOfListOfChars(),
]
)
__snake_case = """\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""
__snake_case = """\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.
CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.
Character error rate can be computed as:
CER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).
CER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
"""
__snake_case = """
Computes CER score of transcribed segments against references.
Args:
references: list of references for each speech input.
predictions: list of transcribtions to score.
concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
(float): the character error rate
Examples:
>>> predictions = [\"this is the prediction\", \"there is an other sample\"]
>>> references = [\"this is the reference\", \"there is another one\"]
>>> cer = datasets.load_metric(\"cer\")
>>> cer_score = cer.compute(predictions=predictions, references=references)
>>> print(cer_score)
0.34146341463414637
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/jitsi/jiwer/"] , reference_urls=[
"https://en.wikipedia.org/wiki/Word_error_rate",
"https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
] , )
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=False ) -> Optional[int]:
'''simple docstring'''
if concatenate_texts:
return jiwer.compute_measures(
UpperCamelCase__ , UpperCamelCase__ , truth_transform=UpperCamelCase__ , hypothesis_transform=UpperCamelCase__ , )["wer"]
snake_case : Optional[int] = 0
snake_case : int = 0
for prediction, reference in zip(UpperCamelCase__ , UpperCamelCase__ ):
snake_case : Dict = jiwer.compute_measures(
UpperCamelCase__ , UpperCamelCase__ , truth_transform=UpperCamelCase__ , hypothesis_transform=UpperCamelCase__ , )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 117
| 1
|
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase__ : List[str] = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
UpperCamelCase__ : Union[str, Any] = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
UpperCamelCase__ : List[str] = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def lowerCAmelCase_ ( _lowerCamelCase: str , _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : List[str] = len([g for position, g in enumerate(_lowerCamelCase ) if g == main_target[position]] )
return (item, float(_lowerCamelCase ))
def lowerCAmelCase_ ( _lowerCamelCase: str , _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : Tuple = random.randint(0 , len(_lowerCamelCase ) - 1 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = parent_a[:random_slice] + parent_a[random_slice:]
__SCREAMING_SNAKE_CASE : int = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def lowerCAmelCase_ ( _lowerCamelCase: str , _lowerCamelCase: list[str] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = list(_lowerCamelCase )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
__SCREAMING_SNAKE_CASE : Optional[Any] = random.choice(_lowerCamelCase )
return "".join(_lowerCamelCase )
def lowerCAmelCase_ ( _lowerCamelCase: tuple[str, float] , _lowerCamelCase: list[tuple[str, float]] , _lowerCamelCase: list[str] , ):
__SCREAMING_SNAKE_CASE : List[Any] = []
# Generate more children proportionally to the fitness score.
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(parent_a[1] * 1_00 ) + 1
__SCREAMING_SNAKE_CASE : Optional[int] = 10 if child_n >= 10 else child_n
for _ in range(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Tuple = population_score[random.randint(0 , _lowerCamelCase )][0]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = crossover(parent_a[0] , _lowerCamelCase )
# Append new string to the population list.
pop.append(mutate(_lowerCamelCase , _lowerCamelCase ) )
pop.append(mutate(_lowerCamelCase , _lowerCamelCase ) )
return pop
def lowerCAmelCase_ ( _lowerCamelCase: str , _lowerCamelCase: list[str] , _lowerCamelCase: bool = True ):
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
__SCREAMING_SNAKE_CASE : List[Any] = F"{N_POPULATION} must be bigger than {N_SELECTED}"
raise ValueError(_lowerCamelCase )
# Verify that the target contains no genes besides the ones inside genes variable.
__SCREAMING_SNAKE_CASE : Union[str, Any] = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
__SCREAMING_SNAKE_CASE : Optional[Any] = F"{not_in_genes_list} is not in genes list, evolution cannot converge"
raise ValueError(_lowerCamelCase )
# Generate random starting population.
__SCREAMING_SNAKE_CASE : str = []
for _ in range(_lowerCamelCase ):
population.append("""""".join([random.choice(_lowerCamelCase ) for i in range(len(_lowerCamelCase ) )] ) )
# Just some logs to know what the algorithms is doing.
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(_lowerCamelCase )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
__SCREAMING_SNAKE_CASE : List[str] = [evaluate(_lowerCamelCase , _lowerCamelCase ) for item in population]
# Check if there is a matching evolution.
__SCREAMING_SNAKE_CASE : Union[str, Any] = sorted(_lowerCamelCase , key=lambda _lowerCamelCase : x[1] , reverse=_lowerCamelCase )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
F"\nGeneration: {generation}"
F"\nTotal Population:{total_population}"
F"\nBest score: {population_score[0][1]}"
F"\nBest string: {population_score[0][0]}" )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
__SCREAMING_SNAKE_CASE : List[Any] = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(_lowerCamelCase )
# Normalize population score to be between 0 and 1.
__SCREAMING_SNAKE_CASE : Optional[Any] = [
(item, score / len(_lowerCamelCase )) for item, score in population_score
]
# This is selection
for i in range(_lowerCamelCase ):
population.extend(select(population_score[int(_lowerCamelCase )] , _lowerCamelCase , _lowerCamelCase ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(_lowerCamelCase ) > N_POPULATION:
break
if __name__ == "__main__":
UpperCamelCase__ : str = (
'''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
)
UpperCamelCase__ : List[str] = list(
''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
'''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
)
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : str = basic(target_str, genes_list)
print(
f"\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"
)
| 578
|
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCAmelCase_ ( _lowerCamelCase: Optional[int] , _lowerCamelCase: str , _lowerCamelCase: str , _lowerCamelCase: Path , _lowerCamelCase: str = None , _lowerCamelCase: str = None , _lowerCamelCase: str = None , ):
if config_name_or_path is None:
__SCREAMING_SNAKE_CASE : List[str] = """facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""
if generator_tokenizer_name_or_path is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
__SCREAMING_SNAKE_CASE : Tuple = question_encoder_name_or_path
__SCREAMING_SNAKE_CASE : int = RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration
# Save model.
__SCREAMING_SNAKE_CASE : List[Any] = RagConfig.from_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = gen_config
__SCREAMING_SNAKE_CASE : Union[str, Any] = question_encoder_config
__SCREAMING_SNAKE_CASE : Dict = model_class.from_pretrained_question_encoder_generator(
_lowerCamelCase , _lowerCamelCase , config=_lowerCamelCase )
rag_model.save_pretrained(_lowerCamelCase )
# Sanity check.
model_class.from_pretrained(_lowerCamelCase )
# Save tokenizers.
__SCREAMING_SNAKE_CASE : str = AutoTokenizer.from_pretrained(_lowerCamelCase )
gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/""" )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoTokenizer.from_pretrained(_lowerCamelCase )
question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/""" )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = 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``'''
),
)
UpperCamelCase__ : Dict = parser.parse_args()
UpperCamelCase__ : Any = 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,
)
| 578
| 1
|
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
UpperCamelCase = "__DUMMY_TRANSFORMERS_USER__"
UpperCamelCase = "Dummy User"
UpperCamelCase = "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"
UpperCamelCase = "https://hub-ci.huggingface.co"
UpperCamelCase = CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
UpperCamelCase = CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
UpperCamelCase = Path("~/.huggingface/hub_ci_token").expanduser()
@pytest.fixture
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> str:
monkeypatch.setattr(
'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE' , SCREAMING_SNAKE_CASE )
@pytest.fixture
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
monkeypatch.setattr('datasets.config.HF_ENDPOINT' , SCREAMING_SNAKE_CASE )
monkeypatch.setattr('datasets.config.HUB_DATASETS_URL' , SCREAMING_SNAKE_CASE )
@pytest.fixture
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> Tuple:
monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token' , SCREAMING_SNAKE_CASE )
@pytest.fixture
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Dict:
HfFolder.save_token(SCREAMING_SNAKE_CASE )
yield
HfFolder.delete_token()
@pytest.fixture(scope='session' )
def __magic_name__ ( ) -> str:
return HfApi(endpoint=SCREAMING_SNAKE_CASE )
@pytest.fixture(scope='session' )
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
_lowercase : Union[str, Any] = HfFolder.get_token()
HfFolder.save_token(SCREAMING_SNAKE_CASE )
yield CI_HUB_USER_TOKEN
if previous_token is not None:
HfFolder.save_token(SCREAMING_SNAKE_CASE )
@pytest.fixture
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> List[str]:
def _cleanup_repo(SCREAMING_SNAKE_CASE ):
hf_api.delete_repo(SCREAMING_SNAKE_CASE , token=SCREAMING_SNAKE_CASE , repo_type='dataset' )
return _cleanup_repo
@pytest.fixture
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> int:
@contextmanager
def _temporary_repo(SCREAMING_SNAKE_CASE ):
try:
yield repo_id
finally:
cleanup_repo(SCREAMING_SNAKE_CASE )
return _temporary_repo
@pytest.fixture(scope='session' )
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
_lowercase : Optional[Any] = F"""repo_txt_data-{int(time.time() * 10E3 )}"""
_lowercase : List[str] = F"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(SCREAMING_SNAKE_CASE , token=SCREAMING_SNAKE_CASE , repo_type='dataset' , private=SCREAMING_SNAKE_CASE )
hf_api.upload_file(
token=SCREAMING_SNAKE_CASE , path_or_fileobj=str(SCREAMING_SNAKE_CASE ) , path_in_repo='data/text_data.txt' , repo_id=SCREAMING_SNAKE_CASE , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(SCREAMING_SNAKE_CASE , token=SCREAMING_SNAKE_CASE , repo_type='dataset' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Tuple:
return hf_private_dataset_repo_txt_data_
@pytest.fixture(scope='session' )
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[str]:
_lowercase : Optional[Any] = F"""repo_zipped_txt_data-{int(time.time() * 10E3 )}"""
_lowercase : Union[str, Any] = F"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(SCREAMING_SNAKE_CASE , token=SCREAMING_SNAKE_CASE , repo_type='dataset' , private=SCREAMING_SNAKE_CASE )
hf_api.upload_file(
token=SCREAMING_SNAKE_CASE , path_or_fileobj=str(SCREAMING_SNAKE_CASE ) , path_in_repo='data.zip' , repo_id=SCREAMING_SNAKE_CASE , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(SCREAMING_SNAKE_CASE , token=SCREAMING_SNAKE_CASE , repo_type='dataset' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]:
return hf_private_dataset_repo_zipped_txt_data_
@pytest.fixture(scope='session' )
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Any:
_lowercase : Optional[int] = F"""repo_zipped_img_data-{int(time.time() * 10E3 )}"""
_lowercase : List[str] = F"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(SCREAMING_SNAKE_CASE , token=SCREAMING_SNAKE_CASE , repo_type='dataset' , private=SCREAMING_SNAKE_CASE )
hf_api.upload_file(
token=SCREAMING_SNAKE_CASE , path_or_fileobj=str(SCREAMING_SNAKE_CASE ) , path_in_repo='data.zip' , repo_id=SCREAMING_SNAKE_CASE , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(SCREAMING_SNAKE_CASE , token=SCREAMING_SNAKE_CASE , repo_type='dataset' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
return hf_private_dataset_repo_zipped_img_data_
| 715
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlenderbotConfig",
"BlenderbotOnnxConfig",
],
"tokenization_blenderbot": ["BlenderbotTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ["BlenderbotTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
"BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotForCausalLM",
"BlenderbotForConditionalGeneration",
"BlenderbotModel",
"BlenderbotPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
"TFBlenderbotForConditionalGeneration",
"TFBlenderbotModel",
"TFBlenderbotPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
"FlaxBlenderbotForConditionalGeneration",
"FlaxBlenderbotModel",
"FlaxBlenderbotPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
UpperCamelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 677
| 0
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class A__ :
def __init__( self : List[str] , _a : Dict , _a : Dict=13 , _a : Union[str, Any]=7 , _a : Dict=True , _a : Any=True , _a : Optional[int]=True , _a : List[Any]=True , _a : str=99 , _a : Union[str, Any]=32 , _a : List[Any]=2 , _a : Union[str, Any]=4 , _a : Dict=37 , _a : List[str]="gelu" , _a : Tuple=0.1 , _a : Optional[Any]=0.1 , _a : List[str]=512 , _a : Optional[Any]=16 , _a : List[Any]=2 , _a : int=0.02 , _a : Optional[Any]=3 , _a : Optional[Any]=4 , _a : Optional[Any]=None , ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =parent
_SCREAMING_SNAKE_CASE =13
_SCREAMING_SNAKE_CASE =7
_SCREAMING_SNAKE_CASE =True
_SCREAMING_SNAKE_CASE =True
_SCREAMING_SNAKE_CASE =True
_SCREAMING_SNAKE_CASE =True
_SCREAMING_SNAKE_CASE =99
_SCREAMING_SNAKE_CASE =32
_SCREAMING_SNAKE_CASE =2
_SCREAMING_SNAKE_CASE =4
_SCREAMING_SNAKE_CASE =37
_SCREAMING_SNAKE_CASE ='gelu'
_SCREAMING_SNAKE_CASE =0.1
_SCREAMING_SNAKE_CASE =0.1
_SCREAMING_SNAKE_CASE =512
_SCREAMING_SNAKE_CASE =16
_SCREAMING_SNAKE_CASE =2
_SCREAMING_SNAKE_CASE =0.02
_SCREAMING_SNAKE_CASE =3
_SCREAMING_SNAKE_CASE =4
_SCREAMING_SNAKE_CASE =None
def A ( self : int ) -> Any:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE =None
if self.use_input_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 =None
_SCREAMING_SNAKE_CASE =None
_SCREAMING_SNAKE_CASE =None
if self.use_labels:
_SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE =RoFormerConfig(
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 , initializer_range=self.initializer_range , return_dict=_a , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : Optional[int] , _a : Dict , _a : Any , _a : Union[str, Any] , _a : Union[str, Any] , _a : List[str] , _a : str , _a : Tuple ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =TFRoFormerModel(config=_a )
_SCREAMING_SNAKE_CASE ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_SCREAMING_SNAKE_CASE =[input_ids, input_mask]
_SCREAMING_SNAKE_CASE =model(_a )
_SCREAMING_SNAKE_CASE =model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : List[Any] , _a : Optional[int] , _a : Tuple , _a : Any , _a : List[str] , _a : int , _a : Dict , _a : str ) -> int:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =True
_SCREAMING_SNAKE_CASE =TFRoFormerForCausalLM(config=_a )
_SCREAMING_SNAKE_CASE ={
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
_SCREAMING_SNAKE_CASE =model(_a )['logits']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def A ( self : List[str] , _a : List[Any] , _a : Any , _a : List[Any] , _a : Any , _a : List[Any] , _a : Any , _a : Union[str, Any] ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =TFRoFormerForMaskedLM(config=_a )
_SCREAMING_SNAKE_CASE ={
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
_SCREAMING_SNAKE_CASE =model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[str] , _a : List[Any] , _a : Optional[Any] , _a : Union[str, Any] , _a : Dict , _a : List[Any] , _a : str , _a : Union[str, Any] ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.num_labels
_SCREAMING_SNAKE_CASE =TFRoFormerForSequenceClassification(config=_a )
_SCREAMING_SNAKE_CASE ={
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
_SCREAMING_SNAKE_CASE =model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Any , _a : int , _a : str , _a : Any , _a : Tuple , _a : int , _a : List[str] , _a : Optional[int] ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.num_choices
_SCREAMING_SNAKE_CASE =TFRoFormerForMultipleChoice(config=_a )
_SCREAMING_SNAKE_CASE =tf.tile(tf.expand_dims(_a , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE =tf.tile(tf.expand_dims(_a , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE =tf.tile(tf.expand_dims(_a , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE ={
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
_SCREAMING_SNAKE_CASE =model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : Tuple , _a : Dict , _a : Any , _a : Optional[Any] , _a : int , _a : Optional[int] , _a : Union[str, Any] , _a : List[str] ) -> int:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.num_labels
_SCREAMING_SNAKE_CASE =TFRoFormerForTokenClassification(config=_a )
_SCREAMING_SNAKE_CASE ={
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
_SCREAMING_SNAKE_CASE =model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Optional[Any] , _a : Tuple , _a : Tuple , _a : Tuple , _a : List[Any] , _a : Any , _a : int , _a : str ) -> Optional[int]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =TFRoFormerForQuestionAnswering(config=_a )
_SCREAMING_SNAKE_CASE ={
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
_SCREAMING_SNAKE_CASE =model(_a )
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 A ( self : List[str] ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_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': input_mask}
return config, inputs_dict
@require_tf
class A__ ( A__ , A__ , unittest.TestCase ):
A__ = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
A__ = (
{
'feature-extraction': TFRoFormerModel,
'fill-mask': TFRoFormerForMaskedLM,
'question-answering': TFRoFormerForQuestionAnswering,
'text-classification': TFRoFormerForSequenceClassification,
'text-generation': TFRoFormerForCausalLM,
'token-classification': TFRoFormerForTokenClassification,
'zero-shot': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
A__ = False
A__ = False
def A ( self : Any , _a : int , _a : str , _a : Dict , _a : Any , _a : Any ) -> Dict:
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def A ( self : List[str] ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =TFRoFormerModelTester(self )
_SCREAMING_SNAKE_CASE =ConfigTester(self , config_class=_a , hidden_size=37 )
def A ( self : str ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def A ( self : str ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def A ( self : Optional[Any] ) -> str:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_a )
def A ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*_a )
def A ( self : Dict ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_a )
def A ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_a )
def A ( self : str ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_a )
def A ( self : Tuple ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_a )
@slow
def A ( self : str ) -> Any:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base' )
self.assertIsNotNone(_a )
@require_tf
class A__ ( unittest.TestCase ):
@slow
def A ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
_SCREAMING_SNAKE_CASE =tf.constant([[0, 1, 2, 3, 4, 5]] )
_SCREAMING_SNAKE_CASE =model(_a )[0]
# TODO Replace vocab size
_SCREAMING_SNAKE_CASE =5_0000
_SCREAMING_SNAKE_CASE =[1, 6, vocab_size]
self.assertEqual(output.shape , _a )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
_SCREAMING_SNAKE_CASE =tf.constant(
[
[
[-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
[-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
[-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , _a , atol=1e-4 )
@require_tf
class A__ ( unittest.TestCase ):
A__ = 1E-4
def A ( self : Dict ) -> int:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =tf.constant([[4, 10]] )
_SCREAMING_SNAKE_CASE =TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
_SCREAMING_SNAKE_CASE =emba(input_ids.shape )
_SCREAMING_SNAKE_CASE =tf.constant(
[[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]] )
tf.debugging.assert_near(_a , _a , atol=self.tolerance )
def A ( self : Any ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =tf.constant(
[
[0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
[0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
[0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
] )
_SCREAMING_SNAKE_CASE =TFRoFormerSinusoidalPositionalEmbedding(num_positions=512 , embedding_dim=512 )
emba([2, 16, 512] )
_SCREAMING_SNAKE_CASE =emba.weight[:3, :5]
tf.debugging.assert_near(_a , _a , atol=self.tolerance )
@require_tf
class A__ ( unittest.TestCase ):
A__ = 1E-4
def A ( self : Dict ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 100
_SCREAMING_SNAKE_CASE =-tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 100
_SCREAMING_SNAKE_CASE =TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
_SCREAMING_SNAKE_CASE =embed_positions([2, 16, 768] )[None, None, :, :]
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =TFRoFormerSelfAttention.apply_rotary_position_embeddings(
_a , _a , _a )
_SCREAMING_SNAKE_CASE =tf.constant(
[
[0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
[-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
[-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
[-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
[0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
[3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
] )
_SCREAMING_SNAKE_CASE =tf.constant(
[
[0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
[0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
[1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
[2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
[-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
[-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , _a , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , _a , atol=self.tolerance )
| 405
|
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : int ) -> int:
"""simple docstring"""
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError('Input value must be a \'int\' type' )
return bin(_UpperCamelCase ).count('1' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 405
| 1
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __magic_name__ ( a__ ):
"""simple docstring"""
lowerCAmelCase : Union[str, Any] = '''sew-d'''
def __init__( self : List[str] , _lowercase : Union[str, Any]=32 , _lowercase : Any=768 , _lowercase : int=12 , _lowercase : str=12 , _lowercase : Any=3_072 , _lowercase : Tuple=2 , _lowercase : str=512 , _lowercase : Dict=256 , _lowercase : List[str]=True , _lowercase : Tuple=True , _lowercase : Dict=("p2c", "c2p") , _lowercase : List[Any]="layer_norm" , _lowercase : Union[str, Any]="gelu_python" , _lowercase : Optional[int]=0.1 , _lowercase : Any=0.1 , _lowercase : Tuple=0.1 , _lowercase : Dict=0.0 , _lowercase : str=0.1 , _lowercase : List[str]=0.02 , _lowercase : Any=1E-7 , _lowercase : Dict=1E-5 , _lowercase : Tuple="group" , _lowercase : Optional[Any]="gelu" , _lowercase : int=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , _lowercase : Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , _lowercase : int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , _lowercase : Optional[int]=False , _lowercase : Optional[int]=128 , _lowercase : Union[str, Any]=16 , _lowercase : Tuple=True , _lowercase : int=0.05 , _lowercase : int=10 , _lowercase : str=2 , _lowercase : Optional[int]=0.0 , _lowercase : List[Any]=10 , _lowercase : Optional[Any]=0 , _lowercase : Any="mean" , _lowercase : Optional[int]=False , _lowercase : Optional[Any]=False , _lowercase : Union[str, Any]=256 , _lowercase : int=0 , _lowercase : Dict=1 , _lowercase : Tuple=2 , **_lowercase : List[str] , ):
"""simple docstring"""
super().__init__(**_A , pad_token_id=_A , bos_token_id=_A , eos_token_id=_A )
_UpperCamelCase: int = hidden_size
_UpperCamelCase: Any = feat_extract_norm
_UpperCamelCase: Optional[int] = feat_extract_activation
_UpperCamelCase: Optional[int] = list(_A )
_UpperCamelCase: List[Any] = list(_A )
_UpperCamelCase: Tuple = list(_A )
_UpperCamelCase: Dict = conv_bias
_UpperCamelCase: Optional[Any] = num_conv_pos_embeddings
_UpperCamelCase: str = num_conv_pos_embedding_groups
_UpperCamelCase: Optional[Any] = len(self.conv_dim )
_UpperCamelCase: Union[str, Any] = num_hidden_layers
_UpperCamelCase: Optional[int] = intermediate_size
_UpperCamelCase: Dict = squeeze_factor
_UpperCamelCase: Any = max_position_embeddings
_UpperCamelCase: Optional[int] = position_buckets
_UpperCamelCase: Union[str, Any] = share_att_key
_UpperCamelCase: Tuple = relative_attention
_UpperCamelCase: int = norm_rel_ebd
_UpperCamelCase: str = list(_A )
_UpperCamelCase: str = hidden_act
_UpperCamelCase: Any = num_attention_heads
_UpperCamelCase: List[Any] = hidden_dropout
_UpperCamelCase: Optional[Any] = attention_dropout
_UpperCamelCase: List[str] = activation_dropout
_UpperCamelCase: int = feat_proj_dropout
_UpperCamelCase: Union[str, Any] = final_dropout
_UpperCamelCase: Any = layer_norm_eps
_UpperCamelCase: str = feature_layer_norm_eps
_UpperCamelCase: List[str] = initializer_range
_UpperCamelCase: Any = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_UpperCamelCase: Tuple = apply_spec_augment
_UpperCamelCase: List[Any] = mask_time_prob
_UpperCamelCase: str = mask_time_length
_UpperCamelCase: Union[str, Any] = mask_time_min_masks
_UpperCamelCase: Any = mask_feature_prob
_UpperCamelCase: List[str] = mask_feature_length
_UpperCamelCase: List[Any] = mask_feature_min_masks
# ctc loss
_UpperCamelCase: Optional[Any] = ctc_loss_reduction
_UpperCamelCase: List[str] = ctc_zero_infinity
# sequence classification
_UpperCamelCase: Union[str, Any] = use_weighted_layer_sum
_UpperCamelCase: str = classifier_proj_size
@property
def lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 716
|
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase_ ( lowercase: ndarray ) -> float:
'''simple docstring'''
return np.dot(lowercase , lowercase )
class __magic_name__ :
"""simple docstring"""
def __init__( self : int , *,
_lowercase : float = np.inf , _lowercase : str = "linear" , _lowercase : float = 0.0 , ):
"""simple docstring"""
_UpperCamelCase: int = regularization
_UpperCamelCase: Optional[int] = gamma
if kernel == "linear":
_UpperCamelCase: Optional[Any] = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError('''rbf kernel requires gamma''' )
if not isinstance(self.gamma , (float, int) ):
raise ValueError('''gamma must be float or int''' )
if not self.gamma > 0:
raise ValueError('''gamma must be > 0''' )
_UpperCamelCase: Optional[Any] = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
_UpperCamelCase: Optional[int] = f"""Unknown kernel: {kernel}"""
raise ValueError(_lowercase )
def lowerCAmelCase ( self : Union[str, Any] , _lowercase : ndarray , _lowercase : ndarray ):
"""simple docstring"""
return np.dot(_lowercase , _lowercase )
def lowerCAmelCase ( self : str , _lowercase : ndarray , _lowercase : ndarray ):
"""simple docstring"""
return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )
def lowerCAmelCase ( self : str , _lowercase : list[ndarray] , _lowercase : ndarray ):
"""simple docstring"""
_UpperCamelCase: List[str] = observations
_UpperCamelCase: Optional[int] = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((_UpperCamelCase) , ): Any = np.shape(_lowercase )
def to_minimize(_lowercase : ndarray ) -> float:
_UpperCamelCase: Optional[int] = 0
((_UpperCamelCase) , ): str = np.shape(_lowercase )
for i in range(_lowercase ):
for j in range(_lowercase ):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j] )
)
return 1 / 2 * s - sum(_lowercase )
_UpperCamelCase: Optional[Any] = LinearConstraint(_lowercase , 0 , 0 )
_UpperCamelCase: Optional[int] = Bounds(0 , self.regularization )
_UpperCamelCase: Dict = minimize(
_lowercase , np.ones(_lowercase ) , bounds=_lowercase , constraints=[ly_contraint] ).x
_UpperCamelCase: Union[str, Any] = l_star
# calculating mean offset of separation plane to points
_UpperCamelCase: List[str] = 0
for i in range(_lowercase ):
for j in range(_lowercase ):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j] )
_UpperCamelCase: str = s / n
def lowerCAmelCase ( self : Optional[Any] , _lowercase : ndarray ):
"""simple docstring"""
_UpperCamelCase: Optional[int] = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , _lowercase )
for n in range(len(self.classes ) ) )
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : List[str] = {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Dict = ["XGLMTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = ["XGLMTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = [
"XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XGLMForCausalLM",
"XGLMModel",
"XGLMPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[str] = [
"FlaxXGLMForCausalLM",
"FlaxXGLMModel",
"FlaxXGLMPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[str] = [
"TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXGLMForCausalLM",
"TFXGLMModel",
"TFXGLMPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 63
|
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, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import GLPNImageProcessor
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Any ,A : Optional[int] ,A : Optional[int]=7 ,A : Optional[Any]=3 ,A : List[str]=18 ,A : Any=30 ,A : Tuple=4_00 ,A : Union[str, Any]=True ,A : Optional[Any]=32 ,A : Union[str, Any]=True ,):
__A = parent
__A = batch_size
__A = num_channels
__A = image_size
__A = min_resolution
__A = max_resolution
__A = do_resize
__A = size_divisor
__A = do_rescale
def UpperCamelCase_ ( self : Union[str, Any] ):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = GLPNImageProcessor if is_vision_available() else None
def UpperCamelCase_ ( self : int ):
__A = GLPNImageProcessingTester(self )
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase_ ( self : Any ):
__A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A ,"do_resize" ) )
self.assertTrue(hasattr(A ,"size_divisor" ) )
self.assertTrue(hasattr(A ,"resample" ) )
self.assertTrue(hasattr(A ,"do_rescale" ) )
def UpperCamelCase_ ( self : str ):
pass
def UpperCamelCase_ ( self : Dict ):
# Initialize image_processing
__A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__A = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A ,Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
__A = image_processing(image_inputs[0] ,return_tensors="pt" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def UpperCamelCase_ ( self : Optional[Any] ):
# Initialize image_processing
__A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__A = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,numpify=A )
for image in image_inputs:
self.assertIsInstance(A ,np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
__A = image_processing(image_inputs[0] ,return_tensors="pt" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def UpperCamelCase_ ( self : int ):
# Initialize image_processing
__A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__A = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,torchify=A )
for image in image_inputs:
self.assertIsInstance(A ,torch.Tensor )
# Test not batched input (GLPNImageProcessor doesn't support batching)
__A = image_processing(image_inputs[0] ,return_tensors="pt" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
| 55
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_A = {
"configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
"LongT5EncoderModel",
"LongT5ForConditionalGeneration",
"LongT5Model",
"LongT5PreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"FlaxLongT5ForConditionalGeneration",
"FlaxLongT5Model",
"FlaxLongT5PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longta import (
LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
LongTaEncoderModel,
LongTaForConditionalGeneration,
LongTaModel,
LongTaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_longta import (
FlaxLongTaForConditionalGeneration,
FlaxLongTaModel,
FlaxLongTaPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 709
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class _lowerCAmelCase ( __a ):
_lowercase ='''transfo-xl'''
_lowercase =['''mems''']
_lowercase ={
'''n_token''': '''vocab_size''',
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , _UpperCamelCase=267_735 , _UpperCamelCase=[20_000, 40_000, 200_000] , _UpperCamelCase=1_024 , _UpperCamelCase=1_024 , _UpperCamelCase=16 , _UpperCamelCase=64 , _UpperCamelCase=4_096 , _UpperCamelCase=4 , _UpperCamelCase=False , _UpperCamelCase=18 , _UpperCamelCase=1_600 , _UpperCamelCase=1_000 , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=0 , _UpperCamelCase=-1 , _UpperCamelCase=True , _UpperCamelCase=0.1 , _UpperCamelCase=0.0 , _UpperCamelCase=True , _UpperCamelCase="normal" , _UpperCamelCase=0.01 , _UpperCamelCase=0.01 , _UpperCamelCase=0.02 , _UpperCamelCase=1e-5 , _UpperCamelCase=0 , **_UpperCamelCase , ) -> Dict:
lowerCAmelCase_ = vocab_size
lowerCAmelCase_ = []
self.cutoffs.extend(_UpperCamelCase )
if proj_share_all_but_first:
lowerCAmelCase_ = [False] + [True] * len(self.cutoffs )
else:
lowerCAmelCase_ = [False] + [False] * len(self.cutoffs )
lowerCAmelCase_ = d_model
lowerCAmelCase_ = d_embed
lowerCAmelCase_ = d_head
lowerCAmelCase_ = d_inner
lowerCAmelCase_ = div_val
lowerCAmelCase_ = pre_lnorm
lowerCAmelCase_ = n_layer
lowerCAmelCase_ = n_head
lowerCAmelCase_ = mem_len
lowerCAmelCase_ = same_length
lowerCAmelCase_ = attn_type
lowerCAmelCase_ = clamp_len
lowerCAmelCase_ = sample_softmax
lowerCAmelCase_ = adaptive
lowerCAmelCase_ = dropout
lowerCAmelCase_ = dropatt
lowerCAmelCase_ = untie_r
lowerCAmelCase_ = init
lowerCAmelCase_ = init_range
lowerCAmelCase_ = proj_init_std
lowerCAmelCase_ = init_std
lowerCAmelCase_ = layer_norm_epsilon
super().__init__(eos_token_id=_UpperCamelCase , **_UpperCamelCase )
@property
def __a ( self ) -> List[Any]:
# Message copied from Transformer-XL documentation
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def __a ( self , _UpperCamelCase ) -> str:
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 279
| 0
|
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__lowerCamelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__lowerCamelCase = """ \"\"\"\n Output class for the scheduler\'s step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n"""
class UpperCAmelCase ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE (self : List[str] ) -> str:
'''simple docstring'''
snake_case : Optional[int] = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , "schedulers/" ) )
snake_case : str = self.diffusers_dir
shutil.copy(
os.path.join(snake_case__ , "src/diffusers/schedulers/scheduling_ddpm.py" ) , os.path.join(self.diffusers_dir , "schedulers/scheduling_ddpm.py" ) , )
def _SCREAMING_SNAKE_CASE (self : List[str] ) -> List[str]:
'''simple docstring'''
snake_case : Union[str, Any] = "src/diffusers"
shutil.rmtree(self.diffusers_dir )
def _SCREAMING_SNAKE_CASE (self : List[Any] , snake_case__ : List[Any] , snake_case__ : str , snake_case__ : List[Any] , snake_case__ : Union[str, Any]=None ) -> Dict:
'''simple docstring'''
snake_case : Optional[int] = comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
snake_case : Dict = comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
snake_case : Optional[Any] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 )
snake_case : Tuple = black.format_str(snake_case__ , mode=snake_case__ )
snake_case : Tuple = os.path.join(self.diffusers_dir , "new_code.py" )
with open(snake_case__ , "w" , newline="\n" ) as f:
f.write(snake_case__ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(snake_case__ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=snake_case__ )
with open(snake_case__ , "r" ) as f:
self.assertTrue(f.read() , snake_case__ )
def _SCREAMING_SNAKE_CASE (self : Union[str, Any] ) -> str:
'''simple docstring'''
snake_case : Dict = check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" )
self.assertEqual(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE (self : str ) -> List[str]:
'''simple docstring'''
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , REFERENCE_CODE + "\n" , )
# With no empty line at the end
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , snake_case__ , )
# Copy consistency with rename
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , re.sub("DDPM" , "Test" , snake_case__ ) , )
# Copy consistency with a really long name
snake_case : str = "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
self.check_copy_consistency(
f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , f"""{long_class_name}SchedulerOutput""" , re.sub("Bert" , snake_case__ , snake_case__ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , snake_case__ , overwrite_result=re.sub("DDPM" , "Test" , snake_case__ ) , )
| 204
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
'processing_layoutlmv2': ['LayoutLMv2Processor'],
'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['LayoutLMv2TokenizerFast']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['LayoutLMv2FeatureExtractor']
lowercase_ = ['LayoutLMv2ImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'LayoutLMv2ForQuestionAnswering',
'LayoutLMv2ForSequenceClassification',
'LayoutLMv2ForTokenClassification',
'LayoutLMv2Layer',
'LayoutLMv2Model',
'LayoutLMv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaLayer,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 552
| 0
|
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# 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.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __lowercase ( lowercase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (
"This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
"It takes two arguments named `image` which should be the original image, and `label` which should be a text "
"describing the elements what should be identified in the segmentation mask. The tool returns the mask."
)
SCREAMING_SNAKE_CASE = "CIDAS/clipseg-rd64-refined"
SCREAMING_SNAKE_CASE = "image_segmenter"
SCREAMING_SNAKE_CASE = CLIPSegForImageSegmentation
SCREAMING_SNAKE_CASE = ["image", "text"]
SCREAMING_SNAKE_CASE = ["image"]
def __init__( self : Any , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : List[Any] ):
"""simple docstring"""
requires_backends(self , ["""vision"""] )
super().__init__(*UpperCamelCase_ , **UpperCamelCase_ )
def lowerCAmelCase_ ( self : Union[str, Any] , UpperCamelCase_ : "Image" , UpperCamelCase_ : str ):
"""simple docstring"""
return self.pre_processor(text=[label] , images=[image] , padding=UpperCamelCase_ , return_tensors="""pt""" )
def lowerCAmelCase_ ( self : Union[str, Any] , UpperCamelCase_ : Dict ):
"""simple docstring"""
with torch.no_grad():
__A = self.model(**UpperCamelCase_ ).logits
return logits
def lowerCAmelCase_ ( self : Dict , UpperCamelCase_ : Dict ):
"""simple docstring"""
__A = outputs.cpu().detach().numpy()
__A = 0
__A = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 199
|
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
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 TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __lowercase :
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase_ : Tuple , UpperCamelCase_ : Union[str, Any]=13 , UpperCamelCase_ : Optional[Any]=30 , UpperCamelCase_ : Optional[int]=2 , UpperCamelCase_ : Optional[Any]=3 , UpperCamelCase_ : int=True , UpperCamelCase_ : str=True , UpperCamelCase_ : str=32 , UpperCamelCase_ : Tuple=2 , UpperCamelCase_ : List[Any]=4 , UpperCamelCase_ : List[str]=37 , UpperCamelCase_ : Any="gelu" , UpperCamelCase_ : Any=0.1 , UpperCamelCase_ : Optional[Any]=0.1 , UpperCamelCase_ : Any=10 , UpperCamelCase_ : str=0.02 , UpperCamelCase_ : Dict=3 , UpperCamelCase_ : int=None , ):
"""simple docstring"""
__A = parent
__A = batch_size
__A = image_size
__A = patch_size
__A = num_channels
__A = is_training
__A = use_labels
__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 = type_sequence_label_size
__A = initializer_range
__A = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
__A = (image_size // patch_size) ** 2
__A = num_patches + 1
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
__A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__A = None
if self.use_labels:
__A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__A = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase_ ( self : int ):
"""simple docstring"""
return ViTConfig(
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 , )
def lowerCAmelCase_ ( self : Tuple , UpperCamelCase_ : int , UpperCamelCase_ : List[str] , UpperCamelCase_ : Optional[int] ):
"""simple docstring"""
__A = TFViTModel(config=UpperCamelCase_ )
__A = model(UpperCamelCase_ , training=UpperCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
__A = self.image_size // 2
__A = pixel_values[:, :, :image_size, :image_size]
__A = model(UpperCamelCase_ , interpolate_pos_encoding=UpperCamelCase_ , training=UpperCamelCase_ )
__A = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def lowerCAmelCase_ ( self : Tuple , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : List[Any] ):
"""simple docstring"""
__A = self.type_sequence_label_size
__A = TFViTForImageClassification(UpperCamelCase_ )
__A = model(UpperCamelCase_ , labels=UpperCamelCase_ , training=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
__A = self.image_size // 2
__A = pixel_values[:, :, :image_size, :image_size]
__A = model(UpperCamelCase_ , interpolate_pos_encoding=UpperCamelCase_ , training=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__A = 1
__A = TFViTForImageClassification(UpperCamelCase_ )
__A = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__A = model(UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCAmelCase_ ( self : Optional[int] ):
"""simple docstring"""
__A = self.prepare_config_and_inputs()
__A , __A , __A = config_and_inputs
__A = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __lowercase ( lowercase_ , lowercase_ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE = (
{"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def lowerCAmelCase_ ( self : List[Any] ):
"""simple docstring"""
__A = TFViTModelTester(self )
__A = ConfigTester(self , config_class=UpperCamelCase_ , has_text_modality=UpperCamelCase_ , hidden_size=37 )
def lowerCAmelCase_ ( self : Tuple ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def lowerCAmelCase_ ( self : Dict ):
"""simple docstring"""
pass
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def lowerCAmelCase_ ( self : Any ):
"""simple docstring"""
pass
def lowerCAmelCase_ ( self : Tuple ):
"""simple docstring"""
__A , __A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A = model_class(UpperCamelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
__A = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCamelCase_ , tf.keras.layers.Layer ) )
def lowerCAmelCase_ ( self : List[str] ):
"""simple docstring"""
__A , __A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A = model_class(UpperCamelCase_ )
__A = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__A = [*signature.parameters.keys()]
__A = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCamelCase_ )
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_ )
def lowerCAmelCase_ ( self : str ):
"""simple docstring"""
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_ )
@slow
def lowerCAmelCase_ ( self : Any ):
"""simple docstring"""
__A = TFViTModel.from_pretrained("""google/vit-base-patch16-224""" )
self.assertIsNotNone(UpperCamelCase_ )
def _SCREAMING_SNAKE_CASE ( ) -> Union[str, Any]:
"""simple docstring"""
__A = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase_ ( self : List[Any] ):
"""simple docstring"""
return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
__A = TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" )
__A = self.default_image_processor
__A = prepare_img()
__A = image_processor(images=UpperCamelCase_ , return_tensors="""tf""" )
# forward pass
__A = model(**UpperCamelCase_ )
# verify the logits
__A = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , UpperCamelCase_ )
__A = tf.constant([-0.2744, 0.8215, -0.0836] )
tf.debugging.assert_near(outputs.logits[0, :3] , UpperCamelCase_ , atol=1e-4 )
| 199
| 1
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer
_a : Tuple = logging.get_logger(__name__)
_a : Any = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
_a : Tuple = {
'vocab_file': {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
),
}
}
_a : str = {
'junnyu/roformer_chinese_small': 15_36,
'junnyu/roformer_chinese_base': 15_36,
'junnyu/roformer_chinese_char_small': 5_12,
'junnyu/roformer_chinese_char_base': 5_12,
'junnyu/roformer_small_discriminator': 1_28,
'junnyu/roformer_small_generator': 1_28,
}
_a : Tuple = {
'junnyu/roformer_chinese_small': {'do_lower_case': True},
'junnyu/roformer_chinese_base': {'do_lower_case': True},
'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
'junnyu/roformer_small_discriminator': {'do_lower_case': True},
'junnyu/roformer_small_generator': {'do_lower_case': True},
}
class UpperCamelCase_ ( __UpperCamelCase ):
"""simple docstring"""
A = VOCAB_FILES_NAMES
A = PRETRAINED_VOCAB_FILES_MAP
A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A = PRETRAINED_INIT_CONFIGURATION
A = RoFormerTokenizer
def __init__( self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=True , UpperCAmelCase="[UNK]" , UpperCAmelCase="[SEP]" , UpperCAmelCase="[PAD]" , UpperCAmelCase="[CLS]" , UpperCAmelCase="[MASK]" , UpperCAmelCase=True , UpperCAmelCase=None , **UpperCAmelCase , ):
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 (
pre_tok_state.get("""lowercase""" , UpperCAmelCase ) != do_lower_case
or pre_tok_state.get("""strip_accents""" , UpperCAmelCase ) != strip_accents
):
__lowerCamelCase = getattr(UpperCAmelCase , pre_tok_state.pop("""type""" ) )
__lowerCamelCase = do_lower_case
__lowerCamelCase = strip_accents
__lowerCamelCase = pre_tok_class(**UpperCAmelCase )
__lowerCamelCase = do_lower_case
def __getstate__( self ):
__lowerCamelCase = self.__dict__.copy()
__lowerCamelCase = BertPreTokenizer()
return state
def __setstate__( self , UpperCAmelCase ):
__lowerCamelCase = d
__lowerCamelCase = self.__dict__["""_tokenizer"""].get_vocab()
__lowerCamelCase = PreTokenizer.custom(JiebaPreTokenizer(UpperCAmelCase ) )
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase=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 lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase = 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 lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase = None ):
__lowerCamelCase = self._tokenizer.model.save(UpperCAmelCase , name=UpperCAmelCase )
return tuple(UpperCAmelCase )
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=False , **UpperCAmelCase , ):
__lowerCamelCase = BertPreTokenizer()
return super().save_pretrained(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
| 479
|
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
_a : Any = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class UpperCamelCase_ ( datasets.BuilderConfig ):
"""simple docstring"""
A = None
def UpperCamelCase__ ( _A: "pyspark.sql.DataFrame" , _A: List[int] , ):
'''simple docstring'''
import pyspark
def generate_fn():
__lowerCamelCase = df.select("""*""" , pyspark.sql.functions.spark_partition_id().alias("""part_id""" ) )
for partition_id in partition_order:
__lowerCamelCase = df_with_partition_id.select("""*""" ).where(f'''part_id = {partition_id}''' ).drop("""part_id""" )
__lowerCamelCase = partition_df.collect()
__lowerCamelCase = 0
for row in rows:
yield f'''{partition_id}_{row_id}''', row.asDict()
row_id += 1
return generate_fn
class UpperCamelCase_ ( _BaseExamplesIterable ):
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase=None , ):
__lowerCamelCase = df
__lowerCamelCase = partition_order or range(self.df.rdd.getNumPartitions() )
__lowerCamelCase = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self ):
yield from self.generate_examples_fn()
def lowerCamelCase_ ( self , UpperCAmelCase ):
__lowerCamelCase = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(UpperCAmelCase )
return SparkExamplesIterable(self.df , partition_order=UpperCAmelCase )
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase ):
__lowerCamelCase = self.split_shard_indices_by_worker(UpperCAmelCase , UpperCAmelCase )
return SparkExamplesIterable(self.df , partition_order=UpperCAmelCase )
@property
def lowerCamelCase_ ( self ):
return len(self.partition_order )
class UpperCamelCase_ ( datasets.DatasetBuilder ):
"""simple docstring"""
A = SparkConfig
def __init__( self , UpperCAmelCase , UpperCAmelCase = None , UpperCAmelCase = None , **UpperCAmelCase , ):
import pyspark
__lowerCamelCase = pyspark.sql.SparkSession.builder.getOrCreate()
__lowerCamelCase = df
__lowerCamelCase = working_dir
super().__init__(
cache_dir=UpperCAmelCase , config_name=str(self.df.semanticHash() ) , **UpperCAmelCase , )
def lowerCamelCase_ ( self ):
# Returns the path of the created file.
def create_cache_and_write_probe(UpperCAmelCase ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=UpperCAmelCase )
__lowerCamelCase = os.path.join(self._cache_dir , """fs_test""" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(UpperCAmelCase , """a""" )
return [probe_file]
if self._spark.conf.get("""spark.master""" , """""" ).startswith("""local""" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
__lowerCamelCase = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(UpperCAmelCase ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"""When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir""" )
def lowerCamelCase_ ( self ):
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase_ ( self , UpperCAmelCase ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def lowerCamelCase_ ( self , UpperCAmelCase ):
import pyspark
def get_arrow_batch_size(UpperCAmelCase ):
for batch in it:
yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]} )
__lowerCamelCase = self.df.count()
__lowerCamelCase = df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
__lowerCamelCase = (
self.df.limit(UpperCAmelCase )
.repartition(1 )
.mapInArrow(UpperCAmelCase , """batch_bytes: long""" )
.agg(pyspark.sql.functions.sum("""batch_bytes""" ).alias("""sample_bytes""" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
__lowerCamelCase = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
__lowerCamelCase = min(UpperCAmelCase , int(approx_total_size / max_shard_size ) )
__lowerCamelCase = self.df.repartition(UpperCAmelCase )
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ):
import pyspark
__lowerCamelCase = ParquetWriter if file_format == """parquet""" else ArrowWriter
__lowerCamelCase = os.path.join(self._working_dir , os.path.basename(UpperCAmelCase ) ) if self._working_dir else fpath
__lowerCamelCase = file_format == """parquet"""
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
__lowerCamelCase = self.config.features
__lowerCamelCase = self._writer_batch_size
__lowerCamelCase = self._fs.storage_options
def write_arrow(UpperCAmelCase ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
__lowerCamelCase = pyspark.TaskContext().taskAttemptId()
__lowerCamelCase = next(UpperCAmelCase , UpperCAmelCase )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=["""task_id""", """num_examples""", """num_bytes"""] , )
__lowerCamelCase = 0
__lowerCamelCase = writer_class(
features=UpperCAmelCase , path=working_fpath.replace("""SSSSS""" , f'''{shard_id:05d}''' ).replace("""TTTTT""" , f'''{task_id:05d}''' ) , writer_batch_size=UpperCAmelCase , storage_options=UpperCAmelCase , embed_local_files=UpperCAmelCase , )
__lowerCamelCase = pa.Table.from_batches([first_batch] )
writer.write_table(UpperCAmelCase )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
__lowerCamelCase , __lowerCamelCase = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=["""task_id""", """num_examples""", """num_bytes"""] , )
shard_id += 1
__lowerCamelCase = writer_class(
features=writer._features , path=working_fpath.replace("""SSSSS""" , f'''{shard_id:05d}''' ).replace("""TTTTT""" , f'''{task_id:05d}''' ) , writer_batch_size=UpperCAmelCase , storage_options=UpperCAmelCase , embed_local_files=UpperCAmelCase , )
__lowerCamelCase = pa.Table.from_batches([batch] )
writer.write_table(UpperCAmelCase )
if writer._num_bytes > 0:
__lowerCamelCase , __lowerCamelCase = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=["""task_id""", """num_examples""", """num_bytes"""] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(UpperCAmelCase ) ):
__lowerCamelCase = os.path.join(os.path.dirname(UpperCAmelCase ) , os.path.basename(UpperCAmelCase ) )
shutil.move(UpperCAmelCase , UpperCAmelCase )
__lowerCamelCase = (
self.df.mapInArrow(UpperCAmelCase , """task_id: long, num_examples: long, num_bytes: long""" )
.groupBy("""task_id""" )
.agg(
pyspark.sql.functions.sum("""num_examples""" ).alias("""total_num_examples""" ) , pyspark.sql.functions.sum("""num_bytes""" ).alias("""total_num_bytes""" ) , pyspark.sql.functions.count("""num_bytes""" ).alias("""num_shards""" ) , pyspark.sql.functions.collect_list("""num_examples""" ).alias("""shard_lengths""" ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase = "arrow" , UpperCAmelCase = None , UpperCAmelCase = None , **UpperCAmelCase , ):
self._validate_cache_dir()
__lowerCamelCase = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(UpperCAmelCase )
__lowerCamelCase = not is_remote_filesystem(self._fs )
__lowerCamelCase = os.path.join if is_local else posixpath.join
__lowerCamelCase = """-TTTTT-SSSSS-of-NNNNN"""
__lowerCamelCase = f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
__lowerCamelCase = path_join(self._output_dir , UpperCAmelCase )
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = []
__lowerCamelCase = []
for task_id, content in self._prepare_split_single(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(UpperCAmelCase )
__lowerCamelCase = total_num_examples
__lowerCamelCase = total_num_bytes
# should rename everything at the end
logger.debug(f'''Renaming {total_shards} shards.''' )
if total_shards > 1:
__lowerCamelCase = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
__lowerCamelCase = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ):
rename(
UpperCAmelCase , fpath.replace("""SSSSS""" , f'''{shard_id:05d}''' ).replace("""TTTTT""" , f'''{task_id:05d}''' ) , fpath.replace("""TTTTT-SSSSS""" , f'''{global_shard_id:05d}''' ).replace("""NNNNN""" , f'''{total_shards:05d}''' ) , )
__lowerCamelCase = []
__lowerCamelCase = 0
for i in range(len(UpperCAmelCase ) ):
__lowerCamelCase , __lowerCamelCase = task_id_and_num_shards[i]
for shard_id in range(UpperCAmelCase ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(UpperCAmelCase , len(UpperCAmelCase ) ).map(lambda UpperCAmelCase : _rename_shard(*UpperCAmelCase ) ).collect()
else:
# don't use any pattern
__lowerCamelCase = 0
__lowerCamelCase = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("""SSSSS""" , f'''{shard_id:05d}''' ).replace("""TTTTT""" , f'''{task_id:05d}''' ) , fpath.replace(UpperCAmelCase , """""" ) , )
def lowerCamelCase_ ( self , UpperCAmelCase , ):
return SparkExamplesIterable(self.df )
| 479
| 1
|
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
class UpperCamelCase :
def __init__(self , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase=None , __UpperCamelCase=None ) -> Any:
if not conversation_id:
UpperCamelCase_ : Dict = uuid.uuida()
if past_user_inputs is None:
UpperCamelCase_ : int = []
if generated_responses is None:
UpperCamelCase_ : Tuple = []
UpperCamelCase_ : Tuple = conversation_id
UpperCamelCase_ : Optional[Any] = past_user_inputs
UpperCamelCase_ : Optional[Any] = generated_responses
UpperCamelCase_ : Union[str, Any] = text
def __eq__(self , __UpperCamelCase ) -> str:
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def A_ (self , __UpperCamelCase , __UpperCamelCase = False ) -> str:
if self.new_user_input:
if overwrite:
logger.warning(
f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
f'''with: "{text}".''' )
UpperCamelCase_ : List[Any] = text
else:
logger.warning(
f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input''' )
else:
UpperCamelCase_ : Optional[Any] = text
def A_ (self ) -> Union[str, Any]:
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
UpperCamelCase_ : Any = None
def A_ (self , __UpperCamelCase ) -> Any:
self.generated_responses.append(UpperCamelCase_ )
def A_ (self ) -> int:
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__(self ) -> Union[str, Any]:
UpperCamelCase_ : str = f'''Conversation id: {self.uuid} \n'''
for is_user, text in self.iter_texts():
UpperCamelCase_ : Union[str, Any] = """user""" if is_user else """bot"""
output += f'''{name} >> {text} \n'''
return output
@add_end_docstrings(
__lowerCamelCase , r'''\n min_length_for_response (`int`, *optional*, defaults to 32):\n The minimum length (in number of tokens) for a response.\n minimum_tokens (`int`, *optional*, defaults to 10):\n The minimum length of tokens to leave for a response.\n ''' , )
class UpperCamelCase ( __lowerCamelCase ):
def __init__(self , *__UpperCamelCase , **__UpperCamelCase ) -> Dict:
super().__init__(*UpperCamelCase_ , **UpperCamelCase_ )
if self.tokenizer.pad_token_id is None:
UpperCamelCase_ : Dict = self.tokenizer.eos_token
def A_ (self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase ) -> str:
UpperCamelCase_ : List[str] = {}
UpperCamelCase_ : Any = {}
UpperCamelCase_ : Any = {}
if min_length_for_response is not None:
UpperCamelCase_ : List[Any] = min_length_for_response
if minimum_tokens is not None:
UpperCamelCase_ : Any = minimum_tokens
if "max_length" in generate_kwargs:
UpperCamelCase_ : Union[str, Any] = generate_kwargs["""max_length"""]
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
UpperCamelCase_ : Any = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(UpperCamelCase_ )
return preprocess_params, forward_params, postprocess_params
def __call__(self , __UpperCamelCase , __UpperCamelCase=0 , **__UpperCamelCase ) -> Optional[Any]:
UpperCamelCase_ : int = super().__call__(UpperCamelCase_ , num_workers=UpperCamelCase_ , **UpperCamelCase_ )
if isinstance(UpperCamelCase_ , UpperCamelCase_ ) and len(UpperCamelCase_ ) == 1:
return outputs[0]
return outputs
def A_ (self , __UpperCamelCase , __UpperCamelCase=32 ) -> str:
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError("""ConversationalPipeline, expects Conversation as inputs""" )
if conversation.new_user_input is None:
raise ValueError(
f'''Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '''
"""Add user inputs with the conversation's `add_user_input` method""" )
if hasattr(self.tokenizer , """_build_conversation_input_ids""" ):
UpperCamelCase_ : int = self.tokenizer._build_conversation_input_ids(UpperCamelCase_ )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
UpperCamelCase_ : List[str] = self._legacy_parse_and_tokenize(UpperCamelCase_ )
if self.framework == "pt":
UpperCamelCase_ : Dict = torch.LongTensor([input_ids] )
elif self.framework == "tf":
UpperCamelCase_ : int = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def A_ (self , __UpperCamelCase , __UpperCamelCase=10 , **__UpperCamelCase ) -> List[Any]:
UpperCamelCase_ : Any = generate_kwargs.get("""max_length""" , self.model.config.max_length )
UpperCamelCase_ : Tuple = model_inputs["""input_ids"""].shape[1]
if max_length - minimum_tokens < n:
logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})''' )
UpperCamelCase_ : Dict = max_length - minimum_tokens
UpperCamelCase_ : Any = model_inputs["""input_ids"""][:, -trim:]
if "attention_mask" in model_inputs:
UpperCamelCase_ : int = model_inputs["""attention_mask"""][:, -trim:]
UpperCamelCase_ : str = model_inputs.pop("""conversation""" )
UpperCamelCase_ : Optional[int] = max_length
UpperCamelCase_ : List[str] = self.model.generate(**UpperCamelCase_ , **UpperCamelCase_ )
if self.model.config.is_encoder_decoder:
UpperCamelCase_ : int = 1
else:
UpperCamelCase_ : Optional[int] = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def A_ (self , __UpperCamelCase , __UpperCamelCase=True ) -> str:
UpperCamelCase_ : Any = model_outputs["""output_ids"""]
UpperCamelCase_ : Optional[Any] = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=UpperCamelCase_ , clean_up_tokenization_spaces=UpperCamelCase_ , )
UpperCamelCase_ : List[Any] = model_outputs["""conversation"""]
conversation.mark_processed()
conversation.append_response(UpperCamelCase_ )
return conversation
def A_ (self , __UpperCamelCase ) -> Union[str, Any]:
UpperCamelCase_ : Optional[Any] = self.tokenizer.eos_token_id
UpperCamelCase_ : int = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ ) )
if len(UpperCamelCase_ ) > self.tokenizer.model_max_length:
UpperCamelCase_ : List[str] = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 714
|
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : Dict ):
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class UpperCamelCase ( __a ):
@staticmethod
def A_ (__UpperCamelCase ) -> str:
UpperCamelCase_ : int = parser.add_parser("""download""" )
download_parser.add_argument(
"""--cache-dir""" , type=__UpperCamelCase , default=__UpperCamelCase , help="""Path to location to store the models""" )
download_parser.add_argument(
"""--force""" , action="""store_true""" , help="""Force the model to be download even if already in cache-dir""" )
download_parser.add_argument(
"""--trust-remote-code""" , action="""store_true""" , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine""" , )
download_parser.add_argument("""model""" , type=__UpperCamelCase , help="""Name of the model to download""" )
download_parser.set_defaults(func=__UpperCamelCase )
def __init__(self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Union[str, Any]:
UpperCamelCase_ : Dict = model
UpperCamelCase_ : Tuple = cache
UpperCamelCase_ : int = force
UpperCamelCase_ : Optional[int] = trust_remote_code
def A_ (self ) -> Any:
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 138
| 0
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _A ( __lowercase , __lowercase , __lowercase , unittest.TestCase ):
lowercase__: Optional[int] = AltDiffusionPipeline
lowercase__: Tuple = TEXT_TO_IMAGE_PARAMS
lowercase__: List[str] = TEXT_TO_IMAGE_BATCH_PARAMS
lowercase__: List[Any] = TEXT_TO_IMAGE_IMAGE_PARAMS
lowercase__: List[str] = TEXT_TO_IMAGE_IMAGE_PARAMS
def lowercase__ ( self : Dict ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
__snake_case : Dict = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
__snake_case : Any = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=__magic_name__ , set_alpha_to_one=__magic_name__ , )
torch.manual_seed(0 )
__snake_case : Any = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
# TODO: address the non-deterministic text encoder (fails for save-load tests)
# torch.manual_seed(0)
# text_encoder_config = RobertaSeriesConfig(
# hidden_size=32,
# project_dim=32,
# intermediate_size=37,
# layer_norm_eps=1e-05,
# num_attention_heads=4,
# num_hidden_layers=5,
# vocab_size=5002,
# )
# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
torch.manual_seed(0 )
__snake_case : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=50_02 , )
__snake_case : Dict = CLIPTextModel(__magic_name__ )
__snake_case : Optional[int] = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
__snake_case : str = 77
__snake_case : List[Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowercase__ ( self : Any , __magic_name__ : Union[str, Any] , __magic_name__ : Any=0 ) -> Optional[int]:
"""simple docstring"""
if str(__magic_name__ ).startswith("""mps""" ):
__snake_case : int = torch.manual_seed(__magic_name__ )
else:
__snake_case : Optional[Any] = torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
__snake_case : List[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def lowercase__ ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def lowercase__ ( self : int ) -> Optional[int]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def lowercase__ ( self : Dict ) -> List[Any]:
"""simple docstring"""
__snake_case : Dict = """cpu""" # ensure determinism for the device-dependent torch.Generator
__snake_case : Optional[Any] = self.get_dummy_components()
torch.manual_seed(0 )
__snake_case : str = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=50_02 , )
# TODO: remove after fixing the non-deterministic text encoder
__snake_case : Optional[int] = RobertaSeriesModelWithTransformation(__magic_name__ )
__snake_case : Optional[Any] = text_encoder
__snake_case : Any = AltDiffusionPipeline(**__magic_name__ )
__snake_case : Any = alt_pipe.to(__magic_name__ )
alt_pipe.set_progress_bar_config(disable=__magic_name__ )
__snake_case : int = self.get_dummy_inputs(__magic_name__ )
__snake_case : Union[str, Any] = """A photo of an astronaut"""
__snake_case : Any = alt_pipe(**__magic_name__ )
__snake_case : List[Any] = output.images
__snake_case : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__snake_case : int = np.array(
[0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowercase__ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__snake_case : Any = """cpu""" # ensure determinism for the device-dependent torch.Generator
__snake_case : Any = self.get_dummy_components()
__snake_case : List[Any] = PNDMScheduler(skip_prk_steps=__magic_name__ )
torch.manual_seed(0 )
__snake_case : int = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=50_02 , )
# TODO: remove after fixing the non-deterministic text encoder
__snake_case : Dict = RobertaSeriesModelWithTransformation(__magic_name__ )
__snake_case : Tuple = text_encoder
__snake_case : Optional[Any] = AltDiffusionPipeline(**__magic_name__ )
__snake_case : Optional[int] = alt_pipe.to(__magic_name__ )
alt_pipe.set_progress_bar_config(disable=__magic_name__ )
__snake_case : Dict = self.get_dummy_inputs(__magic_name__ )
__snake_case : List[Any] = alt_pipe(**__magic_name__ )
__snake_case : Dict = output.images
__snake_case : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__snake_case : str = np.array(
[0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
def lowercase__ ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__snake_case : List[str] = AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion""" , safety_checker=__magic_name__ )
__snake_case : List[str] = alt_pipe.to(__magic_name__ )
alt_pipe.set_progress_bar_config(disable=__magic_name__ )
__snake_case : int = """A painting of a squirrel eating a burger"""
__snake_case : Tuple = torch.manual_seed(0 )
__snake_case : Dict = alt_pipe([prompt] , generator=__magic_name__ , guidance_scale=6.0 , num_inference_steps=20 , output_type="""np""" )
__snake_case : Optional[Any] = output.images
__snake_case : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__snake_case : str = np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowercase__ ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
__snake_case : Union[str, Any] = DDIMScheduler.from_pretrained("""BAAI/AltDiffusion""" , subfolder="""scheduler""" )
__snake_case : Dict = AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion""" , scheduler=__magic_name__ , safety_checker=__magic_name__ )
__snake_case : List[str] = alt_pipe.to(__magic_name__ )
alt_pipe.set_progress_bar_config(disable=__magic_name__ )
__snake_case : Dict = """A painting of a squirrel eating a burger"""
__snake_case : Dict = torch.manual_seed(0 )
__snake_case : Any = alt_pipe([prompt] , generator=__magic_name__ , num_inference_steps=2 , output_type="""numpy""" )
__snake_case : List[Any] = output.images
__snake_case : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__snake_case : Union[str, Any] = np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 26
|
def __lowerCamelCase ( A__ : float , A__ : float , A__ : float , A__ : float , A__ : float , ) -> float:
lowerCamelCase_ : List[str] = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("""All input parameters must be positive""" )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError("""Relative densities cannot be greater than one""" )
else:
lowerCamelCase_ : Union[str, Any] = 1 - (matter_density + radiation_density + dark_energy)
lowerCamelCase_ : int = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
lowerCamelCase_ : Dict = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
snake_case__ : str = 0.3
print(
hubble_parameter(
hubble_constant=6_8.3,
radiation_density=1e-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 278
| 0
|
'''simple docstring'''
def lowercase_ ( lowercase__ ) ->float:
if not nums: # Makes sure that the list is not empty
raise ValueError('List is empty' )
_snake_case: Any = sum(lowercase__ ) / len(lowercase__ ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 273
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : Any = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class lowerCamelCase ( __UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = "biogpt"
def __init__( self : Union[str, Any] , __snake_case : Tuple=4_23_84 , __snake_case : Any=10_24 , __snake_case : Optional[int]=24 , __snake_case : int=16 , __snake_case : str=40_96 , __snake_case : Dict="gelu" , __snake_case : List[str]=0.1 , __snake_case : List[Any]=0.1 , __snake_case : List[Any]=10_24 , __snake_case : Optional[int]=0.02 , __snake_case : Union[str, Any]=1e-12 , __snake_case : List[Any]=True , __snake_case : Union[str, Any]=True , __snake_case : Any=0.0 , __snake_case : int=0.0 , __snake_case : Dict=1 , __snake_case : str=0 , __snake_case : List[str]=2 , **__snake_case : Optional[int] , ):
'''simple docstring'''
_snake_case: List[str] = vocab_size
_snake_case: Optional[int] = max_position_embeddings
_snake_case: Dict = hidden_size
_snake_case: Union[str, Any] = num_hidden_layers
_snake_case: str = num_attention_heads
_snake_case: Any = intermediate_size
_snake_case: List[Any] = hidden_act
_snake_case: int = hidden_dropout_prob
_snake_case: List[str] = attention_probs_dropout_prob
_snake_case: Optional[Any] = initializer_range
_snake_case: Union[str, Any] = layer_norm_eps
_snake_case: Dict = scale_embedding
_snake_case: Dict = use_cache
_snake_case: List[Any] = layerdrop
_snake_case: int = activation_dropout
super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
| 273
| 1
|
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class A_ ( unittest.TestCase ):
'''simple docstring'''
@property
def lowerCAmelCase_ (self ) -> str:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowerCAmelCase_ (self ) -> Union[str, Any]:
__UpperCAmelCase = ort.SessionOptions()
__UpperCAmelCase = False
return options
def lowerCAmelCase_ (self ) -> Tuple:
__UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
__UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
__UpperCAmelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy''' )
# using the PNDM scheduler by default
__UpperCAmelCase = OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''onnx''' , safety_checker=lowercase__ , feature_extractor=lowercase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowercase__ )
__UpperCAmelCase = '''A red cat sitting on a park bench'''
__UpperCAmelCase = np.random.RandomState(0 )
__UpperCAmelCase = pipe(
prompt=lowercase__ , image=lowercase__ , mask_image=lowercase__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=15 , generator=lowercase__ , output_type='''np''' , )
__UpperCAmelCase = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 1E-2
| 303
|
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase = 0
while b > 0:
if b & 1:
__UpperCAmelCase = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res
| 303
| 1
|
"""simple docstring"""
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
UpperCAmelCase__ : Optional[Any] = datasets.logging.get_logger(__name__)
UpperCAmelCase__ : int = '\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation},\n author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n booktitle={ACL},\n year={2020},\n url={https://arxiv.org/abs/2004.04696}\n}\n'
UpperCAmelCase__ : str = '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'
UpperCAmelCase__ : List[Any] = '\nBLEURT score.\n\nArgs:\n `predictions` (list of str): prediction/candidate sentences\n `references` (list of str): reference sentences\n `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n \'scores\': List of scores.\nExamples:\n\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> bleurt = datasets.load_metric("bleurt")\n >>> results = bleurt.compute(predictions=predictions, references=references)\n >>> print([round(v, 2) for v in results["scores"]])\n [1.03, 1.04]\n'
UpperCAmelCase__ : List[Any] = {
'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase_ (datasets.Metric ):
"""simple docstring"""
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://github.com/google-research/bleurt""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/google-research/bleurt"""] , reference_urls=["""https://github.com/google-research/bleurt""", """https://arxiv.org/abs/2004.04696"""] , )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> Any:
"""simple docstring"""
if self.config_name == "default":
logger.warning(
"""Using default BLEURT-Base checkpoint for sequence maximum length 128. """
"""You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').""" )
SCREAMING_SNAKE_CASE__ : Any = """bleurt-base-128"""
if self.config_name.lower() in CHECKPOINT_URLS:
SCREAMING_SNAKE_CASE__ : Any = self.config_name.lower()
elif self.config_name.upper() in CHECKPOINT_URLS:
SCREAMING_SNAKE_CASE__ : int = self.config_name.upper()
else:
raise KeyError(
F'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}''' )
# download the model checkpoint specified by self.config_name and set up the scorer
SCREAMING_SNAKE_CASE__ : Any = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
SCREAMING_SNAKE_CASE__ : Optional[int] = score.BleurtScorer(os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.scorer.score(references=SCREAMING_SNAKE_CASE__ , candidates=SCREAMING_SNAKE_CASE__ )
return {"scores": scores}
| 545
|
"""simple docstring"""
UpperCAmelCase__ : Dict = [
(1_0_0_0, 'M'),
(9_0_0, 'CM'),
(5_0_0, 'D'),
(4_0_0, 'CD'),
(1_0_0, 'C'),
(9_0, 'XC'),
(5_0, 'L'),
(4_0, 'XL'),
(1_0, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
]
def lowercase_ ( _snake_case ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
while place < len(_snake_case ):
if (place + 1 < len(_snake_case )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def lowercase_ ( _snake_case ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for arabic, roman in ROMAN:
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[Any] = divmod(_snake_case ,_snake_case )
result.append(roman * factor )
if number == 0:
break
return "".join(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 545
| 1
|
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_SCREAMING_SNAKE_CASE )
class a ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
A__ : str = field(default="text-classification", metadata={"include_in_asdict_even_if_is_default": True} )
A__ : ClassVar[Features] = Features({"text": Value("string" )} )
A__ : ClassVar[Features] = Features({"labels": ClassLabel} )
A__ : str = "text"
A__ : str = "labels"
def __A ( self , snake_case_ ) -> Tuple:
if self.label_column not in features:
raise ValueError(F"""Column {self.label_column} is not present in features.""" )
if not isinstance(features[self.label_column] , UpperCamelCase__ ):
raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""" )
_UpperCAmelCase = copy.deepcopy(self )
_UpperCAmelCase = self.label_schema.copy()
_UpperCAmelCase = features[self.label_column]
_UpperCAmelCase = label_schema
return task_template
@property
def __A ( self ) -> Dict[str, str]:
return {
self.text_column: "text",
self.label_column: "labels",
}
| 426
|
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
__lowercase : Dict = """src/transformers"""
# Matches is_xxx_available()
__lowercase : Tuple = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
__lowercase : Dict = re.compile(r"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
__lowercase : List[str] = re.compile(r"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
__lowercase : Tuple = re.compile(r"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
__lowercase : List[str] = re.compile(r"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
__lowercase : str = re.compile(r"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
__lowercase : Union[str, Any] = re.compile("""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
__lowercase : Union[str, Any] = re.compile("""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
__lowercase : Optional[Any] = re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
__lowercase : Optional[int] = re.compile(r"""^\s*try:""")
# Catches a line with else:
__lowercase : Union[str, Any] = re.compile(r"""^\s*else:""")
def lowerCamelCase_ ( _lowerCamelCase : Dict ):
if _re_test_backend.search(_lowerCamelCase ) is None:
return None
lowerCamelCase_ = [b[0] for b in _re_backend.findall(_lowerCamelCase )]
backends.sort()
return "_and_".join(_lowerCamelCase )
def lowerCamelCase_ ( _lowerCamelCase : Tuple ):
with open(_lowerCamelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowerCamelCase_ = f.readlines()
lowerCamelCase_ = 0
while line_index < len(_lowerCamelCase ) and not lines[line_index].startswith('''_import_structure = {''' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(_lowerCamelCase ):
return None
# First grab the objects without a specific backend in _import_structure
lowerCamelCase_ = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
lowerCamelCase_ = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(_lowerCamelCase ):
lowerCamelCase_ = _re_one_line_import_struct.search(_lowerCamelCase ).groups()[0]
lowerCamelCase_ = re.findall('''\[([^\]]+)\]''' , _lowerCamelCase )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
lowerCamelCase_ = _re_import_struct_key_value.search(_lowerCamelCase )
if single_line_import_search is not None:
lowerCamelCase_ = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', ''' ) if len(_lowerCamelCase ) > 0]
objects.extend(_lowerCamelCase )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
line_index += 1
lowerCamelCase_ = {'''none''': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('''if TYPE_CHECKING''' ):
# If the line is an if not is_backend_available, we grab all objects associated.
lowerCamelCase_ = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowerCamelCase_ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowerCamelCase_ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
lowerCamelCase_ = lines[line_index]
if _re_import_struct_add_one.search(_lowerCamelCase ) is not None:
objects.append(_re_import_struct_add_one.search(_lowerCamelCase ).groups()[0] )
elif _re_import_struct_add_many.search(_lowerCamelCase ) is not None:
lowerCamelCase_ = _re_import_struct_add_many.search(_lowerCamelCase ).groups()[0].split(''', ''' )
lowerCamelCase_ = [obj[1:-1] for obj in imports if len(_lowerCamelCase ) > 0]
objects.extend(_lowerCamelCase )
elif _re_between_brackets.search(_lowerCamelCase ) is not None:
lowerCamelCase_ = _re_between_brackets.search(_lowerCamelCase ).groups()[0].split(''', ''' )
lowerCamelCase_ = [obj[1:-1] for obj in imports if len(_lowerCamelCase ) > 0]
objects.extend(_lowerCamelCase )
elif _re_quote_object.search(_lowerCamelCase ) is not None:
objects.append(_re_quote_object.search(_lowerCamelCase ).groups()[0] )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
elif line.startswith(''' ''' * 1_2 + '''"''' ):
objects.append(line[1_3:-3] )
line_index += 1
lowerCamelCase_ = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
lowerCamelCase_ = []
while (
line_index < len(_lowerCamelCase )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
lowerCamelCase_ = lines[line_index]
lowerCamelCase_ = _re_import.search(_lowerCamelCase )
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
lowerCamelCase_ = {'''none''': objects}
# Let's continue with backend-specific objects
while line_index < len(_lowerCamelCase ):
# If the line is an if is_backend_available, we grab all objects associated.
lowerCamelCase_ = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowerCamelCase_ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowerCamelCase_ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
lowerCamelCase_ = lines[line_index]
lowerCamelCase_ = _re_import.search(_lowerCamelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 1_2 ):
objects.append(line[1_2:-2] )
line_index += 1
lowerCamelCase_ = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def lowerCamelCase_ ( _lowerCamelCase : List[Any] , _lowerCamelCase : int ):
def find_duplicates(_lowerCamelCase : List[Any] ):
return [k for k, v in collections.Counter(_lowerCamelCase ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
lowerCamelCase_ = []
for key in import_dict_objects.keys():
lowerCamelCase_ = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}""" )
lowerCamelCase_ = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}""" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
lowerCamelCase_ = '''base imports''' if key == '''none''' else F"""{key} backend"""
errors.append(F"""Differences for {name}:""" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F""" {a} in TYPE_HINT but not in _import_structure.""" )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F""" {a} in _import_structure but not in TYPE_HINT.""" )
return errors
def lowerCamelCase_ ( ):
lowerCamelCase_ = []
for root, _, files in os.walk(_lowerCamelCase ):
if "__init__.py" in files:
lowerCamelCase_ = os.path.join(_lowerCamelCase , '''__init__.py''' )
lowerCamelCase_ = parse_init(_lowerCamelCase )
if objects is not None:
lowerCamelCase_ = analyze_results(*_lowerCamelCase )
if len(_lowerCamelCase ) > 0:
lowerCamelCase_ = F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
failures.append('''\n'''.join(_lowerCamelCase ) )
if len(_lowerCamelCase ) > 0:
raise ValueError('''\n\n'''.join(_lowerCamelCase ) )
def lowerCamelCase_ ( ):
lowerCamelCase_ = []
for path, directories, files in os.walk(_lowerCamelCase ):
for folder in directories:
# Ignore private modules
if folder.startswith('''_''' ):
directories.remove(_lowerCamelCase )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(_lowerCamelCase ) / folder).glob('''*.py''' ) ) ) == 0:
continue
lowerCamelCase_ = str((Path(_lowerCamelCase ) / folder).relative_to(_lowerCamelCase ) )
lowerCamelCase_ = short_path.replace(os.path.sep , '''.''' )
submodules.append(_lowerCamelCase )
for fname in files:
if fname == "__init__.py":
continue
lowerCamelCase_ = str((Path(_lowerCamelCase ) / fname).relative_to(_lowerCamelCase ) )
lowerCamelCase_ = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(_lowerCamelCase )
return submodules
__lowercase : int = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
]
def lowerCamelCase_ ( ):
# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase_ = importlib.util.spec_from_file_location(
'''transformers''' , os.path.join(_lowerCamelCase , '''__init__.py''' ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
lowerCamelCase_ = spec.loader.load_module()
lowerCamelCase_ = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(_lowerCamelCase ) > 0:
lowerCamelCase_ = '''\n'''.join(F"""- {module}""" for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registered in the main init of Transformers:\n'''
F"""{list_of_modules}\n"""
'''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.''' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 142
| 0
|
from math import isclose, sqrt
def UpperCamelCase ( snake_case__ : Optional[int] ,snake_case__ : List[Any] ,snake_case__ : Tuple ):
'''simple docstring'''
__snake_case :Dict = point_y / 4 / point_x
__snake_case :List[str] = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
__snake_case :List[Any] = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
__snake_case :Union[str, Any] = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
__snake_case :Tuple = outgoing_gradient**2 + 4
__snake_case :Optional[int] = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
__snake_case :Optional[int] = (point_y - outgoing_gradient * point_x) ** 2 - 100
__snake_case :str = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
__snake_case :Optional[int] = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
__snake_case :List[str] = x_minus if isclose(__lowerCAmelCase ,__lowerCAmelCase ) else x_plus
__snake_case :Tuple = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def UpperCamelCase ( snake_case__ : Union[str, Any] = 1.4 ,snake_case__ : List[str] = -9.6 ):
'''simple docstring'''
__snake_case :int = 0
__snake_case :float = first_x_coord
__snake_case :float = first_y_coord
__snake_case :float = (10.1 - point_y) / (0.0 - point_x)
while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
__snake_case :str = next_point(__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f'''{solution() = }''')
| 718
|
def UpperCamelCase ( snake_case__ : str ,snake_case__ : int ):
'''simple docstring'''
__snake_case :list[list[str]] = [[] for _ in range(snake_case__ )]
__snake_case :Union[str, Any] = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(snake_case__ ) <= key:
return input_string
for position, character in enumerate(snake_case__ ):
__snake_case :Any = position % (lowest * 2) # puts it in bounds
__snake_case :Optional[int] = min(snake_case__ ,lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(snake_case__ )
__snake_case :List[Any] = ["""""".join(snake_case__ ) for row in temp_grid]
__snake_case :Any = """""".join(snake_case__ )
return output_string
def UpperCamelCase ( snake_case__ : str ,snake_case__ : int ):
'''simple docstring'''
__snake_case :Union[str, Any] = []
__snake_case :Optional[int] = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1:
return input_string
__snake_case :list[list[str]] = [[] for _ in range(snake_case__ )] # generates template
for position in range(len(snake_case__ ) ):
__snake_case :List[str] = position % (lowest * 2) # puts it in bounds
__snake_case :int = min(snake_case__ ,lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("""*""" )
__snake_case :str = 0
for row in temp_grid: # fills in the characters
__snake_case :str = input_string[counter : counter + len(snake_case__ )]
grid.append(list(snake_case__ ) )
counter += len(snake_case__ )
__snake_case :Any = """""" # reads as zigzag
for position in range(len(snake_case__ ) ):
__snake_case :Optional[int] = position % (lowest * 2) # puts it in bounds
__snake_case :int = min(snake_case__ ,lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def UpperCamelCase ( snake_case__ : str ):
'''simple docstring'''
__snake_case :Optional[Any] = {}
for key_guess in range(1 ,len(snake_case__ ) ): # tries every key
__snake_case :Dict = decrypt(snake_case__ ,snake_case__ )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 291
| 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_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowercase__ = logging.get_logger(__name__)
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
snake_case = ["""pixel_values"""]
def __init__( self , UpperCAmelCase_ = True , UpperCAmelCase_ = None , UpperCAmelCase_ = PILImageResampling.BICUBIC , UpperCAmelCase_ = True , UpperCAmelCase_ = True , UpperCAmelCase_ = 1 / 2_55 , UpperCAmelCase_ = None , UpperCAmelCase_ = True , UpperCAmelCase_ = None , UpperCAmelCase_ = None , **UpperCAmelCase_ , ):
super().__init__(**UpperCAmelCase_ )
snake_case_ = size if size is not None else {"height": 2_24, "width": 2_24}
snake_case_ = get_size_dict(UpperCAmelCase_ )
snake_case_ = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
snake_case_ = get_size_dict(UpperCAmelCase_ , default_to_square=UpperCAmelCase_ , param_name="crop_size" )
snake_case_ = do_resize
snake_case_ = do_rescale
snake_case_ = do_normalize
snake_case_ = do_center_crop
snake_case_ = crop_size
snake_case_ = size
snake_case_ = resample
snake_case_ = rescale_factor
snake_case_ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
snake_case_ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = PILImageResampling.BILINEAR , UpperCAmelCase_ = None , **UpperCAmelCase_ , ):
snake_case_ = get_size_dict(UpperCAmelCase_ )
if "shortest_edge" in size:
snake_case_ = get_resize_output_image_size(UpperCAmelCase_ , size=size["shortest_edge"] , default_to_square=UpperCAmelCase_ )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
snake_case_ = (size["height"], size["width"])
else:
raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
return resize(UpperCAmelCase_ , size=UpperCAmelCase_ , resample=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = None , **UpperCAmelCase_ , ):
snake_case_ = get_size_dict(UpperCAmelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(UpperCAmelCase_ , size=(size["height"], size["width"]) , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = None , **UpperCAmelCase_ ):
return rescale(UpperCAmelCase_ , scale=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = None , **UpperCAmelCase_ , ):
return normalize(UpperCAmelCase_ , mean=UpperCAmelCase_ , std=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ = None , UpperCAmelCase_ = None , UpperCAmelCase_ = None , UpperCAmelCase_ = None , UpperCAmelCase_ = None , UpperCAmelCase_ = None , UpperCAmelCase_ = None , UpperCAmelCase_ = None , UpperCAmelCase_ = None , UpperCAmelCase_ = None , UpperCAmelCase_ = None , UpperCAmelCase_ = ChannelDimension.FIRST , **UpperCAmelCase_ , ):
snake_case_ = do_resize if do_resize is not None else self.do_resize
snake_case_ = do_rescale if do_rescale is not None else self.do_rescale
snake_case_ = do_normalize if do_normalize is not None else self.do_normalize
snake_case_ = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case_ = crop_size if crop_size is not None else self.crop_size
snake_case_ = get_size_dict(UpperCAmelCase_ , param_name="crop_size" , default_to_square=UpperCAmelCase_ )
snake_case_ = resample if resample is not None else self.resample
snake_case_ = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case_ = image_mean if image_mean is not None else self.image_mean
snake_case_ = image_std if image_std is not None else self.image_std
snake_case_ = size if size is not None else self.size
snake_case_ = get_size_dict(UpperCAmelCase_ )
if not is_batched(UpperCAmelCase_ ):
snake_case_ = [images]
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." )
# All transformations expect numpy arrays.
snake_case_ = [to_numpy_array(UpperCAmelCase_ ) for image in images]
if do_resize:
snake_case_ = [self.resize(image=UpperCAmelCase_ , size=UpperCAmelCase_ , resample=UpperCAmelCase_ ) for image in images]
if do_center_crop:
snake_case_ = [self.center_crop(image=UpperCAmelCase_ , size=UpperCAmelCase_ ) for image in images]
if do_rescale:
snake_case_ = [self.rescale(image=UpperCAmelCase_ , scale=UpperCAmelCase_ ) for image in images]
if do_normalize:
snake_case_ = [self.normalize(image=UpperCAmelCase_ , mean=UpperCAmelCase_ , std=UpperCAmelCase_ ) for image in images]
snake_case_ = [to_channel_dimension_format(UpperCAmelCase_ , UpperCAmelCase_ ) for image in images]
snake_case_ = {"pixel_values": images}
return BatchFeature(data=UpperCAmelCase_ , tensor_type=UpperCAmelCase_ )
| 508
|
'''simple docstring'''
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowercase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self , UpperCAmelCase_ , UpperCAmelCase_=7_68 ):
super().__init__(UpperCAmelCase_ )
snake_case_ = proj_size
snake_case_ = CLIPVisionModel(UpperCAmelCase_ )
snake_case_ = PaintByExampleMapper(UpperCAmelCase_ )
snake_case_ = nn.LayerNorm(config.hidden_size )
snake_case_ = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
snake_case_ = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_=False ):
snake_case_ = self.model(pixel_values=UpperCAmelCase_ )
snake_case_ = clip_output.pooler_output
snake_case_ = self.mapper(latent_states[:, None] )
snake_case_ = self.final_layer_norm(UpperCAmelCase_ )
snake_case_ = self.proj_out(UpperCAmelCase_ )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class UpperCAmelCase_ ( nn.Module ):
"""simple docstring"""
def __init__( self , UpperCAmelCase_ ):
super().__init__()
snake_case_ = (config.num_hidden_layers + 1) // 5
snake_case_ = config.hidden_size
snake_case_ = 1
snake_case_ = nn.ModuleList(
[
BasicTransformerBlock(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , activation_fn="gelu" , attention_bias=UpperCAmelCase_ )
for _ in range(UpperCAmelCase_ )
] )
def _lowercase ( self , UpperCAmelCase_ ):
for block in self.blocks:
snake_case_ = block(UpperCAmelCase_ )
return hidden_states
| 508
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE__ : int =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int ={
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( a_ , a_ ):
"""simple docstring"""
__snake_case = """bit"""
__snake_case = ["""preactivation""", """bottleneck"""]
__snake_case = ["""SAME""", """VALID"""]
def __init__( self , _lowercase=3 , _lowercase=64 , _lowercase=[256, 512, 1024, 2048] , _lowercase=[3, 4, 6, 3] , _lowercase="preactivation" , _lowercase="relu" , _lowercase=None , _lowercase=32 , _lowercase=0.0 , _lowercase=False , _lowercase=32 , _lowercase=1 , _lowercase=None , _lowercase=None , **_lowercase , ) -> str:
super().__init__(**_lowercase )
if layer_type not in self.layer_types:
raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types )}''' )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
_lowerCamelCase : int = global_padding.upper()
else:
raise ValueError(F'''Padding strategy {global_padding} not supported''' )
_lowerCamelCase : Dict = num_channels
_lowerCamelCase : Optional[int] = embedding_size
_lowerCamelCase : int = hidden_sizes
_lowerCamelCase : Optional[int] = depths
_lowerCamelCase : Optional[int] = layer_type
_lowerCamelCase : List[str] = hidden_act
_lowerCamelCase : List[Any] = global_padding
_lowerCamelCase : Tuple = num_groups
_lowerCamelCase : Union[str, Any] = drop_path_rate
_lowerCamelCase : Tuple = embedding_dynamic_padding
_lowerCamelCase : List[str] = output_stride
_lowerCamelCase : Union[str, Any] = width_factor
_lowerCamelCase : List[Any] = ['''stem'''] + [F'''stage{idx}''' for idx in range(1 , len(_lowercase ) + 1 )]
_lowerCamelCase : Any = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
| 714
|
"""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 , _lowercase , _lowercase=13 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=99 , _lowercase=32 , _lowercase=5 , _lowercase=4 , _lowercase=37 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=16 , _lowercase=2 , _lowercase=0.02 , _lowercase=4 , ) -> List[Any]:
_lowerCamelCase : str = parent
_lowerCamelCase : Dict = batch_size
_lowerCamelCase : Optional[Any] = seq_length
_lowerCamelCase : List[str] = is_training
_lowerCamelCase : Dict = use_attention_mask
_lowerCamelCase : Optional[Any] = use_token_type_ids
_lowerCamelCase : Tuple = use_labels
_lowerCamelCase : List[Any] = vocab_size
_lowerCamelCase : Union[str, Any] = hidden_size
_lowerCamelCase : Union[str, Any] = num_hidden_layers
_lowerCamelCase : Union[str, Any] = num_attention_heads
_lowerCamelCase : Tuple = intermediate_size
_lowerCamelCase : Union[str, Any] = hidden_act
_lowerCamelCase : Tuple = hidden_dropout_prob
_lowerCamelCase : Union[str, Any] = attention_probs_dropout_prob
_lowerCamelCase : Tuple = max_position_embeddings
_lowerCamelCase : List[str] = type_vocab_size
_lowerCamelCase : List[str] = type_sequence_label_size
_lowerCamelCase : Union[str, Any] = initializer_range
_lowerCamelCase : str = num_choices
def a__ ( self ) -> Dict:
_lowerCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCamelCase : List[Any] = None
if self.use_attention_mask:
_lowerCamelCase : str = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCamelCase : List[Any] = None
if self.use_token_type_ids:
_lowerCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowerCamelCase : int = 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=_lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def a__ ( self ) -> int:
_lowerCamelCase : Optional[Any] = self.prepare_config_and_inputs()
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase : int = config_and_inputs
_lowerCamelCase : Tuple = {'''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"""
__snake_case = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a__ ( self ) -> Any:
_lowerCamelCase : Union[str, Any] = FlaxAlbertModelTester(self )
@slow
def a__ ( self ) -> int:
for model_class_name in self.all_model_classes:
_lowerCamelCase : str = model_class_name.from_pretrained('''albert-base-v2''' )
_lowerCamelCase : int = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowercase )
@require_flax
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def a__ ( self ) -> Optional[int]:
_lowerCamelCase : Tuple = FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
_lowerCamelCase : List[Any] = np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
_lowerCamelCase : Union[str, Any] = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_lowerCamelCase : str = model(_lowercase , attention_mask=_lowercase )[0]
_lowerCamelCase : List[str] = (1, 11, 768)
self.assertEqual(output.shape , _lowercase )
_lowerCamelCase : Dict = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , _lowercase , atol=1E-4 ) )
| 558
| 0
|
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
SCREAMING_SNAKE_CASE: Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE: List[Any] = TypeVar('''DatasetType''', Dataset, IterableDataset)
def _a ( lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = "first_exhausted" , )-> DatasetType:
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(lowerCAmelCase ):
if not isinstance(lowerCAmelCase , (Dataset, IterableDataset) ):
if isinstance(lowerCAmelCase , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
'is an empty dataset dictionary.' )
raise ValueError(
F'''Dataset at position {i} has at least one split: {list(lowerCAmelCase )}\n'''
F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCAmelCase ) )}\']''' )
raise ValueError(
F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCAmelCase ).__name__}.''' )
if i == 0:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = (
(Dataset, IterableDataset) if isinstance(lowerCAmelCase , lowerCAmelCase ) else (IterableDataset, Dataset)
)
elif not isinstance(lowerCAmelCase , lowerCAmelCase ):
raise ValueError(
F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.''' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , info=lowerCAmelCase , split=lowerCAmelCase , stopping_strategy=lowerCAmelCase )
else:
return _interleave_iterable_datasets(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , info=lowerCAmelCase , split=lowerCAmelCase , stopping_strategy=lowerCAmelCase )
def _a ( lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = 0 , )-> DatasetType:
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(lowerCAmelCase ):
if not isinstance(lowerCAmelCase , (Dataset, IterableDataset) ):
if isinstance(lowerCAmelCase , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
'is an empty dataset dictionary.' )
raise ValueError(
F'''Dataset at position {i} has at least one split: {list(lowerCAmelCase )}\n'''
F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCAmelCase ) )}\']''' )
raise ValueError(
F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCAmelCase ).__name__}.''' )
if i == 0:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = (
(Dataset, IterableDataset) if isinstance(lowerCAmelCase , lowerCAmelCase ) else (IterableDataset, Dataset)
)
elif not isinstance(lowerCAmelCase , lowerCAmelCase ):
raise ValueError(
F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(lowerCAmelCase , info=lowerCAmelCase , split=lowerCAmelCase , axis=lowerCAmelCase )
else:
return _concatenate_iterable_datasets(lowerCAmelCase , info=lowerCAmelCase , split=lowerCAmelCase , axis=lowerCAmelCase )
| 360
|
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE: Optional[int] = 2_9_9_7_9_2_4_5_8
# Symbols
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE: Tuple = symbols('''ct x y z''')
def _a ( lowerCAmelCase )-> float:
if velocity > c:
raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!' )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError('Speed must be greater than or equal to 1!' )
return velocity / c
def _a ( lowerCAmelCase )-> float:
return 1 / sqrt(1 - beta(lowerCAmelCase ) ** 2 )
def _a ( lowerCAmelCase )-> np.ndarray:
return np.array(
[
[gamma(lowerCAmelCase ), -gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), 0, 0],
[-gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), gamma(lowerCAmelCase ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def _a ( lowerCAmelCase , lowerCAmelCase = None )-> np.ndarray:
# Ensure event is not empty
if event is None:
SCREAMING_SNAKE_CASE_ = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(lowerCAmelCase ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
SCREAMING_SNAKE_CASE: int = transform(2_9_9_7_9_2_4_5)
print('''Example of four vector: ''')
print(f"""ct' = {four_vector[0]}""")
print(f"""x' = {four_vector[1]}""")
print(f"""y' = {four_vector[2]}""")
print(f"""z' = {four_vector[3]}""")
# Substitute symbols with numerical values
SCREAMING_SNAKE_CASE: List[Any] = {ct: c, x: 1, y: 1, z: 1}
SCREAMING_SNAKE_CASE: Optional[Any] = [four_vector[i].subs(sub_dict) for i in range(4)]
print(f"""\n{numerical_vector}""")
| 360
| 1
|
'''simple docstring'''
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
_lowercase = logging.get_logger(__name__)
def A ():
_lowerCAmelCase = os.getenv("""SM_HP_MP_PARAMETERS""" , """{}""" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
_lowerCAmelCase = json.loads(_lowerCamelCase )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
_lowerCAmelCase = os.getenv("""SM_FRAMEWORK_PARAMS""" , """{}""" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
_lowerCAmelCase = json.loads(_lowerCamelCase )
if not mpi_options.get("""sagemaker_mpi_enabled""" , _lowerCamelCase ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("""smdistributed""" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class UpperCAmelCase_ ( __lowercase ):
'''simple docstring'''
_lowercase : Optional[int] = field(
default='''''' , metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''} , )
def _lowercase ( self ):
"""simple docstring"""
super().__post_init__()
warnings.warn(
"""`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
"""`TrainingArguments` instead.""" , _lowercase , )
@cached_property
def _lowercase ( self ):
"""simple docstring"""
logger.info("""PyTorch: setting up devices""" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"""torch.distributed process group is initialized, but local_rank == -1. """
"""In order to use Torch DDP, launch your script with `python -m torch.distributed.launch""" )
if self.no_cuda:
_lowerCAmelCase = torch.device("""cpu""" )
_lowerCAmelCase = 0
elif is_sagemaker_model_parallel_available():
_lowerCAmelCase = smp.local_rank()
_lowerCAmelCase = torch.device("""cuda""" , _lowercase )
_lowerCAmelCase = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="""smddp""" , timeout=self.ddp_timeout_delta )
_lowerCAmelCase = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK""" ) )
_lowerCAmelCase = torch.device("""cuda""" , self.local_rank )
_lowerCAmelCase = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
_lowerCAmelCase = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
_lowerCAmelCase = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="""nccl""" , timeout=self.ddp_timeout_delta )
_lowerCAmelCase = torch.device("""cuda""" , self.local_rank )
_lowerCAmelCase = 1
if device.type == "cuda":
torch.cuda.set_device(_lowercase )
return device
@property
def _lowercase ( self ):
"""simple docstring"""
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def _lowercase ( self ):
"""simple docstring"""
return not is_sagemaker_model_parallel_available()
@property
def _lowercase ( self ):
"""simple docstring"""
return False
| 706
|
'''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
_lowercase = 300 # TEMPERATURE (unit = K)
def A (__lowerCamelCase :float , __lowerCamelCase :float , __lowerCamelCase :float , ):
if donor_conc <= 0:
raise ValueError("""Donor concentration should be positive""" )
elif acceptor_conc <= 0:
raise ValueError("""Acceptor concentration should be positive""" )
elif intrinsic_conc <= 0:
raise ValueError("""Intrinsic concentration should be positive""" )
elif donor_conc <= intrinsic_conc:
raise ValueError(
"""Donor concentration should be greater than intrinsic concentration""" )
elif acceptor_conc <= intrinsic_conc:
raise ValueError(
"""Acceptor concentration should be greater than intrinsic concentration""" )
else:
return (
Boltzmann
* T
* log((donor_conc * acceptor_conc) / intrinsic_conc**2 )
/ physical_constants["electron volt"][0]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 162
| 0
|
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
_lowerCAmelCase = """1"""
_lowerCAmelCase = """0"""
_lowerCAmelCase = """1"""
_lowerCAmelCase = ort.SessionOptions()
_lowerCAmelCase = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("""Create inference session...""")
_lowerCAmelCase = ["""TensorrtExecutionProvider""", """CUDAExecutionProvider"""]
_lowerCAmelCase = ort.InferenceSession("""model.onnx""", sess_options=sess_opt, providers=execution_provider)
_lowerCAmelCase = ort.RunOptions()
_lowerCAmelCase = 1_2_8
_lowerCAmelCase = 1
_lowerCAmelCase = np.ones((batch, sequence), dtype=np.intaa)
_lowerCAmelCase = np.ones((batch, sequence), dtype=np.intaa)
_lowerCAmelCase = np.ones((batch, sequence), dtype=np.intaa)
print("""Warm up phase...""")
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Start inference...""")
_lowerCAmelCase = time.time()
_lowerCAmelCase = 2_0_0_0
_lowerCAmelCase = {}
for iter in range(max_iters):
_lowerCAmelCase = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Average Inference Time = {:.3f} ms""".format((time.time() - start_time) * 1_0_0_0 / max_iters))
| 259
|
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
lowerCAmelCase__ = False
class __snake_case ( unittest.TestCase):
pass
@nightly
@require_torch_gpu
class __snake_case ( unittest.TestCase):
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
_lowerCamelCase : int = VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
# remove text_unet
pipe.remove_unused_weights()
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_lowerCamelCase : str = '''A painting of a squirrel eating a burger '''
_lowerCamelCase : Dict = torch.manual_seed(0 )
_lowerCamelCase : List[Any] = pipe(
prompt=__lowerCAmelCase , generator=__lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__lowerCAmelCase )
_lowerCamelCase : Tuple = VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_lowerCamelCase : int = generator.manual_seed(0 )
_lowerCamelCase : List[str] = pipe(
prompt=__lowerCAmelCase , generator=__lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def SCREAMING_SNAKE_CASE ( self : Dict ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = VersatileDiffusionTextToImagePipeline.from_pretrained(
'''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = '''A painting of a squirrel eating a burger '''
_lowerCamelCase : Optional[int] = torch.manual_seed(0 )
_lowerCamelCase : int = pipe(
prompt=__lowerCAmelCase , generator=__lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=5_0 , output_type='''numpy''' ).images
_lowerCamelCase : List[str] = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : Dict = np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 83
| 0
|
import math
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : int ) -> int:
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE_ : Any =f'Input value of [number={number}] must be an integer'
raise TypeError(UpperCAmelCase_ )
if number < 1:
SCREAMING_SNAKE_CASE_ : Optional[int] =f'Input value of [number={number}] must be > 0'
raise ValueError(UpperCAmelCase_ )
elif number == 1:
return 3
elif number == 2:
return 5
else:
SCREAMING_SNAKE_CASE_ : List[Any] =int(math.log(number // 3 , 2 ) ) + 2
SCREAMING_SNAKE_CASE_ : Dict =[3, 5]
SCREAMING_SNAKE_CASE_ : Optional[Any] =2
SCREAMING_SNAKE_CASE_ : Tuple =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):
_lowercase = 0
try:
_lowercase = proth(number)
except ValueError:
print(F"ValueError: there is no {number}th Proth number")
continue
print(F"The {number}th Proth number: {value}")
| 431
|
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowercase = abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Union[str, Any] ) -> Optional[int]:
config.addinivalue_line(
'''markers''' , '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''' , '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''' , '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''' , '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''' , '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''' , '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : List[Any] ) -> List[str]:
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(UpperCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Dict ) -> Optional[int]:
from transformers.testing_utils import pytest_terminal_summary_main
SCREAMING_SNAKE_CASE_ : List[Any] =terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(UpperCAmelCase_ , id=UpperCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[int] ) -> int:
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
SCREAMING_SNAKE_CASE_ : Union[str, Any] =0
# Doctest custom flag to ignore output.
_lowercase = doctest.register_optionflag("""IGNORE_RESULT""")
_lowercase = doctest.OutputChecker
class lowercase_ ( A ):
def _snake_case ( self , __A , __A , __A ) -> List[Any]:
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , __A , __A , __A )
_lowercase = CustomOutputChecker
_lowercase = HfDoctestModule
_lowercase = HfDocTestParser
| 431
| 1
|
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class snake_case ( tf.keras.layers.Layer ):
def __init__( self : Dict , UpperCamelCase__ : Dict[str, int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : int = None , UpperCamelCase__ : int = None)-> List[Any]:
'''simple docstring'''
super().__init__()
__lowerCAmelCase: List[str] = pad_token_id
__lowerCAmelCase: Any = max_length
__lowerCAmelCase: Tuple = vocab
__lowerCAmelCase: Tuple = merges
__lowerCAmelCase: List[str] = BytePairTokenizer(UpperCamelCase__ , UpperCamelCase__ , sequence_length=UpperCamelCase__)
@classmethod
def lowercase_ ( cls : Optional[int] , UpperCamelCase__ : GPTaTokenizer , *UpperCamelCase__ : int , **UpperCamelCase__ : List[Any])-> Union[str, Any]:
'''simple docstring'''
__lowerCAmelCase: Optional[int] = [" ".join(UpperCamelCase__) for m in tokenizer.bpe_ranks.keys()]
__lowerCAmelCase: str = tokenizer.get_vocab()
return cls(UpperCamelCase__ , UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__)
@classmethod
def lowercase_ ( cls : Any , UpperCamelCase__ : Union[str, os.PathLike] , *UpperCamelCase__ : List[str] , **UpperCamelCase__ : Union[str, Any])-> str:
'''simple docstring'''
__lowerCAmelCase: Optional[Any] = GPTaTokenizer.from_pretrained(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__)
return cls.from_tokenizer(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__)
@classmethod
def lowercase_ ( cls : Optional[Any] , UpperCamelCase__ : Optional[int])-> Tuple:
'''simple docstring'''
return cls(**UpperCamelCase__)
def lowercase_ ( self : Optional[int])-> str:
'''simple docstring'''
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def lowercase_ ( self : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : int = None)-> Tuple:
'''simple docstring'''
__lowerCAmelCase: List[Any] = self.tf_tokenizer(UpperCamelCase__)
__lowerCAmelCase: List[Any] = tf.ones_like(UpperCamelCase__)
if self.pad_token_id is not None:
# pad the tokens up to max length
__lowerCAmelCase: Tuple = max_length if max_length is not None else self.max_length
if max_length is not None:
__lowerCAmelCase , __lowerCAmelCase: Union[str, Any] = pad_model_inputs(
UpperCamelCase__ , max_seq_length=UpperCamelCase__ , pad_value=self.pad_token_id)
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 346
|
"""simple docstring"""
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
__A = object()
# For specifying empty leaf dict `{}`
__A = object()
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Dict:
__lowerCAmelCase: Dict = tuple((re.compile(x + "$" ) for x in qs) )
for i in range(len(__SCREAMING_SNAKE_CASE ) - len(__SCREAMING_SNAKE_CASE ) + 1 ):
__lowerCAmelCase: Tuple = [x.match(__SCREAMING_SNAKE_CASE ) for x, y in zip(__SCREAMING_SNAKE_CASE , ks[i:] )]
if matches and all(__SCREAMING_SNAKE_CASE ):
return True
return False
def a__ ( __SCREAMING_SNAKE_CASE ) -> List[Any]:
def replace(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
for rule, replacement in rules:
if _match(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return replacement
return val
return replace
def a__ ( ) -> str:
return [
# embeddings
(("transformer", "wpe", "embedding"), P("mp" , __SCREAMING_SNAKE_CASE )),
(("transformer", "wte", "embedding"), P("mp" , __SCREAMING_SNAKE_CASE )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__SCREAMING_SNAKE_CASE , "mp" )),
(("attention", "out_proj", "kernel"), P("mp" , __SCREAMING_SNAKE_CASE )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(__SCREAMING_SNAKE_CASE , "mp" )),
(("mlp", "c_fc", "bias"), P("mp" )),
(("mlp", "c_proj", "kernel"), P("mp" , __SCREAMING_SNAKE_CASE )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def a__ ( __SCREAMING_SNAKE_CASE ) -> List[str]:
__lowerCAmelCase: Any = _get_partition_rules()
__lowerCAmelCase: List[Any] = _replacement_rules(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Tuple = {k: _unmatched for k in flatten_dict(__SCREAMING_SNAKE_CASE )}
__lowerCAmelCase: Any = {k: replace(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(__SCREAMING_SNAKE_CASE ) )
| 346
| 1
|
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance == 0:
return {"resistance": sqrt(pow(__UpperCamelCase ,2 ) - pow(__UpperCamelCase ,2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__UpperCamelCase ,2 ) - pow(__UpperCamelCase ,2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__UpperCamelCase ,2 ) + pow(__UpperCamelCase ,2 ) )}
else:
raise ValueError('Exactly one argument must be 0' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 384
|
'''simple docstring'''
A_ = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
A_ = ["a", "b", "c", "d", "e"]
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> List[str]:
lowerCamelCase_ = start
# add current to visited
visited.append(__UpperCamelCase )
lowerCamelCase_ = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
lowerCamelCase_ = topological_sort(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# if all neighbors visited add current to sort
sort.append(__UpperCamelCase )
# if all vertices haven't been visited select a new one to visit
if len(__UpperCamelCase ) != len(__UpperCamelCase ):
for vertice in vertices:
if vertice not in visited:
lowerCamelCase_ = topological_sort(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# return sort
return sort
if __name__ == "__main__":
A_ = topological_sort("a", [], [])
print(sort)
| 384
| 1
|
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(SCREAMING_SNAKE_CASE ):
for j in range(SCREAMING_SNAKE_CASE ):
if dist[i][j] != float('''inf''' ):
print(int(dist[i][j] ) , end='''\t''' )
else:
print('''INF''' , end='''\t''' )
print()
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = [[float('''inf''' ) for _ in range(SCREAMING_SNAKE_CASE )] for _ in range(SCREAMING_SNAKE_CASE )]
for i in range(SCREAMING_SNAKE_CASE ):
for j in range(SCREAMING_SNAKE_CASE ):
lowercase__ = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(SCREAMING_SNAKE_CASE ):
# looping through rows of graph array
for i in range(SCREAMING_SNAKE_CASE ):
# looping through columns of graph array
for j in range(SCREAMING_SNAKE_CASE ):
if (
dist[i][k] != float('''inf''' )
and dist[k][j] != float('''inf''' )
and dist[i][k] + dist[k][j] < dist[i][j]
):
lowercase__ = dist[i][k] + dist[k][j]
_print_dist(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return dist, v
if __name__ == "__main__":
lowerCAmelCase = int(input('Enter number of vertices: '))
lowerCAmelCase = int(input('Enter number of edges: '))
lowerCAmelCase = [[float('inf') for i in range(v)] for j in range(v)]
for i in range(v):
lowerCAmelCase = 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)
lowerCAmelCase = int(input('Enter source:'))
lowerCAmelCase = int(input('Enter destination:'))
lowerCAmelCase = float(input('Enter weight:'))
lowerCAmelCase = 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
| 43
|
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def a ( snake_case__: Any ):
'''simple docstring'''
lowercase_ = filter(lambda snake_case__ : p.requires_grad , model.parameters() )
lowercase_ = sum([np.prod(p.size() ) for p in model_parameters] )
return params
__a = logging.getLogger(__name__)
def a ( snake_case__: int , snake_case__: Any ):
'''simple docstring'''
if metric == "rouge2":
lowercase_ = '''{val_avg_rouge2:.4f}-{step_count}'''
elif metric == "bleu":
lowercase_ = '''{val_avg_bleu:.4f}-{step_count}'''
elif metric == "em":
lowercase_ = '''{val_avg_em:.4f}-{step_count}'''
elif metric == "loss":
lowercase_ = '''{val_avg_loss:.4f}-{step_count}'''
else:
raise NotImplementedError(
F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
''' function.''' )
lowercase_ = ModelCheckpoint(
dirpath=snake_case__ , filename=snake_case__ , monitor=F'''val_{metric}''' , mode='''max''' , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def a ( snake_case__: str , snake_case__: List[str] ):
'''simple docstring'''
return EarlyStopping(
monitor=F'''val_{metric}''' , mode='''min''' if '''loss''' in metric else '''max''' , patience=snake_case__ , verbose=snake_case__ , )
class lowercase__( pl.Callback ):
"""simple docstring"""
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Any ) -> Optional[Any]:
lowercase_ = {f'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_ )
@rank_zero_only
def _lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : pl.Trainer , SCREAMING_SNAKE_CASE_ : pl.LightningModule , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Tuple=True ) -> None:
logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
lowercase_ = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']} )
# Log results
lowercase_ = Path(pl_module.hparams.output_dir )
if type_path == "test":
lowercase_ = od / '''test_results.txt'''
lowercase_ = od / '''test_generations.txt'''
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
lowercase_ = od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
lowercase_ = od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ , '''a+''' ) as writer:
for key in sorted(SCREAMING_SNAKE_CASE_ ):
if key in ["log", "progress_bar", "preds"]:
continue
lowercase_ = metrics[key]
if isinstance(SCREAMING_SNAKE_CASE_ , torch.Tensor ):
lowercase_ = val.item()
lowercase_ = f'''{key}: {val:.6f}\n'''
writer.write(SCREAMING_SNAKE_CASE_ )
if not save_generations:
return
if "preds" in metrics:
lowercase_ = '''\n'''.join(metrics['''preds'''] )
generations_file.open('''w+''' ).write(SCREAMING_SNAKE_CASE_ )
@rank_zero_only
def _lowercase ( self : Any , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Any ) -> Union[str, Any]:
try:
lowercase_ = pl_module.model.model.num_parameters()
except AttributeError:
lowercase_ = pl_module.model.num_parameters()
lowercase_ = count_trainable_parameters(SCREAMING_SNAKE_CASE_ )
# mp stands for million parameters
trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6} )
@rank_zero_only
def _lowercase ( self : Tuple , SCREAMING_SNAKE_CASE_ : pl.Trainer , SCREAMING_SNAKE_CASE_ : pl.LightningModule ) -> Any:
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , '''test''' )
@rank_zero_only
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : pl.Trainer , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Dict:
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 97
| 0
|
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase__ ="\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"
lowerCAmelCase__ ="\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while 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 y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - `'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. Note that it can result in an F-score that is not between precision and recall.\n - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n - `0`: If there is a zero division, the return value is `0`.\n - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n Example 1-A simple example with some errors\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n >>> print(results)\n {'recall': 0.6666666666666666}\n\n Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n >>> print(results)\n {'recall': 0.5}\n\n Example 3-The same example as Example 1, but with `sample_weight` included.\n >>> recall_metric = datasets.load_metric('recall')\n >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n >>> print(results)\n {'recall': 0.55}\n\n Example 4-A multiclass example, using different averages.\n >>> recall_metric = datasets.load_metric('recall')\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'recall': array([1., 0., 0.])}\n"
lowerCAmelCase__ ="\n@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}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A__( datasets.Metric ):
def _a ( self : Any ) -> int:
"""simple docstring"""
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.recall_score.html'''] , )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]="binary" , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : List[Any]="warn" , ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = recall_score(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , pos_label=__SCREAMING_SNAKE_CASE , average=__SCREAMING_SNAKE_CASE , sample_weight=__SCREAMING_SNAKE_CASE , zero_division=__SCREAMING_SNAKE_CASE , )
return {"recall": float(__SCREAMING_SNAKE_CASE ) if score.size == 1 else score}
| 690
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ ={"configuration_timm_backbone": ["TimmBackboneConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["TimmBackbone"]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690
| 1
|
"""simple docstring"""
from math import factorial
A = {str(digit): factorial(digit) for digit in range(10)}
def __A ( a_ :int) -> int:
if not isinstance(a_ , a_):
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(a_))
def __A ( a_ :int = 60 , a_ :int = 1_00_00_00) -> int:
if not isinstance(a_ , a_) or not isinstance(a_ , a_):
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
__a : int = 0
# the cached sizes of the previous chains
__a : dict[int, int] = {}
for start_chain_element in range(1 , a_):
# The temporary set will contain the elements of the chain
__a : Tuple = set()
__a : Union[str, Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__a : str = 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(a_)
chain_set_length += 1
__a : Optional[int] = digit_factorial_sum(a_)
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__a : Optional[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()}')
| 52
|
"""simple docstring"""
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
__lowerCAmelCase : Tuple = '''scheduler_config.json'''
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = 1
_lowerCamelCase = 2
_lowerCamelCase = 3
_lowerCamelCase = 4
_lowerCamelCase = 5
@dataclass
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = 42
class _lowerCAmelCase :
"""simple docstring"""
_lowerCamelCase = SCHEDULER_CONFIG_NAME
_lowerCamelCase = ['''dtype''']
_lowerCamelCase = []
_lowerCamelCase = True
@classmethod
def UpperCAmelCase__ ( cls , _lowercase = None , _lowercase = None , _lowercase=False , **_lowercase , ) -> Any:
'''simple docstring'''
snake_case_ , snake_case_ : int = cls.load_config(
pretrained_model_name_or_path=_lowercase , subfolder=_lowercase , return_unused_kwargs=_lowercase , **_lowercase , )
snake_case_ , snake_case_ : Dict = cls.from_config(_lowercase , return_unused_kwargs=_lowercase , **_lowercase )
if hasattr(_lowercase , """create_state""" ) and getattr(_lowercase , """has_state""" , _lowercase ):
snake_case_ : Any = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def UpperCAmelCase__ ( self , _lowercase , _lowercase = False , **_lowercase ) -> Optional[Any]:
'''simple docstring'''
self.save_config(save_directory=_lowercase , push_to_hub=_lowercase , **_lowercase )
@property
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
return self._get_compatibles()
@classmethod
def UpperCAmelCase__ ( cls ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = list(set([cls.__name__] + cls._compatibles ) )
snake_case_ : str = importlib.import_module(__name__.split(""".""" )[0] )
snake_case_ : Optional[int] = [
getattr(_lowercase , _lowercase ) for c in compatible_classes_str if hasattr(_lowercase , _lowercase )
]
return compatible_classes
def __lowerCAmelCase ( __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 __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : Any=0.999 , __UpperCamelCase : Optional[int]=jnp.floataa ):
'''simple docstring'''
def alpha_bar(__UpperCamelCase : Optional[int] ):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2
snake_case_ : Optional[Any] = []
for i in range(__UpperCamelCase ):
snake_case_ : Dict = i / num_diffusion_timesteps
snake_case_ : Union[str, Any] = (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 _lowerCAmelCase :
"""simple docstring"""
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
@classmethod
def UpperCAmelCase__ ( cls , _lowercase ) -> int:
'''simple docstring'''
snake_case_ : Any = scheduler.config
if config.trained_betas is not None:
snake_case_ : Optional[Any] = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
snake_case_ : int = 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.
snake_case_ : 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
snake_case_ : int = 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__}' )
snake_case_ : Optional[Any] = 1.0 - betas
snake_case_ : Any = jnp.cumprod(_lowercase , axis=0 )
return cls(
alphas=_lowercase , betas=_lowercase , alphas_cumprod=_lowercase , )
def __lowerCAmelCase ( __UpperCamelCase : CommonSchedulerState , __UpperCamelCase : jnp.ndarray , __UpperCamelCase : jnp.ndarray , __UpperCamelCase : jnp.ndarray ):
'''simple docstring'''
snake_case_ : Tuple = state.alphas_cumprod
snake_case_ : Optional[int] = alphas_cumprod[timesteps] ** 0.5
snake_case_ : Dict = sqrt_alpha_prod.flatten()
snake_case_ : int = broadcast_to_shape_from_left(__UpperCamelCase , original_samples.shape )
snake_case_ : Optional[Any] = (1 - alphas_cumprod[timesteps]) ** 0.5
snake_case_ : Dict = sqrt_one_minus_alpha_prod.flatten()
snake_case_ : Tuple = broadcast_to_shape_from_left(__UpperCamelCase , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def __lowerCAmelCase ( __UpperCamelCase : CommonSchedulerState , __UpperCamelCase : jnp.ndarray , __UpperCamelCase : jnp.ndarray , __UpperCamelCase : jnp.ndarray ):
'''simple docstring'''
snake_case_ , snake_case_ : str = get_sqrt_alpha_prod(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ : Any = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def __lowerCAmelCase ( __UpperCamelCase : CommonSchedulerState , __UpperCamelCase : jnp.ndarray , __UpperCamelCase : jnp.ndarray , __UpperCamelCase : jnp.ndarray ):
'''simple docstring'''
snake_case_ , snake_case_ : List[Any] = get_sqrt_alpha_prod(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ : Any = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 58
| 0
|
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowercase : int = logging.get_logger(__name__)
@add_end_docstrings(__snake_case )
class A ( __snake_case ):
def __init__( self , **SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
super().__init__(**SCREAMING_SNAKE_CASE )
if self.framework != "pt":
raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
# No specific FOR_XXX available yet
def __call__( self , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
return super().__call__(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , **SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
A : Union[str, Any] = {}
if "candidate_labels" in kwargs:
A : Dict = kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
A : Dict = kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE="This is a sound of {}." ) -> Optional[int]:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
if audio.startswith('''http://''' ) or audio.startswith('''https://''' ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
A : str = requests.get(SCREAMING_SNAKE_CASE ).content
else:
with open(SCREAMING_SNAKE_CASE , '''rb''' ) as f:
A : Tuple = f.read()
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A : List[Any] = ffmpeg_read(SCREAMING_SNAKE_CASE , self.feature_extractor.sampling_rate )
if not isinstance(SCREAMING_SNAKE_CASE , np.ndarray ):
raise ValueError('''We expect a numpy ndarray as input''' )
if len(audio.shape ) != 1:
raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline''' )
A : List[Any] = self.feature_extractor(
[audio] , sampling_rate=self.feature_extractor.sampling_rate , return_tensors='''pt''' )
A : Any = candidate_labels
A : Any = [hypothesis_template.format(SCREAMING_SNAKE_CASE ) for x in candidate_labels]
A : Optional[Any] = self.tokenizer(SCREAMING_SNAKE_CASE , return_tensors=self.framework , padding=SCREAMING_SNAKE_CASE )
A : Union[str, Any] = [text_inputs]
return inputs
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
A : Optional[Any] = model_inputs.pop('''candidate_labels''' )
A : Optional[Any] = model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0] , SCREAMING_SNAKE_CASE ):
A : List[Any] = text_inputs[0]
else:
# Batching case.
A : Dict = text_inputs[0][0]
A : Optional[Any] = self.model(**SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
A : List[Any] = {
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_audio,
}
return model_outputs
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
A : Any = model_outputs.pop('''candidate_labels''' )
A : Optional[Any] = model_outputs['''logits'''][0]
if self.framework == "pt":
A : Union[str, Any] = logits.softmax(dim=0 )
A : Dict = probs.tolist()
else:
raise ValueError('''`tf` framework not supported.''' )
A : Dict = [
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , key=lambda SCREAMING_SNAKE_CASE : -x[0] )
]
return result
| 343
|
'''simple docstring'''
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class A ( __snake_case , unittest.TestCase ):
__magic_name__ = CpmAntTokenizer
__magic_name__ = False
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
super().setUp()
A : Optional[int] = [
'''<d>''',
'''</d>''',
'''<s>''',
'''</s>''',
'''</_>''',
'''<unk>''',
'''<pad>''',
'''</n>''',
'''我''',
'''是''',
'''C''',
'''P''',
'''M''',
'''A''',
'''n''',
'''t''',
]
A : Union[str, Any] = 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] ) )
@tooslow
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : Union[str, Any] = CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b''' )
A : List[str] = '''今天天气真好！'''
A : Union[str, Any] = ['''今天''', '''天气''', '''真''', '''好''', '''！''']
A : List[str] = tokenizer.tokenize(SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
A : Optional[Any] = '''今天天气真好！'''
A : List[str] = [tokenizer.bos_token] + tokens
A : int = [6, 9802, 14962, 2082, 831, 244]
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
A : str = tokenizer.decode(SCREAMING_SNAKE_CASE )
self.assertEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
| 343
| 1
|
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_lowercase: List[Any] = logging.get_logger(__name__)
_lowercase: Optional[int] = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all LED models at https://huggingface.co/models?filter=LED
_lowercase: List[Any] = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
_lowercase: Dict = {
'''allenai/led-base-16384''': 1_6_3_8_4,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def _lowerCamelCase ( ):
_lowerCAmelCase = (
list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) )
)
_lowerCAmelCase = bs[:]
_lowerCAmelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(snake_case )
cs.append(2**8 + n )
n += 1
_lowerCAmelCase = [chr(snake_case ) for n in cs]
return dict(zip(snake_case , snake_case ) )
def _lowerCamelCase ( snake_case ):
_lowerCAmelCase = set()
_lowerCAmelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowerCAmelCase = char
return pairs
class lowerCamelCase__ ( UpperCAmelCase ):
UpperCamelCase__ =VOCAB_FILES_NAMES
UpperCamelCase__ =PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ =["input_ids", "attention_mask"]
def __init__( self : str , lowercase__ : Tuple , lowercase__ : Any , lowercase__ : List[str]="replace" , lowercase__ : List[str]="<s>" , lowercase__ : Optional[Any]="</s>" , lowercase__ : Any="</s>" , lowercase__ : List[Any]="<s>" , lowercase__ : Union[str, Any]="<unk>" , lowercase__ : str="<pad>" , lowercase__ : Any="<mask>" , lowercase__ : List[str]=False , **lowercase__ : Tuple , ):
_lowerCAmelCase = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__ ) if isinstance(lowercase__ , lowercase__ ) else bos_token
_lowerCAmelCase = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__ ) if isinstance(lowercase__ , lowercase__ ) else eos_token
_lowerCAmelCase = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__ ) if isinstance(lowercase__ , lowercase__ ) else sep_token
_lowerCAmelCase = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__ ) if isinstance(lowercase__ , lowercase__ ) else cls_token
_lowerCAmelCase = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__ ) if isinstance(lowercase__ , lowercase__ ) else unk_token
_lowerCAmelCase = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__ ) if isinstance(lowercase__ , lowercase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_lowerCAmelCase = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__ ) if isinstance(lowercase__ , lowercase__ ) else mask_token
super().__init__(
errors=lowercase__ , bos_token=lowercase__ , eos_token=lowercase__ , unk_token=lowercase__ , sep_token=lowercase__ , cls_token=lowercase__ , pad_token=lowercase__ , mask_token=lowercase__ , add_prefix_space=lowercase__ , **lowercase__ , )
with open(lowercase__ , encoding='utf-8' ) as vocab_handle:
_lowerCAmelCase = json.load(lowercase__ )
_lowerCAmelCase = {v: k for k, v in self.encoder.items()}
_lowerCAmelCase = errors # how to handle errors in decoding
_lowerCAmelCase = bytes_to_unicode()
_lowerCAmelCase = {v: k for k, v in self.byte_encoder.items()}
with open(lowercase__ , encoding='utf-8' ) as merges_handle:
_lowerCAmelCase = merges_handle.read().split('\n' )[1:-1]
_lowerCAmelCase = [tuple(merge.split() ) for merge in bpe_merges]
_lowerCAmelCase = dict(zip(lowercase__ , range(len(lowercase__ ) ) ) )
_lowerCAmelCase = {}
_lowerCAmelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowerCAmelCase = re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
return len(self.encoder )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
return dict(self.encoder , **self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE__ ( self : int , lowercase__ : List[str] ):
if token in self.cache:
return self.cache[token]
_lowerCAmelCase = tuple(lowercase__ )
_lowerCAmelCase = get_pairs(lowercase__ )
if not pairs:
return token
while True:
_lowerCAmelCase = min(lowercase__ , key=lambda lowercase__ : self.bpe_ranks.get(lowercase__ , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
_lowerCAmelCase , _lowerCAmelCase = bigram
_lowerCAmelCase = []
_lowerCAmelCase = 0
while i < len(lowercase__ ):
try:
_lowerCAmelCase = word.index(lowercase__ , lowercase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_lowerCAmelCase = j
if word[i] == first and i < len(lowercase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_lowerCAmelCase = tuple(lowercase__ )
_lowerCAmelCase = new_word
if len(lowercase__ ) == 1:
break
else:
_lowerCAmelCase = get_pairs(lowercase__ )
_lowerCAmelCase = ' '.join(lowercase__ )
_lowerCAmelCase = word
return word
def SCREAMING_SNAKE_CASE__ ( self : List[str] , lowercase__ : Tuple ):
_lowerCAmelCase = []
for token in re.findall(self.pat , lowercase__ ):
_lowerCAmelCase = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase__ ).split(' ' ) )
return bpe_tokens
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , lowercase__ : str ):
return self.encoder.get(lowercase__ , self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , lowercase__ : List[Any] ):
return self.decoder.get(lowercase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , lowercase__ : str ):
_lowerCAmelCase = ''.join(lowercase__ )
_lowerCAmelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , lowercase__ : str , lowercase__ : Optional[str] = None ):
if not os.path.isdir(lowercase__ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_lowerCAmelCase = os.path.join(
lowercase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
_lowerCAmelCase = os.path.join(
lowercase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(lowercase__ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowercase__ , ensure_ascii=lowercase__ ) + '\n' )
_lowerCAmelCase = 0
with open(lowercase__ , '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 lowercase__ : 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!' )
_lowerCAmelCase = token_index
writer.write(' '.join(lowercase__ ) + '\n' )
index += 1
return vocab_file, merge_file
def SCREAMING_SNAKE_CASE__ ( self : Dict , lowercase__ : List[int] , lowercase__ : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_lowerCAmelCase = [self.cls_token_id]
_lowerCAmelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__ ( self : Tuple , lowercase__ : List[int] , lowercase__ : Optional[List[int]] = None , lowercase__ : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase__ , token_ids_a=lowercase__ , already_has_special_tokens=lowercase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowercase__ )) + [1]
return [1] + ([0] * len(lowercase__ )) + [1, 1] + ([0] * len(lowercase__ )) + [1]
def SCREAMING_SNAKE_CASE__ ( self : Union[str, 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 + sep + token_ids_a + sep ) * [0]
def SCREAMING_SNAKE_CASE__ ( self : Dict , lowercase__ : List[Any] , lowercase__ : Optional[Any]=False , **lowercase__ : Union[str, Any] ):
_lowerCAmelCase = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowercase__ ) > 0 and not text[0].isspace()):
_lowerCAmelCase = ' ' + text
return (text, kwargs)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , lowercase__ : Union[Dict[str, EncodedInput], BatchEncoding] , lowercase__ : Optional[int] = None , lowercase__ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , lowercase__ : Optional[int] = None , lowercase__ : Optional[bool] = None , ):
_lowerCAmelCase = super()._pad(
encoded_inputs=lowercase__ , max_length=lowercase__ , padding_strategy=lowercase__ , pad_to_multiple_of=lowercase__ , return_attention_mask=lowercase__ , )
# Load from model defaults
if return_attention_mask is None:
_lowerCAmelCase = 'attention_mask' in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_lowerCAmelCase = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_lowerCAmelCase = len(encoded_inputs['global_attention_mask'] ) != len(lowercase__ )
if needs_to_be_padded:
_lowerCAmelCase = len(lowercase__ ) - len(encoded_inputs['global_attention_mask'] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
_lowerCAmelCase = (
encoded_inputs['global_attention_mask'] + [-1] * difference
)
elif self.padding_side == "left":
_lowerCAmelCase = [-1] * difference + encoded_inputs[
'global_attention_mask'
]
else:
raise ValueError('Invalid padding strategy:' + str(self.padding_side ) )
return encoded_inputs
| 192
|
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 : List[str] , lowercase__ : Any , lowercase__ : List[Any]=7 , lowercase__ : List[str]=3 , lowercase__ : str=18 , lowercase__ : List[Any]=30 , lowercase__ : Optional[int]=4_00 , lowercase__ : Dict=True , lowercase__ : List[str]=None , lowercase__ : int=True , lowercase__ : Tuple=None , lowercase__ : int=True , lowercase__ : Tuple=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , lowercase__ : Optional[int]=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , lowercase__ : Any=True , ):
_lowerCAmelCase = size if size is not None else {'height': 2_24, 'width': 2_24}
_lowerCAmelCase = crop_size if crop_size is not None else {'height': 18, 'width': 18}
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = image_size
_lowerCAmelCase = min_resolution
_lowerCAmelCase = max_resolution
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = do_center_crop
_lowerCAmelCase = crop_size
_lowerCAmelCase = do_normalize
_lowerCAmelCase = image_mean
_lowerCAmelCase = image_std
_lowerCAmelCase = do_convert_rgb
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
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 : List[str] , lowercase__ : Tuple=False , lowercase__ : List[Any]=False , lowercase__ : str=False ):
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
_lowerCAmelCase = []
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:
_lowerCAmelCase = []
for i in range(self.batch_size ):
_lowerCAmelCase , _lowerCAmelCase = 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
_lowerCAmelCase = [Image.fromarray(np.moveaxis(lowercase__ , 0 , -1 ) ) for x in image_inputs]
if torchify:
_lowerCAmelCase = [torch.from_numpy(lowercase__ ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class lowerCamelCase__ ( UpperCAmelCase ,unittest.TestCase ):
UpperCamelCase__ =ChineseCLIPImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
_lowerCAmelCase = ChineseCLIPImageProcessingTester(self , do_center_crop=lowercase__ )
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase__ , 'do_resize' ) )
self.assertTrue(hasattr(lowercase__ , 'size' ) )
self.assertTrue(hasattr(lowercase__ , 'do_center_crop' ) )
self.assertTrue(hasattr(lowercase__ , 'center_crop' ) )
self.assertTrue(hasattr(lowercase__ , 'do_normalize' ) )
self.assertTrue(hasattr(lowercase__ , 'image_mean' ) )
self.assertTrue(hasattr(lowercase__ , 'image_std' ) )
self.assertTrue(hasattr(lowercase__ , 'do_convert_rgb' ) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
_lowerCAmelCase = 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} )
_lowerCAmelCase = 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 : List[Any] ):
pass
def SCREAMING_SNAKE_CASE__ ( self : str ):
# Initialize image_processing
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase = self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , Image.Image )
# Test not batched input
_lowerCAmelCase = 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
_lowerCAmelCase = image_processing(lowercase__ , 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 : Any ):
# Initialize image_processing
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowerCAmelCase = self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__ , numpify=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , np.ndarray )
# Test not batched input
_lowerCAmelCase = 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
_lowerCAmelCase = image_processing(lowercase__ , 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 : int ):
# Initialize image_processing
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowerCAmelCase = self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__ , torchify=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , torch.Tensor )
# Test not batched input
_lowerCAmelCase = 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
_lowerCAmelCase = image_processing(lowercase__ , 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 ):
UpperCamelCase__ =ChineseCLIPImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
_lowerCAmelCase = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=lowercase__ )
_lowerCAmelCase = 3
@property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase__ , 'do_resize' ) )
self.assertTrue(hasattr(lowercase__ , 'size' ) )
self.assertTrue(hasattr(lowercase__ , 'do_center_crop' ) )
self.assertTrue(hasattr(lowercase__ , 'center_crop' ) )
self.assertTrue(hasattr(lowercase__ , 'do_normalize' ) )
self.assertTrue(hasattr(lowercase__ , 'image_mean' ) )
self.assertTrue(hasattr(lowercase__ , 'image_std' ) )
self.assertTrue(hasattr(lowercase__ , 'do_convert_rgb' ) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
pass
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
# Initialize image_processing
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase = self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , Image.Image )
# Test not batched input
_lowerCAmelCase = 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
_lowerCAmelCase = image_processing(lowercase__ , 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'],
) , )
| 192
| 1
|
'''simple docstring'''
import argparse
import datetime
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Dict ={
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4""": """Thursday""",
"""5""": """Friday""",
"""6""": """Saturday""",
}
__magic_name__ : Union[str, Any] ={0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(lowerCamelCase ) < 11:
raise ValueError("""Must be 10 characters long""" )
# Get month
__magic_name__ : int =int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError("""Month must be between 1 - 12""" )
__magic_name__ : str =date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("""Date separator must be '-' or '/'""" )
# Get day
__magic_name__ : int =int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError("""Date must be between 1 - 31""" )
# Get second separator
__magic_name__ : str =date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("""Date separator must be '-' or '/'""" )
# Get year
__magic_name__ : int =int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 8500:
raise ValueError(
"""Year out of range. There has to be some sort of limit...right?""" )
# Get datetime obj for validation
__magic_name__ : Optional[Any] =datetime.date(int(lowerCamelCase ) , int(lowerCamelCase ) , int(lowerCamelCase ) )
# Start math
if m <= 2:
__magic_name__ : int =y - 1
__magic_name__ : Union[str, Any] =m + 12
# maths var
__magic_name__ : int =int(str(lowerCamelCase )[:2] )
__magic_name__ : int =int(str(lowerCamelCase )[2:] )
__magic_name__ : int =int(2.6 * m - 5.3_9 )
__magic_name__ : int =int(c / 4 )
__magic_name__ : int =int(k / 4 )
__magic_name__ : int =int(d + k )
__magic_name__ : int =int(t + u + v + x )
__magic_name__ : int =int(z - (2 * c) )
__magic_name__ : int =round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError("""The date was evaluated incorrectly. Contact developer.""" )
# Response
__magic_name__ : str =F"Your date {date_input}, is a {days[str(lowerCamelCase )]}!"
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : Union[str, Any] = argparse.ArgumentParser(
description=(
"Find out what day of the week nearly any date is or was. Enter "
"date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
)
)
parser.add_argument(
"date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
)
UpperCAmelCase_ : str = parser.parse_args()
zeller(args.date_input)
| 713
|
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def lowerCAmelCase_ ( *lowerCamelCase , lowerCamelCase = None , lowerCamelCase=True , lowerCamelCase=2 ):
from .. import __version__
__magic_name__ : Optional[int] =take_from
__magic_name__ : Tuple =()
if not isinstance(args[0] , lowerCamelCase ):
__magic_name__ : List[Any] =(args,)
for attribute, version_name, message in args:
if version.parse(version.parse(lowerCamelCase ).base_version ) >= version.parse(lowerCamelCase ):
raise ValueError(
F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
F" version {__version__} is >= {version_name}" )
__magic_name__ : List[str] =None
if isinstance(lowerCamelCase , lowerCamelCase ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(lowerCamelCase ),)
__magic_name__ : List[str] =F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
elif hasattr(lowerCamelCase , lowerCamelCase ):
values += (getattr(lowerCamelCase , lowerCamelCase ),)
__magic_name__ : List[str] =F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
elif deprecated_kwargs is None:
__magic_name__ : List[str] =F"`{attribute}` is deprecated and will be removed in version {version_name}."
if warning is not None:
__magic_name__ : List[Any] =warning + """ """ if standard_warn else """"""
warnings.warn(warning + message , lowerCamelCase , stacklevel=lowerCamelCase )
if isinstance(lowerCamelCase , lowerCamelCase ) and len(lowerCamelCase ) > 0:
__magic_name__ : List[Any] =inspect.getouterframes(inspect.currentframe() )[1]
__magic_name__ : Optional[int] =call_frame.filename
__magic_name__ : Tuple =call_frame.lineno
__magic_name__ : str =call_frame.function
__magic_name__ , __magic_name__ : Optional[int] =next(iter(deprecated_kwargs.items() ) )
raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`" )
if len(lowerCamelCase ) == 0:
return
elif len(lowerCamelCase ) == 1:
return values[0]
return values
| 367
| 0
|
def lowercase__( A ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
2: [4, 5],
3: [4],
4: [2, 3],
5: [2],
6: [0, 7],
7: [6],
8: [],
9: [1],
},
{
0: [4],
1: [6],
2: [],
3: [5, 6, 7],
4: [0, 6],
5: [3, 8, 9],
6: [1, 3, 4, 7],
7: [3, 6, 8, 9],
8: [5, 7],
9: [5, 7],
},
{
0: [1, 3],
1: [0, 2, 4],
2: [1, 3, 4],
3: [0, 2, 4],
4: [1, 2, 3],
},
][index]
def lowercase__( A ):
snake_case__ : Any = 0
snake_case__ : int = len(A ) # No of vertices in graph
snake_case__ : Dict = [0] * n
snake_case__ : Optional[int] = [False] * n
def dfs(A , A , A , A ):
snake_case__ : Tuple = True
snake_case__ : List[Any] = id_
id_ += 1
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
dfs(A , A , A , id_ )
snake_case__ : int = min(low[at] , low[to] )
if id_ <= low[to]:
bridges.append((at, to) if at < to else (to, at) )
else:
# This edge is a back edge and cannot be a bridge
snake_case__ : Optional[int] = min(low[at] , low[to] )
snake_case__ : list[tuple[int, int]] = []
for i in range(A ):
if not visited[i]:
dfs(A , -1 , A , id_ )
return bridges
if __name__ == "__main__":
import doctest
doctest.testmod()
| 170
|
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[int] = {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class snake_case__ ( unittest.TestCase ):
_lowerCAmelCase =MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
_lowerCAmelCase =TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
_lowerCAmelCase ={config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
_lowerCAmelCase ={
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def UpperCAmelCase__ ( self : List[Any] , _lowerCamelCase : Any , _lowerCamelCase : Tuple , _lowerCamelCase : List[Any] ):
snake_case__ : List[str] = ZeroShotClassificationPipeline(
model=_lowerCamelCase , tokenizer=_lowerCamelCase , candidate_labels=['polics', 'health'] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def UpperCAmelCase__ ( self : Any , _lowerCamelCase : Optional[int] , _lowerCamelCase : Optional[int] ):
snake_case__ : 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
snake_case__ : Any = classifier('Who are you voting for in 2020?' , ['politics'] )
self.assertEqual(_lowerCamelCase , {'sequence': ANY(_lowerCamelCase ), 'labels': [ANY(_lowerCamelCase )], 'scores': [ANY(_lowerCamelCase )]} )
snake_case__ : str = classifier('Who are you voting for in 2020?' , candidate_labels=['politics'] )
self.assertEqual(_lowerCamelCase , {'sequence': ANY(_lowerCamelCase ), 'labels': [ANY(_lowerCamelCase )], 'scores': [ANY(_lowerCamelCase )]} )
snake_case__ : 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 )
snake_case__ : Optional[int] = 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 )
snake_case__ : Optional[Any] = 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
snake_case__ : List[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 )
] , )
snake_case__ : int = 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 UpperCAmelCase__ ( self : Any , _lowerCamelCase : Pipeline ):
snake_case__ : List[str] = zero_shot_classifier.model.config
snake_case__ : Union[str, Any] = config.labelaid
snake_case__ : Dict = zero_shot_classifier.entailment_id
snake_case__ : List[str] = {'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
snake_case__ : List[Any] = {'entailment': 0, 'neutral': 1, 'contradiction': 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
snake_case__ : Dict = {'ENTAIL': 0, 'NON-ENTAIL': 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
snake_case__ : List[str] = {'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
snake_case__ : Optional[int] = original_labelaid
self.assertEqual(_lowerCamelCase , zero_shot_classifier.entailment_id )
@require_torch
def UpperCAmelCase__ ( self : int ):
snake_case__ : 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_0_0 , candidate_labels=['politics', 'public health', 'science'] )
@require_torch
def UpperCAmelCase__ ( self : Optional[int] ):
snake_case__ : List[Any] = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='pt' , )
snake_case__ : 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 UpperCAmelCase__ ( self : Optional[Any] ):
snake_case__ : str = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='tf' , )
snake_case__ : 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 UpperCAmelCase__ ( self : int ):
snake_case__ : Optional[Any] = pipeline('zero-shot-classification' , model='roberta-large-mnli' , framework='pt' )
snake_case__ : int = 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],
} , )
snake_case__ : Union[str, Any] = 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 UpperCAmelCase__ ( self : List[Any] ):
snake_case__ : List[Any] = pipeline('zero-shot-classification' , model='roberta-large-mnli' , framework='tf' )
snake_case__ : int = 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],
} , )
snake_case__ : List[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],
} , )
| 170
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Any = logging.get_logger(__name__)
a__ : List[Any] = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
"""RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
"""RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
"""RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
"""RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}
class lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
snake_case_ = 'rwkv'
snake_case_ = {'max_position_embeddings': 'context_length'}
def __init__( self : Union[str, Any] , a_ : List[str]=5_02_77 , a_ : Any=10_24 , a_ : List[Any]=40_96 , a_ : Optional[Any]=32 , a_ : Dict=None , a_ : Dict=None , a_ : Optional[Any]=1e-5 , a_ : Optional[int]=0 , a_ : Union[str, Any]=0 , a_ : Optional[int]=6 , a_ : Optional[int]=False , a_ : Union[str, Any]=True , **a_ : Dict , ):
"""simple docstring"""
lowerCamelCase__ = vocab_size
lowerCamelCase__ = context_length
lowerCamelCase__ = hidden_size
lowerCamelCase__ = num_hidden_layers
lowerCamelCase__ = attention_hidden_size if attention_hidden_size is not None else hidden_size
lowerCamelCase__ = intermediate_size if intermediate_size is not None else 4 * hidden_size
lowerCamelCase__ = layer_norm_epsilon
lowerCamelCase__ = rescale_every
lowerCamelCase__ = use_cache
lowerCamelCase__ = bos_token_id
lowerCamelCase__ = eos_token_id
super().__init__(
tie_word_embeddings=a_ , bos_token_id=a_ , eos_token_id=a_ , **a_ )
| 702
|
import collections
import importlib.util
import os
import re
from pathlib import Path
a__ : str = """src/transformers"""
# Matches is_xxx_available()
a__ : Any = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
a__ : List[str] = re.compile(r"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
a__ : Dict = re.compile(r"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
a__ : int = re.compile(r"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
a__ : Optional[int] = re.compile(r"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
a__ : Optional[Any] = re.compile(r"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
a__ : Union[str, Any] = re.compile("""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
a__ : List[str] = re.compile("""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
a__ : Dict = re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
a__ : int = re.compile(r"""^\s*try:""")
# Catches a line with else:
a__ : List[str] = re.compile(r"""^\s*else:""")
def snake_case (UpperCamelCase : Tuple ):
'''simple docstring'''
if _re_test_backend.search(UpperCamelCase ) is None:
return None
lowerCamelCase__ = [b[0] for b in _re_backend.findall(UpperCamelCase )]
backends.sort()
return "_and_".join(UpperCamelCase )
def snake_case (UpperCamelCase : Tuple ):
'''simple docstring'''
with open(UpperCamelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase__ = f.readlines()
lowerCamelCase__ = 0
while line_index < len(UpperCamelCase ) and not lines[line_index].startswith("""_import_structure = {""" ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(UpperCamelCase ):
return None
# First grab the objects without a specific backend in _import_structure
lowerCamelCase__ = []
while not lines[line_index].startswith("""if TYPE_CHECKING""" ) and find_backend(lines[line_index] ) is None:
lowerCamelCase__ = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(UpperCamelCase ):
lowerCamelCase__ = _re_one_line_import_struct.search(UpperCamelCase ).groups()[0]
lowerCamelCase__ = re.findall("""\[([^\]]+)\]""" , UpperCamelCase )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(""", """ )] )
line_index += 1
continue
lowerCamelCase__ = _re_import_struct_key_value.search(UpperCamelCase )
if single_line_import_search is not None:
lowerCamelCase__ = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """ ) if len(UpperCamelCase ) > 0]
objects.extend(UpperCamelCase )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
line_index += 1
lowerCamelCase__ = {"""none""": objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith("""if TYPE_CHECKING""" ):
# If the line is an if not is_backend_available, we grab all objects associated.
lowerCamelCase__ = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowerCamelCase__ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowerCamelCase__ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 4 ):
lowerCamelCase__ = lines[line_index]
if _re_import_struct_add_one.search(UpperCamelCase ) is not None:
objects.append(_re_import_struct_add_one.search(UpperCamelCase ).groups()[0] )
elif _re_import_struct_add_many.search(UpperCamelCase ) is not None:
lowerCamelCase__ = _re_import_struct_add_many.search(UpperCamelCase ).groups()[0].split(""", """ )
lowerCamelCase__ = [obj[1:-1] for obj in imports if len(UpperCamelCase ) > 0]
objects.extend(UpperCamelCase )
elif _re_between_brackets.search(UpperCamelCase ) is not None:
lowerCamelCase__ = _re_between_brackets.search(UpperCamelCase ).groups()[0].split(""", """ )
lowerCamelCase__ = [obj[1:-1] for obj in imports if len(UpperCamelCase ) > 0]
objects.extend(UpperCamelCase )
elif _re_quote_object.search(UpperCamelCase ) is not None:
objects.append(_re_quote_object.search(UpperCamelCase ).groups()[0] )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
elif line.startswith(""" """ * 12 + """\"""" ):
objects.append(line[13:-3] )
line_index += 1
lowerCamelCase__ = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
lowerCamelCase__ = []
while (
line_index < len(UpperCamelCase )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith("""else""" )
):
lowerCamelCase__ = lines[line_index]
lowerCamelCase__ = _re_import.search(UpperCamelCase )
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
lowerCamelCase__ = {"""none""": objects}
# Let's continue with backend-specific objects
while line_index < len(UpperCamelCase ):
# If the line is an if is_backend_available, we grab all objects associated.
lowerCamelCase__ = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowerCamelCase__ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowerCamelCase__ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
lowerCamelCase__ = lines[line_index]
lowerCamelCase__ = _re_import.search(UpperCamelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 12 ):
objects.append(line[12:-2] )
line_index += 1
lowerCamelCase__ = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def snake_case (UpperCamelCase : Any , UpperCamelCase : List[str] ):
'''simple docstring'''
def find_duplicates(UpperCamelCase : Union[str, Any] ):
return [k for k, v in collections.Counter(UpperCamelCase ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
lowerCamelCase__ = []
for key in import_dict_objects.keys():
lowerCamelCase__ = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
lowerCamelCase__ = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(f'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
lowerCamelCase__ = """base imports""" if key == """none""" else f'''{key} backend'''
errors.append(f'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(f''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(f''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def snake_case ():
'''simple docstring'''
lowerCamelCase__ = []
for root, _, files in os.walk(UpperCamelCase ):
if "__init__.py" in files:
lowerCamelCase__ = os.path.join(UpperCamelCase , """__init__.py""" )
lowerCamelCase__ = parse_init(UpperCamelCase )
if objects is not None:
lowerCamelCase__ = analyze_results(*UpperCamelCase )
if len(UpperCamelCase ) > 0:
lowerCamelCase__ = f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append("""\n""".join(UpperCamelCase ) )
if len(UpperCamelCase ) > 0:
raise ValueError("""\n\n""".join(UpperCamelCase ) )
def snake_case ():
'''simple docstring'''
lowerCamelCase__ = []
for path, directories, files in os.walk(UpperCamelCase ):
for folder in directories:
# Ignore private modules
if folder.startswith("""_""" ):
directories.remove(UpperCamelCase )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(UpperCamelCase ) / folder).glob("""*.py""" ) ) ) == 0:
continue
lowerCamelCase__ = str((Path(UpperCamelCase ) / folder).relative_to(UpperCamelCase ) )
lowerCamelCase__ = short_path.replace(os.path.sep , """.""" )
submodules.append(UpperCamelCase )
for fname in files:
if fname == "__init__.py":
continue
lowerCamelCase__ = str((Path(UpperCamelCase ) / fname).relative_to(UpperCamelCase ) )
lowerCamelCase__ = short_path.replace(""".py""" , """""" ).replace(os.path.sep , """.""" )
if len(submodule.split(""".""" ) ) == 1:
submodules.append(UpperCamelCase )
return submodules
a__ : Optional[Any] = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
]
def snake_case ():
'''simple docstring'''
lowerCamelCase__ = importlib.util.spec_from_file_location(
"""transformers""" , os.path.join(UpperCamelCase , """__init__.py""" ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
lowerCamelCase__ = spec.loader.load_module()
lowerCamelCase__ = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(UpperCamelCase ) > 0:
lowerCamelCase__ = """\n""".join(f'''- {module}''' for module in module_not_registered )
raise ValueError(
"""The following submodules are not properly registered in the main init of Transformers:\n"""
f'''{list_of_modules}\n'''
"""Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.""" )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 235
| 0
|
"""simple docstring"""
import math
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ = 1_0001 ) ->int:
try:
_lowerCamelCase : List[Any] = int(SCREAMING_SNAKE_CASE_ )
except (TypeError, ValueError):
raise TypeError('''Parameter nth must be int or castable to int.''' ) from None
if nth <= 0:
raise ValueError('''Parameter nth must be greater than or equal to one.''' )
_lowerCamelCase : list[int] = []
_lowerCamelCase : List[Any] = 2
while len(SCREAMING_SNAKE_CASE_ ) < nth:
if is_prime(SCREAMING_SNAKE_CASE_ ):
primes.append(SCREAMING_SNAKE_CASE_ )
num += 1
else:
num += 1
return primes[len(SCREAMING_SNAKE_CASE_ ) - 1]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 434
|
"""simple docstring"""
from jiwer import compute_measures
import datasets
SCREAMING_SNAKE_CASE__ : Dict ='\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'
SCREAMING_SNAKE_CASE__ : Dict ='\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'
SCREAMING_SNAKE_CASE__ : Dict ='\nCompute WER score of transcribed segments against references.\n\nArgs:\n references: List of references for each speech input.\n predictions: List of transcriptions to score.\n concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n (float): the word error rate\n\nExamples:\n\n >>> predictions = ["this is the prediction", "there is an other sample"]\n >>> references = ["this is the reference", "there is another one"]\n >>> wer = datasets.load_metric("wer")\n >>> wer_score = wer.compute(predictions=predictions, references=references)\n >>> print(wer_score)\n 0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
"""simple docstring"""
def a__ ( self ) -> Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/jitsi/jiwer/'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/Word_error_rate''',
] , )
def a__ ( self , _lowercase=None , _lowercase=None , _lowercase=False ) -> Dict:
if concatenate_texts:
return compute_measures(_lowercase , _lowercase )["wer"]
else:
_lowerCamelCase : Any = 0
_lowerCamelCase : Tuple = 0
for prediction, reference in zip(_lowercase , _lowercase ):
_lowerCamelCase : Any = compute_measures(_lowercase , _lowercase )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 434
| 1
|
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
lowerCAmelCase__ = {
"tokenizer_file": {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
},
}
lowerCAmelCase__ = {
"gpt-neox-20b": 2_0_4_8,
}
class _a ( lowerCamelCase_ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_="<|endoftext|>" , lowerCAmelCase_="<|endoftext|>" , lowerCAmelCase_="<|endoftext|>" , lowerCAmelCase_=False , **lowerCAmelCase_ , ):
super().__init__(
lowerCAmelCase_ , lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , bos_token=lowerCAmelCase_ , eos_token=lowerCAmelCase_ , add_prefix_space=lowerCAmelCase_ , **lowerCAmelCase_ , )
_lowercase =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , lowerCAmelCase_ ) != add_prefix_space:
_lowercase =getattr(lowerCAmelCase_ , pre_tok_state.pop("type" ) )
_lowercase =add_prefix_space
_lowercase =pre_tok_class(**lowerCAmelCase_ )
_lowercase =add_prefix_space
def __lowerCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None ):
_lowercase =self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_ )
return tuple(lowerCAmelCase_ )
def __lowerCAmelCase ( self , lowerCAmelCase_ ):
_lowercase =[]
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:
_lowercase =input_ids[-self.model_max_length :]
return input_ids
| 594
|
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class _a ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 1
@register_to_config
def __init__( self , lowerCAmelCase_=2000 , lowerCAmelCase_=0.1 , lowerCAmelCase_=20 , lowerCAmelCase_=1e-3 ):
_lowercase =None
_lowercase =None
_lowercase =None
def __lowerCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None ):
_lowercase =torch.linspace(1 , self.config.sampling_eps , lowerCAmelCase_ , device=lowerCAmelCase_ )
def __lowerCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=None ):
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
_lowercase =(
-0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
_lowercase =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
_lowercase =std.flatten()
while len(std.shape ) < len(score.shape ):
_lowercase =std.unsqueeze(-1 )
_lowercase =-score / std
# compute
_lowercase =-1.0 / len(self.timesteps )
_lowercase =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
_lowercase =beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
_lowercase =beta_t.unsqueeze(-1 )
_lowercase =-0.5 * beta_t * x
_lowercase =torch.sqrt(lowerCAmelCase_ )
_lowercase =drift - diffusion**2 * score
_lowercase =x + drift * dt
# add noise
_lowercase =randn_tensor(x.shape , layout=x.layout , generator=lowerCAmelCase_ , device=x.device , dtype=x.dtype )
_lowercase =x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self ):
return self.config.num_train_timesteps
| 594
| 1
|
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> list:
if len(lowerCAmelCase__ ) <= 1:
return [tuple(lowerCAmelCase__ )]
UpperCAmelCase__ : int = []
def generate(lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase__ : int = [0] * n
res.append(tuple(lowerCAmelCase__ ) )
UpperCAmelCase__ : Any = 0
while i < n:
if c[i] < i:
if i % 2 == 0:
UpperCAmelCase__ , UpperCAmelCase__ : str = arr[i], arr[0]
else:
UpperCAmelCase__ , UpperCAmelCase__ : int = arr[i], arr[c[i]]
res.append(tuple(lowerCAmelCase__ ) )
c[i] += 1
UpperCAmelCase__ : List[Any] = 0
else:
UpperCAmelCase__ : Optional[Any] = 0
i += 1
generate(len(lowerCAmelCase__ ) , lowerCAmelCase__ )
return res
if __name__ == "__main__":
UpperCamelCase__ = input('''Enter numbers separated by a comma:\n''').strip()
UpperCamelCase__ = [int(item) for item in user_input.split(''',''')]
print(heaps(arr))
| 75
|
'''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> np.ndarray:
UpperCAmelCase__ : List[str] = cva.getAffineTransform(lowerCAmelCase__ , lowerCAmelCase__ )
return cva.warpAffine(lowerCAmelCase__ , lowerCAmelCase__ , (rows, cols) )
if __name__ == "__main__":
# read original image
UpperCamelCase__ = cva.imread(
str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
)
# turn image in gray scale value
UpperCamelCase__ = cva.cvtColor(image, cva.COLOR_BGR2GRAY)
# get image shape
UpperCamelCase__ , UpperCamelCase__ = gray_img.shape
# set different points to rotate image
UpperCamelCase__ = np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa)
UpperCamelCase__ = np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa)
UpperCamelCase__ = np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa)
UpperCamelCase__ = np.array([[1_0, 1_0_0], [8_0, 5_0], [1_8_0, 2_5_0]], np.floataa)
# add all rotated images in a list
UpperCamelCase__ = [
gray_img,
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
]
# plot different image rotations
UpperCamelCase__ = plt.figure(1)
UpperCamelCase__ = ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
for i, image in enumerate(images):
plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
plt.title(titles[i])
plt.axis('''off''')
plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
plt.show()
| 75
| 1
|
'''simple docstring'''
class __magic_name__ :
"""simple docstring"""
def __init__( self , _lowercase , _lowercase , _lowercase ) -> Union[str, Any]:
lowercase_ : str = None
lowercase_ : Dict = None
lowercase_ : str = graph
self._normalize_graph(_lowercase , _lowercase )
lowercase_ : List[str] = len(_lowercase )
lowercase_ : Optional[Any] = None
def lowerCamelCase__ ( self , _lowercase , _lowercase ) -> List[str]:
if sources is int:
lowercase_ : Tuple = [sources]
if sinks is int:
lowercase_ : Union[str, Any] = [sinks]
if len(_lowercase ) == 0 or len(_lowercase ) == 0:
return
lowercase_ : Dict = sources[0]
lowercase_ : Union[str, Any] = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(_lowercase ) > 1 or len(_lowercase ) > 1:
lowercase_ : str = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
lowercase_ : List[Any] = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
lowercase_ : List[Any] = max_input_flow
lowercase_ : List[Any] = 0
lowercase_ : Any = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
lowercase_ : str = max_input_flow
lowercase_ : str = size - 1
def lowerCamelCase__ ( self ) -> int:
if self.maximum_flow_algorithm is None:
raise Exception('You need to set maximum flow algorithm before.' )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def lowerCamelCase__ ( self , _lowercase ) -> Optional[Any]:
lowercase_ : Union[str, Any] = algorithm(self )
class __magic_name__ :
"""simple docstring"""
def __init__( self , _lowercase ) -> Dict:
lowercase_ : List[Any] = flow_network
lowercase_ : Tuple = flow_network.verticesCount
lowercase_ : str = flow_network.sourceIndex
lowercase_ : Optional[int] = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
lowercase_ : Dict = flow_network.graph
lowercase_ : Optional[int] = False
def lowerCamelCase__ ( self ) -> Optional[int]:
if not self.executed:
self._algorithm()
lowercase_ : str = True
def lowerCamelCase__ ( self ) -> Any:
pass
class __magic_name__ ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self , _lowercase ) -> int:
super().__init__(_lowercase )
# use this to save your result
lowercase_ : List[Any] = -1
def lowerCamelCase__ ( self ) -> Dict:
if not self.executed:
raise Exception('You should execute algorithm before using its result!' )
return self.maximum_flow
class __magic_name__ ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self , _lowercase ) -> List[Any]:
super().__init__(_lowercase )
lowercase_ : Union[str, Any] = [[0] * self.verticies_count for i in range(self.verticies_count )]
lowercase_ : Any = [0] * self.verticies_count
lowercase_ : List[Any] = [0] * self.verticies_count
def lowerCamelCase__ ( self ) -> List[Any]:
lowercase_ : Dict = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
lowercase_ : str = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
lowercase_ : List[Any] = 0
while i < len(_lowercase ):
lowercase_ : Any = vertices_list[i]
lowercase_ : Optional[Any] = self.heights[vertex_index]
self.process_vertex(_lowercase )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(_lowercase ) )
lowercase_ : Union[str, Any] = 0
else:
i += 1
lowercase_ : str = sum(self.preflow[self.source_index] )
def lowerCamelCase__ ( self , _lowercase ) -> List[Any]:
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(_lowercase , _lowercase )
self.relabel(_lowercase )
def lowerCamelCase__ ( self , _lowercase , _lowercase ) -> List[Any]:
lowercase_ : List[str] = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def lowerCamelCase__ ( self , _lowercase ) -> Optional[int]:
lowercase_ : Union[str, Any] = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
lowercase_ : Tuple = self.heights[to_index]
if min_height is not None:
lowercase_ : Union[str, Any] = min_height + 1
if __name__ == "__main__":
A: str = [0]
A: Dict = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
A: str = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
A: Any = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
A: Tuple = flow_network.find_maximum_flow()
print(f"""maximum flow is {maximum_flow}""")
| 7
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A: int = logging.get_logger(__name__)
A: int = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
class __magic_name__ ( UpperCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = 'gpt_bigcode'
SCREAMING_SNAKE_CASE_ : int = ['past_key_values']
SCREAMING_SNAKE_CASE_ : Any = {
'hidden_size': 'n_embd',
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , _lowercase=5_0257 , _lowercase=1024 , _lowercase=768 , _lowercase=12 , _lowercase=12 , _lowercase=None , _lowercase="gelu_pytorch_tanh" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=0.1 , _lowercase=1E-5 , _lowercase=0.02 , _lowercase=True , _lowercase=True , _lowercase=5_0256 , _lowercase=5_0256 , _lowercase=True , _lowercase=True , _lowercase=True , **_lowercase , ) -> Any:
lowercase_ : Tuple = vocab_size
lowercase_ : str = n_positions
lowercase_ : List[str] = n_embd
lowercase_ : str = n_layer
lowercase_ : Optional[Any] = n_head
lowercase_ : Optional[int] = n_inner
lowercase_ : Union[str, Any] = activation_function
lowercase_ : Dict = resid_pdrop
lowercase_ : str = embd_pdrop
lowercase_ : Optional[Any] = attn_pdrop
lowercase_ : List[Any] = layer_norm_epsilon
lowercase_ : Optional[int] = initializer_range
lowercase_ : List[Any] = scale_attn_weights
lowercase_ : Any = use_cache
lowercase_ : List[str] = attention_softmax_in_fpaa
lowercase_ : Any = scale_attention_softmax_in_fpaa
lowercase_ : Optional[Any] = multi_query
lowercase_ : Optional[Any] = bos_token_id
lowercase_ : Optional[Any] = eos_token_id
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
| 7
| 1
|
import warnings
from ..trainer import Trainer
from ..utils import logging
__a : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__=None , **lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , lowerCAmelCase__ , )
super().__init__(args=lowerCAmelCase__ , **lowerCAmelCase__ )
| 534
|
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging
__a : Tuple = logging.get_logger(__name__) # pylint: disable=invalid-name
class _UpperCamelCase ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> Optional[int]:
'''simple docstring'''
super().__init__()
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(
speech_model=lowerCAmelCase__ , speech_processor=lowerCAmelCase__ , vae=lowerCAmelCase__ , text_encoder=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ = "auto" ) -> Dict:
'''simple docstring'''
if slice_size == "auto":
__lowercase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
self.enable_attention_slicing(lowerCAmelCase__ )
@torch.no_grad()
def __call__( self , lowerCAmelCase__ , lowerCAmelCase__=1_60_00 , lowerCAmelCase__ = 5_12 , lowerCAmelCase__ = 5_12 , lowerCAmelCase__ = 50 , lowerCAmelCase__ = 7.5 , lowerCAmelCase__ = None , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = "pil" , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = 1 , **lowerCAmelCase__ , ) -> List[str]:
'''simple docstring'''
__lowercase = self.speech_processor.feature_extractor(
lowerCAmelCase__ , return_tensors='''pt''' , sampling_rate=lowerCAmelCase__ ).input_features.to(self.device )
__lowercase = self.speech_model.generate(lowerCAmelCase__ , max_length=48_00_00 )
__lowercase = self.speech_processor.tokenizer.batch_decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ , normalize=lowerCAmelCase__ )[
0
]
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__lowercase = 1
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__lowercase = len(lowerCAmelCase__ )
else:
raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__ )}" )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}." )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or callback_steps <= 0)
):
raise ValueError(
F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
F" {type(lowerCAmelCase__ )}." )
# get prompt text embeddings
__lowercase = self.tokenizer(
lowerCAmelCase__ , padding='''max_length''' , max_length=self.tokenizer.model_max_length , return_tensors='''pt''' , )
__lowercase = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
__lowercase = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
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}" )
__lowercase = text_input_ids[:, : self.tokenizer.model_max_length]
__lowercase = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
__lowercase , __lowercase , __lowercase = text_embeddings.shape
__lowercase = text_embeddings.repeat(1 , lowerCAmelCase__ , 1 )
__lowercase = text_embeddings.view(bs_embed * num_images_per_prompt , lowerCAmelCase__ , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
__lowercase = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
__lowercase = 42
if negative_prompt is None:
__lowercase = [''''''] * batch_size
elif type(lowerCAmelCase__ ) is not type(lowerCAmelCase__ ):
raise TypeError(
F"`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase__ )} !="
F" {type(lowerCAmelCase__ )}." )
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__lowercase = [negative_prompt]
elif batch_size != len(lowerCAmelCase__ ):
raise ValueError(
F"`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase__ )}, but `prompt`:"
F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
''' the batch size of `prompt`.''' )
else:
__lowercase = negative_prompt
__lowercase = text_input_ids.shape[-1]
__lowercase = self.tokenizer(
lowerCAmelCase__ , padding='''max_length''' , max_length=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors='''pt''' , )
__lowercase = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__lowercase = uncond_embeddings.shape[1]
__lowercase = uncond_embeddings.repeat(1 , lowerCAmelCase__ , 1 )
__lowercase = uncond_embeddings.view(batch_size * num_images_per_prompt , lowerCAmelCase__ , -1 )
# 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
__lowercase = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
__lowercase = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
__lowercase = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
__lowercase = torch.randn(lowerCAmelCase__ , generator=lowerCAmelCase__ , device='''cpu''' , dtype=lowerCAmelCase__ ).to(
self.device )
else:
__lowercase = torch.randn(lowerCAmelCase__ , generator=lowerCAmelCase__ , device=self.device , dtype=lowerCAmelCase__ )
else:
if latents.shape != latents_shape:
raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
__lowercase = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(lowerCAmelCase__ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
__lowercase = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
__lowercase = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
__lowercase = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__lowercase = {}
if accepts_eta:
__lowercase = eta
for i, t in enumerate(self.progress_bar(lowerCAmelCase__ ) ):
# expand the latents if we are doing classifier free guidance
__lowercase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__lowercase = self.scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
# predict the noise residual
__lowercase = self.unet(lowerCAmelCase__ , lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ ).sample
# perform guidance
if do_classifier_free_guidance:
__lowercase , __lowercase = noise_pred.chunk(2 )
__lowercase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
__lowercase = self.scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
__lowercase = 1 / 0.1_8215 * latents
__lowercase = self.vae.decode(lowerCAmelCase__ ).sample
__lowercase = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
__lowercase = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
__lowercase = self.numpy_to_pil(lowerCAmelCase__ )
if not return_dict:
return image
return StableDiffusionPipelineOutput(images=lowerCAmelCase__ , nsfw_content_detected=lowerCAmelCase__ )
| 534
| 1
|
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
lowerCamelCase__ : Dict = """\
Text data.
Second line of data."""
lowerCamelCase__ : int = """file"""
@pytest.fixture(scope="""session""" )
def UpperCamelCase ( lowercase_ ) -> Dict:
'''simple docstring'''
lowercase__ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / (FILE_PATH + """.zstd""")
lowercase__ : int = bytes(lowercase_ , """utf-8""" )
with zstd.open(lowercase_ , """wb""" ) as f:
f.write(lowercase_ )
return path
@pytest.fixture
def UpperCamelCase ( lowercase_ ) -> Dict:
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , lowercase_ ) , """w""" ) as f:
f.write(lowercase_ )
return FILE_PATH
@pytest.mark.parametrize("""compression_format""" , ["""gzip""", """xz""", """zstd"""] )
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
lowercase__ : List[Any] = {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
lowercase__ : int = input_paths[compression_format]
lowercase__ : Optional[Any] = tmp_path / """cache"""
lowercase__ : Tuple = DownloadConfig(cache_dir=lowercase_ , extract_compressed_file=lowercase_ )
lowercase__ : str = cached_path(lowercase_ , download_config=lowercase_ )
with open(lowercase_ ) as f:
lowercase__ : Dict = f.read()
with open(lowercase_ ) as f:
lowercase__ : List[str] = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize("""default_extracted""" , [True, False] )
@pytest.mark.parametrize("""default_cache_dir""" , [True, False] )
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Tuple:
'''simple docstring'''
lowercase__ : Union[str, Any] = """custom_cache"""
lowercase__ : Union[str, Any] = """custom_extracted_dir"""
lowercase__ : List[Any] = tmp_path / """custom_extracted_path"""
if default_extracted:
lowercase__ : Dict = ("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
else:
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR""" , lowercase_ )
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH""" , str(lowercase_ ) )
lowercase__ : Tuple = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
lowercase__ : List[str] = xz_file
lowercase__ : str = (
DownloadConfig(extract_compressed_file=lowercase_ )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=lowercase_ )
)
lowercase__ : str = cached_path(lowercase_ , download_config=lowercase_ )
assert Path(lowercase_ ).parent.parts[-2:] == expected
def UpperCamelCase ( lowercase_ ) -> Tuple:
'''simple docstring'''
lowercase__ : Optional[int] = str(Path(lowercase_ ).resolve() )
assert cached_path(lowercase_ ) == text_file
# relative path
lowercase__ : Any = str(Path(lowercase_ ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(lowercase_ ) == text_file
def UpperCamelCase ( lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
lowercase__ : Optional[int] = str(tmp_path.resolve() / """__missing_file__.txt""" )
with pytest.raises(lowercase_ ):
cached_path(lowercase_ )
# relative path
lowercase__ : Any = """./__missing_file__.txt"""
with pytest.raises(lowercase_ ):
cached_path(lowercase_ )
def UpperCamelCase ( lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
lowercase__ : Union[str, Any] = get_from_cache(F'tmp://{tmpfs_file}' )
with open(lowercase_ ) as f:
lowercase__ : Tuple = f.read()
assert output_file_content == FILE_CONTENT
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , lowercase_ )
def UpperCamelCase ( ) -> Tuple:
'''simple docstring'''
with pytest.raises(lowercase_ ):
cached_path("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , lowercase_ )
def UpperCamelCase ( lowercase_ ) -> List[str]:
'''simple docstring'''
lowercase__ : List[str] = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(lowercase_ ):
http_get("""https://huggingface.co""" , temp_file=lowercase_ )
with pytest.raises(lowercase_ ):
http_head("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , lowercase_ )
def UpperCamelCase ( lowercase_ ) -> Any:
'''simple docstring'''
lowercase__ : Any = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(lowercase_ ):
ftp_get("""ftp://huggingface.co""" , temp_file=lowercase_ )
with pytest.raises(lowercase_ ):
ftp_head("""ftp://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , lowercase_ )
def UpperCamelCase ( lowercase_ ) -> Any:
'''simple docstring'''
lowercase__ : Any = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(lowercase_ ):
fsspec_get("""s3://huggingface.co""" , temp_file=lowercase_ )
with pytest.raises(lowercase_ ):
fsspec_head("""s3://huggingface.co""" )
| 495
|
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _snake_case ( UpperCAmelCase_ ):
@staticmethod
@abstractmethod
def lowercase__ ( SCREAMING_SNAKE_CASE_):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def lowercase__ ( self):
'''simple docstring'''
raise NotImplementedError()
| 495
| 1
|
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class __A ( unittest.TestCase ):
def A__ ( self :List[str] , __snake_case :List[str] ):
'''simple docstring'''
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["""bs"""] , model_result["""ss"""] ):
__magic_name__ : str =model_result["""result"""][batch_size][sequence_length]
self.assertIsNotNone(__snake_case )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Dict ="""sshleifer/tiny-gpt2"""
__magic_name__ : Optional[int] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=__snake_case , multi_process=__snake_case , )
__magic_name__ : List[str] =TensorFlowBenchmark(__snake_case )
__magic_name__ : List[Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[int] ="""sgugger/tiny-distilbert-classification"""
__magic_name__ : List[str] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__snake_case , only_pretrain_model=__snake_case , )
__magic_name__ : Dict =TensorFlowBenchmark(__snake_case )
__magic_name__ : List[Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : str ="""sshleifer/tiny-gpt2"""
__magic_name__ : List[Any] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__snake_case , )
__magic_name__ : int =TensorFlowBenchmark(__snake_case )
__magic_name__ : List[Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ : int ="""sshleifer/tiny-gpt2"""
__magic_name__ : Optional[int] =AutoConfig.from_pretrained(__snake_case )
__magic_name__ : Optional[int] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=__snake_case , multi_process=__snake_case , )
__magic_name__ : str =TensorFlowBenchmark(__snake_case , [config] )
__magic_name__ : List[Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : List[Any] ="""sshleifer/tiny-gpt2"""
__magic_name__ : Tuple =AutoConfig.from_pretrained(__snake_case )
__magic_name__ : Dict =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__snake_case , )
__magic_name__ : Union[str, Any] =TensorFlowBenchmark(__snake_case , [config] )
__magic_name__ : str =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : Optional[int] ="""sshleifer/tiny-gpt2"""
__magic_name__ : Optional[int] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__snake_case , )
__magic_name__ : Optional[Any] =TensorFlowBenchmark(__snake_case )
__magic_name__ : Any =benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] ="""sshleifer/tiny-gpt2"""
__magic_name__ : Tuple =AutoConfig.from_pretrained(__snake_case )
__magic_name__ : str =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__snake_case , )
__magic_name__ : Optional[Any] =TensorFlowBenchmark(__snake_case , [config] )
__magic_name__ : int =benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ : List[str] ="""patrickvonplaten/t5-tiny-random"""
__magic_name__ : Optional[Any] =AutoConfig.from_pretrained(__snake_case )
__magic_name__ : str =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__snake_case , )
__magic_name__ : List[Any] =TensorFlowBenchmark(__snake_case , configs=[config] )
__magic_name__ : Union[str, Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , """Cannot do xla on CPU.""" )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : List[Any] ="""sshleifer/tiny-gpt2"""
__magic_name__ : List[Any] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , use_xla=__snake_case , multi_process=__snake_case , )
__magic_name__ : List[str] =TensorFlowBenchmark(__snake_case )
__magic_name__ : Optional[Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : int ="""sshleifer/tiny-gpt2"""
with tempfile.TemporaryDirectory() as tmp_dir:
__magic_name__ : Tuple =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=__snake_case , save_to_csv=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(__snake_case , """inf_time.csv""" ) , inference_memory_csv_file=os.path.join(__snake_case , """inf_mem.csv""" ) , env_info_csv_file=os.path.join(__snake_case , """env.csv""" ) , multi_process=__snake_case , )
__magic_name__ : Optional[Any] =TensorFlowBenchmark(__snake_case )
benchmark.run()
self.assertTrue(Path(os.path.join(__snake_case , """inf_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(__snake_case , """inf_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(__snake_case , """env.csv""" ) ).exists() )
def A__ ( self :str ):
'''simple docstring'''
__magic_name__ : int ="""sshleifer/tiny-gpt2"""
def _check_summary_is_not_empty(__snake_case :Dict ):
self.assertTrue(hasattr(__snake_case , """sequential""" ) )
self.assertTrue(hasattr(__snake_case , """cumulative""" ) )
self.assertTrue(hasattr(__snake_case , """current""" ) )
self.assertTrue(hasattr(__snake_case , """total""" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
__magic_name__ : List[Any] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(__snake_case , """log.txt""" ) , log_print=__snake_case , trace_memory_line_by_line=__snake_case , eager_mode=__snake_case , multi_process=__snake_case , )
__magic_name__ : List[Any] =TensorFlowBenchmark(__snake_case )
__magic_name__ : Optional[int] =benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(__snake_case , """log.txt""" ) ).exists() )
| 21
|
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__( self :List[str] , __snake_case :int , __snake_case :int , __snake_case :float , **__snake_case :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =feature_size
__magic_name__ : Union[str, Any] =sampling_rate
__magic_name__ : List[Any] =padding_value
__magic_name__ : List[str] =kwargs.pop("""padding_side""" , """right""" )
__magic_name__ : Tuple =kwargs.pop("""return_attention_mask""" , __snake_case )
super().__init__(**__snake_case )
def A__ ( self :Any , __snake_case :Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
] , __snake_case :Union[bool, str, PaddingStrategy] = True , __snake_case :Optional[int] = None , __snake_case :bool = False , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , __snake_case :Optional[Union[str, TensorType]] = None , ):
'''simple docstring'''
if isinstance(__snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
__magic_name__ : Union[str, Any] ={
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"""You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
f" to this method that includes {self.model_input_names[0]}, but you provided"
f" {list(processed_features.keys() )}" )
__magic_name__ : int =processed_features[self.model_input_names[0]]
__magic_name__ : Union[str, Any] =(
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(__snake_case ) == 0:
if return_attention_mask:
__magic_name__ : List[str] =[]
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
__magic_name__ : Optional[int] =required_input[0]
if isinstance(__snake_case , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
__magic_name__ : Optional[Any] =0
while len(required_input[index] ) == 0:
index += 1
if index < len(__snake_case ):
__magic_name__ : List[str] =required_input[index][0]
if return_tensors is None:
if is_tf_tensor(__snake_case ):
__magic_name__ : int ="""tf"""
elif is_torch_tensor(__snake_case ):
__magic_name__ : str ="""pt"""
elif isinstance(__snake_case , (int, float, list, tuple, np.ndarray) ):
__magic_name__ : List[Any] ="""np"""
else:
raise ValueError(
f"type of {first_element} unknown: {type(__snake_case )}. "
"""Should be one of a python, numpy, pytorch or tensorflow object.""" )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
__magic_name__ : List[str] =to_numpy(__snake_case )
else:
__magic_name__ : str =[to_numpy(__snake_case ) for v in value]
# Convert padding_strategy in PaddingStrategy
__magic_name__ : Dict =self._get_padding_strategies(padding=__snake_case , max_length=__snake_case )
__magic_name__ : Optional[Any] =processed_features[self.model_input_names[0]]
__magic_name__ : Dict =len(__snake_case )
if not all(len(__snake_case ) == batch_size for v in processed_features.values() ):
raise ValueError("""Some items in the output dictionary have a different batch size than others.""" )
__magic_name__ : Optional[int] =[]
for i in range(__snake_case ):
__magic_name__ : Any ={k: v[i] for k, v in processed_features.items()}
# truncation
__magic_name__ : List[str] =self._truncate(
__snake_case , max_length=__snake_case , pad_to_multiple_of=__snake_case , truncation=__snake_case , )
truncated_inputs.append(__snake_case )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
__magic_name__ : Optional[int] =max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
__magic_name__ : Tuple =PaddingStrategy.MAX_LENGTH
__magic_name__ : str ={}
for i in range(__snake_case ):
# padding
__magic_name__ : List[str] =self._pad(
truncated_inputs[i] , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , )
for key, value in outputs.items():
if key not in batch_outputs:
__magic_name__ : Dict =[]
if value.dtype is np.dtype(np.floataa ):
__magic_name__ : Optional[int] =value.astype(np.floataa )
batch_outputs[key].append(__snake_case )
return BatchFeature(__snake_case , tensor_type=__snake_case )
def A__ ( self :Any , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
__magic_name__ : Dict =processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
__magic_name__ : Any =len(__snake_case )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : Dict =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : List[Any] =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
__magic_name__ : int =np.ones(len(__snake_case ) , dtype=np.intaa )
if needs_to_be_padded:
__magic_name__ : List[Any] =max_length - len(__snake_case )
if self.padding_side == "right":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (0, difference) )
__magic_name__ : Tuple =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
__magic_name__ : str =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (difference, 0) )
__magic_name__ : Optional[int] =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
__magic_name__ : List[Any] =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return processed_features
def A__ ( self :Optional[Any] , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" )
__magic_name__ : Union[str, Any] =processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : List[str] =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : Any =len(__snake_case ) > max_length
if needs_to_be_truncated:
__magic_name__ : List[Any] =processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
__magic_name__ : List[str] =processed_features["""attention_mask"""][:max_length]
return processed_features
def A__ ( self :List[Any] , __snake_case :str=False , __snake_case :Optional[int]=None ):
'''simple docstring'''
if padding is not False:
if padding is True:
__magic_name__ : Union[str, Any] =PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(__snake_case , __snake_case ):
__magic_name__ : Optional[int] =PaddingStrategy(__snake_case )
elif isinstance(__snake_case , __snake_case ):
__magic_name__ : Any =padding
else:
__magic_name__ : Any =PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"""Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
""" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" )
return padding_strategy
| 21
| 1
|
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowercase( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self : List[Any] , _lowerCAmelCase : TransformeraDModel , _lowerCAmelCase : AutoencoderKL , _lowerCAmelCase : KarrasDiffusionSchedulers , _lowerCAmelCase : Optional[Dict[int, str]] = None , ) -> int:
super().__init__()
self.register_modules(transformer=_lowerCAmelCase , vae=_lowerCAmelCase , scheduler=_lowerCAmelCase )
# create a imagenet -> id dictionary for easier use
_lowerCAmelCase = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(',' ):
_lowerCAmelCase = int(_lowerCAmelCase )
_lowerCAmelCase = dict(sorted(self.labels.items() ) )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , _lowerCAmelCase : Union[str, List[str]] ) -> List[int]:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCAmelCase = list(_lowerCAmelCase )
for l in label:
if l not in self.labels:
raise ValueError(
F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''' )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self : List[Any] , _lowerCAmelCase : List[int] , _lowerCAmelCase : float = 4.0 , _lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowerCAmelCase : int = 50 , _lowerCAmelCase : Optional[str] = "pil" , _lowerCAmelCase : bool = True , ) -> Union[ImagePipelineOutput, Tuple]:
_lowerCAmelCase = len(_lowerCAmelCase )
_lowerCAmelCase = self.transformer.config.sample_size
_lowerCAmelCase = self.transformer.config.in_channels
_lowerCAmelCase = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=_lowerCAmelCase , device=self.device , dtype=self.transformer.dtype , )
_lowerCAmelCase = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
_lowerCAmelCase = torch.tensor(_lowerCAmelCase , device=self.device ).reshape(-1 )
_lowerCAmelCase = torch.tensor([1000] * batch_size , device=self.device )
_lowerCAmelCase = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(_lowerCAmelCase )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
_lowerCAmelCase = latent_model_input[: len(_lowerCAmelCase ) // 2]
_lowerCAmelCase = torch.cat([half, half] , dim=0 )
_lowerCAmelCase = self.scheduler.scale_model_input(_lowerCAmelCase , _lowerCAmelCase )
_lowerCAmelCase = t
if not torch.is_tensor(_lowerCAmelCase ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
_lowerCAmelCase = latent_model_input.device.type == 'mps'
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCAmelCase = torch.floataa if is_mps else torch.floataa
else:
_lowerCAmelCase = torch.intaa if is_mps else torch.intaa
_lowerCAmelCase = torch.tensor([timesteps] , dtype=_lowerCAmelCase , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
_lowerCAmelCase = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
_lowerCAmelCase = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
_lowerCAmelCase = self.transformer(
_lowerCAmelCase , timestep=_lowerCAmelCase , class_labels=_lowerCAmelCase ).sample
# perform guidance
if guidance_scale > 1:
_lowerCAmelCase , _lowerCAmelCase = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
_lowerCAmelCase , _lowerCAmelCase = torch.split(_lowerCAmelCase , len(_lowerCAmelCase ) // 2 , dim=0 )
_lowerCAmelCase = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
_lowerCAmelCase = torch.cat([half_eps, half_eps] , dim=0 )
_lowerCAmelCase = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
_lowerCAmelCase , _lowerCAmelCase = torch.split(_lowerCAmelCase , _lowerCAmelCase , dim=1 )
else:
_lowerCAmelCase = noise_pred
# compute previous image: x_t -> x_t-1
_lowerCAmelCase = self.scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ).prev_sample
if guidance_scale > 1:
_lowerCAmelCase , _lowerCAmelCase = latent_model_input.chunk(2 , dim=0 )
else:
_lowerCAmelCase = latent_model_input
_lowerCAmelCase = 1 / self.vae.config.scaling_factor * latents
_lowerCAmelCase = self.vae.decode(_lowerCAmelCase ).sample
_lowerCAmelCase = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
_lowerCAmelCase = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
_lowerCAmelCase = self.numpy_to_pil(_lowerCAmelCase )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=_lowerCAmelCase )
| 585
|
import copy
import random
from transformers import CLIPTokenizer
class __lowercase( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self : Optional[Any] , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : Optional[int] ) -> str:
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
_lowerCAmelCase = {}
def SCREAMING_SNAKE_CASE_ ( self : List[str] , _lowerCAmelCase : List[Any] , *_lowerCAmelCase : List[Any] , **_lowerCAmelCase : List[str] ) -> List[Any]:
_lowerCAmelCase = super().add_tokens(_lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase )
if num_added_tokens == 0:
raise ValueError(
F'''The tokenizer already contains the token {placeholder_token}. Please pass a different'''
' `placeholder_token` that is not already in the tokenizer.' )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , _lowerCAmelCase : int , *_lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any]=1 , **_lowerCAmelCase : Optional[Any] ) -> Optional[Any]:
_lowerCAmelCase = []
if num_vec_per_token == 1:
self.try_adding_tokens(_lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase )
output.append(_lowerCAmelCase )
else:
_lowerCAmelCase = []
for i in range(_lowerCAmelCase ):
_lowerCAmelCase = placeholder_token + F'''_{i}'''
self.try_adding_tokens(_lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase )
output.append(_lowerCAmelCase )
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
F'''The tokenizer already has placeholder token {token} that can get confused with'''
F''' {placeholder_token}keep placeholder tokens independent''' )
_lowerCAmelCase = output
def SCREAMING_SNAKE_CASE_ ( self : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any=False , _lowerCAmelCase : int=1.0 ) -> List[str]:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCAmelCase = []
for i in range(len(_lowerCAmelCase ) ):
output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=_lowerCAmelCase ) )
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
_lowerCAmelCase = self.token_map[placeholder_token]
_lowerCAmelCase = tokens[: 1 + int(len(_lowerCAmelCase ) * prop_tokens_to_load )]
if vector_shuffle:
_lowerCAmelCase = copy.copy(_lowerCAmelCase )
random.shuffle(_lowerCAmelCase )
_lowerCAmelCase = text.replace(_lowerCAmelCase , ' '.join(_lowerCAmelCase ) )
return text
def __call__( self : Any , _lowerCAmelCase : Any , *_lowerCAmelCase : str , _lowerCAmelCase : Tuple=False , _lowerCAmelCase : Optional[int]=1.0 , **_lowerCAmelCase : int ) -> int:
return super().__call__(
self.replace_placeholder_tokens_in_text(
_lowerCAmelCase , vector_shuffle=_lowerCAmelCase , prop_tokens_to_load=_lowerCAmelCase ) , *_lowerCAmelCase , **_lowerCAmelCase , )
def SCREAMING_SNAKE_CASE_ ( self : Any , _lowerCAmelCase : str , *_lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str]=False , _lowerCAmelCase : List[Any]=1.0 , **_lowerCAmelCase : str ) -> List[str]:
return super().encode(
self.replace_placeholder_tokens_in_text(
_lowerCAmelCase , vector_shuffle=_lowerCAmelCase , prop_tokens_to_load=_lowerCAmelCase ) , *_lowerCAmelCase , **_lowerCAmelCase , )
| 585
| 1
|
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase__ )
class lowerCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
_A = field(default='question-answering-extractive' , metadata={'include_in_asdict_even_if_is_default': True} )
_A = Features({'question': Value('string' ), 'context': Value('string' )} )
_A = Features(
{
'answers': Sequence(
{
'text': Value('string' ),
'answer_start': Value('int32' ),
} )
} )
_A = "question"
_A = "context"
_A = "answers"
@property
def __magic_name__ ( self ) -> Dict[str, str]:
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 597
|
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __magic_name__ ( self ) -> Optional[Any]:
__a : Any = tempfile.mkdtemp()
__a : Union[str, Any] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'的',
'价',
'格',
'是',
'15',
'便',
'alex',
'##andra',
'，',
'。',
'-',
't',
'shirt',
]
__a : int = 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] ) )
__a : Optional[Any] = {
'do_resize': True,
'size': {'height': 224, 'width': 224},
'do_center_crop': True,
'crop_size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
'do_convert_rgb': True,
}
__a : Any = os.path.join(self.tmpdirname , _A )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(_A , _A )
def __magic_name__ ( self , **_A ) -> str:
return BertTokenizer.from_pretrained(self.tmpdirname , **_A )
def __magic_name__ ( self , **_A ) -> Dict:
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_A )
def __magic_name__ ( self , **_A ) -> str:
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **_A )
def __magic_name__ ( self ) -> Any:
shutil.rmtree(self.tmpdirname )
def __magic_name__ ( self ) -> Any:
__a : str = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__a : Union[str, Any] = [Image.fromarray(np.moveaxis(_A , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __magic_name__ ( self ) -> List[str]:
__a : str = self.get_tokenizer()
__a : int = self.get_rust_tokenizer()
__a : Any = self.get_image_processor()
__a : int = ChineseCLIPProcessor(tokenizer=_A , image_processor=_A )
processor_slow.save_pretrained(self.tmpdirname )
__a : Optional[int] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=_A )
__a : Optional[Any] = ChineseCLIPProcessor(tokenizer=_A , image_processor=_A )
processor_fast.save_pretrained(self.tmpdirname )
__a : List[Any] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _A )
self.assertIsInstance(processor_fast.tokenizer , _A )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _A )
self.assertIsInstance(processor_fast.image_processor , _A )
def __magic_name__ ( self ) -> Union[str, Any]:
__a : int = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__a : List[str] = self.get_tokenizer(cls_token='(CLS)' , sep_token='(SEP)' )
__a : Optional[Any] = self.get_image_processor(do_normalize=_A )
__a : Dict = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token='(CLS)' , sep_token='(SEP)' , do_normalize=_A )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _A )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _A )
def __magic_name__ ( self ) -> Dict:
__a : Dict = self.get_image_processor()
__a : List[Any] = self.get_tokenizer()
__a : Optional[int] = ChineseCLIPProcessor(tokenizer=_A , image_processor=_A )
__a : Optional[int] = self.prepare_image_inputs()
__a : Dict = image_processor(_A , return_tensors='np' )
__a : int = processor(images=_A , 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 __magic_name__ ( self ) -> Tuple:
__a : Union[str, Any] = self.get_image_processor()
__a : Tuple = self.get_tokenizer()
__a : Dict = ChineseCLIPProcessor(tokenizer=_A , image_processor=_A )
__a : Dict = 'Alexandra，T-shirt的价格是15便士。'
__a : int = processor(text=_A )
__a : List[Any] = tokenizer(_A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __magic_name__ ( self ) -> Dict:
__a : List[str] = self.get_image_processor()
__a : Dict = self.get_tokenizer()
__a : Tuple = ChineseCLIPProcessor(tokenizer=_A , image_processor=_A )
__a : str = 'Alexandra，T-shirt的价格是15便士。'
__a : Union[str, Any] = self.prepare_image_inputs()
__a : Union[str, Any] = processor(text=_A , images=_A )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(_A ):
processor()
def __magic_name__ ( self ) -> Tuple:
__a : int = self.get_image_processor()
__a : str = self.get_tokenizer()
__a : Optional[int] = ChineseCLIPProcessor(tokenizer=_A , image_processor=_A )
__a : Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__a : Union[str, Any] = processor.batch_decode(_A )
__a : Optional[Any] = tokenizer.batch_decode(_A )
self.assertListEqual(_A , _A )
def __magic_name__ ( self ) -> Any:
__a : int = self.get_image_processor()
__a : str = self.get_tokenizer()
__a : List[str] = ChineseCLIPProcessor(tokenizer=_A , image_processor=_A )
__a : str = 'Alexandra，T-shirt的价格是15便士。'
__a : Any = self.prepare_image_inputs()
__a : List[str] = processor(text=_A , images=_A )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 597
| 1
|
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class UpperCamelCase__:
__magic_name__ : CommonSchedulerState
# setable values
__magic_name__ : jnp.ndarray
__magic_name__ : jnp.ndarray
__magic_name__ : Optional[int] = None
@classmethod
def a__( cls : str , lowerCAmelCase : CommonSchedulerState , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : jnp.ndarray )-> List[str]:
"""simple docstring"""
return cls(common=lowerCAmelCase , init_noise_sigma=lowerCAmelCase , timesteps=lowerCAmelCase )
@dataclass
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : DDPMSchedulerState
class UpperCamelCase__( lowerCAmelCase , lowerCAmelCase ):
__magic_name__ : List[str] = [e.name for e in FlaxKarrasDiffusionSchedulers]
__magic_name__ : jnp.dtype
@property
def a__( self : str )-> str:
"""simple docstring"""
return True
@register_to_config
def __init__( self : List[Any] , lowerCAmelCase : int = 1000 , lowerCAmelCase : float = 0.0001 , lowerCAmelCase : float = 0.02 , lowerCAmelCase : str = "linear" , lowerCAmelCase : Optional[jnp.ndarray] = None , lowerCAmelCase : str = "fixed_small" , lowerCAmelCase : bool = True , lowerCAmelCase : str = "epsilon" , lowerCAmelCase : jnp.dtype = jnp.floataa , )-> Tuple:
"""simple docstring"""
UpperCAmelCase = dtype
def a__( self : Union[str, Any] , lowerCAmelCase : Optional[CommonSchedulerState] = None )-> DDPMSchedulerState:
"""simple docstring"""
if common is None:
UpperCAmelCase = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
UpperCAmelCase = jnp.array(1.0 , dtype=self.dtype )
UpperCAmelCase = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=lowerCAmelCase , init_noise_sigma=lowerCAmelCase , timesteps=lowerCAmelCase , )
def a__( self : Union[str, Any] , lowerCAmelCase : DDPMSchedulerState , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : Optional[int] = None )-> jnp.ndarray:
"""simple docstring"""
return sample
def a__( self : str , lowerCAmelCase : DDPMSchedulerState , lowerCAmelCase : int , lowerCAmelCase : Tuple = () )-> DDPMSchedulerState:
"""simple docstring"""
UpperCAmelCase = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
UpperCAmelCase = (jnp.arange(0 , lowerCAmelCase ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=lowerCAmelCase , timesteps=lowerCAmelCase , )
def a__( self : Any , lowerCAmelCase : DDPMSchedulerState , lowerCAmelCase : str , lowerCAmelCase : Any=None , lowerCAmelCase : Any=None )-> Optional[int]:
"""simple docstring"""
UpperCAmelCase = state.common.alphas_cumprod[t]
UpperCAmelCase = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
UpperCAmelCase = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
UpperCAmelCase = jnp.clip(lowerCAmelCase , a_min=1E-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
UpperCAmelCase = jnp.log(jnp.clip(lowerCAmelCase , a_min=1E-20 ) )
elif variance_type == "fixed_large":
UpperCAmelCase = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
UpperCAmelCase = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
UpperCAmelCase = variance
UpperCAmelCase = state.common.betas[t]
UpperCAmelCase = (predicted_variance + 1) / 2
UpperCAmelCase = frac * max_log + (1 - frac) * min_log
return variance
def a__( self : List[str] , lowerCAmelCase : DDPMSchedulerState , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : int , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : Optional[jax.random.KeyArray] = None , lowerCAmelCase : bool = True , )-> Union[FlaxDDPMSchedulerOutput, Tuple]:
"""simple docstring"""
UpperCAmelCase = timestep
if key is None:
UpperCAmelCase = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
UpperCAmelCase , UpperCAmelCase = jnp.split(lowerCAmelCase , sample.shape[1] , axis=1 )
else:
UpperCAmelCase = None
# 1. compute alphas, betas
UpperCAmelCase = state.common.alphas_cumprod[t]
UpperCAmelCase = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
UpperCAmelCase = 1 - alpha_prod_t
UpperCAmelCase = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase = model_output
elif self.config.prediction_type == "v_prediction":
UpperCAmelCase = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
''' for the FlaxDDPMScheduler.''' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase = jnp.clip(lowerCAmelCase , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
UpperCAmelCase = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
UpperCAmelCase = jax.random.split(lowerCAmelCase , num=1 )
UpperCAmelCase = jax.random.normal(lowerCAmelCase , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(lowerCAmelCase , lowerCAmelCase , predicted_variance=lowerCAmelCase ) ** 0.5) * noise
UpperCAmelCase = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
UpperCAmelCase = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=lowerCAmelCase , state=lowerCAmelCase )
def a__( self : List[Any] , lowerCAmelCase : DDPMSchedulerState , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : jnp.ndarray , )-> jnp.ndarray:
"""simple docstring"""
return add_noise_common(state.common , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def a__( self : Union[str, Any] , lowerCAmelCase : DDPMSchedulerState , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : jnp.ndarray , )-> jnp.ndarray:
"""simple docstring"""
return get_velocity_common(state.common , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def __len__( self : str )-> str:
"""simple docstring"""
return self.config.num_train_timesteps
| 50
|
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : List[str] = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
"""facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : List[str] = "encodec"
def __init__( self : List[str] , lowerCAmelCase : int=[1.5, 3.0, 6.0, 12.0, 24.0] , lowerCAmelCase : Tuple=24000 , lowerCAmelCase : List[Any]=1 , lowerCAmelCase : Union[str, Any]=False , lowerCAmelCase : str=None , lowerCAmelCase : Dict=None , lowerCAmelCase : str=128 , lowerCAmelCase : Any=32 , lowerCAmelCase : Any=1 , lowerCAmelCase : List[Any]=[8, 5, 4, 2] , lowerCAmelCase : Union[str, Any]="weight_norm" , lowerCAmelCase : str=7 , lowerCAmelCase : Optional[int]=7 , lowerCAmelCase : Any=3 , lowerCAmelCase : Tuple=2 , lowerCAmelCase : Optional[Any]=True , lowerCAmelCase : List[str]="reflect" , lowerCAmelCase : Optional[int]=2 , lowerCAmelCase : List[Any]=2 , lowerCAmelCase : Union[str, Any]=1.0 , lowerCAmelCase : Optional[Any]=1024 , lowerCAmelCase : Optional[Any]=None , lowerCAmelCase : str=True , **lowerCAmelCase : str , )-> List[str]:
"""simple docstring"""
UpperCAmelCase = target_bandwidths
UpperCAmelCase = sampling_rate
UpperCAmelCase = audio_channels
UpperCAmelCase = normalize
UpperCAmelCase = chunk_length_s
UpperCAmelCase = overlap
UpperCAmelCase = hidden_size
UpperCAmelCase = num_filters
UpperCAmelCase = num_residual_layers
UpperCAmelCase = upsampling_ratios
UpperCAmelCase = norm_type
UpperCAmelCase = kernel_size
UpperCAmelCase = last_kernel_size
UpperCAmelCase = residual_kernel_size
UpperCAmelCase = dilation_growth_rate
UpperCAmelCase = use_causal_conv
UpperCAmelCase = pad_mode
UpperCAmelCase = compress
UpperCAmelCase = num_lstm_layers
UpperCAmelCase = trim_right_ratio
UpperCAmelCase = codebook_size
UpperCAmelCase = codebook_dim if codebook_dim is not None else hidden_size
UpperCAmelCase = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""" )
super().__init__(**lowerCAmelCase )
@property
def a__( self : str )-> Optional[int]:
"""simple docstring"""
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def a__( self : List[str] )-> Optional[int]:
"""simple docstring"""
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def a__( self : List[Any] )-> int:
"""simple docstring"""
UpperCAmelCase = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def a__( self : int )-> int:
"""simple docstring"""
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 50
| 1
|
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {"""vocab_file""": """spiece.model"""}
snake_case_ = {
"""vocab_file""": {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
}
}
snake_case_ = {
"""xlnet-base-cased""": None,
"""xlnet-large-cased""": None,
}
# Segments (not really needed)
snake_case_ = 0
snake_case_ = 1
snake_case_ = 2
snake_case_ = 3
snake_case_ = 4
class a__ ( _lowercase ):
__magic_name__ : int = VOCAB_FILES_NAMES
__magic_name__ : List[str] = PRETRAINED_VOCAB_FILES_MAP
__magic_name__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__magic_name__ : Tuple = "left"
def __init__(self : Optional[int], __UpperCAmelCase : Optional[Any], __UpperCAmelCase : str=False, __UpperCAmelCase : List[str]=True, __UpperCAmelCase : int=False, __UpperCAmelCase : List[str]="<s>", __UpperCAmelCase : List[str]="</s>", __UpperCAmelCase : Optional[Any]="<unk>", __UpperCAmelCase : List[Any]="<sep>", __UpperCAmelCase : int="<pad>", __UpperCAmelCase : Optional[Any]="<cls>", __UpperCAmelCase : int="<mask>", __UpperCAmelCase : List[str]=["<eop>", "<eod>"], __UpperCAmelCase : Optional[Dict[str, Any]] = None, **__UpperCAmelCase : Optional[int], ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = AddedToken(__UpperCAmelCase, lstrip=__UpperCAmelCase, rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase, __UpperCAmelCase ) else mask_token
SCREAMING_SNAKE_CASE : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__UpperCAmelCase, remove_space=__UpperCAmelCase, keep_accents=__UpperCAmelCase, bos_token=__UpperCAmelCase, eos_token=__UpperCAmelCase, unk_token=__UpperCAmelCase, sep_token=__UpperCAmelCase, pad_token=__UpperCAmelCase, cls_token=__UpperCAmelCase, mask_token=__UpperCAmelCase, additional_special_tokens=__UpperCAmelCase, sp_model_kwargs=self.sp_model_kwargs, **__UpperCAmelCase, )
SCREAMING_SNAKE_CASE : List[Any] = 3
SCREAMING_SNAKE_CASE : Any = do_lower_case
SCREAMING_SNAKE_CASE : Dict = remove_space
SCREAMING_SNAKE_CASE : Optional[Any] = keep_accents
SCREAMING_SNAKE_CASE : Optional[int] = vocab_file
SCREAMING_SNAKE_CASE : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
@property
def lowercase__ (self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
return len(self.sp_model )
def lowercase__ (self : str ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : 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 __getstate__(self : Any ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.__dict__.copy()
SCREAMING_SNAKE_CASE : str = None
return state
def __setstate__(self : int, __UpperCAmelCase : List[str] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = d
# for backward compatibility
if not hasattr(self, '''sp_model_kwargs''' ):
SCREAMING_SNAKE_CASE : List[Any] = {}
SCREAMING_SNAKE_CASE : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowercase__ (self : Any, __UpperCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
if self.remove_space:
SCREAMING_SNAKE_CASE : str = ''' '''.join(inputs.strip().split() )
else:
SCREAMING_SNAKE_CASE : Optional[int] = inputs
SCREAMING_SNAKE_CASE : Any = outputs.replace('''``''', '''"''' ).replace('''\'\'''', '''"''' )
if not self.keep_accents:
SCREAMING_SNAKE_CASE : Optional[Any] = unicodedata.normalize('''NFKD''', __UpperCAmelCase )
SCREAMING_SNAKE_CASE : str = ''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase )] )
if self.do_lower_case:
SCREAMING_SNAKE_CASE : Dict = outputs.lower()
return outputs
def lowercase__ (self : int, __UpperCAmelCase : str ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.preprocess_text(__UpperCAmelCase )
SCREAMING_SNAKE_CASE : Any = self.sp_model.encode(__UpperCAmelCase, out_type=__UpperCAmelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = []
for piece in pieces:
if len(__UpperCAmelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
SCREAMING_SNAKE_CASE : List[str] = self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase, '''''' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
SCREAMING_SNAKE_CASE : Dict = cur_pieces[1:]
else:
SCREAMING_SNAKE_CASE : Tuple = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(__UpperCAmelCase )
else:
new_pieces.append(__UpperCAmelCase )
return new_pieces
def lowercase__ (self : str, __UpperCAmelCase : List[str] ) -> Optional[Any]:
"""simple docstring"""
return self.sp_model.PieceToId(__UpperCAmelCase )
def lowercase__ (self : List[Any], __UpperCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
return self.sp_model.IdToPiece(__UpperCAmelCase )
def lowercase__ (self : Tuple, __UpperCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = ''''''.join(__UpperCAmelCase ).replace(__UpperCAmelCase, ''' ''' ).strip()
return out_string
def lowercase__ (self : Optional[Any], __UpperCAmelCase : List[int], __UpperCAmelCase : bool = False, __UpperCAmelCase : bool = None, __UpperCAmelCase : bool = True, **__UpperCAmelCase : int, ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = kwargs.pop('''use_source_tokenizer''', __UpperCAmelCase )
SCREAMING_SNAKE_CASE : Optional[int] = self.convert_ids_to_tokens(__UpperCAmelCase, skip_special_tokens=__UpperCAmelCase )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Optional[Any] = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(__UpperCAmelCase ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = []
sub_texts.append(__UpperCAmelCase )
else:
current_sub_text.append(__UpperCAmelCase )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(__UpperCAmelCase ) )
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
SCREAMING_SNAKE_CASE : str = ''''''.join(__UpperCAmelCase )
SCREAMING_SNAKE_CASE : int = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
SCREAMING_SNAKE_CASE : Optional[Any] = self.clean_up_tokenization(__UpperCAmelCase )
return clean_text
else:
return text
def lowercase__ (self : Optional[Any], __UpperCAmelCase : List[int], __UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def lowercase__ (self : Optional[Any], __UpperCAmelCase : List[int], __UpperCAmelCase : Optional[List[int]] = None, __UpperCAmelCase : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCAmelCase, token_ids_a=__UpperCAmelCase, already_has_special_tokens=__UpperCAmelCase )
if token_ids_a is not None:
return ([0] * len(__UpperCAmelCase )) + [1] + ([0] * len(__UpperCAmelCase )) + [1, 1]
return ([0] * len(__UpperCAmelCase )) + [1, 1]
def lowercase__ (self : Optional[Any], __UpperCAmelCase : List[int], __UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = [self.sep_token_id]
SCREAMING_SNAKE_CASE : List[str] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def lowercase__ (self : Optional[int], __UpperCAmelCase : str, __UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE : Optional[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:
SCREAMING_SNAKE_CASE : Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
| 507
|
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __lowercase (_SCREAMING_SNAKE_CASE :List[str] ):
monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings''' , set() )
@pytest.fixture
def __lowercase (_SCREAMING_SNAKE_CASE :int ):
class a__ :
def __init__(self : Optional[int], __UpperCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = metric_id
class a__ :
__magic_name__ : List[Any] = [MetricMock(_lowercase ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
def lowercase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
return self._metrics
monkeypatch.setattr('''datasets.inspect.huggingface_hub''' , HfhMock() )
@pytest.mark.parametrize(
'''func, args''' , [(load_metric, ('''metrics/mse''',)), (list_metrics, ()), (inspect_metric, ('''metrics/mse''', '''tmp_path'''))] )
def __lowercase (_SCREAMING_SNAKE_CASE :Union[str, Any] , _SCREAMING_SNAKE_CASE :Tuple , _SCREAMING_SNAKE_CASE :Any , _SCREAMING_SNAKE_CASE :Optional[int] , _SCREAMING_SNAKE_CASE :int ):
if "tmp_path" in args:
SCREAMING_SNAKE_CASE : List[Any] = tuple(arg if arg != '''tmp_path''' else tmp_path for arg in args )
with pytest.warns(_SCREAMING_SNAKE_CASE , match='''https://huggingface.co/docs/evaluate''' ):
func(*_SCREAMING_SNAKE_CASE )
| 507
| 1
|
"""simple docstring"""
import pprint
import requests
A : Optional[int] = "https://zenquotes.io/api"
def _lowerCamelCase ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def _lowerCamelCase ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + "/random" ).json()
if __name__ == "__main__":
A : Optional[int] = random_quotes()
pprint.pprint(response)
| 716
|
"""simple docstring"""
import string
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = ""
for i in sequence:
__lowerCAmelCase = ord(_UpperCamelCase )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <= 122:
output += chr(219 - extract )
else:
output += i
return output
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = string.ascii_letters
__lowerCAmelCase = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(_UpperCamelCase )] if c in letters else c for c in sequence )
def _lowerCamelCase ( ):
'''simple docstring'''
from timeit import timeit
print("Running performance benchmarks..." )
__lowerCAmelCase = "from string import printable ; from __main__ import atbash, atbash_slow"
print(f"> atbash_slow(): {timeit('atbash_slow(printable)' , setup=_UpperCamelCase )} seconds" )
print(f"> atbash(): {timeit('atbash(printable)' , setup=_UpperCamelCase )} seconds" )
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(f'''{example} encrypted in atbash: {atbash(example)}''')
benchmark()
| 282
| 0
|
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _A ( __snake_case :str ) -> str:
"""simple docstring"""
return "".join(sorted(__snake_case ) )
def _A ( __snake_case :str ) -> list[str]:
"""simple docstring"""
return word_by_signature[signature(__snake_case )]
_snake_case : str = Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
_snake_case : List[str] = sorted({word.strip().lower() for word in data.splitlines()})
_snake_case : Any = 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))
| 693
|
from __future__ import annotations
_snake_case : str = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_snake_case : Optional[int] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def _A ( __snake_case :list[float] ) -> list[float]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = len(__snake_case )
for i in range(__snake_case ):
__SCREAMING_SNAKE_CASE = -1
for j in range(i + 1 , __snake_case ):
if arr[i] < arr[j]:
__SCREAMING_SNAKE_CASE = arr[j]
break
result.append(__snake_case )
return result
def _A ( __snake_case :list[float] ) -> list[float]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
for i, outer in enumerate(__snake_case ):
__SCREAMING_SNAKE_CASE = -1
for inner in arr[i + 1 :]:
if outer < inner:
__SCREAMING_SNAKE_CASE = inner
break
result.append(__snake_case )
return result
def _A ( __snake_case :list[float] ) -> list[float]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = len(__snake_case )
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = [-1] * arr_size
for index in reversed(range(__snake_case ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
__SCREAMING_SNAKE_CASE = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
_snake_case : Optional[Any] = (
'from __main__ import arr, next_greatest_element_slow, '
'next_greatest_element_fast, next_greatest_element'
)
print(
'next_greatest_element_slow():',
timeit('next_greatest_element_slow(arr)', setup=setup),
)
print(
'next_greatest_element_fast():',
timeit('next_greatest_element_fast(arr)', setup=setup),
)
print(
' next_greatest_element():',
timeit('next_greatest_element(arr)', setup=setup),
)
| 693
| 1
|
"""simple docstring"""
import logging
from transformers import PretrainedConfig
_lowerCAmelCase = logging.getLogger(__name__)
_lowerCAmelCase = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}
class UpperCamelCase (__snake_case ):
_SCREAMING_SNAKE_CASE : Any = """bertabs"""
def __init__( self :str , __magic_name__ :Optional[int]=30_522 , __magic_name__ :int=512 , __magic_name__ :Optional[Any]=6 , __magic_name__ :Optional[Any]=512 , __magic_name__ :Optional[Any]=8 , __magic_name__ :int=512 , __magic_name__ :List[Any]=0.2 , __magic_name__ :Union[str, Any]=6 , __magic_name__ :int=768 , __magic_name__ :List[str]=8 , __magic_name__ :List[Any]=2_048 , __magic_name__ :Optional[Any]=0.2 , **__magic_name__ :Dict , ) ->Optional[int]:
super().__init__(**__magic_name__ )
lowercase : Optional[int] = vocab_size
lowercase : List[Any] = max_pos
lowercase : int = enc_layers
lowercase : str = enc_hidden_size
lowercase : List[str] = enc_heads
lowercase : List[Any] = enc_ff_size
lowercase : List[Any] = enc_dropout
lowercase : Any = dec_layers
lowercase : str = dec_hidden_size
lowercase : List[Any] = dec_heads
lowercase : Union[str, Any] = dec_ff_size
lowercase : Any = dec_dropout
| 721
|
"""simple docstring"""
from __future__ import annotations
_lowerCAmelCase = [True] * 1_00_00_01
_lowerCAmelCase = 2
while i * i <= 1_00_00_00:
if seive[i]:
for j in range(i * i, 1_00_00_01, i):
_lowerCAmelCase = False
i += 1
def UpperCamelCase ( _A ) -> bool:
return seive[n]
def UpperCamelCase ( _A ) -> bool:
return any(digit in """02468""" for digit in str(_A ) )
def UpperCamelCase ( _A = 1_000_000 ) -> list[int]:
lowercase : Dict = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(_A ) and not contains_an_even_digit(_A ):
lowercase : str = str(_A )
lowercase : int = [int(str_num[j:] + str_num[:j] ) for j in range(len(_A ) )]
if all(is_prime(_A ) for i in list_nums ):
result.append(_A )
return result
def UpperCamelCase ( ) -> int:
return len(find_circular_primes() )
if __name__ == "__main__":
print(F'{len(find_circular_primes()) = }')
| 348
| 0
|
'''simple docstring'''
from __future__ import annotations
import queue
class SCREAMING_SNAKE_CASE__ :
def __init__( self , lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = data
SCREAMING_SNAKE_CASE_ : int = None
SCREAMING_SNAKE_CASE_ : Tuple = None
def __lowerCamelCase ( ) -> TreeNode:
"""simple docstring"""
print("\n********Press N to stop entering at any point of time********\n" )
SCREAMING_SNAKE_CASE_ : Any = input("Enter the value of the root node: " ).strip().lower()
SCREAMING_SNAKE_CASE_ : queue.Queue = queue.Queue()
SCREAMING_SNAKE_CASE_ : Tuple = TreeNode(int(SCREAMING_SNAKE_CASE_ ) )
q.put(SCREAMING_SNAKE_CASE_ )
while not q.empty():
SCREAMING_SNAKE_CASE_ : Any = q.get()
SCREAMING_SNAKE_CASE_ : str = F"Enter the left node of {node_found.data}: "
SCREAMING_SNAKE_CASE_ : int = input(SCREAMING_SNAKE_CASE_ ).strip().lower() or "n"
if check == "n":
return tree_node
SCREAMING_SNAKE_CASE_ : List[str] = TreeNode(int(SCREAMING_SNAKE_CASE_ ) )
SCREAMING_SNAKE_CASE_ : Tuple = left_node
q.put(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ : Tuple = F"Enter the right node of {node_found.data}: "
SCREAMING_SNAKE_CASE_ : Union[str, Any] = input(SCREAMING_SNAKE_CASE_ ).strip().lower() or "n"
if check == "n":
return tree_node
SCREAMING_SNAKE_CASE_ : Optional[int] = TreeNode(int(SCREAMING_SNAKE_CASE_ ) )
SCREAMING_SNAKE_CASE_ : Tuple = right_node
q.put(SCREAMING_SNAKE_CASE_ )
raise
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or not node:
return
print(node.data , end="," )
pre_order(node.left )
pre_order(node.right )
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or not node:
return
in_order(node.left )
print(node.data , end="," )
in_order(node.right )
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end="," )
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or not node:
return
SCREAMING_SNAKE_CASE_ : queue.Queue = queue.Queue()
q.put(SCREAMING_SNAKE_CASE_ )
while not q.empty():
SCREAMING_SNAKE_CASE_ : Any = q.get()
print(node_dequeued.data , end="," )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or not node:
return
SCREAMING_SNAKE_CASE_ : queue.Queue = queue.Queue()
q.put(SCREAMING_SNAKE_CASE_ )
while not q.empty():
SCREAMING_SNAKE_CASE_ : str = []
while not q.empty():
SCREAMING_SNAKE_CASE_ : Union[str, Any] = q.get()
print(node_dequeued.data , end="," )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(SCREAMING_SNAKE_CASE_ )
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or not node:
return
SCREAMING_SNAKE_CASE_ : list[TreeNode] = []
SCREAMING_SNAKE_CASE_ : List[str] = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end="," )
stack.append(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ : Dict = n.left
# end of while means current node doesn't have left child
SCREAMING_SNAKE_CASE_ : Any = stack.pop()
# start to traverse its right child
SCREAMING_SNAKE_CASE_ : Union[str, Any] = n.right
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or not node:
return
SCREAMING_SNAKE_CASE_ : list[TreeNode] = []
SCREAMING_SNAKE_CASE_ : Optional[Any] = node
while n or stack:
while n:
stack.append(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ : Optional[int] = n.left
SCREAMING_SNAKE_CASE_ : Any = stack.pop()
print(n.data , end="," )
SCREAMING_SNAKE_CASE_ : List[str] = n.right
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or not node:
return
SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ : str = [], []
SCREAMING_SNAKE_CASE_ : List[str] = node
stacka.append(SCREAMING_SNAKE_CASE_ )
while stacka: # to find the reversed order of post order, store it in stack2
SCREAMING_SNAKE_CASE_ : Union[str, Any] = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(SCREAMING_SNAKE_CASE_ )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end="," )
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : str = "" , SCREAMING_SNAKE_CASE_ : Tuple=5_0 , SCREAMING_SNAKE_CASE_ : Optional[Any]="*" ) -> str:
"""simple docstring"""
if not s:
return "\n" + width * char
SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ : Optional[int] = divmod(width - len(SCREAMING_SNAKE_CASE_ ) - 2 , 2 )
return F"{left * char} {s} {(left + extra) * char}"
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('Binary Tree Traversals'))
snake_case_ = build_tree()
print(prompt('Pre Order Traversal'))
pre_order(node)
print(prompt() + '\n')
print(prompt('In Order Traversal'))
in_order(node)
print(prompt() + '\n')
print(prompt('Post Order Traversal'))
post_order(node)
print(prompt() + '\n')
print(prompt('Level Order Traversal'))
level_order(node)
print(prompt() + '\n')
print(prompt('Actual Level Order Traversal'))
level_order_actual(node)
print('*' * 5_0 + '\n')
print(prompt('Pre Order Traversal - Iteration Version'))
pre_order_iter(node)
print(prompt() + '\n')
print(prompt('In Order Traversal - Iteration Version'))
in_order_iter(node)
print(prompt() + '\n')
print(prompt('Post Order Traversal - Iteration Version'))
post_order_iter(node)
print(prompt())
| 421
|
'''simple docstring'''
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
snake_case_ = re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
class SCREAMING_SNAKE_CASE__ :
_A = 42
_A = None
_A = None
_A = None
_A = None
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ : Optional[int] = _str_to_version_tuple(self.version_str )
def __repr__( self ):
"""simple docstring"""
return F"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"
@property
def __lowerCamelCase ( self ):
"""simple docstring"""
return self.major, self.minor, self.patch
def __lowerCamelCase ( self , lowercase__ ):
"""simple docstring"""
if isinstance(lowercase__ , lowercase__ ):
return Version(lowercase__ )
elif isinstance(lowercase__ , lowercase__ ):
return other
raise TypeError(F"{other} (type {type(lowercase__ )}) cannot be compared to version." )
def __eq__( self , lowercase__ ):
"""simple docstring"""
try:
SCREAMING_SNAKE_CASE_ : List[Any] = self._validate_operand(lowercase__ )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self , lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = self._validate_operand(lowercase__ )
return self.tuple < other.tuple
def __hash__( self ):
"""simple docstring"""
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def __lowerCamelCase ( cls , lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def __lowerCamelCase ( self ):
"""simple docstring"""
return self.version_str
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = _VERSION_REG.match(SCREAMING_SNAKE_CASE_ )
if not res:
raise ValueError(F"Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits." )
return tuple(int(SCREAMING_SNAKE_CASE_ ) for v in [res.group("major" ), res.group("minor" ), res.group("patch" )] )
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : List[str] ) -> Optional[Any]:
"""simple docstring"""
return ".".join(str(SCREAMING_SNAKE_CASE_ ) for v in version_tuple )
| 421
| 1
|
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 494
|
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class lowerCAmelCase ( snake_case , unittest.TestCase ):
lowerCAmelCase__ = RoFormerTokenizer
lowerCAmelCase__ = RoFormerTokenizerFast
lowerCAmelCase__ = True
lowerCAmelCase__ = True
def __A ( self ):
super().setUp()
def __A ( self , **a__ ):
return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **a__ )
def __A ( self , **a__ ):
return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **a__ )
def __A ( self ):
_UpperCAmelCase = '永和服装饰品有限公司,今天天气非常好'
_UpperCAmelCase = '永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
return input_text, output_text
def __A ( self ):
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase , _UpperCAmelCase = self.get_chinese_input_output_texts()
_UpperCAmelCase = tokenizer.tokenize(a__ )
self.assertListEqual(a__ , output_text.split() )
_UpperCAmelCase = tokens + [tokenizer.unk_token]
_UpperCAmelCase = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(a__ ) , a__ )
def __A ( self ):
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase , _UpperCAmelCase = self.get_chinese_input_output_texts()
_UpperCAmelCase = tokenizer.tokenize(a__ )
self.assertListEqual(a__ , output_text.split() )
_UpperCAmelCase = tokens + [tokenizer.unk_token]
_UpperCAmelCase = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(a__ ) , a__ )
def __A ( self ):
pass
def __A ( self ):
pass
def __A ( self ):
pass
| 494
| 1
|
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase__ = Lock()
def lowerCAmelCase_ ( __A, __A, __A, __A, __A, __A, __A ) -> str:
'''simple docstring'''
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0, 10 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(__A )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
UpperCAmelCase__ = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
UpperCAmelCase__ = min(__A, __A )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(__A )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
UpperCAmelCase__ = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
UpperCAmelCase__ = max(__A, __A )
# after all swaps are performed, send the values back to main
result_pipe[1].send(__A )
def lowerCAmelCase_ ( __A ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ = []
UpperCAmelCase__ = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
UpperCAmelCase__ = Pipe()
UpperCAmelCase__ = Pipe()
process_array_.append(
Process(
target=__A, args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]), ) )
UpperCAmelCase__ = temp_rs
UpperCAmelCase__ = temp_rr
for i in range(1, len(__A ) - 1 ):
UpperCAmelCase__ = Pipe()
UpperCAmelCase__ = Pipe()
process_array_.append(
Process(
target=__A, args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]), ) )
UpperCAmelCase__ = temp_rs
UpperCAmelCase__ = temp_rr
process_array_.append(
Process(
target=__A, args=(
len(__A ) - 1,
arr[len(__A ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(__A ) - 1],
), ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0, len(__A ) ):
UpperCAmelCase__ = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def lowerCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ = list(range(10, 0, -1 ) )
print("Initial List" )
print(*__A )
UpperCAmelCase__ = odd_even_transposition(__A )
print("Sorted List\n" )
print(*__A )
if __name__ == "__main__":
main()
| 486
|
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_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
UpperCamelCase__ = logging.get_logger(__name__)
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : List[str] = ['pixel_values']
def __init__(self : Optional[int] , __UpperCAmelCase : bool = True , __UpperCAmelCase : Optional[Dict[str, int]] = None , __UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , __UpperCAmelCase : bool = True , __UpperCAmelCase : bool = True , __UpperCAmelCase : Union[int, float] = 1 / 2_5_5 , __UpperCAmelCase : Dict[str, int] = None , __UpperCAmelCase : bool = True , __UpperCAmelCase : Optional[Union[float, List[float]]] = None , __UpperCAmelCase : Optional[Union[float, List[float]]] = None , **__UpperCAmelCase : Optional[Any] , ) -> None:
"""simple docstring"""
super().__init__(**__UpperCAmelCase )
UpperCAmelCase__ = size if size is not None else {"height": 2_2_4, "width": 2_2_4}
UpperCAmelCase__ = get_size_dict(__UpperCAmelCase )
UpperCAmelCase__ = crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
UpperCAmelCase__ = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase , param_name="crop_size" )
UpperCAmelCase__ = do_resize
UpperCAmelCase__ = do_rescale
UpperCAmelCase__ = do_normalize
UpperCAmelCase__ = do_center_crop
UpperCAmelCase__ = crop_size
UpperCAmelCase__ = size
UpperCAmelCase__ = resample
UpperCAmelCase__ = rescale_factor
UpperCAmelCase__ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
UpperCAmelCase__ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def lowercase_ (self : Dict , __UpperCAmelCase : np.ndarray , __UpperCAmelCase : Dict[str, int] , __UpperCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , __UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__UpperCAmelCase : List[Any] , ) -> np.ndarray:
"""simple docstring"""
UpperCAmelCase__ = get_size_dict(__UpperCAmelCase )
if "shortest_edge" in size:
UpperCAmelCase__ = get_resize_output_image_size(__UpperCAmelCase , size=size["shortest_edge"] , default_to_square=__UpperCAmelCase )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
UpperCAmelCase__ = (size["height"], size["width"])
else:
raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}""" )
return resize(__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowercase_ (self : Tuple , __UpperCAmelCase : np.ndarray , __UpperCAmelCase : Dict[str, int] , __UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__UpperCAmelCase : Any , ) -> np.ndarray:
"""simple docstring"""
UpperCAmelCase__ = get_size_dict(__UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(__UpperCAmelCase , size=(size["height"], size["width"]) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowercase_ (self : int , __UpperCAmelCase : np.ndarray , __UpperCAmelCase : float , __UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__UpperCAmelCase : Tuple ) -> np.ndarray:
"""simple docstring"""
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowercase_ (self : int , __UpperCAmelCase : np.ndarray , __UpperCAmelCase : Union[float, List[float]] , __UpperCAmelCase : Union[float, List[float]] , __UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__UpperCAmelCase : Optional[Any] , ) -> np.ndarray:
"""simple docstring"""
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowercase_ (self : Union[str, Any] , __UpperCAmelCase : ImageInput , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Dict[str, int] = None , __UpperCAmelCase : PILImageResampling = None , __UpperCAmelCase : bool = None , __UpperCAmelCase : 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 : Any , ) -> BatchFeature:
"""simple docstring"""
UpperCAmelCase__ = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ = do_normalize if do_normalize is not None else self.do_normalize
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 , param_name="crop_size" , default_to_square=__UpperCAmelCase )
UpperCAmelCase__ = resample if resample is not None else self.resample
UpperCAmelCase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
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__ = size if size is not None else self.size
UpperCAmelCase__ = get_size_dict(__UpperCAmelCase )
if not is_batched(__UpperCAmelCase ):
UpperCAmelCase__ = [images]
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." )
# 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 )
| 486
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: List[str] = logging.get_logger(__name__)
_lowercase: Optional[Any] = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/falcon-7b''': '''https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json''',
}
class lowerCamelCase__ ( UpperCAmelCase ):
UpperCamelCase__ ="falcon"
UpperCamelCase__ =["past_key_values"]
def __init__( self : Optional[Any] , lowercase__ : List[Any]=6_50_24 , lowercase__ : Optional[Any]=45_44 , lowercase__ : int=32 , lowercase__ : List[Any]=71 , lowercase__ : Any=1e-5 , lowercase__ : Dict=0.0_2 , lowercase__ : Union[str, Any]=True , lowercase__ : Optional[Any]=0.0 , lowercase__ : int=0.0 , lowercase__ : Optional[Any]=None , lowercase__ : List[Any]=False , lowercase__ : Tuple=False , lowercase__ : int=True , lowercase__ : List[Any]=True , lowercase__ : Optional[Any]=False , lowercase__ : Optional[Any]=11 , lowercase__ : Optional[Any]=11 , **lowercase__ : Union[str, Any] , ):
_lowerCAmelCase = vocab_size
# Backward compatibility with n_embed kwarg
_lowerCAmelCase = kwargs.pop('n_embed' , lowercase__ )
_lowerCAmelCase = hidden_size if n_embed is None else n_embed
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = layer_norm_epsilon
_lowerCAmelCase = initializer_range
_lowerCAmelCase = use_cache
_lowerCAmelCase = hidden_dropout
_lowerCAmelCase = attention_dropout
_lowerCAmelCase = bos_token_id
_lowerCAmelCase = eos_token_id
_lowerCAmelCase = num_attention_heads if num_kv_heads is None else num_kv_heads
_lowerCAmelCase = alibi
_lowerCAmelCase = new_decoder_architecture
_lowerCAmelCase = multi_query # Ignored when new_decoder_architecture is True
_lowerCAmelCase = parallel_attn
_lowerCAmelCase = bias
super().__init__(bos_token_id=lowercase__ , eos_token_id=lowercase__ , **lowercase__ )
@property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
return self.hidden_size // self.num_attention_heads
@property
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
return not self.alibi
| 225
|
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( UpperCAmelCase ):
UpperCamelCase__ =(UniPCMultistepScheduler,)
UpperCamelCase__ =(("num_inference_steps", 2_5),)
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , **lowercase__ : Dict ):
_lowerCAmelCase = {
'num_train_timesteps': 10_00,
'beta_start': 0.0_0_0_1,
'beta_end': 0.0_2,
'beta_schedule': 'linear',
'solver_order': 2,
'solver_type': 'bh2',
}
config.update(**lowercase__ )
return config
def SCREAMING_SNAKE_CASE__ ( self : Any , lowercase__ : List[str]=0 , **lowercase__ : Union[str, Any] ):
_lowerCAmelCase = dict(self.forward_default_kwargs )
_lowerCAmelCase = kwargs.pop('num_inference_steps' , lowercase__ )
_lowerCAmelCase = self.dummy_sample
_lowerCAmelCase = 0.1 * sample
_lowerCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase = self.get_scheduler_config(**lowercase__ )
_lowerCAmelCase = scheduler_class(**lowercase__ )
scheduler.set_timesteps(lowercase__ )
# copy over dummy past residuals
_lowerCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase__ )
_lowerCAmelCase = scheduler_class.from_pretrained(lowercase__ )
new_scheduler.set_timesteps(lowercase__ )
# copy over dummy past residuals
_lowerCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_lowerCAmelCase , _lowerCAmelCase = sample, sample
for t in range(lowercase__ , time_step + scheduler.config.solver_order + 1 ):
_lowerCAmelCase = scheduler.step(lowercase__ , lowercase__ , lowercase__ , **lowercase__ ).prev_sample
_lowerCAmelCase = new_scheduler.step(lowercase__ , lowercase__ , lowercase__ , **lowercase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , lowercase__ : Tuple=0 , **lowercase__ : List[Any] ):
_lowerCAmelCase = dict(self.forward_default_kwargs )
_lowerCAmelCase = kwargs.pop('num_inference_steps' , lowercase__ )
_lowerCAmelCase = self.dummy_sample
_lowerCAmelCase = 0.1 * sample
_lowerCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**lowercase__ )
scheduler.set_timesteps(lowercase__ )
# copy over dummy past residuals (must be after setting timesteps)
_lowerCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase__ )
_lowerCAmelCase = scheduler_class.from_pretrained(lowercase__ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowercase__ )
# copy over dummy past residual (must be after setting timesteps)
_lowerCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_lowerCAmelCase = scheduler.step(lowercase__ , lowercase__ , lowercase__ , **lowercase__ ).prev_sample
_lowerCAmelCase = new_scheduler.step(lowercase__ , lowercase__ , lowercase__ , **lowercase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def SCREAMING_SNAKE_CASE__ ( self : Tuple , lowercase__ : int=None , **lowercase__ : List[Any] ):
if scheduler is None:
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(**lowercase__ )
_lowerCAmelCase = scheduler_class(**lowercase__ )
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(**lowercase__ )
_lowerCAmelCase = scheduler_class(**lowercase__ )
_lowerCAmelCase = 10
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(lowercase__ )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase = model(lowercase__ , lowercase__ )
_lowerCAmelCase = scheduler.step(lowercase__ , lowercase__ , lowercase__ ).prev_sample
return sample
def SCREAMING_SNAKE_CASE__ ( self : str ):
_lowerCAmelCase = dict(self.forward_default_kwargs )
_lowerCAmelCase = kwargs.pop('num_inference_steps' , lowercase__ )
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**lowercase__ )
_lowerCAmelCase = self.dummy_sample
_lowerCAmelCase = 0.1 * sample
if num_inference_steps is not None and hasattr(lowercase__ , 'set_timesteps' ):
scheduler.set_timesteps(lowercase__ )
elif num_inference_steps is not None and not hasattr(lowercase__ , 'set_timesteps' ):
_lowerCAmelCase = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_lowerCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
_lowerCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
_lowerCAmelCase = scheduler.timesteps[5]
_lowerCAmelCase = scheduler.timesteps[6]
_lowerCAmelCase = scheduler.step(lowercase__ , lowercase__ , lowercase__ , **lowercase__ ).prev_sample
_lowerCAmelCase = scheduler.step(lowercase__ , lowercase__ , lowercase__ , **lowercase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def SCREAMING_SNAKE_CASE__ ( self : str ):
# make sure that iterating over schedulers with same config names gives same results
# for defaults
_lowerCAmelCase = UniPCMultistepScheduler(**self.get_scheduler_config() )
_lowerCAmelCase = self.full_loop(scheduler=lowercase__ )
_lowerCAmelCase = torch.mean(torch.abs(lowercase__ ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3
_lowerCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_lowerCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
_lowerCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
_lowerCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
_lowerCAmelCase = self.full_loop(scheduler=lowercase__ )
_lowerCAmelCase = torch.mean(torch.abs(lowercase__ ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
for timesteps in [25, 50, 1_00, 9_99, 10_00]:
self.check_over_configs(num_train_timesteps=lowercase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
self.check_over_configs(thresholding=lowercase__ )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=lowercase__ , prediction_type=lowercase__ , sample_max_value=lowercase__ , solver_order=lowercase__ , solver_type=lowercase__ , )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=lowercase__ , solver_type=lowercase__ , prediction_type=lowercase__ , )
_lowerCAmelCase = self.full_loop(
solver_order=lowercase__ , solver_type=lowercase__ , prediction_type=lowercase__ , )
assert not torch.isnan(lowercase__ ).any(), "Samples have nan numbers"
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
self.check_over_configs(lower_order_final=lowercase__ )
self.check_over_configs(lower_order_final=lowercase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
self.check_over_forward(num_inference_steps=lowercase__ , time_step=0 )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
_lowerCAmelCase = self.full_loop()
_lowerCAmelCase = torch.mean(torch.abs(lowercase__ ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3
def SCREAMING_SNAKE_CASE__ ( self : int ):
_lowerCAmelCase = self.full_loop(prediction_type='v_prediction' )
_lowerCAmelCase = torch.mean(torch.abs(lowercase__ ) )
assert abs(result_mean.item() - 0.1_0_1_4 ) < 1e-3
def SCREAMING_SNAKE_CASE__ ( self : str ):
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(thresholding=lowercase__ , dynamic_thresholding_ratio=0 )
_lowerCAmelCase = scheduler_class(**lowercase__ )
_lowerCAmelCase = 10
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(lowercase__ )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase = model(lowercase__ , lowercase__ )
_lowerCAmelCase = scheduler.step(lowercase__ , lowercase__ , lowercase__ ).prev_sample
assert sample.dtype == torch.floataa
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , **lowercase__ : List[Any] ):
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase = self.get_scheduler_config(**lowercase__ )
_lowerCAmelCase = scheduler_class(**lowercase__ )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 225
| 1
|
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class snake_case ( a_ , unittest.TestCase ):
lowercase_ = BertTokenizer
lowercase_ = BertTokenizerFast
lowercase_ = True
lowercase_ = True
lowercase_ = filter_non_english
def __lowercase( self : Union[str, Any] )-> List[str]:
"""simple docstring"""
super().setUp()
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
SCREAMING_SNAKE_CASE__ : Optional[int] = 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 __lowercase( self : str , a_ : Union[str, Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = 'UNwant\u00E9d,running'
SCREAMING_SNAKE_CASE__ : Optional[int] = 'unwanted, running'
return input_text, output_text
def __lowercase( self : List[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.tokenizer_class(self.vocab_file )
SCREAMING_SNAKE_CASE__ : str = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(lowerCAmelCase_ , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , [9, 6, 7, 12, 10, 11] )
def __lowercase( self : List[str] )-> Optional[Any]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : List[Any] = 'UNwant\u00E9d,running'
SCREAMING_SNAKE_CASE__ : int = tokenizer.tokenize(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : str = rust_tokenizer.tokenize(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[Any] = rust_tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Tuple = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.encode(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = rust_tokenizer.encode(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
# With lower casing
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer(do_lower_case=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Dict = self.get_rust_tokenizer(do_lower_case=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = 'UNwant\u00E9d,running'
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.tokenize(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = rust_tokenizer.tokenize(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[Any] = rust_tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Any = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer.encode(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Dict = rust_tokenizer.encode(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
def __lowercase( self : List[Any] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def __lowercase( self : Tuple )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = BasicTokenizer(do_lower_case=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __lowercase( self : List[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BasicTokenizer(do_lower_case=lowerCAmelCase_ , strip_accents=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def __lowercase( self : List[str] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = BasicTokenizer(do_lower_case=lowerCAmelCase_ , strip_accents=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __lowercase( self : Optional[int] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = BasicTokenizer(do_lower_case=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __lowercase( self : Union[str, Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = BasicTokenizer(do_lower_case=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __lowercase( self : Optional[int] )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = BasicTokenizer(do_lower_case=lowerCAmelCase_ , strip_accents=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __lowercase( self : Union[str, Any] )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BasicTokenizer(do_lower_case=lowerCAmelCase_ , strip_accents=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __lowercase( self : Union[str, Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = BasicTokenizer(do_lower_case=lowerCAmelCase_ , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def __lowercase( self : Optional[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = BasicTokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 'a\n\'ll !!to?\'d of, can\'t.'
SCREAMING_SNAKE_CASE__ : List[Any] = ['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
self.assertListEqual(tokenizer.tokenize(lowerCAmelCase_ ) , lowerCAmelCase_ )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
SCREAMING_SNAKE_CASE__ : List[Any] = {}
for i, token in enumerate(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE__ : Any = i
SCREAMING_SNAKE_CASE__ : List[str] = WordpieceTokenizer(vocab=lowerCAmelCase_ , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
def __lowercase( self : Tuple )-> Union[str, Any]:
"""simple docstring"""
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def __lowercase( self : int )-> Optional[int]:
"""simple docstring"""
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
def __lowercase( self : str )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(lowerCAmelCase_ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
self.assertListEqual(
[rust_tokenizer.tokenize(lowerCAmelCase_ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
@slow
def __lowercase( self : List[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.tokenizer_class.from_pretrained('bert-base-uncased' )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.encode('sequence builders' , add_special_tokens=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.encode('multi-sequence build' , add_special_tokens=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ , lowerCAmelCase_ )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def __lowercase( self : str )-> Optional[int]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
SCREAMING_SNAKE_CASE__ : str = tokenizer_r.encode_plus(
lowerCAmelCase_ , return_attention_mask=lowerCAmelCase_ , return_token_type_ids=lowerCAmelCase_ , return_offsets_mapping=lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ , )
SCREAMING_SNAKE_CASE__ : int = tokenizer_r.do_lower_case if hasattr(lowerCAmelCase_ , 'do_lower_case' ) else False
SCREAMING_SNAKE_CASE__ : int = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'] )
def __lowercase( self : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = ['的', '人', '有']
SCREAMING_SNAKE_CASE__ : Dict = ''.join(lowerCAmelCase_ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE__ : Any = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer_p.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer_r.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Any = tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer_p.convert_ids_to_tokens(lowerCAmelCase_ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = False
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Tuple = self.tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_r.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer_p.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer_p.convert_ids_to_tokens(lowerCAmelCase_ )
# it is expected that only the first Chinese character is not preceded by "##".
SCREAMING_SNAKE_CASE__ : str = [
F'''##{token}''' if idx != 0 else token for idx, token in enumerate(lowerCAmelCase_ )
]
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
| 85
|
'''simple docstring'''
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__ = {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
'''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class a__ ( a_ ):
'''simple docstring'''
A : Union[str, Any] = '''roberta'''
def __init__( self : Any , lowerCAmelCase_ : int=50_265 , lowerCAmelCase_ : int=768 , lowerCAmelCase_ : Optional[Any]=12 , lowerCAmelCase_ : Tuple=12 , lowerCAmelCase_ : Optional[int]=3_072 , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : List[str]=0.1 , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : List[Any]=512 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : List[Any]=0.02 , lowerCAmelCase_ : Optional[int]=1E-12 , lowerCAmelCase_ : str=1 , lowerCAmelCase_ : Dict=0 , lowerCAmelCase_ : Any=2 , lowerCAmelCase_ : List[str]="absolute" , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : Dict=None , **lowerCAmelCase_ : Any , ) -> int:
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= hidden_act
__A= intermediate_size
__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= position_embedding_type
__A= use_cache
__A= classifier_dropout
class a__ ( a_ ):
'''simple docstring'''
@property
def lowerCAmelCase ( self : Any ) -> Mapping[str, Mapping[int, str]]:
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),
] )
| 186
| 0
|
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__A =logging.get_logger(__name__)
def _UpperCamelCase ( UpperCamelCase__ ):
UpperCAmelCase__ : List[str] = R"""\w+[.]\d+"""
UpperCAmelCase__ : List[Any] = re.findall(UpperCamelCase__ , UpperCamelCase__ )
for pat in pats:
UpperCAmelCase__ : str = key.replace(UpperCamelCase__ , """_""".join(pat.split(""".""" ) ) )
return key
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : str = pt_tuple_key[:-1] + ("""scale""",)
if (
any("""norm""" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
UpperCAmelCase__ : Union[str, Any] = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
UpperCAmelCase__ : str = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
UpperCAmelCase__ : Union[str, Any] = pt_tuple_key[:-1] + ("""embedding""",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
UpperCAmelCase__ : Any = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
UpperCAmelCase__ : Any = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
UpperCAmelCase__ : Optional[int] = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight":
UpperCAmelCase__ : Any = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
UpperCAmelCase__ : Optional[Any] = pt_tuple_key[:-1] + ("""weight""",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
UpperCAmelCase__ : Dict = pt_tuple_key[:-1] + ("""bias""",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=4_2 ):
# Step 1: Convert pytorch tensor to numpy
UpperCAmelCase__ : str = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
UpperCAmelCase__ : str = flax_model.init_weights(PRNGKey(UpperCamelCase__ ) )
UpperCAmelCase__ : Union[str, Any] = flatten_dict(UpperCamelCase__ )
UpperCAmelCase__ : int = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
UpperCAmelCase__ : str = rename_key(UpperCamelCase__ )
UpperCAmelCase__ : Optional[Any] = tuple(renamed_pt_key.split(""".""" ) )
# Correctly rename weight parameters
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = rename_key_and_reshape_tensor(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )
# also add unexpected weight so that warning is thrown
UpperCAmelCase__ : str = jnp.asarray(UpperCamelCase__ )
return unflatten_dict(UpperCamelCase__ )
| 113
|
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__A =numpy.array([0, 0])
__A =numpy.array([0.5, 0.8_6_6_0_2_5_4])
__A =numpy.array([1, 0])
__A =[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : Union[str, Any] = initial_vectors
for _ in range(UpperCamelCase__ ):
UpperCAmelCase__ : Dict = iteration_step(UpperCamelCase__ )
return vectors
def _UpperCamelCase ( UpperCamelCase__ ):
UpperCAmelCase__ : Optional[Any] = []
for i, start_vector in enumerate(vectors[:-1] ):
UpperCAmelCase__ : Tuple = vectors[i + 1]
new_vectors.append(UpperCamelCase__ )
UpperCAmelCase__ : Optional[Any] = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3 )
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3 , 6_0 ) )
new_vectors.append(start_vector + difference_vector * 2 / 3 )
new_vectors.append(vectors[-1] )
return new_vectors
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : Any = numpy.radians(UpperCamelCase__ )
UpperCAmelCase__ , UpperCAmelCase__ : int = numpy.cos(UpperCamelCase__ ), numpy.sin(UpperCamelCase__ )
UpperCAmelCase__ : Dict = numpy.array(((c, -s), (s, c)) )
return numpy.dot(UpperCamelCase__ , UpperCamelCase__ )
def _UpperCamelCase ( UpperCamelCase__ ):
UpperCAmelCase__ : Any = plt.gca()
axes.set_aspect("""equal""" )
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = zip(*UpperCamelCase__ )
plt.plot(UpperCamelCase__ , UpperCamelCase__ )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
__A =iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 113
| 1
|
def lowerCAmelCase_ ( __A ) -> float:
'''simple docstring'''
return 10 - x * x
def lowerCAmelCase_ ( __A, __A ) -> float:
'''simple docstring'''
if equation(__A ) * equation(__A ) >= 0:
raise ValueError("Wrong space!" )
UpperCAmelCase__ = a
while (b - a) >= 0.01:
# Find middle point
UpperCAmelCase__ = (a + b) / 2
# Check if middle point is root
if equation(__A ) == 0.0:
break
# Decide the side to repeat the steps
if equation(__A ) * equation(__A ) < 0:
UpperCAmelCase__ = c
else:
UpperCAmelCase__ = c
return c
if __name__ == "__main__":
import doctest
doctest.testmod()
print(bisection(-2, 5))
print(bisection(0, 6))
| 486
|
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
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.linear_k': 'encoder.layers.*.self_attn.linear_k',
'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
'self_attn.rotary_emb': 'encoder.embed_positions',
'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
'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 lowerCAmelCase_ ( __A, __A, __A, __A, __A ) -> Union[str, Any]:
'''simple docstring'''
for attribute in key.split("." ):
UpperCAmelCase__ = getattr(__A, __A )
if weight_type is not None:
UpperCAmelCase__ = getattr(__A, __A ).shape
else:
UpperCAmelCase__ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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__ = value
elif weight_type == "weight_g":
UpperCAmelCase__ = value
elif weight_type == "weight_v":
UpperCAmelCase__ = value
elif weight_type == "bias":
UpperCAmelCase__ = value
elif weight_type == "running_mean":
UpperCAmelCase__ = value
elif weight_type == "running_var":
UpperCAmelCase__ = value
elif weight_type == "num_batches_tracked":
UpperCAmelCase__ = value
elif weight_type == "inv_freq":
UpperCAmelCase__ = value
else:
UpperCAmelCase__ = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def lowerCAmelCase_ ( __A, __A, __A ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ = []
UpperCAmelCase__ = fairseq_model.state_dict()
UpperCAmelCase__ = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
UpperCAmelCase__ = False
if "conv_layers" in name:
load_conv_layer(
__A, __A, __A, __A, hf_model.config.feat_extract_norm == "group", )
UpperCAmelCase__ = True
else:
for key, mapped_key in MAPPING.items():
UpperCAmelCase__ = "wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
UpperCAmelCase__ = True
if "*" in mapped_key:
UpperCAmelCase__ = name.split(__A )[0].split("." )[-2]
UpperCAmelCase__ = mapped_key.replace("*", __A )
if "pos_bias_u" in name:
UpperCAmelCase__ = None
elif "pos_bias_v" in name:
UpperCAmelCase__ = None
elif "weight_g" in name:
UpperCAmelCase__ = "weight_g"
elif "weight_v" in name:
UpperCAmelCase__ = "weight_v"
elif "bias" in name:
UpperCAmelCase__ = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
UpperCAmelCase__ = "weight"
elif "running_mean" in name:
UpperCAmelCase__ = "running_mean"
elif "inv_freq" in name:
UpperCAmelCase__ = "inv_freq"
elif "running_var" in name:
UpperCAmelCase__ = "running_var"
elif "num_batches_tracked" in name:
UpperCAmelCase__ = "num_batches_tracked"
else:
UpperCAmelCase__ = None
set_recursively(__A, __A, __A, __A, __A )
continue
if not is_used:
unused_weights.append(__A )
logger.warning(f"""Unused weights: {unused_weights}""" )
def lowerCAmelCase_ ( __A, __A, __A, __A, __A ) -> Any:
'''simple docstring'''
UpperCAmelCase__ = full_name.split("conv_layers." )[-1]
UpperCAmelCase__ = name.split("." )
UpperCAmelCase__ = int(items[0] )
UpperCAmelCase__ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
UpperCAmelCase__ = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
UpperCAmelCase__ = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
UpperCAmelCase__ = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
UpperCAmelCase__ = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__A )
@torch.no_grad()
def lowerCAmelCase_ ( __A, __A, __A=None, __A=None, __A=True ) -> Optional[Any]:
'''simple docstring'''
if config_path is not None:
UpperCAmelCase__ = WavaVecaConformerConfig.from_pretrained(__A, hidden_act="swish" )
else:
UpperCAmelCase__ = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
UpperCAmelCase__ = "rotary"
if is_finetuned:
if dict_path:
UpperCAmelCase__ = Dictionary.load(__A )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
UpperCAmelCase__ = target_dict.pad_index
UpperCAmelCase__ = target_dict.bos_index
UpperCAmelCase__ = target_dict.eos_index
UpperCAmelCase__ = len(target_dict.symbols )
UpperCAmelCase__ = os.path.join(__A, "vocab.json" )
if not os.path.isdir(__A ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__A ) )
return
os.makedirs(__A, exist_ok=__A )
UpperCAmelCase__ = target_dict.indices
# fairseq has the <pad> and <s> switched
UpperCAmelCase__ = 0
UpperCAmelCase__ = 1
with open(__A, "w", encoding="utf-8" ) as vocab_handle:
json.dump(__A, __A )
UpperCAmelCase__ = WavaVecaCTCTokenizer(
__A, 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=__A, )
UpperCAmelCase__ = True if config.feat_extract_norm == "layer" else False
UpperCAmelCase__ = WavaVecaFeatureExtractor(
feature_size=1, sampling_rate=16_000, padding_value=0, do_normalize=__A, return_attention_mask=__A, )
UpperCAmelCase__ = WavaVecaProcessor(feature_extractor=__A, tokenizer=__A )
processor.save_pretrained(__A )
UpperCAmelCase__ = WavaVecaConformerForCTC(__A )
else:
UpperCAmelCase__ = WavaVecaConformerForPreTraining(__A )
if is_finetuned:
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
UpperCAmelCase__ = argparse.Namespace(task="audio_pretraining" )
UpperCAmelCase__ = fairseq.tasks.setup_task(__A )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path], task=__A )
UpperCAmelCase__ = model[0].eval()
recursively_load_weights(__A, __A, not is_finetuned )
hf_wavavec.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('--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_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 486
| 1
|
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
'''b0''': efficientnet.EfficientNetBa,
'''b1''': efficientnet.EfficientNetBa,
'''b2''': efficientnet.EfficientNetBa,
'''b3''': efficientnet.EfficientNetBa,
'''b4''': efficientnet.EfficientNetBa,
'''b5''': efficientnet.EfficientNetBa,
'''b6''': efficientnet.EfficientNetBa,
'''b7''': efficientnet.EfficientNetBa,
}
a : Dict = {
'''b0''': {
'''hidden_dim''': 1_2_8_0,
'''width_coef''': 1.0,
'''depth_coef''': 1.0,
'''image_size''': 2_2_4,
'''dropout_rate''': 0.2,
'''dw_padding''': [],
},
'''b1''': {
'''hidden_dim''': 1_2_8_0,
'''width_coef''': 1.0,
'''depth_coef''': 1.1,
'''image_size''': 2_4_0,
'''dropout_rate''': 0.2,
'''dw_padding''': [1_6],
},
'''b2''': {
'''hidden_dim''': 1_4_0_8,
'''width_coef''': 1.1,
'''depth_coef''': 1.2,
'''image_size''': 2_6_0,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 8, 1_6],
},
'''b3''': {
'''hidden_dim''': 1_5_3_6,
'''width_coef''': 1.2,
'''depth_coef''': 1.4,
'''image_size''': 3_0_0,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 1_8],
},
'''b4''': {
'''hidden_dim''': 1_7_9_2,
'''width_coef''': 1.4,
'''depth_coef''': 1.8,
'''image_size''': 3_8_0,
'''dropout_rate''': 0.4,
'''dw_padding''': [6],
},
'''b5''': {
'''hidden_dim''': 2_0_4_8,
'''width_coef''': 1.6,
'''depth_coef''': 2.2,
'''image_size''': 4_5_6,
'''dropout_rate''': 0.4,
'''dw_padding''': [1_3, 2_7],
},
'''b6''': {
'''hidden_dim''': 2_3_0_4,
'''width_coef''': 1.8,
'''depth_coef''': 2.6,
'''image_size''': 5_2_8,
'''dropout_rate''': 0.5,
'''dw_padding''': [3_1],
},
'''b7''': {
'''hidden_dim''': 2_5_6_0,
'''width_coef''': 2.0,
'''depth_coef''': 3.1,
'''image_size''': 6_0_0,
'''dropout_rate''': 0.5,
'''dw_padding''': [1_8],
},
}
def _UpperCamelCase ( _A ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = EfficientNetConfig()
_UpperCAmelCase = CONFIG_MAP[model_name]["""hidden_dim"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""width_coef"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""depth_coef"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""image_size"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""dropout_rate"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""dw_padding"""]
_UpperCAmelCase = """huggingface/label-files"""
_UpperCAmelCase = """imagenet-1k-id2label.json"""
_UpperCAmelCase = 1_0_0_0
_UpperCAmelCase = json.load(open(hf_hub_download(_A , _A , repo_type="""dataset""" ) , """r""" ) )
_UpperCAmelCase = {int(_A ): v for k, v in idalabel.items()}
_UpperCAmelCase = idalabel
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def _UpperCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_UpperCAmelCase = Image.open(requests.get(_A , stream=_A ).raw )
return im
def _UpperCamelCase ( _A ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = CONFIG_MAP[model_name]["""image_size"""]
_UpperCAmelCase = EfficientNetImageProcessor(
size={"""height""": size, """width""": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163] , do_center_crop=_A , )
return preprocessor
def _UpperCamelCase ( _A ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
_UpperCAmelCase = sorted(set(_A ) )
_UpperCAmelCase = len(_A )
_UpperCAmelCase = {b: str(_A ) for b, i in zip(_A , range(_A ) )}
_UpperCAmelCase = []
rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""") )
rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""") )
rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""") )
rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""") )
rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""") )
for b in block_names:
_UpperCAmelCase = block_name_mapping[b]
rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""") )
rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""") )
rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""") )
rename_keys.append(
(F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""") )
rename_keys.append(
(F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""") )
rename_keys.append(
(F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""") )
rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""") )
rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""") )
rename_keys.append(
(F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""") )
rename_keys.append(
(F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""") )
rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""") )
rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""") )
rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""") )
rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""") )
rename_keys.append(
(F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""") )
rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""") )
rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""") )
rename_keys.append(
(F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""") )
rename_keys.append(
(F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""") )
rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""") )
rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""") )
rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""") )
rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""") )
rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""") )
_UpperCAmelCase = {}
for item in rename_keys:
if item[0] in original_param_names:
_UpperCAmelCase = """efficientnet.""" + item[1]
_UpperCAmelCase = """classifier.weight"""
_UpperCAmelCase = """classifier.bias"""
return key_mapping
def _UpperCamelCase ( _A , _A , _A ) -> List[str]:
"""simple docstring"""
for key, value in tf_params.items():
if "normalization" in key:
continue
_UpperCAmelCase = key_mapping[key]
if "_conv" in key and "kernel" in key:
_UpperCAmelCase = torch.from_numpy(_A ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
_UpperCAmelCase = torch.from_numpy(_A ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
_UpperCAmelCase = torch.from_numpy(np.transpose(_A ) )
else:
_UpperCAmelCase = torch.from_numpy(_A )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_A )
@torch.no_grad()
def _UpperCamelCase ( _A , _A , _A , _A ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = model_classes[model_name](
include_top=_A , weights="""imagenet""" , input_tensor=_A , input_shape=_A , pooling=_A , classes=1_0_0_0 , classifier_activation="""softmax""" , )
_UpperCAmelCase = original_model.trainable_variables
_UpperCAmelCase = original_model.non_trainable_variables
_UpperCAmelCase = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
_UpperCAmelCase = param.numpy()
_UpperCAmelCase = list(tf_params.keys() )
# Load HuggingFace model
_UpperCAmelCase = get_efficientnet_config(_A )
_UpperCAmelCase = EfficientNetForImageClassification(_A ).eval()
_UpperCAmelCase = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("""Converting parameters...""" )
_UpperCAmelCase = rename_keys(_A )
replace_params(_A , _A , _A )
# Initialize preprocessor and preprocess input image
_UpperCAmelCase = convert_image_processor(_A )
_UpperCAmelCase = preprocessor(images=prepare_img() , return_tensors="""pt""" )
# HF model inference
hf_model.eval()
with torch.no_grad():
_UpperCAmelCase = hf_model(**_A )
_UpperCAmelCase = outputs.logits.detach().numpy()
# Original model inference
_UpperCAmelCase = False
_UpperCAmelCase = CONFIG_MAP[model_name]["""image_size"""]
_UpperCAmelCase = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
_UpperCAmelCase = image.img_to_array(_A )
_UpperCAmelCase = np.expand_dims(_A , axis=0 )
_UpperCAmelCase = original_model.predict(_A )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_A , _A , atol=1e-3 ), "The predicted logits are not the same."
print("""Model outputs match!""" )
if save_model:
# Create folder to save model
if not os.path.isdir(_A ):
os.mkdir(_A )
# Save converted model and image processor
hf_model.save_pretrained(_A )
preprocessor.save_pretrained(_A )
if push_to_hub:
# Push model and image processor to hub
print(F"""Pushing converted {model_name} to the hub...""" )
_UpperCAmelCase = F"""efficientnet-{model_name}"""
preprocessor.push_to_hub(_A )
hf_model.push_to_hub(_A )
if __name__ == "__main__":
a : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''b0''',
type=str,
help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''hf_model''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
a : Any = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 19
|
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( _A , _A , _A ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = LxmertConfig.from_json_file(_A )
print(F"""Building PyTorch model from configuration: {config}""" )
_UpperCAmelCase = LxmertForPreTraining(_A )
# Load weights from tf checkpoint
load_tf_weights_in_lxmert(_A , _A , _A )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , _A )
if __name__ == "__main__":
a : Dict = 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(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
a : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 19
| 1
|
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __UpperCamelCase :
@staticmethod
def UpperCamelCase( *_UpperCamelCase , **_UpperCamelCase ):
pass
@is_pipeline_test
@require_vision
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
__A : Union[str, Any] = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
_UpperCAmelCase = [
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
]
return object_detector, examples
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase = object_detector(examples[0] , threshold=0.0 )
_UpperCAmelCase = len(_UpperCamelCase )
self.assertGreater(_UpperCamelCase , 0 )
self.assertEqual(
_UpperCamelCase , [
{
'''score''': ANY(_UpperCamelCase ),
'''label''': ANY(_UpperCamelCase ),
'''box''': {'''xmin''': ANY(_UpperCamelCase ), '''ymin''': ANY(_UpperCamelCase ), '''xmax''': ANY(_UpperCamelCase ), '''ymax''': ANY(_UpperCamelCase )},
}
for i in range(_UpperCamelCase )
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def UpperCamelCase( self ):
pass
@require_torch
def UpperCamelCase( self ):
_UpperCAmelCase = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
_UpperCAmelCase = object_detector(
'''./tests/fixtures/tests_samples/COCO/000000039769.png''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=0.64 , )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
{'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
] , )
_UpperCAmelCase = object_detector(
[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
] , threshold=0.64 , )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
[
{'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
]
] , )
@require_torch
@slow
def UpperCamelCase( self ):
_UpperCAmelCase = pipeline('''zero-shot-object-detection''' )
_UpperCAmelCase = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
{'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
] , )
_UpperCAmelCase = object_detector(
[
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
] , )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
[
{'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
[
{'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def UpperCamelCase( self ):
pass
@require_torch
@slow
def UpperCamelCase( self ):
_UpperCAmelCase = 0.2
_UpperCAmelCase = pipeline('''zero-shot-object-detection''' )
_UpperCAmelCase = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=_UpperCamelCase , )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
{'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
] , )
@require_torch
@slow
def UpperCamelCase( self ):
_UpperCAmelCase = 2
_UpperCAmelCase = pipeline('''zero-shot-object-detection''' )
_UpperCAmelCase = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , top_k=_UpperCamelCase , )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
{'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
] , )
| 32
|
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
_lowerCamelCase : Optional[List[str]] = None
_lowerCamelCase : int = """<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowerCamelCase : Union[str, Any] = [
np.dtype("""|b1"""),
np.dtype("""|u1"""),
np.dtype("""<u2"""),
np.dtype(""">u2"""),
np.dtype("""<i2"""),
np.dtype(""">i2"""),
np.dtype("""<u4"""),
np.dtype(""">u4"""),
np.dtype("""<i4"""),
np.dtype(""">i4"""),
np.dtype("""<f4"""),
np.dtype(""">f4"""),
np.dtype("""<f8"""),
np.dtype(""">f8"""),
]
@dataclass
class UpperCamelCase_ :
'''simple docstring'''
UpperCAmelCase__ = True
UpperCAmelCase__ = None
# Automatically constructed
UpperCAmelCase__ = "PIL.Image.Image"
UpperCAmelCase__ = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
UpperCAmelCase__ = field(default='''Image''' , init=UpperCAmelCase__ , repr=UpperCAmelCase__ )
def __call__( self : List[str]) ->List[str]:
'''simple docstring'''
return self.pa_type
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , UpperCAmelCase__ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]) ->dict:
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support encoding images, please install \'Pillow\'.''')
if isinstance(UpperCAmelCase__ , UpperCAmelCase__):
A__ = np.array(UpperCAmelCase__)
if isinstance(UpperCAmelCase__ , UpperCAmelCase__):
return {"path": value, "bytes": None}
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__):
return {"path": None, "bytes": value}
elif isinstance(UpperCAmelCase__ , np.ndarray):
# convert the image array to PNG/TIFF bytes
return encode_np_array(UpperCAmelCase__)
elif isinstance(UpperCAmelCase__ , PIL.Image.Image):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(UpperCAmelCase__)
elif value.get('''path''') is not None and os.path.isfile(value['''path''']):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get('''path''')}
elif value.get('''bytes''') is not None or value.get('''path''') is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get('''bytes'''), "path": value.get('''path''')}
else:
raise ValueError(
f"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""")
def SCREAMING_SNAKE_CASE ( self : Optional[int] , UpperCAmelCase__ : dict , UpperCAmelCase__ : str=None) ->"PIL.Image.Image":
'''simple docstring'''
if not self.decode:
raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.''')
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support decoding images, please install \'Pillow\'.''')
if token_per_repo_id is None:
A__ = {}
A__ , A__ = value['''path'''], value['''bytes''']
if bytes_ is None:
if path is None:
raise ValueError(f"""An image should have one of 'path' or 'bytes' but both are None in {value}.""")
else:
if is_local_path(UpperCAmelCase__):
A__ = PIL.Image.open(UpperCAmelCase__)
else:
A__ = path.split('''::''')[-1]
try:
A__ = string_to_dict(UpperCAmelCase__ , config.HUB_DATASETS_URL)['''repo_id''']
A__ = token_per_repo_id.get(UpperCAmelCase__)
except ValueError:
A__ = None
with xopen(UpperCAmelCase__ , '''rb''' , use_auth_token=UpperCAmelCase__) as f:
A__ = BytesIO(f.read())
A__ = PIL.Image.open(bytes_)
else:
A__ = PIL.Image.open(BytesIO(bytes_))
image.load() # to avoid "Too many open files" errors
return image
def SCREAMING_SNAKE_CASE ( self : Dict) ->Union["FeatureType", Dict[str, "FeatureType"]]:
'''simple docstring'''
from .features import Value
return (
self
if self.decode
else {
"bytes": Value('''binary'''),
"path": Value('''string'''),
}
)
def SCREAMING_SNAKE_CASE ( self : Dict , UpperCAmelCase__ : Union[pa.StringArray, pa.StructArray, pa.ListArray]) ->pa.StructArray:
'''simple docstring'''
if pa.types.is_string(storage.type):
A__ = pa.array([None] * len(UpperCAmelCase__) , type=pa.binary())
A__ = pa.StructArray.from_arrays([bytes_array, storage] , ['''bytes''', '''path'''] , mask=storage.is_null())
elif pa.types.is_binary(storage.type):
A__ = pa.array([None] * len(UpperCAmelCase__) , type=pa.string())
A__ = pa.StructArray.from_arrays([storage, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null())
elif pa.types.is_struct(storage.type):
if storage.type.get_field_index('''bytes''') >= 0:
A__ = storage.field('''bytes''')
else:
A__ = pa.array([None] * len(UpperCAmelCase__) , type=pa.binary())
if storage.type.get_field_index('''path''') >= 0:
A__ = storage.field('''path''')
else:
A__ = pa.array([None] * len(UpperCAmelCase__) , type=pa.string())
A__ = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null())
elif pa.types.is_list(storage.type):
A__ = pa.array(
[encode_np_array(np.array(UpperCAmelCase__))['''bytes'''] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
A__ = pa.array([None] * len(UpperCAmelCase__) , type=pa.string())
A__ = pa.StructArray.from_arrays(
[bytes_array, path_array] , ['''bytes''', '''path'''] , mask=bytes_array.is_null())
return array_cast(UpperCAmelCase__ , self.pa_type)
def SCREAMING_SNAKE_CASE ( self : List[Any] , UpperCAmelCase__ : pa.StructArray) ->pa.StructArray:
'''simple docstring'''
@no_op_if_value_is_null
def path_to_bytes(UpperCAmelCase__ : Dict):
with xopen(UpperCAmelCase__ , '''rb''') as f:
A__ = f.read()
return bytes_
A__ = pa.array(
[
(path_to_bytes(x['''path''']) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
A__ = pa.array(
[os.path.basename(UpperCAmelCase__) if path is not None else None for path in storage.field('''path''').to_pylist()] , type=pa.string() , )
A__ = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=bytes_array.is_null())
return array_cast(UpperCAmelCase__ , self.pa_type)
def SCREAMING_SNAKE_CASE ( ) -> List[str]:
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support encoding images, please install \'Pillow\'.''' )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
A__ = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> bytes:
"""simple docstring"""
A__ = BytesIO()
if image.format in list_image_compression_formats():
A__ = image.format
else:
A__ = '''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
image.save(lowercase_ , format=lowercase_ )
return buffer.getvalue()
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> dict:
"""simple docstring"""
if hasattr(lowercase_ , '''filename''' ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(lowercase_ )}
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> dict:
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support encoding images, please install \'Pillow\'.''' )
A__ = array.dtype
A__ = dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
A__ = dtype.kind
A__ = dtype.itemsize
A__ = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
A__ = np.dtype('''|u1''' )
if dtype_kind not in ["u", "i"]:
raise TypeError(
f"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.""" )
if dtype is not dest_dtype:
warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
A__ = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
A__ = dtype_byteorder + dtype_kind + str(lowercase_ )
A__ = np.dtype(lowercase_ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
f"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}""" )
A__ = PIL.Image.fromarray(array.astype(lowercase_ ) )
return {"path": None, "bytes": image_to_bytes(lowercase_ )}
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> List[dict]:
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support encoding images, please install \'Pillow\'.''' )
if objs:
A__ , A__ = first_non_null_value(lowercase_ )
if isinstance(lowercase_ , lowercase_ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(lowercase_ , np.ndarray ):
A__ = no_op_if_value_is_null(lowercase_ )
return [obj_to_image_dict_func(lowercase_ ) for obj in objs]
elif isinstance(lowercase_ , PIL.Image.Image ):
A__ = no_op_if_value_is_null(lowercase_ )
return [obj_to_image_dict_func(lowercase_ ) for obj in objs]
else:
return objs
else:
return objs
| 87
| 0
|
def _SCREAMING_SNAKE_CASE ( a , a ) -> str:
__A : int = len(a )
__A : int = len(a )
__A : int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
__A : list = []
for char_count in range(a ):
if char_count < first_str_length:
output_list.append(first_str[char_count] )
if char_count < second_str_length:
output_list.append(second_str[char_count] )
return "".join(a )
if __name__ == "__main__":
print(alternative_string_arrange('''AB''', '''XYZ'''), end=''' ''')
| 77
|
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _A( nn.Module ):
"""simple docstring"""
def __init__( self ):
super().__init__()
__A : List[str] = nn.Linear(3 , 4 )
__A : Optional[Any] = nn.BatchNormad(4 )
__A : List[Any] = nn.Linear(4 , 5 )
def UpperCAmelCase_ ( self , _A ):
return self.lineara(self.batchnorm(self.lineara(_A ) ) )
class _A( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
__A : Dict = ModelForTest()
with TemporaryDirectory() as tmp_dir:
offload_state_dict(_A , model.state_dict() )
__A : str = os.path.join(_A , 'index.json' )
self.assertTrue(os.path.isfile(_A ) )
# TODO: add tests on what is inside the index
for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
__A : Optional[int] = os.path.join(_A , F"""{key}.dat""" )
self.assertTrue(os.path.isfile(_A ) )
# TODO: add tests on the fact weights are properly loaded
def UpperCAmelCase_ ( self ):
__A : Dict = [torch.floataa, torch.floataa, torch.bfloataa]
for dtype in dtypes:
__A : Tuple = torch.randn(2 , 3 , dtype=_A )
with TemporaryDirectory() as tmp_dir:
__A : int = offload_weight(_A , 'weight' , _A , {} )
__A : Union[str, Any] = os.path.join(_A , 'weight.dat' )
self.assertTrue(os.path.isfile(_A ) )
self.assertDictEqual(_A , {'weight': {'shape': [2, 3], 'dtype': str(_A ).split('.' )[1]}} )
__A : List[str] = load_offloaded_weight(_A , index['weight'] )
self.assertTrue(torch.equal(_A , _A ) )
def UpperCAmelCase_ ( self ):
__A : int = ModelForTest()
__A : Union[str, Any] = model.state_dict()
__A : Optional[Any] = {k: v for k, v in state_dict.items() if 'linear2' not in k}
__A : str = {k: v for k, v in state_dict.items() if 'linear2' in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(_A , _A )
__A : List[str] = OffloadedWeightsLoader(state_dict=_A , save_folder=_A )
# Every key is there with the right value
self.assertEqual(sorted(_A ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(_A , weight_map[key] ) )
__A : Union[str, Any] = {k: v for k, v in state_dict.items() if 'weight' in k}
__A : List[Any] = {k: v for k, v in state_dict.items() if 'weight' not in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(_A , _A )
__A : Optional[int] = OffloadedWeightsLoader(state_dict=_A , save_folder=_A )
# Every key is there with the right value
self.assertEqual(sorted(_A ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(_A , weight_map[key] ) )
with TemporaryDirectory() as tmp_dir:
offload_state_dict(_A , _A )
# Duplicates are removed
__A : str = OffloadedWeightsLoader(state_dict=_A , save_folder=_A )
# Every key is there with the right value
self.assertEqual(sorted(_A ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(_A , weight_map[key] ) )
def UpperCAmelCase_ ( self ):
__A : Dict = {'a.1': 0, 'a.10': 1, 'a.2': 2}
__A : str = extract_submodules_state_dict(_A , ['a.1', 'a.2'] )
self.assertDictEqual(_A , {'a.1': 0, 'a.2': 2} )
__A : Optional[Any] = {'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
__A : Any = extract_submodules_state_dict(_A , ['a.1', 'a.2'] )
self.assertDictEqual(_A , {'a.1.a': 0, 'a.2.a': 2} )
| 77
| 1
|
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