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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()
_lowerCAmelCase = logging.get_logger(__name__)
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
A_ : Any = DPTConfig()
if "large" in checkpoint_url:
A_ : Dict = 1_0_2_4
A_ : Any = 4_0_9_6
A_ : Any = 2_4
A_ : Optional[int] = 1_6
A_ : Dict = [5, 1_1, 1_7, 2_3]
A_ : Tuple = [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
A_ : Optional[int] = (1, 3_8_4, 3_8_4)
if "ade" in checkpoint_url:
A_ : List[str] = True
A_ : Any = 1_5_0
A_ : str = """huggingface/label-files"""
A_ : Union[str, Any] = """ade20k-id2label.json"""
A_ : List[str] = json.load(open(cached_download(hf_hub_url(_lowerCAmelCase ,_lowerCAmelCase ,repo_type="""dataset""" ) ) ,"""r""" ) )
A_ : Tuple = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
A_ : Dict = idalabel
A_ : Tuple = {v: k for k, v in idalabel.items()}
A_ : Optional[Any] = [1, 1_5_0, 4_8_0, 4_8_0]
return config, expected_shape
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
A_ : Any = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(_lowerCAmelCase ,_lowerCAmelCase )
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
A_ : Optional[int] = name.replace("""pretrained.model""" ,"""dpt.encoder""" )
if "pretrained.model" in name:
A_ : str = name.replace("""pretrained.model""" ,"""dpt.embeddings""" )
if "patch_embed" in name:
A_ : Any = name.replace("""patch_embed""" ,"""patch_embeddings""" )
if "pos_embed" in name:
A_ : int = name.replace("""pos_embed""" ,"""position_embeddings""" )
if "attn.proj" in name:
A_ : str = name.replace("""attn.proj""" ,"""attention.output.dense""" )
if "proj" in name and "project" not in name:
A_ : Union[str, Any] = name.replace("""proj""" ,"""projection""" )
if "blocks" in name:
A_ : List[str] = name.replace("""blocks""" ,"""layer""" )
if "mlp.fc1" in name:
A_ : str = name.replace("""mlp.fc1""" ,"""intermediate.dense""" )
if "mlp.fc2" in name:
A_ : Union[str, Any] = name.replace("""mlp.fc2""" ,"""output.dense""" )
if "norm1" in name:
A_ : Dict = name.replace("""norm1""" ,"""layernorm_before""" )
if "norm2" in name:
A_ : Optional[int] = name.replace("""norm2""" ,"""layernorm_after""" )
if "scratch.output_conv" in name:
A_ : List[Any] = name.replace("""scratch.output_conv""" ,"""head""" )
if "scratch" in name:
A_ : List[Any] = name.replace("""scratch""" ,"""neck""" )
if "layer1_rn" in name:
A_ : Tuple = name.replace("""layer1_rn""" ,"""convs.0""" )
if "layer2_rn" in name:
A_ : Optional[Any] = name.replace("""layer2_rn""" ,"""convs.1""" )
if "layer3_rn" in name:
A_ : Optional[Any] = name.replace("""layer3_rn""" ,"""convs.2""" )
if "layer4_rn" in name:
A_ : Dict = name.replace("""layer4_rn""" ,"""convs.3""" )
if "refinenet" in name:
A_ : int = 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
A_ : int = name.replace(f"""refinenet{layer_idx}""" ,f"""fusion_stage.layers.{abs(layer_idx-4 )}""" )
if "out_conv" in name:
A_ : Tuple = name.replace("""out_conv""" ,"""projection""" )
if "resConfUnit1" in name:
A_ : Dict = name.replace("""resConfUnit1""" ,"""residual_layer1""" )
if "resConfUnit2" in name:
A_ : Tuple = name.replace("""resConfUnit2""" ,"""residual_layer2""" )
if "conv1" in name:
A_ : List[str] = name.replace("""conv1""" ,"""convolution1""" )
if "conv2" in name:
A_ : Tuple = name.replace("""conv2""" ,"""convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
A_ : Optional[int] = name.replace("""pretrained.act_postprocess1.0.project.0""" ,"""neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
A_ : int = name.replace("""pretrained.act_postprocess2.0.project.0""" ,"""neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
A_ : Optional[Any] = name.replace("""pretrained.act_postprocess3.0.project.0""" ,"""neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
A_ : Dict = name.replace("""pretrained.act_postprocess4.0.project.0""" ,"""neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
A_ : Optional[Any] = name.replace("""pretrained.act_postprocess1.3""" ,"""neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
A_ : Any = name.replace("""pretrained.act_postprocess1.4""" ,"""neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
A_ : Dict = name.replace("""pretrained.act_postprocess2.3""" ,"""neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
A_ : Union[str, Any] = name.replace("""pretrained.act_postprocess2.4""" ,"""neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
A_ : List[str] = name.replace("""pretrained.act_postprocess3.3""" ,"""neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
A_ : Optional[int] = name.replace("""pretrained.act_postprocess4.3""" ,"""neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
A_ : int = name.replace("""pretrained.act_postprocess4.4""" ,"""neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
A_ : Any = name.replace("""pretrained""" ,"""dpt""" )
if "bn" in name:
A_ : Any = name.replace("""bn""" ,"""batch_norm""" )
if "head" in name:
A_ : List[Any] = name.replace("""head""" ,"""head.head""" )
if "encoder.norm" in name:
A_ : Optional[int] = name.replace("""encoder.norm""" ,"""layernorm""" )
if "auxlayer" in name:
A_ : Optional[int] = name.replace("""auxlayer""" ,"""auxiliary_head.head""" )
return name
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
A_ : Union[str, Any] = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" )
A_ : Tuple = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
A_ : Tuple = in_proj_weight[: config.hidden_size, :]
A_ : Tuple = in_proj_bias[: config.hidden_size]
A_ : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
A_ : List[str] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
A_ : List[str] = in_proj_weight[
-config.hidden_size :, :
]
A_ : Optional[Any] = in_proj_bias[-config.hidden_size :]
def _lowerCAmelCase ( ):
'''simple docstring'''
A_ : Optional[int] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
A_ : str = Image.open(requests.get(_lowerCAmelCase ,stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
A_ , A_ : Union[str, Any] = get_dpt_config(_lowerCAmelCase )
# load original state_dict from URL
A_ : List[str] = torch.hub.load_state_dict_from_url(_lowerCAmelCase ,map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(_lowerCAmelCase )
# rename keys
for key in state_dict.copy().keys():
A_ : Tuple = state_dict.pop(_lowerCAmelCase )
A_ : List[str] = val
# read in qkv matrices
read_in_q_k_v(_lowerCAmelCase ,_lowerCAmelCase )
# load HuggingFace model
A_ : Dict = DPTForSemanticSegmentation(_lowerCAmelCase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase )
model.eval()
# Check outputs on an image
A_ : Union[str, Any] = 4_8_0 if """ade""" in checkpoint_url else 3_8_4
A_ : Optional[int] = DPTImageProcessor(size=_lowerCAmelCase )
A_ : int = prepare_img()
A_ : Dict = image_processor(_lowerCAmelCase ,return_tensors="""pt""" )
# forward pass
A_ : List[Any] = model(**_lowerCAmelCase ).logits if """ade""" in checkpoint_url else model(**_lowerCAmelCase ).predicted_depth
# Assert logits
A_ : Union[str, Any] = torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
if "ade" in checkpoint_url:
A_ : Union[str, Any] = torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
assert outputs.shape == torch.Size(_lowerCAmelCase )
assert (
torch.allclose(outputs[0, 0, :3, :3] ,_lowerCAmelCase ,atol=1e-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] ,_lowerCAmelCase )
)
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(_lowerCAmelCase )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_lowerCAmelCase )
if push_to_hub:
print("""Pushing model to hub...""" )
model.push_to_hub(
repo_path_or_name=Path(_lowerCAmelCase ,_lowerCAmelCase ) ,organization="""nielsr""" ,commit_message="""Add model""" ,use_temp_dir=_lowerCAmelCase ,)
image_processor.push_to_hub(
repo_path_or_name=Path(_lowerCAmelCase ,_lowerCAmelCase ) ,organization="""nielsr""" ,commit_message="""Add image processor""" ,use_temp_dir=_lowerCAmelCase ,)
if __name__ == "__main__":
_lowerCAmelCase = 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.""",
)
_lowerCAmelCase = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 569
|
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
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 PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
a = StableDiffusionSAGPipeline
a = TEXT_TO_IMAGE_PARAMS
a = TEXT_TO_IMAGE_BATCH_PARAMS
a = TEXT_TO_IMAGE_IMAGE_PARAMS
a = TEXT_TO_IMAGE_IMAGE_PARAMS
a = False
def _lowerCamelCase ( self ):
torch.manual_seed(0 )
A_ : 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 , )
A_ : List[Any] = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=a__ , set_alpha_to_one=a__ , )
torch.manual_seed(0 )
A_ : Optional[int] = 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 , )
torch.manual_seed(0 )
A_ : Union[str, Any] = 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=1000 , )
A_ : Optional[int] = CLIPTextModel(a__ )
A_ : List[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
A_ : Union[str, Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _lowerCamelCase ( self , a__ , a__=0 ):
if str(a__ ).startswith("""mps""" ):
A_ : Union[str, Any] = torch.manual_seed(a__ )
else:
A_ : Optional[int] = torch.Generator(device=a__ ).manual_seed(a__ )
A_ : List[Any] = {
"""prompt""": """.""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 1.0,
"""sag_scale""": 1.0,
"""output_type""": """numpy""",
}
return inputs
def _lowerCamelCase ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCamelCase ( self ):
A_ : Optional[int] = StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" )
A_ : Tuple = sag_pipe.to(a__ )
sag_pipe.set_progress_bar_config(disable=a__ )
A_ : Optional[Any] = """."""
A_ : Optional[Any] = torch.manual_seed(0 )
A_ : str = sag_pipe(
[prompt] , generator=a__ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" )
A_ : Tuple = output.images
A_ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
A_ : List[Any] = np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def _lowerCamelCase ( self ):
A_ : Dict = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
A_ : List[str] = sag_pipe.to(a__ )
sag_pipe.set_progress_bar_config(disable=a__ )
A_ : List[str] = """."""
A_ : List[Any] = torch.manual_seed(0 )
A_ : List[str] = sag_pipe(
[prompt] , generator=a__ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" )
A_ : Union[str, Any] = output.images
A_ : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
A_ : str = np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def _lowerCamelCase ( self ):
A_ : Optional[Any] = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
A_ : Tuple = sag_pipe.to(a__ )
sag_pipe.set_progress_bar_config(disable=a__ )
A_ : Optional[Any] = """."""
A_ : Any = torch.manual_seed(0 )
A_ : Optional[int] = sag_pipe(
[prompt] , width=768 , height=512 , generator=a__ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" , )
A_ : Optional[int] = output.images
assert image.shape == (1, 512, 768, 3)
| 569
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : List[str] = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class __lowerCAmelCase ( lowerCAmelCase):
_a = '''fnet'''
def __init__( self: str , _lowerCAmelCase: Any=3_20_00 , _lowerCAmelCase: Tuple=7_68 , _lowerCAmelCase: List[str]=12 , _lowerCAmelCase: Dict=30_72 , _lowerCAmelCase: Union[str, Any]="gelu_new" , _lowerCAmelCase: str=0.1 , _lowerCAmelCase: str=5_12 , _lowerCAmelCase: Optional[Any]=4 , _lowerCAmelCase: Dict=0.02 , _lowerCAmelCase: Tuple=1e-1_2 , _lowerCAmelCase: Union[str, Any]=False , _lowerCAmelCase: Optional[int]=5_12 , _lowerCAmelCase: List[Any]=3 , _lowerCAmelCase: Optional[int]=1 , _lowerCAmelCase: List[str]=2 , **_lowerCAmelCase: Any , ):
super().__init__(pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
lowercase :List[str] = vocab_size
lowercase :str = max_position_embeddings
lowercase :int = hidden_size
lowercase :Tuple = num_hidden_layers
lowercase :str = intermediate_size
lowercase :Optional[int] = hidden_act
lowercase :Optional[int] = hidden_dropout_prob
lowercase :Optional[Any] = initializer_range
lowercase :List[str] = type_vocab_size
lowercase :List[Any] = layer_norm_eps
lowercase :Union[str, Any] = use_tpu_fourier_optimizations
lowercase :Union[str, Any] = tpu_short_seq_length
| 453
|
def UpperCAmelCase__ ( lowerCamelCase ):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
lowercase :str = 1
lowercase :Tuple = 1
while repunit:
lowercase :Dict = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def UpperCAmelCase__ ( lowerCamelCase = 1000000 ):
lowercase :List[Any] = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(lowerCamelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'''{solution() = }''')
| 453
| 1
|
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
snake_case_ = logging.get_logger(__name__)
@add_end_docstrings(_lowercase )
class a__ ( _lowercase ):
def __init__(self : int, *__UpperCAmelCase : List[str], **__UpperCAmelCase : List[Any] ) -> Dict:
"""simple docstring"""
super().__init__(*__UpperCAmelCase, **__UpperCAmelCase )
self.check_model_type(__UpperCAmelCase )
def lowercase__ (self : Any, __UpperCAmelCase : Optional[int]=None, __UpperCAmelCase : List[Any]=None, __UpperCAmelCase : str=None, **__UpperCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = {}, {}
if padding is not None:
SCREAMING_SNAKE_CASE : Optional[int] = padding
if truncation is not None:
SCREAMING_SNAKE_CASE : List[Any] = truncation
if top_k is not None:
SCREAMING_SNAKE_CASE : str = top_k
return preprocess_params, {}, postprocess_params
def __call__(self : str, __UpperCAmelCase : Union["Image.Image", str], __UpperCAmelCase : str = None, **__UpperCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
if isinstance(__UpperCAmelCase, (Image.Image, str) ) and isinstance(__UpperCAmelCase, __UpperCAmelCase ):
SCREAMING_SNAKE_CASE : Tuple = {'''image''': image, '''question''': question}
else:
SCREAMING_SNAKE_CASE : str = image
SCREAMING_SNAKE_CASE : List[str] = super().__call__(__UpperCAmelCase, **__UpperCAmelCase )
return results
def lowercase__ (self : int, __UpperCAmelCase : Union[str, Any], __UpperCAmelCase : Optional[Any]=False, __UpperCAmelCase : str=False ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(inputs['''image'''] )
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer(
inputs['''question'''], return_tensors=self.framework, padding=__UpperCAmelCase, truncation=__UpperCAmelCase )
SCREAMING_SNAKE_CASE : Tuple = self.image_processor(images=__UpperCAmelCase, return_tensors=self.framework )
model_inputs.update(__UpperCAmelCase )
return model_inputs
def lowercase__ (self : str, __UpperCAmelCase : Optional[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.model(**__UpperCAmelCase )
return model_outputs
def lowercase__ (self : List[Any], __UpperCAmelCase : Union[str, Any], __UpperCAmelCase : int=5 ) -> Optional[Any]:
"""simple docstring"""
if top_k > self.model.config.num_labels:
SCREAMING_SNAKE_CASE : List[str] = self.model.config.num_labels
if self.framework == "pt":
SCREAMING_SNAKE_CASE : Union[str, Any] = model_outputs.logits.sigmoid()[0]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = probs.topk(__UpperCAmelCase )
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
SCREAMING_SNAKE_CASE : Tuple = scores.tolist()
SCREAMING_SNAKE_CASE : str = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__UpperCAmelCase, __UpperCAmelCase )]
| 507
|
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a__ ( nn.Module ):
def __init__(self : Union[str, Any], __UpperCAmelCase : int = 16, __UpperCAmelCase : int = 88, __UpperCAmelCase : Optional[int] = None, __UpperCAmelCase : int = 1, __UpperCAmelCase : float = 0.0, __UpperCAmelCase : int = 32, __UpperCAmelCase : Optional[int] = None, __UpperCAmelCase : bool = False, __UpperCAmelCase : Optional[int] = None, __UpperCAmelCase : Optional[int] = None, __UpperCAmelCase : str = "geglu", __UpperCAmelCase : Optional[int] = None, ) -> str:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE : Any = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=__UpperCAmelCase, attention_head_dim=__UpperCAmelCase, in_channels=__UpperCAmelCase, num_layers=__UpperCAmelCase, dropout=__UpperCAmelCase, norm_num_groups=__UpperCAmelCase, cross_attention_dim=__UpperCAmelCase, attention_bias=__UpperCAmelCase, sample_size=__UpperCAmelCase, num_vector_embeds=__UpperCAmelCase, activation_fn=__UpperCAmelCase, num_embeds_ada_norm=__UpperCAmelCase, )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
SCREAMING_SNAKE_CASE : int = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
SCREAMING_SNAKE_CASE : Dict = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
SCREAMING_SNAKE_CASE : Optional[Any] = [1, 0]
def lowercase__ (self : Union[str, Any], __UpperCAmelCase : Dict, __UpperCAmelCase : Union[str, Any], __UpperCAmelCase : List[str]=None, __UpperCAmelCase : List[Any]=None, __UpperCAmelCase : List[str]=None, __UpperCAmelCase : bool = True, ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = hidden_states
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : List[str] = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
SCREAMING_SNAKE_CASE : Tuple = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
SCREAMING_SNAKE_CASE : str = self.transformer_index_for_condition[i]
SCREAMING_SNAKE_CASE : Dict = self.transformers[transformer_index](
__UpperCAmelCase, encoder_hidden_states=__UpperCAmelCase, timestep=__UpperCAmelCase, cross_attention_kwargs=__UpperCAmelCase, return_dict=__UpperCAmelCase, )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
SCREAMING_SNAKE_CASE : Union[str, Any] = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
SCREAMING_SNAKE_CASE : Optional[int] = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=__UpperCAmelCase )
| 507
| 1
|
from __future__ import annotations
from typing import Any
class a__ :
def __init__( self , UpperCAmelCase = 6 ) -> None:
__a = None
__a = None
self.create_linked_list(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> None:
__a = Node()
__a = current_node
__a = current_node
__a = current_node
for _ in range(1 , UpperCAmelCase ):
__a = Node()
__a = current_node
__a = previous_node
__a = current_node
__a = self.front
__a = previous_node
def __SCREAMING_SNAKE_CASE ( self ) -> bool:
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def __SCREAMING_SNAKE_CASE ( self ) -> Any | None:
self.check_can_perform_operation()
return self.front.data if self.front else None
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> None:
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
__a = self.rear.next
if self.rear:
__a = data
def __SCREAMING_SNAKE_CASE ( self ) -> Any:
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
__a = self.front.data
__a = None
return data
__a = self.front
__a = old_front.next
__a = old_front.data
__a = None
return data
def __SCREAMING_SNAKE_CASE ( self ) -> None:
if self.is_empty():
raise Exception('Empty Queue' )
def __SCREAMING_SNAKE_CASE ( self ) -> None:
if self.rear and self.rear.next == self.front:
raise Exception('Full Queue' )
class a__ :
def __init__( self ) -> None:
__a = None
__a = None
__a = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 246
|
from __future__ import annotations
lowerCamelCase_ : List[Any] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
class a__ :
def __init__( self , UpperCAmelCase , UpperCAmelCase ) -> None:
__a = graph
# mapping node to its parent in resulting breadth first tree
__a = {}
__a = source_vertex
def __SCREAMING_SNAKE_CASE ( self ) -> None:
__a = {self.source_vertex}
__a = None
__a = [self.source_vertex] # first in first out queue
while queue:
__a = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(UpperCAmelCase )
__a = vertex
queue.append(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> str:
if target_vertex == self.source_vertex:
return self.source_vertex
__a = self.parent.get(UpperCAmelCase )
if target_vertex_parent is None:
__a = (
f'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
)
raise ValueError(UpperCAmelCase )
return self.shortest_path(UpperCAmelCase ) + f'''->{target_vertex}'''
if __name__ == "__main__":
lowerCamelCase_ : Optional[int] = Graph(graph, """G""")
g.breath_first_search()
print(g.shortest_path("""D"""))
print(g.shortest_path("""G"""))
print(g.shortest_path("""Foo"""))
| 246
| 1
|
"""simple docstring"""
def lowercase_ ( _lowercase : int = 10**12 ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = 1
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Dict = 1
UpperCAmelCase : Union[str, Any] = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev_denominator += 2 * denominator
denominator += 2 * prev_denominator
return (denominator + 1) // 2
if __name__ == "__main__":
print(f'''{solution() = }''')
| 595
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowercase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''MLukeTokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 272
| 0
|
import os
import time
import numpy as np
import onnxruntime as ort
a = '''1'''
a = '''0'''
a = '''1'''
a = ort.SessionOptions()
a = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
a = ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
a = ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
a = ort.RunOptions()
a = 1_2_8
a = 1
a = np.ones((batch, sequence), dtype=np.intaa)
a = np.ones((batch, sequence), dtype=np.intaa)
a = 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...')
a = time.time()
a = 2_0_0_0
a = {}
for iter in range(max_iters):
a = 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))
| 709
|
import cva
import numpy as np
class UpperCamelCase__ :
def __init__( self : List[str] , UpperCamelCase__ : float , UpperCamelCase__ : int ):
'''simple docstring'''
if k in (0.04, 0.06):
lowercase_ = k
lowercase_ = window_size
else:
raise ValueError("""invalid k value""" )
def __str__( self : Optional[int] ):
'''simple docstring'''
return str(self.k )
def UpperCAmelCase__ ( self : Optional[Any] , UpperCamelCase__ : str ):
'''simple docstring'''
lowercase_ = cva.imread(UpperCamelCase__ , 0 )
lowercase_ , lowercase_ = img.shape
lowercase_ = []
lowercase_ = img.copy()
lowercase_ = cva.cvtColor(UpperCamelCase__ , cva.COLOR_GRAY2RGB )
lowercase_ , lowercase_ = np.gradient(UpperCamelCase__ )
lowercase_ = dx**2
lowercase_ = dy**2
lowercase_ = dx * dy
lowercase_ = 0.04
lowercase_ = self.window_size // 2
for y in range(UpperCamelCase__ , h - offset ):
for x in range(UpperCamelCase__ , w - offset ):
lowercase_ = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
lowercase_ = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
lowercase_ = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
lowercase_ = (wxx * wyy) - (wxy**2)
lowercase_ = wxx + wyy
lowercase_ = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) , 0 )
color_img.itemset((y, x, 1) , 0 )
color_img.itemset((y, x, 2) , 255 )
return color_img, corner_list
if __name__ == "__main__":
a = HarrisCorner(0.04, 3)
a , a = edge_detect.detect('path_to_image')
cva.imwrite('detect.png', color_img)
| 650
| 0
|
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers import (
BertTokenizer,
BlipConfig,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
)
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
SCREAMING_SNAKE_CASE = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase).raw).convert('RGB')
SCREAMING_SNAKE_CASE = transforms.Compose(
[
transforms.Resize((image_size, image_size) , interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73) , (0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)),
])
SCREAMING_SNAKE_CASE = transform(_UpperCAmelCase).unsqueeze(0).to(_UpperCAmelCase)
return image
def lowerCamelCase__ (_UpperCAmelCase):
if "visual_encoder" in key:
SCREAMING_SNAKE_CASE = re.sub('visual_encoder*' , 'vision_model.encoder' , _UpperCAmelCase)
if "blocks" in key:
SCREAMING_SNAKE_CASE = re.sub(R'blocks' , 'layers' , _UpperCAmelCase)
if "attn" in key:
SCREAMING_SNAKE_CASE = re.sub(R'attn' , 'self_attn' , _UpperCAmelCase)
if "norm1" in key:
SCREAMING_SNAKE_CASE = re.sub(R'norm1' , 'layer_norm1' , _UpperCAmelCase)
if "norm2" in key:
SCREAMING_SNAKE_CASE = re.sub(R'norm2' , 'layer_norm2' , _UpperCAmelCase)
if "encoder.norm" in key:
SCREAMING_SNAKE_CASE = re.sub(R'encoder.norm' , 'post_layernorm' , _UpperCAmelCase)
if "encoder.patch_embed.proj" in key:
SCREAMING_SNAKE_CASE = re.sub(R'encoder.patch_embed.proj' , 'embeddings.patch_embedding' , _UpperCAmelCase)
if "encoder.pos_embed" in key:
SCREAMING_SNAKE_CASE = re.sub(R'encoder.pos_embed' , 'embeddings.position_embedding' , _UpperCAmelCase)
if "encoder.cls_token" in key:
SCREAMING_SNAKE_CASE = re.sub(R'encoder.cls_token' , 'embeddings.class_embedding' , _UpperCAmelCase)
if "self_attn" in key:
SCREAMING_SNAKE_CASE = re.sub(R'self_attn.proj' , 'self_attn.projection' , _UpperCAmelCase)
return key
@torch.no_grad()
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase=None):
if config_path is not None:
SCREAMING_SNAKE_CASE = BlipConfig.from_pretrained(_UpperCAmelCase)
else:
SCREAMING_SNAKE_CASE = BlipConfig(projection_dim=512 , text_config={} , vision_config={})
SCREAMING_SNAKE_CASE = BlipForConditionalGeneration(_UpperCAmelCase).eval()
SCREAMING_SNAKE_CASE = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'
SCREAMING_SNAKE_CASE = blip_decoder(pretrained=_UpperCAmelCase , image_size=384 , vit='base')
SCREAMING_SNAKE_CASE = pt_model.eval()
SCREAMING_SNAKE_CASE = pt_model.state_dict()
for key in modified_state_dict.copy():
SCREAMING_SNAKE_CASE = modified_state_dict.pop(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = rename_key(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = value
hf_model.load_state_dict(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = 384
SCREAMING_SNAKE_CASE = load_demo_image(image_size=_UpperCAmelCase , device='cpu')
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained('bert-base-uncased')
SCREAMING_SNAKE_CASE = tokenizer(['a picture of']).input_ids
SCREAMING_SNAKE_CASE = hf_model.generate(_UpperCAmelCase , _UpperCAmelCase)
assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]
SCREAMING_SNAKE_CASE = hf_model.generate(_UpperCAmelCase)
assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]
if pytorch_dump_folder_path is not None:
hf_model.save_pretrained(_UpperCAmelCase)
# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
SCREAMING_SNAKE_CASE = (
'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
)
SCREAMING_SNAKE_CASE = blip_vqa(pretrained=_UpperCAmelCase , image_size=_UpperCAmelCase , vit='base')
vqa_model.eval()
SCREAMING_SNAKE_CASE = vqa_model.state_dict()
for key in modified_state_dict.copy():
SCREAMING_SNAKE_CASE = modified_state_dict.pop(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = rename_key(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = value
SCREAMING_SNAKE_CASE = BlipForQuestionAnswering(_UpperCAmelCase)
hf_vqa_model.load_state_dict(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = ['How many dogs are in this image?']
SCREAMING_SNAKE_CASE = tokenizer(_UpperCAmelCase , return_tensors='pt').input_ids
SCREAMING_SNAKE_CASE = hf_vqa_model.generate(_UpperCAmelCase , _UpperCAmelCase)
print(tokenizer.decode(answer[0]))
assert tokenizer.decode(answer[0]) == "[UNK] 1 [SEP]"
if pytorch_dump_folder_path is not None:
hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa')
SCREAMING_SNAKE_CASE = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'
SCREAMING_SNAKE_CASE = blip_itm(pretrained=_UpperCAmelCase , image_size=_UpperCAmelCase , vit='base')
itm_model.eval()
SCREAMING_SNAKE_CASE = itm_model.state_dict()
for key in modified_state_dict.copy():
SCREAMING_SNAKE_CASE = modified_state_dict.pop(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = rename_key(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = value
SCREAMING_SNAKE_CASE = BlipForImageTextRetrieval(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = ['A picture of a woman with a dog sitting in a beach']
SCREAMING_SNAKE_CASE = tokenizer(
_UpperCAmelCase , return_tensors='pt' , padding='max_length' , truncation=_UpperCAmelCase , max_length=35 , ).input_ids
hf_itm_model.load_state_dict(_UpperCAmelCase)
hf_itm_model.eval()
SCREAMING_SNAKE_CASE = hf_itm_model(_UpperCAmelCase , _UpperCAmelCase , use_itm_head=_UpperCAmelCase)
SCREAMING_SNAKE_CASE = hf_itm_model(_UpperCAmelCase , _UpperCAmelCase , use_itm_head=_UpperCAmelCase)
assert out[0].item() == 0.21_10_68_74_94_27_79_54
assert torch.nn.functional.softmax(out_itm[0] , dim=1)[:, 1].item() == 0.4_56_98_84_53_86_50_51_27
if pytorch_dump_folder_path is not None:
hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm')
if __name__ == "__main__":
a_ : List[Any] = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
a_ : Union[str, Any] = parser.parse_args()
convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 73
|
'''simple docstring'''
from math import factorial
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> float:
if successes > trials:
raise ValueError('''successes must be lower or equal to trials''' )
if trials < 0 or successes < 0:
raise ValueError('''the function is defined for non-negative integers''' )
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError('''the function is defined for non-negative integers''' )
if not 0 < prob < 1:
raise ValueError('''prob has to be in range of 1 - 0''' )
UpperCAmelCase__ : Any = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
UpperCAmelCase__ : Any = float(factorial(lowerCAmelCase__ ) )
coefficient /= factorial(lowerCAmelCase__ ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('''Probability of 2 successes out of 4 trails''')
print('''with probability of 0.75 is:''', end=''' ''')
print(binomial_distribution(2, 4, 0.75))
| 75
| 0
|
def A ( lowercase = 4_000_000 ) -> int:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase , UpperCamelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(lowercase )
UpperCamelCase , UpperCamelCase = b, a + b
return sum(lowercase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 714
|
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
_UpperCAmelCase : Any = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n"
_UpperCAmelCase : str = "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"
_UpperCAmelCase : List[str] = "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n predictions: list of predictions to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n \"accuracy\": Accuracy\n \"f1\": F1 score\n \"pearson\": Pearson Correlation\n \"spearmanr\": Spearman Correlation\n \"matthews_correlation\": Matthew Correlation\nExamples:\n\n >>> glue_metric = datasets.load_metric('glue', 'sst2') # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> glue_metric = datasets.load_metric('glue', 'mrpc') # 'mrpc' or 'qqp'\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> glue_metric = datasets.load_metric('glue', 'stsb')\n >>> references = [0., 1., 2., 3., 4., 5.]\n >>> predictions = [0., 1., 2., 3., 4., 5.]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n {'pearson': 1.0, 'spearmanr': 1.0}\n\n >>> glue_metric = datasets.load_metric('glue', 'cola')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'matthews_correlation': 1.0}\n"
def A ( lowercase , lowercase ) -> List[str]:
'''simple docstring'''
return float((preds == labels).mean() )
def A ( lowercase , lowercase ) -> Tuple:
'''simple docstring'''
UpperCamelCase = simple_accuracy(lowercase , lowercase )
UpperCamelCase = float(fa_score(y_true=lowercase , y_pred=lowercase ) )
return {
"accuracy": acc,
"f1": fa,
}
def A ( lowercase , lowercase ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase = float(pearsonr(lowercase , lowercase )[0] )
UpperCamelCase = float(spearmanr(lowercase , lowercase )[0] )
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase ( datasets.Metric ):
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
if self.config_name not in [
"sst2",
"mnli",
"mnli_mismatched",
"mnli_matched",
"cola",
"stsb",
"mrpc",
"qqp",
"qnli",
"rte",
"wnli",
"hans",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
'"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32' ),
'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32' ),
} ) , codebase_urls=[] , reference_urls=[] , format='numpy' , )
def __UpperCamelCase ( self , A_ , A_ ) -> Any:
"""simple docstring"""
if self.config_name == "cola":
return {"matthews_correlation": matthews_corrcoef(A_ , A_ )}
elif self.config_name == "stsb":
return pearson_and_spearman(A_ , A_ )
elif self.config_name in ["mrpc", "qqp"]:
return acc_and_fa(A_ , A_ )
elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
return {"accuracy": simple_accuracy(A_ , A_ )}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
'"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]' )
| 3
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE_ = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ['DeiTFeatureExtractor']
SCREAMING_SNAKE_CASE_ = ['DeiTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DeiTForImageClassification',
'DeiTForImageClassificationWithTeacher',
'DeiTForMaskedImageModeling',
'DeiTModel',
'DeiTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDeiTForImageClassification',
'TFDeiTForImageClassificationWithTeacher',
'TFDeiTForMaskedImageModeling',
'TFDeiTModel',
'TFDeiTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 582
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class lowerCAmelCase_ ( snake_case__ ):
"""simple docstring"""
a_ :List[str] ="""biogpt"""
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any]=4_2_3_8_4 , SCREAMING_SNAKE_CASE__ : Tuple=1_0_2_4 , SCREAMING_SNAKE_CASE__ : List[str]=2_4 , SCREAMING_SNAKE_CASE__ : Tuple=1_6 , SCREAMING_SNAKE_CASE__ : List[Any]=4_0_9_6 , SCREAMING_SNAKE_CASE__ : List[Any]="gelu" , SCREAMING_SNAKE_CASE__ : List[str]=0.1 , SCREAMING_SNAKE_CASE__ : Dict=0.1 , SCREAMING_SNAKE_CASE__ : List[str]=1_0_2_4 , SCREAMING_SNAKE_CASE__ : str=0.0_2 , SCREAMING_SNAKE_CASE__ : str=1E-12 , SCREAMING_SNAKE_CASE__ : Optional[int]=True , SCREAMING_SNAKE_CASE__ : Optional[Any]=True , SCREAMING_SNAKE_CASE__ : int=0.0 , SCREAMING_SNAKE_CASE__ : str=0.0 , SCREAMING_SNAKE_CASE__ : str=1 , SCREAMING_SNAKE_CASE__ : int=0 , SCREAMING_SNAKE_CASE__ : int=2 , **SCREAMING_SNAKE_CASE__ : Tuple , ):
'''simple docstring'''
__a = vocab_size
__a = max_position_embeddings
__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 = initializer_range
__a = layer_norm_eps
__a = scale_embedding
__a = use_cache
__a = layerdrop
__a = activation_dropout
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__ , bos_token_id=SCREAMING_SNAKE_CASE__ , eos_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
| 582
| 1
|
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __lowerCAmelCase ( snake_case : str = "isbn/0140328726" ) -> dict:
__lowerCamelCase: Tuple = olid.strip().strip("""/""" ) # Remove leading/trailing whitespace & slashes
if new_olid.count("""/""" ) != 1:
__lowerCamelCase: Dict = f'{olid} is not a valid Open Library olid'
raise ValueError(UpperCAmelCase__ )
return requests.get(f'https://openlibrary.org/{new_olid}.json' ).json()
def __lowerCAmelCase ( snake_case : dict ) -> dict:
__lowerCamelCase: Union[str, Any] = {
"""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)""",
}
__lowerCamelCase: int = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
__lowerCamelCase: Optional[Any] = [
get_openlibrary_data(author["""key"""] )["""name"""] for author in data["""Authors"""]
]
__lowerCamelCase: str = data["""First sentence"""]["""value"""]
for key, value in data.items():
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__lowerCamelCase: Dict = """, """.join(UpperCAmelCase__ )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
_A : Optional[int] = 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 (10, 13) 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:
_A : List[str] = 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}.""")
| 715
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, 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, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a ( _UpperCAmelCase ,_UpperCAmelCase ,unittest.TestCase ):
UpperCAmelCase__ : Tuple = CycleDiffusionPipeline
UpperCAmelCase__ : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"negative_prompt",
"height",
"width",
"negative_prompt_embeds",
}
UpperCAmelCase__ : Union[str, Any] = PipelineTesterMixin.required_optional_params - {"latents"}
UpperCAmelCase__ : Optional[int] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"} )
UpperCAmelCase__ : Optional[int] = IMAGE_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase__ : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def SCREAMING_SNAKE_CASE__ ( self : str ):
torch.manual_seed(0 )
__lowerCamelCase: Tuple = 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 , )
__lowerCamelCase: Dict = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , num_train_timesteps=1000 , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , )
torch.manual_seed(0 )
__lowerCamelCase: Tuple = 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 , )
torch.manual_seed(0 )
__lowerCamelCase: str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__lowerCamelCase: int = CLIPTextModel(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Tuple = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__lowerCamelCase: Optional[int] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : str=0 ):
__lowerCamelCase: Optional[Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: List[str] = image / 2 + 0.5
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
__lowerCamelCase: int = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
__lowerCamelCase: Tuple = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Any = {
"""prompt""": """An astronaut riding an elephant""",
"""source_prompt""": """An astronaut riding a horse""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""eta""": 0.1,
"""strength""": 0.8,
"""guidance_scale""": 3,
"""source_guidance_scale""": 1,
"""output_type""": """numpy""",
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Any ):
__lowerCamelCase: Tuple = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase: Union[str, Any] = self.get_dummy_components()
__lowerCamelCase: Union[str, Any] = CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: List[Any] = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: List[Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Dict = pipe(**SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Union[str, Any] = output.images
__lowerCamelCase: int = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
__lowerCamelCase: Dict = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
__lowerCamelCase: Optional[int] = self.get_dummy_components()
for name, module in components.items():
if hasattr(SCREAMING_SNAKE_CASE_ , """half""" ):
__lowerCamelCase: Tuple = module.half()
__lowerCamelCase: Union[str, Any] = CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Optional[Any] = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: str = pipe(**SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: int = output.images
__lowerCamelCase: Optional[int] = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
__lowerCamelCase: List[str] = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def SCREAMING_SNAKE_CASE__ ( self : int ):
return super().test_save_load_local()
@unittest.skip("""non-deterministic pipeline""" )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
return super().test_inference_batch_single_identical()
@skip_mps
def SCREAMING_SNAKE_CASE__ ( self : str ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
return super().test_save_load_optional_components()
@skip_mps
def SCREAMING_SNAKE_CASE__ ( self : int ):
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
__lowerCamelCase: Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/cycle-diffusion/black_colored_car.png""" )
__lowerCamelCase: Tuple = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy""" )
__lowerCamelCase: str = init_image.resize((512, 512) )
__lowerCamelCase: Dict = """CompVis/stable-diffusion-v1-4"""
__lowerCamelCase: Optional[int] = DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_ , subfolder="""scheduler""" )
__lowerCamelCase: List[Any] = CycleDiffusionPipeline.from_pretrained(
SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ , torch_dtype=torch.floataa , revision="""fp16""" )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
pipe.enable_attention_slicing()
__lowerCamelCase: List[Any] = """A black colored car"""
__lowerCamelCase: List[Any] = """A blue colored car"""
__lowerCamelCase: List[Any] = torch.manual_seed(0 )
__lowerCamelCase: Any = pipe(
prompt=SCREAMING_SNAKE_CASE_ , source_prompt=SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=SCREAMING_SNAKE_CASE_ , output_type="""np""" , )
__lowerCamelCase: Dict = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5E-1
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
__lowerCamelCase: Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/cycle-diffusion/black_colored_car.png""" )
__lowerCamelCase: Tuple = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy""" )
__lowerCamelCase: List[str] = init_image.resize((512, 512) )
__lowerCamelCase: List[Any] = """CompVis/stable-diffusion-v1-4"""
__lowerCamelCase: Optional[Any] = DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_ , subfolder="""scheduler""" )
__lowerCamelCase: Dict = CycleDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
pipe.enable_attention_slicing()
__lowerCamelCase: Optional[int] = """A black colored car"""
__lowerCamelCase: int = """A blue colored car"""
__lowerCamelCase: str = torch.manual_seed(0 )
__lowerCamelCase: Optional[Any] = pipe(
prompt=SCREAMING_SNAKE_CASE_ , source_prompt=SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=SCREAMING_SNAKE_CASE_ , output_type="""np""" , )
__lowerCamelCase: Any = output.images
assert np.abs(image - expected_image ).max() < 2E-2
| 189
| 0
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_A: Dict = logging.get_logger(__name__)
_A: Union[str, Any] = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
_A: Tuple = {
"""vocab_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
),
"""google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
},
"""tokenizer_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-openqa""": (
"""https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-reader""": (
"""https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-openqa""": (
"""https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-reader""": (
"""https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
),
},
}
_A: Dict = {
"""google/realm-cc-news-pretrained-embedder""": 512,
"""google/realm-cc-news-pretrained-encoder""": 512,
"""google/realm-cc-news-pretrained-scorer""": 512,
"""google/realm-cc-news-pretrained-openqa""": 512,
"""google/realm-orqa-nq-openqa""": 512,
"""google/realm-orqa-nq-reader""": 512,
"""google/realm-orqa-wq-openqa""": 512,
"""google/realm-orqa-wq-reader""": 512,
}
_A: Any = {
"""google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : int = VOCAB_FILES_NAMES
_A : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
_A : Union[str, Any] = PRETRAINED_INIT_CONFIGURATION
_A : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A : Dict = RealmTokenizer
def __init__( self , __A=None , __A=None , __A=True , __A="[UNK]" , __A="[SEP]" , __A="[PAD]" , __A="[CLS]" , __A="[MASK]" , __A=True , __A=None , **__A , ):
super().__init__(
__A , tokenizer_file=__A , do_lower_case=__A , unk_token=__A , sep_token=__A , pad_token=__A , cls_token=__A , mask_token=__A , tokenize_chinese_chars=__A , strip_accents=__A , **__A , )
__UpperCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __A ) != do_lower_case
or normalizer_state.get('strip_accents' , __A ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __A ) != tokenize_chinese_chars
):
__UpperCAmelCase = getattr(__A , normalizer_state.pop('type' ) )
__UpperCAmelCase = do_lower_case
__UpperCAmelCase = strip_accents
__UpperCAmelCase = tokenize_chinese_chars
__UpperCAmelCase = normalizer_class(**__A )
__UpperCAmelCase = do_lower_case
def __lowerCamelCase ( self , __A , **__A ):
__UpperCAmelCase = PaddingStrategy.MAX_LENGTH
__UpperCAmelCase = text
__UpperCAmelCase = kwargs.pop('text_pair' , __A )
__UpperCAmelCase = kwargs.pop('return_tensors' , __A )
__UpperCAmelCase = {
'input_ids': [],
'attention_mask': [],
'token_type_ids': [],
}
for idx, candidate_text in enumerate(__A ):
if batch_text_pair is not None:
__UpperCAmelCase = batch_text_pair[idx]
else:
__UpperCAmelCase = None
__UpperCAmelCase = super().__call__(__A , __A , return_tensors=__A , **__A )
__UpperCAmelCase = encoded_candidates.get('input_ids' )
__UpperCAmelCase = encoded_candidates.get('attention_mask' )
__UpperCAmelCase = encoded_candidates.get('token_type_ids' )
if encoded_input_ids is not None:
output_data["input_ids"].append(__A )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(__A )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(__A )
__UpperCAmelCase = {key: item for key, item in output_data.items() if len(__A ) != 0}
return BatchEncoding(__A , tensor_type=__A )
def __lowerCamelCase ( self , __A , __A=None ):
__UpperCAmelCase = [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 , __A , __A = None ):
__UpperCAmelCase = [self.sep_token_id]
__UpperCAmelCase = [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 , __A , __A = None ):
__UpperCAmelCase = self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
| 126
|
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN'] )
__UpperCAmelCase = g.get_repo('huggingface/accelerate' )
__UpperCAmelCase = repo.get_issues(state='open' )
for issue in open_issues:
__UpperCAmelCase = sorted([comment for comment in issue.get_comments()] , key=lambda _lowerCAmelCase : i.created_at , reverse=_lowerCAmelCase )
__UpperCAmelCase = comments[0] if len(_lowerCAmelCase ) > 0 else None
__UpperCAmelCase = dt.utcnow()
__UpperCAmelCase = (current_time - issue.updated_at).days
__UpperCAmelCase = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state='closed' )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
if __name__ == "__main__":
main()
| 126
| 1
|
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> Tuple:
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE : Tuple = np.full((len(__SCREAMING_SNAKE_CASE ), sequence_length, 2) , __SCREAMING_SNAKE_CASE )
else:
_SCREAMING_SNAKE_CASE : Any = np.full((len(__SCREAMING_SNAKE_CASE ), sequence_length) , __SCREAMING_SNAKE_CASE )
for i, tensor in enumerate(__SCREAMING_SNAKE_CASE ):
if padding_side == "right":
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE : Optional[Any] = tensor[:sequence_length]
else:
_SCREAMING_SNAKE_CASE : int = tensor[:sequence_length]
else:
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE : Any = tensor[:sequence_length]
else:
_SCREAMING_SNAKE_CASE : List[str] = tensor[:sequence_length]
return out_tensor.tolist()
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> List[Any]:
_SCREAMING_SNAKE_CASE : List[str] = ord(__SCREAMING_SNAKE_CASE )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
_SCREAMING_SNAKE_CASE : Tuple = unicodedata.category(__SCREAMING_SNAKE_CASE )
if cat.startswith("""P""" ):
return True
return False
@dataclass
class _snake_case ( __snake_case ):
"""simple docstring"""
a = 42
a = True
a = None
a = None
a = -1_00
a = "pt"
def _lowerCAmelCase ( self : List[str] , _A : str):
"""simple docstring"""
import torch
_SCREAMING_SNAKE_CASE : List[str] = """label""" if """label""" in features[0].keys() else """labels"""
_SCREAMING_SNAKE_CASE : Optional[Any] = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
_SCREAMING_SNAKE_CASE : List[str] = self.tokenizer.pad(
_A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="""pt""" if labels is None else None , )
if labels is None:
return batch
_SCREAMING_SNAKE_CASE : Tuple = torch.tensor(batch["""entity_ids"""]).shape[1]
_SCREAMING_SNAKE_CASE : Tuple = self.tokenizer.padding_side
if padding_side == "right":
_SCREAMING_SNAKE_CASE : Union[str, Any] = [
list(_A) + [self.label_pad_token_id] * (sequence_length - len(_A)) for label in labels
]
else:
_SCREAMING_SNAKE_CASE : List[str] = [
[self.label_pad_token_id] * (sequence_length - len(_A)) + list(_A) for label in labels
]
_SCREAMING_SNAKE_CASE : Optional[int] = [feature["""ner_tags"""] for feature in features]
_SCREAMING_SNAKE_CASE : Tuple = padding_tensor(_A , -1 , _A , _A)
_SCREAMING_SNAKE_CASE : Optional[Any] = [feature["""original_entity_spans"""] for feature in features]
_SCREAMING_SNAKE_CASE : Dict = padding_tensor(_A , (-1, -1) , _A , _A)
_SCREAMING_SNAKE_CASE : Optional[Any] = {k: torch.tensor(_A , dtype=torch.intaa) for k, v in batch.items()}
return batch
| 721
|
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
lowerCAmelCase_ = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> List[str]:
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> Any:
return max(metric_fn(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) for gt in ground_truths )
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> List[str]:
_SCREAMING_SNAKE_CASE : List[str] = [line.strip() for line in open(__SCREAMING_SNAKE_CASE , """r""" ).readlines()]
_SCREAMING_SNAKE_CASE : Dict = []
if args.gold_data_mode == "qa":
_SCREAMING_SNAKE_CASE : int = pd.read_csv(__SCREAMING_SNAKE_CASE , sep="""\t""" , header=__SCREAMING_SNAKE_CASE )
for answer_list in data[1]:
_SCREAMING_SNAKE_CASE : Union[str, Any] = ast.literal_eval(__SCREAMING_SNAKE_CASE )
answers.append(__SCREAMING_SNAKE_CASE )
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = [line.strip() for line in open(__SCREAMING_SNAKE_CASE , """r""" ).readlines()]
_SCREAMING_SNAKE_CASE : Optional[int] = [[reference] for reference in references]
_SCREAMING_SNAKE_CASE : Optional[int] = 0
for prediction, ground_truths in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
total += 1
em += metric_max_over_ground_truths(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
fa += metric_max_over_ground_truths(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE : Dict = 1_00.0 * em / total
_SCREAMING_SNAKE_CASE : Optional[Any] = 1_00.0 * fa / total
logger.info(F"""F1: {fa:.2f}""" )
logger.info(F"""EM: {em:.2f}""" )
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> Union[str, Any]:
_SCREAMING_SNAKE_CASE : Tuple = args.k
_SCREAMING_SNAKE_CASE : int = [line.strip() for line in open(__SCREAMING_SNAKE_CASE , """r""" ).readlines()]
_SCREAMING_SNAKE_CASE : Any = [line.strip() for line in open(__SCREAMING_SNAKE_CASE , """r""" ).readlines()]
_SCREAMING_SNAKE_CASE : Optional[Any] = 0
for hypo, reference in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE : Optional[Any] = set(hypo.split("""\t""" )[:k] )
_SCREAMING_SNAKE_CASE : Union[str, Any] = set(reference.split("""\t""" ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
_SCREAMING_SNAKE_CASE : int = 1_00.0 * em / total
logger.info(F"""Precision@{k}: {em: .2f}""" )
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> Dict:
def strip_title(__SCREAMING_SNAKE_CASE ):
if title.startswith("""\"""" ):
_SCREAMING_SNAKE_CASE : Optional[int] = title[1:]
if title.endswith("""\"""" ):
_SCREAMING_SNAKE_CASE : str = title[:-1]
return title
_SCREAMING_SNAKE_CASE : Dict = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
__SCREAMING_SNAKE_CASE , return_tensors="""pt""" , padding=__SCREAMING_SNAKE_CASE , truncation=__SCREAMING_SNAKE_CASE , )["""input_ids"""].to(args.device )
_SCREAMING_SNAKE_CASE : List[str] = rag_model.rag.question_encoder(__SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE : Any = question_enc_outputs[0]
_SCREAMING_SNAKE_CASE : List[Any] = rag_model.retriever(
__SCREAMING_SNAKE_CASE , question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors="""pt""" , )
_SCREAMING_SNAKE_CASE : Optional[int] = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
_SCREAMING_SNAKE_CASE : Union[str, Any] = []
for docs in all_docs:
_SCREAMING_SNAKE_CASE : str = [strip_title(__SCREAMING_SNAKE_CASE ) for title in docs["""title"""]]
provenance_strings.append("""\t""".join(__SCREAMING_SNAKE_CASE ) )
return provenance_strings
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> List[Any]:
with torch.no_grad():
_SCREAMING_SNAKE_CASE : Optional[Any] = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
__SCREAMING_SNAKE_CASE , return_tensors="""pt""" , padding=__SCREAMING_SNAKE_CASE , truncation=__SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE : Union[str, Any] = inputs_dict.input_ids.to(args.device )
_SCREAMING_SNAKE_CASE : Union[str, Any] = inputs_dict.attention_mask.to(args.device )
_SCREAMING_SNAKE_CASE : Optional[Any] = rag_model.generate( # rag_model overwrites generate
__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=__SCREAMING_SNAKE_CASE , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
_SCREAMING_SNAKE_CASE : Tuple = rag_model.retriever.generator_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE )
if args.print_predictions:
for q, a in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
logger.info("""Q: {} - A: {}""".format(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
return answers
def lowerCamelCase_()-> List[Any]:
_SCREAMING_SNAKE_CASE : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""" , choices=["""rag_sequence""", """rag_token""", """bart"""] , type=__SCREAMING_SNAKE_CASE , help=(
"""RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
""" model_name_or_path"""
) , )
parser.add_argument(
"""--index_name""" , default=__SCREAMING_SNAKE_CASE , choices=["""exact""", """compressed""", """legacy"""] , type=__SCREAMING_SNAKE_CASE , help="""RAG model retriever type""" , )
parser.add_argument(
"""--index_path""" , default=__SCREAMING_SNAKE_CASE , type=__SCREAMING_SNAKE_CASE , help="""Path to the retrieval index""" , )
parser.add_argument("""--n_docs""" , default=5 , type=__SCREAMING_SNAKE_CASE , help="""Number of retrieved docs""" )
parser.add_argument(
"""--model_name_or_path""" , default=__SCREAMING_SNAKE_CASE , type=__SCREAMING_SNAKE_CASE , required=__SCREAMING_SNAKE_CASE , help="""Path to pretrained checkpoints or model identifier from huggingface.co/models""" , )
parser.add_argument(
"""--eval_mode""" , choices=["""e2e""", """retrieval"""] , default="""e2e""" , type=__SCREAMING_SNAKE_CASE , help=(
"""Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
""" precision@k."""
) , )
parser.add_argument("""--k""" , default=1 , type=__SCREAMING_SNAKE_CASE , help="""k for the precision@k calculation""" )
parser.add_argument(
"""--evaluation_set""" , default=__SCREAMING_SNAKE_CASE , type=__SCREAMING_SNAKE_CASE , required=__SCREAMING_SNAKE_CASE , help="""Path to a file containing evaluation samples""" , )
parser.add_argument(
"""--gold_data_path""" , default=__SCREAMING_SNAKE_CASE , type=__SCREAMING_SNAKE_CASE , required=__SCREAMING_SNAKE_CASE , help="""Path to a tab-separated file with gold samples""" , )
parser.add_argument(
"""--gold_data_mode""" , default="""qa""" , type=__SCREAMING_SNAKE_CASE , choices=["""qa""", """ans"""] , help=(
"""Format of the gold data file"""
"""qa - a single line in the following format: question [tab] answer_list"""
"""ans - a single line of the gold file contains the expected answer string"""
) , )
parser.add_argument(
"""--predictions_path""" , type=__SCREAMING_SNAKE_CASE , default="""predictions.txt""" , help="""Name of the predictions file, to be stored in the checkpoints directory""" , )
parser.add_argument(
"""--eval_all_checkpoints""" , action="""store_true""" , help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number""" , )
parser.add_argument(
"""--eval_batch_size""" , default=8 , type=__SCREAMING_SNAKE_CASE , help="""Batch size per GPU/CPU for evaluation.""" , )
parser.add_argument(
"""--recalculate""" , help="""Recalculate predictions even if the prediction file exists""" , action="""store_true""" , )
parser.add_argument(
"""--num_beams""" , default=4 , type=__SCREAMING_SNAKE_CASE , help="""Number of beams to be used when generating answers""" , )
parser.add_argument("""--min_length""" , default=1 , type=__SCREAMING_SNAKE_CASE , help="""Min length of the generated answers""" )
parser.add_argument("""--max_length""" , default=50 , type=__SCREAMING_SNAKE_CASE , help="""Max length of the generated answers""" )
parser.add_argument(
"""--print_predictions""" , action="""store_true""" , help="""If True, prints predictions while evaluating.""" , )
parser.add_argument(
"""--print_docs""" , action="""store_true""" , help="""If True, prints docs retried while generating.""" , )
_SCREAMING_SNAKE_CASE : Dict = parser.parse_args()
_SCREAMING_SNAKE_CASE : Any = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
return args
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> int:
_SCREAMING_SNAKE_CASE : Union[str, Any] = {}
if args.model_type is None:
_SCREAMING_SNAKE_CASE : Optional[int] = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith("""rag""" ):
_SCREAMING_SNAKE_CASE : List[Any] = RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
_SCREAMING_SNAKE_CASE : Optional[Any] = args.n_docs
if args.index_name is not None:
_SCREAMING_SNAKE_CASE : Optional[Any] = args.index_name
if args.index_path is not None:
_SCREAMING_SNAKE_CASE : Any = args.index_path
else:
_SCREAMING_SNAKE_CASE : Any = BartForConditionalGeneration
_SCREAMING_SNAKE_CASE : int = (
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info("""Evaluate the following checkpoints: %s""" , __SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE : Tuple = get_scores if args.eval_mode == """e2e""" else get_precision_at_k
_SCREAMING_SNAKE_CASE : Tuple = evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path ) )
score_fn(__SCREAMING_SNAKE_CASE , args.predictions_path , args.gold_data_path )
continue
logger.info("""***** Running evaluation for {} *****""".format(__SCREAMING_SNAKE_CASE ) )
logger.info(""" Batch size = %d""" , args.eval_batch_size )
logger.info(""" Predictions will be stored under {}""".format(args.predictions_path ) )
if args.model_type.startswith("""rag""" ):
_SCREAMING_SNAKE_CASE : str = RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE : Tuple = model_class.from_pretrained(__SCREAMING_SNAKE_CASE , retriever=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
model.retriever.init_retrieval()
else:
_SCREAMING_SNAKE_CASE : str = model_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
model.to(args.device )
with open(args.evaluation_set , """r""" ) as eval_file, open(args.predictions_path , """w""" ) as preds_file:
_SCREAMING_SNAKE_CASE : str = []
for line in tqdm(__SCREAMING_SNAKE_CASE ):
questions.append(line.strip() )
if len(__SCREAMING_SNAKE_CASE ) == args.eval_batch_size:
_SCREAMING_SNAKE_CASE : str = evaluate_batch_fn(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
preds_file.write("""\n""".join(__SCREAMING_SNAKE_CASE ) + """\n""" )
preds_file.flush()
_SCREAMING_SNAKE_CASE : Any = []
if len(__SCREAMING_SNAKE_CASE ) > 0:
_SCREAMING_SNAKE_CASE : List[str] = evaluate_batch_fn(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
preds_file.write("""\n""".join(__SCREAMING_SNAKE_CASE ) )
preds_file.flush()
score_fn(__SCREAMING_SNAKE_CASE , args.predictions_path , args.gold_data_path )
if __name__ == "__main__":
lowerCAmelCase_ = get_args()
main(args)
| 635
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ : Union[str, Any] = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['''XLMTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = [
'''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMForMultipleChoice''',
'''XLMForQuestionAnswering''',
'''XLMForQuestionAnsweringSimple''',
'''XLMForSequenceClassification''',
'''XLMForTokenClassification''',
'''XLMModel''',
'''XLMPreTrainedModel''',
'''XLMWithLMHeadModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Any = [
'''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMForMultipleChoice''',
'''TFXLMForQuestionAnsweringSimple''',
'''TFXLMForSequenceClassification''',
'''TFXLMForTokenClassification''',
'''TFXLMMainLayer''',
'''TFXLMModel''',
'''TFXLMPreTrainedModel''',
'''TFXLMWithLMHeadModel''',
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
UpperCamelCase__ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 105
|
def lowerCAmelCase_ ( _lowercase : list , _lowercase : int , _lowercase : int = 0 , _lowercase : int = 0) -> int:
"""simple docstring"""
a__ : str = right or len(_lowercase) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(_lowercase , _lowercase , left + 1 , right - 1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 136
| 0
|
"""simple docstring"""
import string
from math import logaa
def _UpperCamelCase ( _A , _A ) -> int:
"""simple docstring"""
_UpperCAmelCase = document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" , """""" )
_UpperCAmelCase = document_without_punctuation.split(""" """ ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def _UpperCamelCase ( _A , _A ) -> tuple[int, int]:
"""simple docstring"""
_UpperCAmelCase = corpus.lower().translate(
str.maketrans("""""" , """""" , string.punctuation ) ) # strip all punctuation and replace it with ''
_UpperCAmelCase = corpus_without_punctuation.split("""\n""" )
_UpperCAmelCase = term.lower()
return (len([doc for doc in docs if term in doc] ), len(_A ))
def _UpperCamelCase ( _A , _A , _A=False ) -> float:
"""simple docstring"""
if smoothing:
if n == 0:
raise ValueError("""log10(0) is undefined.""" )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError("""df must be > 0""" )
elif n == 0:
raise ValueError("""log10(0) is undefined.""" )
return round(logaa(n / df ) , 3 )
def _UpperCamelCase ( _A , _A ) -> float:
"""simple docstring"""
return round(tf * idf , 3 )
| 713
|
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class a_ :
def __init__( self : Tuple , __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any]=99 , __UpperCamelCase : Optional[int]=13 , __UpperCamelCase : List[str]=7 , __UpperCamelCase : List[Any]=9 , __UpperCamelCase : List[Any]=True , __UpperCamelCase : str=True , __UpperCamelCase : int=False , __UpperCamelCase : int=32 , __UpperCamelCase : Dict=5 , __UpperCamelCase : Optional[int]=4 , __UpperCamelCase : Any=37 , __UpperCamelCase : List[Any]=8 , __UpperCamelCase : Optional[int]=0.1 , __UpperCamelCase : str=0.0_0_2 , __UpperCamelCase : Union[str, Any]=1 , __UpperCamelCase : List[Any]=0 , __UpperCamelCase : Tuple=0 , __UpperCamelCase : Optional[int]=None , __UpperCamelCase : Any=None , ) ->List[Any]:
'''simple docstring'''
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = encoder_seq_length
_UpperCAmelCase = decoder_seq_length
# For common tests
_UpperCAmelCase = self.decoder_seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_mask
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = d_ff
_UpperCAmelCase = relative_attention_num_buckets
_UpperCAmelCase = dropout_rate
_UpperCAmelCase = initializer_factor
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = pad_token_id
_UpperCAmelCase = decoder_start_token_id
_UpperCAmelCase = None
_UpperCAmelCase = decoder_layers
def _snake_case ( self : Union[str, Any] ) ->Union[str, Any]:
'''simple docstring'''
return TaConfig.from_pretrained("""google/umt5-base""" )
def _snake_case ( self : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Tuple=None , __UpperCamelCase : Optional[Any]=None , __UpperCamelCase : Optional[Any]=None , __UpperCamelCase : Any=None , __UpperCamelCase : str=None , ) ->int:
'''simple docstring'''
if attention_mask is None:
_UpperCAmelCase = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_UpperCAmelCase = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_UpperCAmelCase = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=__UpperCamelCase )
if decoder_head_mask is None:
_UpperCAmelCase = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=__UpperCamelCase )
if cross_attn_head_mask is None:
_UpperCAmelCase = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=__UpperCamelCase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _snake_case ( self : List[Any] ) ->Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_UpperCAmelCase = input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = decoder_input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = self.get_config()
_UpperCAmelCase = config.num_attention_heads
_UpperCAmelCase = self.prepare_inputs_dict(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return config, input_dict
def _snake_case ( self : Union[str, Any] ) ->Any:
'''simple docstring'''
_UpperCAmelCase ,_UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def _snake_case ( self : Dict ) ->List[str]:
'''simple docstring'''
return TaConfig(
vocab_size=1_66 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _snake_case ( self : Tuple ) ->Dict:
'''simple docstring'''
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _snake_case ( self : List[Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : Dict , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Dict , __UpperCamelCase : List[str] , ) ->List[str]:
'''simple docstring'''
_UpperCAmelCase = UMTaModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
input_ids=__UpperCamelCase , decoder_input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase , decoder_attention_mask=__UpperCamelCase , )
_UpperCAmelCase = model(input_ids=__UpperCamelCase , decoder_input_ids=__UpperCamelCase )
_UpperCAmelCase = result.last_hidden_state
_UpperCAmelCase = result.past_key_values
_UpperCAmelCase = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(__UpperCamelCase ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _snake_case ( self : Union[str, Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Dict , __UpperCamelCase : Optional[int] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str] , ) ->str:
'''simple docstring'''
_UpperCAmelCase = UMTaModel(config=__UpperCamelCase ).get_decoder().to(__UpperCamelCase ).eval()
# first forward pass
_UpperCAmelCase = model(__UpperCamelCase , use_cache=__UpperCamelCase )
_UpperCAmelCase = model(__UpperCamelCase )
_UpperCAmelCase = model(__UpperCamelCase , use_cache=__UpperCamelCase )
self.parent.assertTrue(len(__UpperCamelCase ) == len(__UpperCamelCase ) )
self.parent.assertTrue(len(__UpperCamelCase ) == len(__UpperCamelCase ) + 1 )
_UpperCAmelCase ,_UpperCAmelCase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_UpperCAmelCase = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
_UpperCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase = model(__UpperCamelCase )["""last_hidden_state"""]
_UpperCAmelCase = model(__UpperCamelCase , past_key_values=__UpperCamelCase )["""last_hidden_state"""]
# select random slice
_UpperCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase = output_from_no_past[:, -1, random_slice_idx].detach()
_UpperCAmelCase = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
def _snake_case ( self : Optional[int] , __UpperCamelCase : int , __UpperCamelCase : Dict , ) ->List[str]:
'''simple docstring'''
_UpperCAmelCase = UMTaModel(config=__UpperCamelCase ).to(__UpperCamelCase ).half().eval()
_UpperCAmelCase = model(**__UpperCamelCase )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(__UpperCamelCase ).any().item() )
@require_torch
class a_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
a : List[str] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
a : Tuple = (UMTaForConditionalGeneration,) if is_torch_available() else ()
a : Optional[Any] = (
{
'conversational': UMTaForConditionalGeneration,
'feature-extraction': UMTaModel,
'summarization': UMTaForConditionalGeneration,
'text2text-generation': UMTaForConditionalGeneration,
'translation': UMTaForConditionalGeneration,
'question-answering': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
a : Any = True
a : Optional[int] = False
a : Any = False
a : Optional[int] = True
a : Optional[Any] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
a : int = [0.8, 0.9]
def _snake_case ( self : Optional[Any] ) ->Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _snake_case ( self : Union[str, Any] ) ->Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = UMTaModel(config_and_inputs[0] ).to(__UpperCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
__UpperCamelCase , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f"""{tmpdirname}/t5_test.onnx""" , export_params=__UpperCamelCase , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _snake_case ( self : Tuple ) ->Optional[int]:
'''simple docstring'''
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*__UpperCamelCase )
def _snake_case ( self : Any ) ->Any:
'''simple docstring'''
_UpperCAmelCase = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = config_and_inputs[0]
_UpperCAmelCase = UMTaForConditionalGeneration(__UpperCamelCase ).eval()
model.to(__UpperCamelCase )
_UpperCAmelCase = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=__UpperCamelCase ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=__UpperCamelCase ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=__UpperCamelCase ),
}
for attn_name, (name, mask) in zip(__UpperCamelCase , head_masking.items() ):
_UpperCAmelCase = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
_UpperCAmelCase = torch.ones(
config.num_decoder_layers , config.num_heads , device=__UpperCamelCase )
_UpperCAmelCase = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=__UpperCamelCase , return_dict_in_generate=__UpperCamelCase , **__UpperCamelCase , )
# We check the state of decoder_attentions and cross_attentions just from the last step
_UpperCAmelCase = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _snake_case ( self : Tuple ) ->List[Any]:
'''simple docstring'''
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class a_ ( unittest.TestCase ):
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _snake_case ( self : Optional[Any] ) ->Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=__UpperCamelCase ).to(__UpperCamelCase )
_UpperCAmelCase = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=__UpperCamelCase , legacy=__UpperCamelCase )
_UpperCAmelCase = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
_UpperCAmelCase = tokenizer(__UpperCamelCase , return_tensors="""pt""" , padding=__UpperCamelCase ).input_ids
# fmt: off
_UpperCAmelCase = torch.tensor(
[
[ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
] )
# fmt: on
torch.testing.assert_allclose(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = model.generate(input_ids.to(__UpperCamelCase ) )
_UpperCAmelCase = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
_UpperCAmelCase = tokenizer.batch_decode(__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
| 19
| 0
|
from __future__ import annotations
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ):
"""simple docstring"""
UpperCAmelCase = len(__UpperCAmelCase )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append([". " * i + "Q " + ". " * (n - 1 - i) for i in possible_board] )
return
# We iterate each column in the row to find all possible results in each row
for col in range(__UpperCAmelCase ):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
[*possible_board, col] , [*diagonal_right_collisions, row - col] , [*diagonal_left_collisions, row + col] , __UpperCAmelCase , __UpperCAmelCase , )
def __UpperCamelCase ( _lowerCAmelCase ):
"""simple docstring"""
UpperCAmelCase = []
depth_first_search([] , [] , [] , __UpperCAmelCase , __UpperCAmelCase )
# Print all the boards
for board in boards:
for column in board:
print(__UpperCAmelCase )
print("" )
print(len(__UpperCAmelCase ) , "solutions were found." )
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| 333
|
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
__lowerCamelCase : Tuple = [
"""cross_validation.py""",
"""gradient_accumulation.py""",
"""local_sgd.py""",
"""multi_process_metrics.py""",
"""memory.py""",
"""automatic_gradient_accumulation.py""",
"""fsdp_with_peak_mem_tracking.py""",
"""deepspeed_with_config_support.py""",
"""megatron_lm_gpt_pretraining.py""",
]
class lowerCAmelCase__ ( unittest.TestCase ):
def __UpperCamelCase ( self : int , UpperCamelCase_ : str , UpperCamelCase_ : bool , UpperCamelCase_ : str = None , UpperCamelCase_ : list = None ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ : int = None
lowerCamelCase_ : Union[str, Any] = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
lowerCamelCase_ : List[Any] = os.path.abspath('''examples''' )
for item in os.listdir(UpperCamelCase_ ):
if item not in EXCLUDE_EXAMPLES:
lowerCamelCase_ : List[str] = os.path.join(UpperCamelCase_ , UpperCamelCase_ )
if os.path.isfile(UpperCamelCase_ ) and ".py" in item_path:
with self.subTest(
tested_script=UpperCamelCase_ , feature_script=UpperCamelCase_ , tested_section='''main()''' if parser_only else '''training_function()''' , ):
lowerCamelCase_ : Optional[int] = compare_against_test(
os.path.join(UpperCamelCase_ , UpperCamelCase_ ) , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
lowerCamelCase_ : Optional[Any] = '''\n'''.join(UpperCamelCase_ )
if special_strings is not None:
for string in special_strings:
lowerCamelCase_ : List[Any] = diff.replace(UpperCamelCase_ , '''''' )
self.assertEqual(UpperCamelCase_ , '''''' )
def __UpperCamelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
self.one_complete_example('''complete_nlp_example.py''' , UpperCamelCase_ )
self.one_complete_example('''complete_nlp_example.py''' , UpperCamelCase_ )
def __UpperCamelCase ( self : Tuple ) -> int:
"""simple docstring"""
lowerCamelCase_ : Any = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
lowerCamelCase_ : int = [
''' ''' * 16 + '''{\n\n''',
''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 20 + '''"epoch": epoch,\n\n''',
''' ''' * 16 + '''},\n\n''',
''' ''' * 16 + '''step=epoch,\n''',
''' ''' * 12,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
self.one_complete_example('''complete_cv_example.py''' , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
@mock.patch.dict(os.environ ,{"TESTING_MOCKED_DATALOADERS": "1"} )
class lowerCAmelCase__ ( _lowerCAmelCase ):
A = False
@classmethod
def __UpperCamelCase ( cls : int ) -> Tuple:
"""simple docstring"""
super().setUpClass()
lowerCamelCase_ : Any = tempfile.mkdtemp()
lowerCamelCase_ : int = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
lowerCamelCase_ : List[str] = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def __UpperCamelCase ( cls : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def __UpperCamelCase ( self : List[str] ) -> Any:
"""simple docstring"""
lowerCamelCase_ : Dict = F"""
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
""".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def __UpperCamelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ : List[str] = F"""
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
""".split()
lowerCamelCase_ : str = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ : Dict = F"""
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
""".split()
lowerCamelCase_ : Any = run_command(self._launch_args + testargs , return_stdout=UpperCamelCase_ )
self.assertNotIn('''epoch 0:''' , UpperCamelCase_ )
self.assertIn('''epoch 1:''' , UpperCamelCase_ )
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ : Union[str, Any] = F"""
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
""".split()
lowerCamelCase_ : List[str] = run_command(self._launch_args + testargs , return_stdout=UpperCamelCase_ )
if torch.cuda.is_available():
lowerCamelCase_ : str = torch.cuda.device_count()
else:
lowerCamelCase_ : List[Any] = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , UpperCamelCase_ )
self.assertIn('''epoch 1:''' , UpperCamelCase_ )
else:
self.assertIn('''epoch 0:''' , UpperCamelCase_ )
self.assertIn('''epoch 1:''' , UpperCamelCase_ )
@slow
def __UpperCamelCase ( self : Tuple ) -> Any:
"""simple docstring"""
lowerCamelCase_ : int = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
lowerCamelCase_ : Union[str, Any] = run_command(self._launch_args + testargs , return_stdout=UpperCamelCase_ )
lowerCamelCase_ : Any = re.findall('''({.+})''' , UpperCamelCase_ )
lowerCamelCase_ : int = [r for r in results if '''accuracy''' in r][-1]
lowerCamelCase_ : int = ast.literal_eval(UpperCamelCase_ )
self.assertGreaterEqual(results['''accuracy'''] , 0.75 )
def __UpperCamelCase ( self : Dict ) -> Any:
"""simple docstring"""
lowerCamelCase_ : Optional[Any] = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __UpperCamelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdir:
lowerCamelCase_ : Union[str, Any] = F"""
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
""".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_ , '''tracking''' ) ) )
def __UpperCamelCase ( self : int ) -> str:
"""simple docstring"""
lowerCamelCase_ : Optional[Any] = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def __UpperCamelCase ( self : Dict ) -> str:
"""simple docstring"""
lowerCamelCase_ : List[Any] = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 501
| 0
|
"""simple docstring"""
def UpperCAmelCase__ ( A__ , A__ ) -> bool:
"""simple docstring"""
lowerCamelCase__ = len(A__ )
lowerCamelCase__ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
lowerCamelCase__ = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
lowerCamelCase__ = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
lowerCamelCase__ = subset[i - 1][j]
if arr[i - 1] <= j:
lowerCamelCase__ = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 274
|
"""simple docstring"""
def UpperCAmelCase__ ( A__ ) -> list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(A__ , A__ ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(A__ )]
if __name__ == "__main__":
print(hexagonal_numbers(length=5))
print(hexagonal_numbers(length=10))
| 274
| 1
|
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __a ( A__ : Any ):
SCREAMING_SNAKE_CASE = SwinConfig()
SCREAMING_SNAKE_CASE = swin_name.split("_" )
SCREAMING_SNAKE_CASE = name_split[1]
SCREAMING_SNAKE_CASE = int(name_split[4] )
SCREAMING_SNAKE_CASE = int(name_split[3][-1] )
if model_size == "tiny":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 6, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "small":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "base":
SCREAMING_SNAKE_CASE = 128
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (4, 8, 16, 32)
else:
SCREAMING_SNAKE_CASE = 192
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (6, 12, 24, 48)
if "in22k" in swin_name:
SCREAMING_SNAKE_CASE = 21841
else:
SCREAMING_SNAKE_CASE = 1000
SCREAMING_SNAKE_CASE = "huggingface/label-files"
SCREAMING_SNAKE_CASE = "imagenet-1k-id2label.json"
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(A__ , A__ , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE = {int(A__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = img_size
SCREAMING_SNAKE_CASE = num_classes
SCREAMING_SNAKE_CASE = embed_dim
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = window_size
return config
def __a ( A__ : List[str] ):
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE = name.replace("patch_embed.norm" , "embeddings.norm" )
if "layers" in name:
SCREAMING_SNAKE_CASE = "encoder." + name
if "attn.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
SCREAMING_SNAKE_CASE = name.replace("attn" , "attention.self" )
if "norm1" in name:
SCREAMING_SNAKE_CASE = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
SCREAMING_SNAKE_CASE = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE = name.replace("mlp.fc2" , "output.dense" )
if name == "norm.weight":
SCREAMING_SNAKE_CASE = "layernorm.weight"
if name == "norm.bias":
SCREAMING_SNAKE_CASE = "layernorm.bias"
if "head" in name:
SCREAMING_SNAKE_CASE = name.replace("head" , "classifier" )
else:
SCREAMING_SNAKE_CASE = "swin." + name
return name
def __a ( A__ : Optional[Any] , A__ : Any ):
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(A__ )
if "mask" in key:
continue
elif "qkv" in key:
SCREAMING_SNAKE_CASE = key.split("." )
SCREAMING_SNAKE_CASE = int(key_split[1] )
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[
:dim
]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __a ( A__ : Optional[Any] , A__ : Any ):
SCREAMING_SNAKE_CASE = timm.create_model(A__ , pretrained=A__ )
timm_model.eval()
SCREAMING_SNAKE_CASE = get_swin_config(A__ )
SCREAMING_SNAKE_CASE = SwinForImageClassification(A__ )
model.eval()
SCREAMING_SNAKE_CASE = convert_state_dict(timm_model.state_dict() , A__ )
model.load_state_dict(A__ )
SCREAMING_SNAKE_CASE = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_" , "-" ) ) )
SCREAMING_SNAKE_CASE = Image.open(requests.get(A__ , stream=A__ ).raw )
SCREAMING_SNAKE_CASE = image_processor(images=A__ , return_tensors="pt" )
SCREAMING_SNAKE_CASE = timm_model(inputs["pixel_values"] )
SCREAMING_SNAKE_CASE = model(**A__ ).logits
assert torch.allclose(A__ , A__ , atol=1E-3 )
print(F"Saving model {swin_name} 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 __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--swin_name',
default='swin_tiny_patch4_window7_224',
type=str,
help='Name of the Swin timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
__A : List[Any] = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 16
|
from collections.abc import Callable
import numpy as np
def __a ( A__ : Callable , A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = int(np.ceil((x_end - xa) / step_size ) )
SCREAMING_SNAKE_CASE = np.zeros((n + 1,) )
SCREAMING_SNAKE_CASE = ya
SCREAMING_SNAKE_CASE = xa
for k in range(A__ ):
SCREAMING_SNAKE_CASE = y[k] + step_size * ode_func(A__ , y[k] )
SCREAMING_SNAKE_CASE = y[k] + (
(step_size / 2) * (ode_func(A__ , y[k] ) + ode_func(x + step_size , A__ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 16
| 1
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ : Any = logging.get_logger(__name__)
A__ : str = {
"hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class __UpperCamelCase ( SCREAMING_SNAKE_CASE ):
_lowercase : Any = "yolos"
def __init__( self: Dict , __UpperCamelCase: str=7_68 , __UpperCamelCase: List[Any]=12 , __UpperCamelCase: List[Any]=12 , __UpperCamelCase: List[str]=30_72 , __UpperCamelCase: List[str]="gelu" , __UpperCamelCase: List[str]=0.0 , __UpperCamelCase: List[str]=0.0 , __UpperCamelCase: Optional[Any]=0.02 , __UpperCamelCase: List[str]=1E-12 , __UpperCamelCase: List[Any]=[5_12, 8_64] , __UpperCamelCase: Tuple=16 , __UpperCamelCase: Union[str, Any]=3 , __UpperCamelCase: Optional[Any]=True , __UpperCamelCase: Optional[int]=1_00 , __UpperCamelCase: List[Any]=True , __UpperCamelCase: Optional[Any]=False , __UpperCamelCase: Tuple=1 , __UpperCamelCase: Any=5 , __UpperCamelCase: Dict=2 , __UpperCamelCase: Optional[Any]=5 , __UpperCamelCase: Optional[Any]=2 , __UpperCamelCase: Union[str, Any]=0.1 , **__UpperCamelCase: Tuple , ):
'''simple docstring'''
super().__init__(**__UpperCamelCase )
__magic_name__ = hidden_size
__magic_name__ = num_hidden_layers
__magic_name__ = num_attention_heads
__magic_name__ = intermediate_size
__magic_name__ = hidden_act
__magic_name__ = hidden_dropout_prob
__magic_name__ = attention_probs_dropout_prob
__magic_name__ = initializer_range
__magic_name__ = layer_norm_eps
__magic_name__ = image_size
__magic_name__ = patch_size
__magic_name__ = num_channels
__magic_name__ = qkv_bias
__magic_name__ = num_detection_tokens
__magic_name__ = use_mid_position_embeddings
__magic_name__ = auxiliary_loss
# Hungarian matcher
__magic_name__ = class_cost
__magic_name__ = bbox_cost
__magic_name__ = giou_cost
# Loss coefficients
__magic_name__ = bbox_loss_coefficient
__magic_name__ = giou_loss_coefficient
__magic_name__ = eos_coefficient
class __UpperCamelCase ( SCREAMING_SNAKE_CASE ):
_lowercase : str = version.parse("1.11" )
@property
def _SCREAMING_SNAKE_CASE ( self: Dict ):
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def _SCREAMING_SNAKE_CASE ( self: Dict ):
'''simple docstring'''
return 1E-4
@property
def _SCREAMING_SNAKE_CASE ( self: Optional[int] ):
'''simple docstring'''
return 12
| 705
|
from math import factorial, radians
def _lowercase ( a_ : float ,a_ : int = 1_8 ,a_ : int = 1_0 ) -> float:
'''simple docstring'''
__magic_name__ = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
__magic_name__ = radians(a_ )
__magic_name__ = angle_in_radians
__magic_name__ = 3
__magic_name__ = -1
for _ in range(a_ ):
result += (b * (angle_in_radians**a)) / factorial(a_ )
__magic_name__ = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(a_ ,a_ )
if __name__ == "__main__":
__import__("doctest").testmod()
| 184
| 0
|
import os
import string
import sys
a_ = 1 << 8
a_ = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_FLAG,
"""right""": 67 + ARROW_KEY_FLAG,
"""left""": 68 + ARROW_KEY_FLAG,
"""mod_int""": 91,
"""undefined""": sys.maxsize,
"""interrupt""": 3,
"""insert""": 50,
"""delete""": 51,
"""pg_up""": 53,
"""pg_down""": 54,
}
a_ = KEYMAP["""up"""]
a_ = KEYMAP["""left"""]
if sys.platform == "win32":
a_ = []
a_ = {
b"""\xe0H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
b"""\x00H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
b"""\xe0P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
b"""\x00P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
b"""\xe0M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
b"""\x00M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
b"""\xe0K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
b"""\x00K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
}
for i in range(10):
a_ = ord(str(i))
def a__ ( ):
if os.name == "nt":
import msvcrt
__lowerCamelCase = '''mbcs'''
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(UpperCamelCase__ ) == 0:
# Read the keystroke
__lowerCamelCase = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
__lowerCamelCase = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
__lowerCamelCase = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['''mod_int'''] ) )
WIN_CH_BUFFER.append(UpperCamelCase__ )
if ord(UpperCamelCase__ ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(1_26 ) )
__lowerCamelCase = chr(KEYMAP['''esc'''] )
except KeyError:
__lowerCamelCase = cha[1]
else:
__lowerCamelCase = ch.decode(UpperCamelCase__ )
else:
__lowerCamelCase = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
__lowerCamelCase = sys.stdin.fileno()
__lowerCamelCase = termios.tcgetattr(UpperCamelCase__ )
try:
tty.setraw(UpperCamelCase__ )
__lowerCamelCase = sys.stdin.read(1 )
finally:
termios.tcsetattr(UpperCamelCase__ ,termios.TCSADRAIN ,UpperCamelCase__ )
return ch
def a__ ( ):
__lowerCamelCase = get_raw_chars()
if ord(UpperCamelCase__ ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(UpperCamelCase__ ) == KEYMAP["esc"]:
__lowerCamelCase = get_raw_chars()
if ord(UpperCamelCase__ ) == KEYMAP["mod_int"]:
__lowerCamelCase = get_raw_chars()
if ord(UpperCamelCase__ ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(UpperCamelCase__ ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(UpperCamelCase__ ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 175
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
'PegasusXForConditionalGeneration',
'PegasusXModel',
'PegasusXPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 296
| 0
|
'''simple docstring'''
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
A_ = {
"debug": logging.DEBUG,
"info": logging.INFO,
"warning": logging.WARNING,
"error": logging.ERROR,
"critical": logging.CRITICAL,
}
A_ = logging.WARNING
def A_ ( ):
SCREAMING_SNAKE_CASE:Dict = os.getenv("DATASETS_VERBOSITY" , snake_case )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
F'''has to be one of: { ", ".join(log_levels.keys() ) }''' )
return _default_log_level
def A_ ( ):
return __name__.split("." )[0]
def A_ ( ):
return logging.getLogger(_get_library_name() )
def A_ ( ):
# Apply our default configuration to the library root logger.
SCREAMING_SNAKE_CASE:List[Any] = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def A_ ( ):
SCREAMING_SNAKE_CASE:List[str] = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def A_ ( snake_case = None ):
if name is None:
SCREAMING_SNAKE_CASE:Any = _get_library_name()
return logging.getLogger(snake_case )
def A_ ( ):
return _get_library_root_logger().getEffectiveLevel()
def A_ ( snake_case ):
_get_library_root_logger().setLevel(snake_case )
def A_ ( ):
return set_verbosity(snake_case )
def A_ ( ):
return set_verbosity(snake_case )
def A_ ( ):
return set_verbosity(snake_case )
def A_ ( ):
return set_verbosity(snake_case )
def A_ ( ):
SCREAMING_SNAKE_CASE:List[Any] = False
def A_ ( ):
SCREAMING_SNAKE_CASE:Union[str, Any] = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class _snake_case :
def __init__( self : int ,*SCREAMING_SNAKE_CASE__ : List[Any] ,**SCREAMING_SNAKE_CASE__ : Dict ): # pylint: disable=unused-argument
SCREAMING_SNAKE_CASE:int = args[0] if args else None
def __iter__( self : List[str] ):
return iter(self._iterator )
def __getattr__( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
def empty_fn(*SCREAMING_SNAKE_CASE__ : Optional[Any] ,**SCREAMING_SNAKE_CASE__ : Tuple ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self : List[Any] ):
return self
def __exit__( self : Tuple ,SCREAMING_SNAKE_CASE__ : str ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : List[str] ):
return
A_ = True
class _snake_case :
def __call__( self : Optional[Any] ,*SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : Dict=False ,**SCREAMING_SNAKE_CASE__ : List[str] ):
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
else:
return EmptyTqdm(*SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : Dict ,*SCREAMING_SNAKE_CASE__ : Any ,**SCREAMING_SNAKE_CASE__ : Tuple ):
SCREAMING_SNAKE_CASE:Dict = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : Tuple ):
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
A_ = _tqdm_cls()
def A_ ( ):
global _tqdm_active
return bool(_tqdm_active )
def A_ ( ):
global _tqdm_active
SCREAMING_SNAKE_CASE:str = True
def A_ ( ):
global _tqdm_active
SCREAMING_SNAKE_CASE:str = False
| 709
|
'''simple docstring'''
import random
def A_ ( snake_case , snake_case , snake_case = False ):
SCREAMING_SNAKE_CASE:dict = {i: [] for i in range(snake_case )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_graph(snake_case )
# if probability is lower or equal than 0, then return a graph without edges
if probability <= 0:
return graph
# for each couple of nodes, add an edge from u to v
# if the number randomly generated is greater than probability probability
for i in range(snake_case ):
for j in range(i + 1 , snake_case ):
if random.random() < probability:
graph[i].append(snake_case )
if not directed:
# if the graph is undirected, add an edge in from j to i, either
graph[j].append(snake_case )
return graph
def A_ ( snake_case ):
return {
i: [j for j in range(snake_case ) if i != j] for i in range(snake_case )
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 465
| 0
|
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
snake_case__ : str = open # noqa: we just need to have a builtin inside this module to test it properly
| 408
|
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 243
| 0
|
import collections
import importlib.util
import os
import re
from pathlib import Path
_SCREAMING_SNAKE_CASE = """src/transformers"""
# Matches is_xxx_available()
_SCREAMING_SNAKE_CASE = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
_SCREAMING_SNAKE_CASE = re.compile(R"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_SCREAMING_SNAKE_CASE = re.compile(R"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
_SCREAMING_SNAKE_CASE = re.compile(R"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
_SCREAMING_SNAKE_CASE = re.compile(R"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_SCREAMING_SNAKE_CASE = re.compile(R"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
_SCREAMING_SNAKE_CASE = re.compile("""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
_SCREAMING_SNAKE_CASE = re.compile("""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
_SCREAMING_SNAKE_CASE = re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
_SCREAMING_SNAKE_CASE = re.compile(R"""^\s*try:""")
# Catches a line with else:
_SCREAMING_SNAKE_CASE = re.compile(R"""^\s*else:""")
def SCREAMING_SNAKE_CASE__ ( __a ):
if _re_test_backend.search(__a ) is None:
return None
snake_case_ : Optional[Any] = [b[0] for b in _re_backend.findall(__a )]
backends.sort()
return "_and_".join(__a )
def SCREAMING_SNAKE_CASE__ ( __a ):
with open(__a , 'r' , encoding='utf-8' , newline='\n' ) as f:
snake_case_ : int = f.readlines()
snake_case_ : int = 0
while line_index < len(__a ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(__a ):
return None
# First grab the objects without a specific backend in _import_structure
snake_case_ : Union[str, Any] = []
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
snake_case_ : Dict = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(__a ):
snake_case_ : Any = _re_one_line_import_struct.search(__a ).groups()[0]
snake_case_ : Optional[Any] = re.findall('\[([^\]]+)\]' , __a )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
snake_case_ : str = _re_import_struct_key_value.search(__a )
if single_line_import_search is not None:
snake_case_ : str = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(__a ) > 0]
objects.extend(__a )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
line_index += 1
snake_case_ : List[str] = {'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.
snake_case_ : Optional[Any] = 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:
snake_case_ : Optional[int] = 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
snake_case_ : Union[str, Any] = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
snake_case_ : Dict = lines[line_index]
if _re_import_struct_add_one.search(__a ) is not None:
objects.append(_re_import_struct_add_one.search(__a ).groups()[0] )
elif _re_import_struct_add_many.search(__a ) is not None:
snake_case_ : int = _re_import_struct_add_many.search(__a ).groups()[0].split(', ' )
snake_case_ : Dict = [obj[1:-1] for obj in imports if len(__a ) > 0]
objects.extend(__a )
elif _re_between_brackets.search(__a ) is not None:
snake_case_ : Optional[int] = _re_between_brackets.search(__a ).groups()[0].split(', ' )
snake_case_ : Optional[Any] = [obj[1:-1] for obj in imports if len(__a ) > 0]
objects.extend(__a )
elif _re_quote_object.search(__a ) is not None:
objects.append(_re_quote_object.search(__a ).groups()[0] )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '"' ):
objects.append(line[13:-3] )
line_index += 1
snake_case_ : int = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
snake_case_ : Optional[Any] = []
while (
line_index < len(__a )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
snake_case_ : Optional[int] = lines[line_index]
snake_case_ : Tuple = _re_import.search(__a )
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
snake_case_ : Tuple = {'none': objects}
# Let's continue with backend-specific objects
while line_index < len(__a ):
# If the line is an if is_backend_available, we grab all objects associated.
snake_case_ : Optional[int] = 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:
snake_case_ : Any = 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
snake_case_ : Dict = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
snake_case_ : Tuple = lines[line_index]
snake_case_ : Any = _re_import.search(__a )
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
snake_case_ : Dict = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
def find_duplicates(__a ):
return [k for k, v in collections.Counter(__a ).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!"]
snake_case_ : List[str] = []
for key in import_dict_objects.keys():
snake_case_ : Any = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(f"""Duplicate _import_structure definitions for: {duplicate_imports}""" )
snake_case_ : Tuple = 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] ) ):
snake_case_ : Tuple = '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 SCREAMING_SNAKE_CASE__ ( ):
snake_case_ : int = []
for root, _, files in os.walk(__a ):
if "__init__.py" in files:
snake_case_ : Dict = os.path.join(__a , '__init__.py' )
snake_case_ : Dict = parse_init(__a )
if objects is not None:
snake_case_ : Tuple = analyze_results(*__a )
if len(__a ) > 0:
snake_case_ : Dict = f"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
failures.append('\n'.join(__a ) )
if len(__a ) > 0:
raise ValueError('\n\n'.join(__a ) )
def SCREAMING_SNAKE_CASE__ ( ):
snake_case_ : Optional[int] = []
for path, directories, files in os.walk(__a ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(__a )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(__a ) / folder).glob('*.py' ) ) ) == 0:
continue
snake_case_ : Dict = str((Path(__a ) / folder).relative_to(__a ) )
snake_case_ : Union[str, Any] = short_path.replace(os.path.sep , '.' )
submodules.append(__a )
for fname in files:
if fname == "__init__.py":
continue
snake_case_ : Any = str((Path(__a ) / fname).relative_to(__a ) )
snake_case_ : str = short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(__a )
return submodules
_SCREAMING_SNAKE_CASE = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
]
def SCREAMING_SNAKE_CASE__ ( ):
# This is to make sure the transformers module imported is the one in the repo.
snake_case_ : Optional[int] = importlib.util.spec_from_file_location(
'transformers' , os.path.join(__a , '__init__.py' ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
snake_case_ : Optional[int] = spec.loader.load_module()
snake_case_ : Dict = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(__a ) > 0:
snake_case_ : Any = '\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()
| 534
|
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
# Load checkpoint
snake_case_ : Union[str, Any] = torch.load(__a , map_location='cpu' )
snake_case_ : Union[str, Any] = chkpt['model']
# We have the base model one level deeper than the original XLM repository
snake_case_ : str = {}
for k, v in state_dict.items():
if "pred_layer" in k:
snake_case_ : Tuple = v
else:
snake_case_ : Dict = v
snake_case_ : Tuple = chkpt['params']
snake_case_ : List[Any] = {n: v for n, v in config.items() if not isinstance(__a , (torch.FloatTensor, numpy.ndarray) )}
snake_case_ : Optional[int] = chkpt['dico_word2id']
snake_case_ : List[str] = {s + '</w>' if s.find('@@' ) == -1 and i > 13 else s.replace('@@' , '' ): i for s, i in vocab.items()}
# Save pytorch-model
snake_case_ : List[str] = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
snake_case_ : Dict = pytorch_dump_folder_path + '/' + CONFIG_NAME
snake_case_ : Any = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']
print(f"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(__a , __a )
print(f"""Save configuration file to {pytorch_config_dump_path}""" )
with open(__a , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(__a , indent=2 ) + '\n' )
print(f"""Save vocab file to {pytorch_config_dump_path}""" )
with open(__a , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(__a , indent=2 ) + '\n' )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
_SCREAMING_SNAKE_CASE = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 534
| 1
|
'''simple docstring'''
lowerCAmelCase : Tuple ='''Input must be a string of 8 numbers plus letter'''
lowerCAmelCase : Optional[Any] ='''TRWAGMYFPDXBNJZSQVHLCKE'''
def UpperCAmelCase_ ( __lowerCamelCase : str ):
if not isinstance(__lowerCamelCase ,__lowerCamelCase ):
lowercase_ :Dict = F'Expected string as input, found {type(__lowerCamelCase ).__name__}'
raise TypeError(__lowerCamelCase )
lowercase_ :List[Any] = spanish_id.replace("-" ,"" ).upper()
if len(__lowerCamelCase ) != 9:
raise ValueError(__lowerCamelCase )
try:
lowercase_ :List[str] = int(spanish_id_clean[0:8] )
lowercase_ :Any = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__lowerCamelCase ) from ex
if letter.isdigit():
raise ValueError(__lowerCamelCase )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 172
|
'''simple docstring'''
def UpperCAmelCase_ ( __lowerCamelCase : int ):
if number > 0:
raise ValueError("input must be a negative integer" )
lowercase_ :Optional[Any] = len(bin(__lowerCamelCase )[3:] )
lowercase_ :Optional[int] = bin(abs(__lowerCamelCase ) - (1 << binary_number_length) )[3:]
lowercase_ :Dict = (
(
"1"
+ "0" * (binary_number_length - len(__lowerCamelCase ))
+ twos_complement_number
)
if number < 0
else "0"
)
return "0b" + twos_complement_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 172
| 1
|
'''simple docstring'''
import os
def A (__lowerCamelCase :Dict ):
_lowerCAmelCase = len(grid[0] )
_lowerCAmelCase = len(__lowerCamelCase )
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(__lowerCamelCase ):
for j in range(n_rows - 3 ):
_lowerCAmelCase = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
_lowerCAmelCase = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
_lowerCAmelCase = (
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
_lowerCAmelCase = (
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
_lowerCAmelCase = max(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if max_product > largest:
_lowerCAmelCase = max_product
return largest
def A ():
_lowerCAmelCase = []
with open(os.path.dirname(__lowerCamelCase ) + """/grid.txt""" ) as file:
for line in file:
grid.append(line.strip("""\n""" ).split(""" """ ) )
_lowerCAmelCase = [[int(__lowerCamelCase ) for i in grid[j]] for j in range(len(__lowerCamelCase ) )]
return largest_product(__lowerCamelCase )
if __name__ == "__main__":
print(solution())
| 162
|
'''simple docstring'''
def A (__lowerCamelCase :int ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
_lowerCAmelCase = f'Input value of [number={number}] must be an integer'
raise TypeError(__lowerCamelCase )
if number < 1:
_lowerCAmelCase = f'Input value of [number={number}] must be > 0'
raise ValueError(__lowerCamelCase )
_lowerCAmelCase = 1
for i in range(1 , __lowerCamelCase ):
current_number *= 4 * i - 2
current_number //= i + 1
return current_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 162
| 1
|
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class snake_case ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = StableDiffusionDiffEditPipeline
_lowerCAmelCase = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
_lowerCAmelCase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
_lowerCAmelCase = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_lowerCAmelCase = frozenset([] )
def lowercase__ ( self ) -> str:
"""simple docstring"""
torch.manual_seed(0 )
snake_case__ : Tuple = 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 , attention_head_dim=(2, 4) , use_linear_projection=lowerCamelCase , )
snake_case__ : List[str] = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=lowerCamelCase , set_alpha_to_one=lowerCamelCase , )
snake_case__ : List[Any] = DDIMInverseScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=lowerCamelCase , set_alpha_to_zero=lowerCamelCase , )
torch.manual_seed(0 )
snake_case__ : Optional[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 , sample_size=128 , )
torch.manual_seed(0 )
snake_case__ : Any = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=512 , )
snake_case__ : Tuple = CLIPTextModel(lowerCamelCase )
snake_case__ : Optional[int] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
snake_case__ : Optional[Any] = {
'''unet''': unet,
'''scheduler''': scheduler,
'''inverse_scheduler''': inverse_scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowercase__ ( self , lowerCamelCase , lowerCamelCase=0 ) -> Any:
"""simple docstring"""
snake_case__ : str = floats_tensor((1, 16, 16) , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
snake_case__ : Optional[int] = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
if str(lowerCamelCase ).startswith('''mps''' ):
snake_case__ : Tuple = torch.manual_seed(lowerCamelCase )
else:
snake_case__ : str = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
snake_case__ : int = {
'''prompt''': '''a dog and a newt''',
'''mask_image''': mask,
'''image_latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 2,
'''inpaint_strength''': 1.0,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self , lowerCamelCase , lowerCamelCase=0 ) -> Optional[int]:
"""simple docstring"""
snake_case__ : List[str] = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
snake_case__ : Any = image.cpu().permute(0 , 2 , 3 , 1 )[0]
snake_case__ : Optional[int] = Image.fromarray(np.uinta(lowerCamelCase ) ).convert('''RGB''' )
if str(lowerCamelCase ).startswith('''mps''' ):
snake_case__ : Tuple = torch.manual_seed(lowerCamelCase )
else:
snake_case__ : List[Any] = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
snake_case__ : int = {
'''image''': image,
'''source_prompt''': '''a cat and a frog''',
'''target_prompt''': '''a dog and a newt''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''num_maps_per_mask''': 2,
'''mask_encode_strength''': 1.0,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self , lowerCamelCase , lowerCamelCase=0 ) -> List[str]:
"""simple docstring"""
snake_case__ : Tuple = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
snake_case__ : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
snake_case__ : Tuple = Image.fromarray(np.uinta(lowerCamelCase ) ).convert('''RGB''' )
if str(lowerCamelCase ).startswith('''mps''' ):
snake_case__ : List[Any] = torch.manual_seed(lowerCamelCase )
else:
snake_case__ : str = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
snake_case__ : int = {
'''image''': image,
'''prompt''': '''a cat and a frog''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''inpaint_strength''': 1.0,
'''guidance_scale''': 6.0,
'''decode_latents''': True,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self ) -> str:
"""simple docstring"""
if not hasattr(self.pipeline_class , '''_optional_components''' ):
return
snake_case__ : int = self.get_dummy_components()
snake_case__ : str = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(lowerCamelCase , lowerCamelCase , lowerCamelCase )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
snake_case__ : List[Any] = self.get_dummy_inputs(lowerCamelCase )
snake_case__ : Optional[Any] = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
snake_case__ : Any = self.pipeline_class.from_pretrained(lowerCamelCase )
pipe_loaded.to(lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowerCamelCase , lowerCamelCase ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
snake_case__ : Optional[int] = self.get_dummy_inputs(lowerCamelCase )
snake_case__ : List[Any] = pipe_loaded(**lowerCamelCase )[0]
snake_case__ : List[str] = np.abs(output - output_loaded ).max()
self.assertLess(lowerCamelCase , 1E-4 )
def lowercase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : Dict = '''cpu'''
snake_case__ : int = self.get_dummy_components()
snake_case__ : str = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
snake_case__ : Any = self.get_dummy_mask_inputs(lowerCamelCase )
snake_case__ : str = pipe.generate_mask(**lowerCamelCase )
snake_case__ : Optional[Any] = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
snake_case__ : str = np.array([0] * 9 )
snake_case__ : Dict = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def lowercase__ ( self ) -> List[Any]:
"""simple docstring"""
snake_case__ : Tuple = '''cpu'''
snake_case__ : List[str] = self.get_dummy_components()
snake_case__ : Optional[Any] = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
snake_case__ : Union[str, Any] = self.get_dummy_inversion_inputs(lowerCamelCase )
snake_case__ : Any = pipe.invert(**lowerCamelCase ).images
snake_case__ : List[str] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
snake_case__ : Any = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
snake_case__ : List[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase , 1E-3 )
def lowercase__ ( self ) -> Optional[int]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def lowercase__ ( self ) -> Optional[int]:
"""simple docstring"""
snake_case__ : Optional[Any] = '''cpu'''
snake_case__ : str = self.get_dummy_components()
snake_case__ : Tuple = {'''beta_start''': 0.00_085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
snake_case__ : Optional[Any] = DPMSolverMultistepScheduler(**lowerCamelCase )
snake_case__ : Any = DPMSolverMultistepInverseScheduler(**lowerCamelCase )
snake_case__ : Optional[Any] = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
snake_case__ : int = self.get_dummy_inversion_inputs(lowerCamelCase )
snake_case__ : Tuple = pipe.invert(**lowerCamelCase ).images
snake_case__ : List[str] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
snake_case__ : str = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
snake_case__ : Tuple = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase , 1E-3 )
@require_torch_gpu
@slow
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def lowercase__ ( self ) -> Dict:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def lowercase__ ( cls ) -> List[Any]:
"""simple docstring"""
snake_case__ : Optional[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png''' )
snake_case__ : List[Any] = raw_image.convert('''RGB''' ).resize((768, 768) )
snake_case__ : Dict = raw_image
def lowercase__ ( self ) -> List[Any]:
"""simple docstring"""
snake_case__ : Dict = torch.manual_seed(0 )
snake_case__ : Optional[Any] = StableDiffusionDiffEditPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2-1''' , safety_checker=lowerCamelCase , torch_dtype=torch.floataa )
snake_case__ : Tuple = DDIMScheduler.from_config(pipe.scheduler.config )
snake_case__ : Tuple = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowerCamelCase )
snake_case__ : Dict = '''a bowl of fruit'''
snake_case__ : Any = '''a bowl of pears'''
snake_case__ : List[Any] = pipe.generate_mask(
image=self.raw_image , source_prompt=lowerCamelCase , target_prompt=lowerCamelCase , generator=lowerCamelCase , )
snake_case__ : Union[str, Any] = pipe.invert(
prompt=lowerCamelCase , image=self.raw_image , inpaint_strength=0.7 , generator=lowerCamelCase ).latents
snake_case__ : Optional[Any] = pipe(
prompt=lowerCamelCase , mask_image=lowerCamelCase , image_latents=lowerCamelCase , generator=lowerCamelCase , negative_prompt=lowerCamelCase , inpaint_strength=0.7 , output_type='''numpy''' , ).images[0]
snake_case__ : Optional[Any] = (
np.array(
load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/diffedit/pears.png''' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def lowercase__ ( self ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = torch.manual_seed(0 )
snake_case__ : int = StableDiffusionDiffEditPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2-1''' , safety_checker=lowerCamelCase , torch_dtype=torch.floataa )
snake_case__ : int = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
snake_case__ : Optional[Any] = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowerCamelCase )
snake_case__ : Dict = '''a bowl of fruit'''
snake_case__ : int = '''a bowl of pears'''
snake_case__ : str = pipe.generate_mask(
image=self.raw_image , source_prompt=lowerCamelCase , target_prompt=lowerCamelCase , generator=lowerCamelCase , )
snake_case__ : Optional[Any] = pipe.invert(
prompt=lowerCamelCase , image=self.raw_image , inpaint_strength=0.7 , generator=lowerCamelCase , num_inference_steps=25 , ).latents
snake_case__ : Any = pipe(
prompt=lowerCamelCase , mask_image=lowerCamelCase , image_latents=lowerCamelCase , generator=lowerCamelCase , negative_prompt=lowerCamelCase , inpaint_strength=0.7 , num_inference_steps=25 , output_type='''numpy''' , ).images[0]
snake_case__ : Dict = (
np.array(
load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/diffedit/pears.png''' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 261
|
'''simple docstring'''
def _A ( snake_case__ : list[int] , snake_case__ : list[int] ):
snake_case__ : Tuple = len(snake_case__ )
print('''The following activities are selected:''' )
# The first activity is always selected
snake_case__ : Optional[Any] = 0
print(snake_case__ , end=''',''' )
# Consider rest of the activities
for j in range(snake_case__ ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(snake_case__ , end=''',''' )
snake_case__ : int = j
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase : List[str] = [1, 3, 0, 5, 8, 5]
_lowerCAmelCase : Dict = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 261
| 1
|
import re
import string
import numpy as np
import datasets
_lowerCamelCase : Tuple = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
_lowerCamelCase : List[Any] = "\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results[\"exact_match\"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 33.3\n\n"
_lowerCamelCase : Union[str, Any] = "\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase (datasets.Metric ):
"""simple docstring"""
def A_ ( self : Dict ) -> Dict:
"""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" ),
} ), reference_urls=[], )
def A_ ( self : Optional[Any], _UpperCAmelCase : Optional[int], _UpperCAmelCase : Optional[Any], _UpperCAmelCase : int=None, _UpperCAmelCase : List[Any]=False, _UpperCAmelCase : Tuple=False, _UpperCAmelCase : List[Any]=False, ) -> int:
"""simple docstring"""
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
SCREAMING_SNAKE_CASE__ : List[str] = np.array([re.sub(_lowercase, "", _lowercase ) for x in predictions] )
SCREAMING_SNAKE_CASE__ : Dict = np.array([re.sub(_lowercase, "", _lowercase ) for x in references] )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = np.asarray(_lowercase )
SCREAMING_SNAKE_CASE__ : Any = np.asarray(_lowercase )
if ignore_case:
SCREAMING_SNAKE_CASE__ : Optional[int] = np.char.lower(_lowercase )
SCREAMING_SNAKE_CASE__ : int = np.char.lower(_lowercase )
if ignore_punctuation:
SCREAMING_SNAKE_CASE__ : Optional[Any] = string.punctuation.maketrans("", "", string.punctuation )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.char.translate(_lowercase, table=_lowercase )
SCREAMING_SNAKE_CASE__ : Tuple = np.char.translate(_lowercase, table=_lowercase )
if ignore_numbers:
SCREAMING_SNAKE_CASE__ : List[Any] = string.digits.maketrans("", "", string.digits )
SCREAMING_SNAKE_CASE__ : List[str] = np.char.translate(_lowercase, table=_lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] = np.char.translate(_lowercase, table=_lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = predictions == references
return {"exact_match": np.mean(_lowercase ) * 1_0_0}
| 703
|
from collections.abc import Generator
def _a ( ) -> Generator[int, None, None]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Any = 0, 1
while True:
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Optional[Any] = b, a + b
yield b
def _a ( SCREAMING_SNAKE_CASE__ : int = 10_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = fibonacci_generator()
while len(str(next(SCREAMING_SNAKE_CASE__ ) ) ) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 157
| 0
|
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class __UpperCamelCase :
# setable values
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None # sigma(t_i)
@classmethod
def __A ( cls : List[str] ):
'''simple docstring'''
return cls()
@dataclass
class __UpperCamelCase ( lowercase ):
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = 42
class __UpperCamelCase ( lowercase , lowercase ):
@property
def __A ( self : Tuple ):
'''simple docstring'''
return True
@register_to_config
def __init__( self : Any , lowerCAmelCase : float = 0.02 , lowerCAmelCase : float = 100 , lowerCAmelCase : float = 1.007 , lowerCAmelCase : float = 80 , lowerCAmelCase : float = 0.05 , lowerCAmelCase : float = 50 , ):
'''simple docstring'''
pass
def __A ( self : List[str] ):
'''simple docstring'''
return KarrasVeSchedulerState.create()
def __A ( self : Dict , lowerCAmelCase : KarrasVeSchedulerState , lowerCAmelCase : int , lowerCAmelCase : Tuple = () ):
'''simple docstring'''
UpperCAmelCase_ = jnp.arange(0 , lowerCAmelCase )[::-1].copy()
UpperCAmelCase_ = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=lowerCAmelCase , schedule=jnp.array(lowerCAmelCase , dtype=jnp.floataa ) , timesteps=lowerCAmelCase , )
def __A ( self : Union[str, Any] , lowerCAmelCase : KarrasVeSchedulerState , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : float , lowerCAmelCase : random.KeyArray , ):
'''simple docstring'''
if self.config.s_min <= sigma <= self.config.s_max:
UpperCAmelCase_ = min(self.config.s_churn / state.num_inference_steps , 2**0.5 - 1 )
else:
UpperCAmelCase_ = 0
# sample eps ~ N(0, S_noise^2 * I)
UpperCAmelCase_ = random.split(lowerCAmelCase , num=1 )
UpperCAmelCase_ = self.config.s_noise * random.normal(key=lowerCAmelCase , shape=sample.shape )
UpperCAmelCase_ = sigma + gamma * sigma
UpperCAmelCase_ = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def __A ( self : Optional[int] , lowerCAmelCase : KarrasVeSchedulerState , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : float , lowerCAmelCase : float , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : bool = True , ):
'''simple docstring'''
UpperCAmelCase_ = sample_hat + sigma_hat * model_output
UpperCAmelCase_ = (sample_hat - pred_original_sample) / sigma_hat
UpperCAmelCase_ = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase , derivative=lowerCAmelCase , state=lowerCAmelCase )
def __A ( self : int , lowerCAmelCase : KarrasVeSchedulerState , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : float , lowerCAmelCase : float , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : jnp.ndarray , lowerCAmelCase : bool = True , ):
'''simple docstring'''
UpperCAmelCase_ = sample_prev + sigma_prev * model_output
UpperCAmelCase_ = (sample_prev - pred_original_sample) / sigma_prev
UpperCAmelCase_ = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase , derivative=lowerCAmelCase , state=lowerCAmelCase )
def __A ( self : Any , lowerCAmelCase : KarrasVeSchedulerState , lowerCAmelCase : Any , lowerCAmelCase : List[str] , lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
raise NotImplementedError()
| 162
|
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
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_a: Union[str, Any] = logging.get_logger(__name__)
_a: Dict = {"""tokenizer_file""": """tokenizer.json"""}
_a: Dict = {
"""tokenizer_file""": {
"""bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
"""bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
"""bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
"""bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
"""bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
"""bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
"""bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
},
}
class __UpperCamelCase ( lowercase ):
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = ['input_ids', 'attention_mask']
SCREAMING_SNAKE_CASE__ = None
def __init__( self : Tuple , lowerCAmelCase : Optional[int]=None , lowerCAmelCase : int=None , lowerCAmelCase : str=None , lowerCAmelCase : List[Any]="<unk>" , lowerCAmelCase : int="<s>" , lowerCAmelCase : Dict="</s>" , lowerCAmelCase : List[str]="<pad>" , lowerCAmelCase : int=False , lowerCAmelCase : Optional[int]=False , **lowerCAmelCase : Union[str, Any] , ):
'''simple docstring'''
super().__init__(
lowerCAmelCase , lowerCAmelCase , tokenizer_file=lowerCAmelCase , unk_token=lowerCAmelCase , bos_token=lowerCAmelCase , eos_token=lowerCAmelCase , pad_token=lowerCAmelCase , add_prefix_space=lowerCAmelCase , clean_up_tokenization_spaces=lowerCAmelCase , **lowerCAmelCase , )
UpperCAmelCase_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , lowerCAmelCase ) != add_prefix_space:
UpperCAmelCase_ = getattr(lowerCAmelCase , pre_tok_state.pop("type" ) )
UpperCAmelCase_ = add_prefix_space
UpperCAmelCase_ = pre_tok_class(**lowerCAmelCase )
UpperCAmelCase_ = add_prefix_space
def __A ( self : int , *lowerCAmelCase : Any , **lowerCAmelCase : str ):
'''simple docstring'''
UpperCAmelCase_ = kwargs.get("is_split_into_words" , lowerCAmelCase )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
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 __A ( self : Dict , *lowerCAmelCase : Union[str, Any] , **lowerCAmelCase : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ = kwargs.get("is_split_into_words" , lowerCAmelCase )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
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 __A ( self : Tuple , lowerCAmelCase : str , lowerCAmelCase : Optional[str] = None ):
'''simple docstring'''
UpperCAmelCase_ = self._tokenizer.model.save(lowerCAmelCase , name=lowerCAmelCase )
return tuple(lowerCAmelCase )
def __A ( self : str , lowerCAmelCase : "Conversation" ):
'''simple docstring'''
UpperCAmelCase_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) + [self.eos_token_id] )
if len(lowerCAmelCase ) > self.model_max_length:
UpperCAmelCase_ = input_ids[-self.model_max_length :]
return input_ids
| 162
| 1
|
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase__ : int = ''
UpperCAmelCase__ : List[str] = ''
UpperCAmelCase__ : List[str] = ''
UpperCAmelCase__ : List[str] = 1 # (0 is vertical, 1 is horizontal)
def _A ( ):
_UpperCAmelCase : Optional[Any] = get_dataset(_UpperCamelCase , _UpperCamelCase )
print('''Processing...''' )
_UpperCAmelCase : Tuple = update_image_and_anno(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
for index, image in enumerate(_UpperCamelCase ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
_UpperCAmelCase : Any = random_chars(32 )
_UpperCAmelCase : Optional[Any] = paths[index].split(os.sep )[-1].rsplit('''.''' , 1 )[0]
_UpperCAmelCase : List[Any] = F'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(F'''/{file_root}.jpg''' , _UpperCamelCase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F'''Success {index+1}/{len(_UpperCamelCase )} with {file_name}''' )
_UpperCAmelCase : Any = []
for anno in new_annos[index]:
_UpperCAmelCase : List[Any] = F'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(_UpperCamelCase )
with open(F'''/{file_root}.txt''' , '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def _A ( _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase : Union[str, Any] = []
_UpperCAmelCase : Optional[int] = []
for label_file in glob.glob(os.path.join(_UpperCamelCase , '''*.txt''' ) ):
_UpperCAmelCase : Optional[Any] = label_file.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
with open(_UpperCamelCase ) as in_file:
_UpperCAmelCase : Tuple = in_file.readlines()
_UpperCAmelCase : str = os.path.join(_UpperCamelCase , F'''{label_name}.jpg''' )
_UpperCAmelCase : str = []
for obj_list in obj_lists:
_UpperCAmelCase : Any = obj_list.rstrip('''\n''' ).split(''' ''' )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(_UpperCamelCase )
labels.append(_UpperCamelCase )
return img_paths, labels
def _A ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 1 ):
_UpperCAmelCase : str = []
_UpperCAmelCase : Optional[int] = []
_UpperCAmelCase : List[Any] = []
for idx in range(len(_UpperCamelCase ) ):
_UpperCAmelCase : Optional[int] = []
_UpperCAmelCase : Dict = img_list[idx]
path_list.append(_UpperCamelCase )
_UpperCAmelCase : int = anno_list[idx]
_UpperCAmelCase : Dict = cva.imread(_UpperCamelCase )
if flip_type == 1:
_UpperCAmelCase : Tuple = cva.flip(_UpperCamelCase , _UpperCamelCase )
for bbox in img_annos:
_UpperCAmelCase : List[Any] = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
_UpperCAmelCase : Any = cva.flip(_UpperCamelCase , _UpperCamelCase )
for bbox in img_annos:
_UpperCAmelCase : str = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(_UpperCamelCase )
new_imgs_list.append(_UpperCamelCase )
return new_imgs_list, new_annos_lists, path_list
def _A ( _UpperCamelCase = 32 ):
assert number_char > 1, "The number of character should greater than 1"
_UpperCAmelCase : Dict = ascii_lowercase + digits
return "".join(random.choice(_UpperCamelCase ) for _ in range(_UpperCamelCase ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 710
|
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase__ : Any = Dict[str, Any]
UpperCAmelCase__ : List[str] = List[Prediction]
@add_end_docstrings(lowercase_ )
class lowerCAmelCase_ ( lowercase_ ):
def __init__( self : List[Any] , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : List[Any] ) -> Any:
'''simple docstring'''
super().__init__(*UpperCAmelCase_ , **UpperCAmelCase_ )
if self.framework == "tf":
raise ValueError(F'''The {self.__class__} is only available in PyTorch.''' )
requires_backends(self , '''vision''' )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def a_ ( self : str , **UpperCAmelCase_ : int ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : List[str] = {}
if "threshold" in kwargs:
_UpperCAmelCase : List[str] = kwargs['''threshold''']
return {}, {}, postprocess_kwargs
def __call__( self : Optional[Any] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : List[Any] ) -> Union[Predictions, List[Prediction]]:
'''simple docstring'''
return super().__call__(*UpperCAmelCase_ , **UpperCAmelCase_ )
def a_ ( self : Optional[Any] , UpperCAmelCase_ : List[Any] ) -> Any:
'''simple docstring'''
_UpperCAmelCase : Dict = load_image(UpperCAmelCase_ )
_UpperCAmelCase : Dict = torch.IntTensor([[image.height, image.width]] )
_UpperCAmelCase : Dict = self.image_processor(images=[image] , return_tensors='''pt''' )
if self.tokenizer is not None:
_UpperCAmelCase : Optional[Any] = self.tokenizer(text=inputs['''words'''] , boxes=inputs['''boxes'''] , return_tensors='''pt''' )
_UpperCAmelCase : Tuple = target_size
return inputs
def a_ ( self : List[str] , UpperCAmelCase_ : Optional[int] ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Dict = model_inputs.pop('''target_size''' )
_UpperCAmelCase : int = self.model(**UpperCAmelCase_ )
_UpperCAmelCase : int = outputs.__class__({'''target_size''': target_size, **outputs} )
if self.tokenizer is not None:
_UpperCAmelCase : Tuple = model_inputs['''bbox''']
return model_outputs
def a_ ( self : Dict , UpperCAmelCase_ : Dict , UpperCAmelCase_ : int=0.9 ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = model_outputs['''target_size''']
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
_UpperCAmelCase , _UpperCAmelCase : Tuple = target_size[0].tolist()
def unnormalize(UpperCAmelCase_ : List[Any] ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1000),
(height * bbox[1] / 1000),
(width * bbox[2] / 1000),
(height * bbox[3] / 1000),
] ) )
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = model_outputs['''logits'''].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
_UpperCAmelCase : List[Any] = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
_UpperCAmelCase : List[Any] = [unnormalize(UpperCAmelCase_ ) for bbox in model_outputs['''bbox'''].squeeze(0 )]
_UpperCAmelCase : Union[str, Any] = ['''score''', '''label''', '''box''']
_UpperCAmelCase : Optional[Any] = [dict(zip(UpperCAmelCase_ , UpperCAmelCase_ ) ) for vals in zip(scores.tolist() , UpperCAmelCase_ , UpperCAmelCase_ ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
_UpperCAmelCase : Optional[int] = self.image_processor.post_process_object_detection(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
_UpperCAmelCase : Any = raw_annotations[0]
_UpperCAmelCase : List[str] = raw_annotation['''scores''']
_UpperCAmelCase : str = raw_annotation['''labels''']
_UpperCAmelCase : Dict = raw_annotation['''boxes''']
_UpperCAmelCase : List[str] = scores.tolist()
_UpperCAmelCase : int = [self.model.config.idalabel[label.item()] for label in labels]
_UpperCAmelCase : Any = [self._get_bounding_box(UpperCAmelCase_ ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
_UpperCAmelCase : Tuple = ['''score''', '''label''', '''box''']
_UpperCAmelCase : Any = [
dict(zip(UpperCAmelCase_ , UpperCAmelCase_ ) )
for vals in zip(raw_annotation['''scores'''] , raw_annotation['''labels'''] , raw_annotation['''boxes'''] )
]
return annotation
def a_ ( self : Optional[int] , UpperCAmelCase_ : "torch.Tensor" ) -> Dict[str, int]:
'''simple docstring'''
if self.framework != "pt":
raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.''' )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = box.int().tolist()
_UpperCAmelCase : Optional[Any] = {
'''xmin''': xmin,
'''ymin''': ymin,
'''xmax''': xmax,
'''ymax''': ymax,
}
return bbox
| 416
| 0
|
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
'''simple docstring'''
if len(lowercase_ ) == 0:
return []
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = min(lowercase_ ), max(lowercase_ )
__UpperCAmelCase : Any = int(max_value - min_value ) + 1
__UpperCAmelCase : list[list] = [[] for _ in range(lowercase_ )]
for i in my_list:
buckets[int(i - min_value )].append(lowercase_ )
return [v for bucket in buckets for v in sorted(lowercase_ )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
| 462
|
from __future__ import annotations
import requests
lowerCAmelCase = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ = 1 , lowercase_ = "new" , lowercase_ = None ) -> dict:
'''simple docstring'''
__UpperCAmelCase : Tuple = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(lowercase_ ) - valid_terms ) ):
__UpperCAmelCase : List[Any] = f"Invalid search term: {invalid_search_terms}"
raise ValueError(lowercase_ )
__UpperCAmelCase : Optional[int] = requests.get(
f"https://reddit.com/r/{subreddit}/{age}.json?limit={limit}" , headers={'''User-agent''': '''A random string'''} , )
if response.status_code == 429:
raise requests.HTTPError
__UpperCAmelCase : List[str] = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(lowercase_ )}
__UpperCAmelCase : List[Any] = {}
for id_ in range(lowercase_ ):
__UpperCAmelCase : str = {
item: data['''data''']['''children'''][id_]['''data'''][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data("""learnpython""", wanted_data=["""title""", """url""", """selftext"""]))
| 462
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase : Any = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : List[Any] = ['BartphoTokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
UpperCAmelCase : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 47
|
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class lowerCamelCase (unittest.TestCase ):
@slow
def UpperCAmelCase_ ( self ) -> int:
"""simple docstring"""
_snake_case : Tuple = FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''' )
_snake_case : Any = AutoTokenizer.from_pretrained('''google/mt5-small''' )
_snake_case : List[str] = tokenizer('''Hello there''' , return_tensors='''np''' ).input_ids
_snake_case : Dict = tokenizer('''Hi I am''' , return_tensors='''np''' ).input_ids
_snake_case : Any = shift_tokens_right(lowercase__ , model.config.pad_token_id , model.config.decoder_start_token_id )
_snake_case : Any = model(lowercase__ , decoder_input_ids=lowercase__ ).logits
_snake_case : Tuple = optax.softmax_cross_entropy(lowercase__ , onehot(lowercase__ , logits.shape[-1] ) ).mean()
_snake_case : Tuple = -(labels.shape[-1] * loss.item())
_snake_case : Union[str, Any] = -84.9_127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 47
| 1
|
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class lowerCAmelCase_ ( unittest.TestCase ):
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=1_3 , _lowerCAmelCase=7 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=9_9 , _lowerCAmelCase=3_2 , _lowerCAmelCase=5 , _lowerCAmelCase=4 , _lowerCAmelCase=3_7 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=5_1_2 , _lowerCAmelCase=1_6 , _lowerCAmelCase=2 , _lowerCAmelCase=0.02 , _lowerCAmelCase=4 , ):
_lowercase : Optional[Any] = parent
_lowercase : Any = batch_size
_lowercase : str = seq_length
_lowercase : Union[str, Any] = is_training
_lowercase : Tuple = use_attention_mask
_lowercase : List[str] = use_token_type_ids
_lowercase : Tuple = use_labels
_lowercase : Tuple = vocab_size
_lowercase : List[Any] = hidden_size
_lowercase : List[Any] = num_hidden_layers
_lowercase : List[Any] = num_attention_heads
_lowercase : Any = intermediate_size
_lowercase : List[str] = hidden_act
_lowercase : Dict = hidden_dropout_prob
_lowercase : int = attention_probs_dropout_prob
_lowercase : str = max_position_embeddings
_lowercase : Tuple = type_vocab_size
_lowercase : Optional[int] = type_sequence_label_size
_lowercase : Optional[int] = initializer_range
_lowercase : List[Any] = num_choices
def __a ( self ):
_lowercase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowercase : str = None
if self.use_attention_mask:
_lowercase : int = random_attention_mask([self.batch_size, self.seq_length] )
_lowercase : Optional[Any] = None
if self.use_token_type_ids:
_lowercase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowercase : Any = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def __a ( self ):
_lowercase : List[Any] = self.prepare_config_and_inputs()
_lowercase , _lowercase , _lowercase , _lowercase : Dict = config_and_inputs
_lowercase : Dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def __a ( self ):
_lowercase : Tuple = self.prepare_config_and_inputs()
_lowercase , _lowercase , _lowercase , _lowercase : List[Any] = config_and_inputs
_lowercase : Any = True
_lowercase : str = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_lowercase : Tuple = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class lowerCAmelCase_ ( __snake_case , unittest.TestCase ):
_UpperCamelCase : Optional[Any] = True
_UpperCamelCase : int = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __a ( self ):
_lowercase : Optional[Any] = FlaxBertModelTester(self )
@slow
def __a ( self ):
# Only check this for base model, not necessary for all model classes.
# This will also help speed-up tests.
_lowercase : Union[str, Any] = FlaxBertModel.from_pretrained('bert-base-cased' )
_lowercase : int = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowerCAmelCase )
| 66
|
"""simple docstring"""
import functools
def A ( _A, _A ):
"""simple docstring"""
snake_case_ :Optional[Any] = len(_A )
snake_case_ :Optional[int] = len(_A )
@functools.cache
def min_distance(_A, _A ) -> int:
# if first word index is overflow - delete all from the second word
if indexa >= len_worda:
return len_worda - indexa
# if second word index is overflow - delete all from the first word
if indexa >= len_worda:
return len_worda - indexa
snake_case_ :str = int(worda[indexa] != worda[indexa] ) # current letters not identical
return min(
1 + min_distance(indexa + 1, _A ), 1 + min_distance(_A, indexa + 1 ), diff + min_distance(indexa + 1, indexa + 1 ), )
return min_distance(0, 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 584
| 0
|
"""simple docstring"""
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __lowercase ( _a , _a , _a ):
snake_case_ : Tuple = AutoConfig.from_pretrained(_a )
snake_case_ : Tuple = FlaxAutoModelForSeqaSeqLM.from_config(config=_a )
snake_case_ : Union[str, Any] = checkpoints.load_tax_checkpoint(_a )
snake_case_ : Optional[int] = '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
if config.model_type == "t5":
snake_case_ : str = '''SelfAttention'''
if config.model_type == "longt5" and config.encoder_attention_type == "local":
snake_case_ : List[str] = '''LocalSelfAttention'''
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
snake_case_ : Dict = '''TransientGlobalSelfAttention'''
else:
raise ValueError(
'''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
''' attribute with a value from [\'local\', \'transient-global].''' )
# Encoder
for layer_index in range(config.num_layers ):
snake_case_ : Any = f"layers_{str(_a )}"
# Self-Attention
snake_case_ : Dict = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
snake_case_ : Tuple = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
snake_case_ : Tuple = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
snake_case_ : Optional[int] = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
snake_case_ : Tuple = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']
# Layer Normalization
snake_case_ : List[str] = tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
if split_mlp_wi:
snake_case_ : int = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
snake_case_ : List[str] = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
snake_case_ : int = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
snake_case_ : str = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
snake_case_ : Optional[Any] = tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
snake_case_ : Optional[Any] = flax_model.params['''encoder''']['''block'''][str(_a )]['''layer''']
snake_case_ : List[str] = tax_attention_key
snake_case_ : Optional[Any] = tax_attention_out
snake_case_ : Any = tax_attention_query
snake_case_ : str = tax_attention_value
snake_case_ : Dict = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
snake_case_ : Union[str, Any] = tax_global_layer_norm
if split_mlp_wi:
snake_case_ : Any = tax_mlp_wi_a
snake_case_ : List[Any] = tax_mlp_wi_a
else:
snake_case_ : Union[str, Any] = tax_mlp_wi
snake_case_ : List[Any] = tax_mlp_wo
snake_case_ : int = tax_mlp_layer_norm
snake_case_ : Any = flax_model_encoder_layer_block
# Only for layer 0:
snake_case_ : Optional[int] = tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
snake_case_ : Any = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
snake_case_ : List[str] = tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
snake_case_ : Tuple = tax_encoder_global_rel_embedding
# Assigning
snake_case_ : Dict = tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
snake_case_ : Any = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
snake_case_ : Tuple = f"layers_{str(_a )}"
# Self-Attention
snake_case_ : List[str] = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
snake_case_ : Optional[int] = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
snake_case_ : List[Any] = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
snake_case_ : Optional[int] = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
# Layer Normalization
snake_case_ : str = tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
'''scale'''
]
# Encoder-Decoder-Attention
snake_case_ : Any = tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
snake_case_ : Optional[Any] = tax_enc_dec_attention_module['''key''']['''kernel''']
snake_case_ : str = tax_enc_dec_attention_module['''out''']['''kernel''']
snake_case_ : Union[str, Any] = tax_enc_dec_attention_module['''query''']['''kernel''']
snake_case_ : List[str] = tax_enc_dec_attention_module['''value''']['''kernel''']
# Layer Normalization
snake_case_ : Optional[int] = tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
# MLP
if split_mlp_wi:
snake_case_ : List[Any] = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
snake_case_ : List[str] = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
snake_case_ : Dict = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
snake_case_ : List[Any] = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
snake_case_ : List[Any] = tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
snake_case_ : Dict = flax_model.params['''decoder''']['''block'''][str(_a )]['''layer''']
snake_case_ : int = tax_attention_key
snake_case_ : List[Any] = tax_attention_out
snake_case_ : Any = tax_attention_query
snake_case_ : Dict = tax_attention_value
snake_case_ : str = tax_pre_attention_layer_norm
snake_case_ : Any = tax_enc_dec_attention_key
snake_case_ : str = tax_enc_dec_attention_out
snake_case_ : int = tax_enc_dec_attention_query
snake_case_ : Any = tax_enc_dec_attention_value
snake_case_ : Optional[Any] = tax_cross_layer_norm
if split_mlp_wi:
snake_case_ : Tuple = tax_mlp_wi_a
snake_case_ : List[Any] = tax_mlp_wi_a
else:
snake_case_ : List[Any] = tax_mlp_wi
snake_case_ : Dict = tax_mlp_wo
snake_case_ : List[Any] = txa_mlp_layer_norm
snake_case_ : Optional[int] = flax_model_decoder_layer_block
# Decoder Normalization
snake_case_ : str = tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
snake_case_ : Tuple = txa_decoder_norm
# Only for layer 0:
snake_case_ : str = tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
snake_case_ : Optional[Any] = tax_decoder_rel_embedding
# Token Embeddings
snake_case_ : Union[str, Any] = tax_model['''target''']['''token_embedder''']['''embedding''']
snake_case_ : Optional[int] = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
snake_case_ : Union[str, Any] = tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
flax_model.save_pretrained(_a )
print('''T5X Model was sucessfully converted!''' )
if __name__ == "__main__":
lowercase__ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
)
parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
parser.add_argument(
'''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
)
lowercase__ : Dict = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 485
|
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowercase__ : Optional[Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class _UpperCAmelCase :
_lowerCAmelCase : Optional[str] = field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""})
_lowerCAmelCase : Optional[str] = field(
default=lowerCAmelCase__ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
_lowerCAmelCase : Optional[str] = field(
default=lowerCAmelCase__ , metadata={"""help""": """The column name of the images in the files."""})
_lowerCAmelCase : Optional[str] = field(default=lowerCAmelCase__ , metadata={"""help""": """A folder containing the training data."""})
_lowerCAmelCase : Optional[str] = field(default=lowerCAmelCase__ , metadata={"""help""": """A folder containing the validation data."""})
_lowerCAmelCase : Optional[float] = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""})
_lowerCAmelCase : Optional[int] = field(
default=lowerCAmelCase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_lowerCAmelCase : Optional[int] = field(
default=lowerCAmelCase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def _snake_case ( self : Union[str, Any] ):
snake_case_ : List[Any] = {}
if self.train_dir is not None:
snake_case_ : str = self.train_dir
if self.validation_dir is not None:
snake_case_ : Union[str, Any] = self.validation_dir
snake_case_ : Tuple = data_files if data_files else None
@dataclass
class _UpperCAmelCase :
_lowerCAmelCase : str = field(
default=lowerCAmelCase__ , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
_lowerCAmelCase : Optional[str] = field(
default=lowerCAmelCase__ , metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""})
_lowerCAmelCase : Optional[str] = field(
default=lowerCAmelCase__ , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
_lowerCAmelCase : Optional[str] = field(
default=lowerCAmelCase__ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""})
_lowerCAmelCase : str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_lowerCAmelCase : str = field(default=lowerCAmelCase__ , metadata={"""help""": """Name or path of preprocessor config."""})
_lowerCAmelCase : bool = field(
default=lowerCAmelCase__ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_lowerCAmelCase : float = field(
default=0.75 , metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""})
_lowerCAmelCase : bool = field(
default=lowerCAmelCase__ , metadata={"""help""": """Whether or not to train with normalized pixel values as target."""})
@dataclass
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : float = field(
default=1e-3 , metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""})
def __lowercase ( _a ):
snake_case_ : Tuple = torch.stack([example['''pixel_values'''] for example in examples] )
return {"pixel_values": pixel_values}
def __lowercase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
snake_case_ : Optional[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case_, snake_case_, snake_case_ : Optional[Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case_, snake_case_, snake_case_ : List[Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_mae''' , _a , _a )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
snake_case_ : List[str] = training_args.get_process_log_level()
logger.setLevel(_a )
transformers.utils.logging.set_verbosity(_a )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(f"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
snake_case_ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case_ : int = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset.
snake_case_ : Optional[int] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
snake_case_ : Optional[Any] = None if '''validation''' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _a ) and data_args.train_val_split > 0.0:
snake_case_ : List[Any] = ds['''train'''].train_test_split(data_args.train_val_split )
snake_case_ : Tuple = split['''train''']
snake_case_ : str = split['''test''']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case_ : Optional[int] = {
'''cache_dir''': model_args.cache_dir,
'''revision''': model_args.model_revision,
'''use_auth_token''': True if model_args.use_auth_token else None,
}
if model_args.config_name:
snake_case_ : List[Any] = ViTMAEConfig.from_pretrained(model_args.config_name , **_a )
elif model_args.model_name_or_path:
snake_case_ : Dict = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **_a )
else:
snake_case_ : Optional[int] = ViTMAEConfig()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(f"New config: {config}" )
# adapt config
config.update(
{
'''mask_ratio''': model_args.mask_ratio,
'''norm_pix_loss''': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
snake_case_ : Union[str, Any] = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **_a )
elif model_args.model_name_or_path:
snake_case_ : Union[str, Any] = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **_a )
else:
snake_case_ : Tuple = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
snake_case_ : Tuple = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=_a , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('''Training new model from scratch''' )
snake_case_ : Tuple = ViTMAEForPreTraining(_a )
if training_args.do_train:
snake_case_ : List[str] = ds['''train'''].column_names
else:
snake_case_ : Optional[Any] = ds['''validation'''].column_names
if data_args.image_column_name is not None:
snake_case_ : Tuple = data_args.image_column_name
elif "image" in column_names:
snake_case_ : Tuple = '''image'''
elif "img" in column_names:
snake_case_ : str = '''img'''
else:
snake_case_ : Union[str, Any] = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
snake_case_ : str = image_processor.size['''shortest_edge''']
else:
snake_case_ : Dict = (image_processor.size['''height'''], image_processor.size['''width'''])
snake_case_ : str = Compose(
[
Lambda(lambda _a : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
RandomResizedCrop(_a , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(_a ):
snake_case_ : Tuple = [transforms(_a ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('''--do_train requires a train dataset''' )
if data_args.max_train_samples is not None:
snake_case_ : List[str] = ds['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_a )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('''--do_eval requires a validation dataset''' )
if data_args.max_eval_samples is not None:
snake_case_ : Optional[Any] = (
ds['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_a )
# Compute absolute learning rate
snake_case_ : Any = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
snake_case_ : Union[str, Any] = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
snake_case_ : str = Trainer(
model=_a , args=_a , train_dataset=ds['''train'''] if training_args.do_train else None , eval_dataset=ds['''validation'''] if training_args.do_eval else None , tokenizer=_a , data_collator=_a , )
# Training
if training_args.do_train:
snake_case_ : Any = None
if training_args.resume_from_checkpoint is not None:
snake_case_ : Optional[int] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case_ : str = last_checkpoint
snake_case_ : List[str] = trainer.train(resume_from_checkpoint=_a )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
snake_case_ : Any = trainer.evaluate()
trainer.log_metrics('''eval''' , _a )
trainer.save_metrics('''eval''' , _a )
# Write model card and (optionally) push to hub
snake_case_ : Optional[int] = {
'''tasks''': '''masked-auto-encoding''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''masked-auto-encoding'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_a )
else:
trainer.create_model_card(**_a )
def __lowercase ( _a ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 485
| 1
|
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_UpperCAmelCase : Union[str, Any] = (720, 1280) # Height, Width
_UpperCAmelCase : str = (0.4, 0.6) # if height or width lower than this scale, drop it.
_UpperCAmelCase : Optional[Any] = 1 / 100
_UpperCAmelCase : Optional[Any] = """"""
_UpperCAmelCase : int = """"""
_UpperCAmelCase : Union[str, Any] = """"""
_UpperCAmelCase : List[Any] = 250
def snake_case__ ( ) -> None:
_UpperCamelCase, _UpperCamelCase : List[Any] = get_dataset(UpperCamelCase ,UpperCamelCase )
for index in range(UpperCamelCase ):
_UpperCamelCase : List[str] = random.sample(range(len(UpperCamelCase ) ) ,4 )
_UpperCamelCase, _UpperCamelCase, _UpperCamelCase : List[str] = update_image_and_anno(
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,filter_scale=UpperCamelCase ,)
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
_UpperCamelCase : List[str] = random_chars(32 )
_UpperCamelCase : List[str] = path.split(os.sep )[-1].rsplit('''.''' ,1 )[0]
_UpperCamelCase : Any = f'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
cva.imwrite(f'''{file_root}.jpg''' ,UpperCamelCase ,[cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}''' )
_UpperCamelCase : Any = []
for anno in new_annos:
_UpperCamelCase : List[Any] = anno[3] - anno[1]
_UpperCamelCase : int = anno[4] - anno[2]
_UpperCamelCase : int = anno[1] + width / 2
_UpperCamelCase : int = anno[2] + height / 2
_UpperCamelCase : Optional[Any] = f'''{anno[0]} {x_center} {y_center} {width} {height}'''
annos_list.append(UpperCamelCase )
with open(f'''{file_root}.txt''' ,'''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> tuple[list, list]:
_UpperCamelCase : List[str] = []
_UpperCamelCase : Union[str, Any] = []
for label_file in glob.glob(os.path.join(UpperCamelCase ,'''*.txt''' ) ):
_UpperCamelCase : int = label_file.split(os.sep )[-1].rsplit('''.''' ,1 )[0]
with open(UpperCamelCase ) as in_file:
_UpperCamelCase : Dict = in_file.readlines()
_UpperCamelCase : Tuple = os.path.join(UpperCamelCase ,f'''{label_name}.jpg''' )
_UpperCamelCase : Tuple = []
for obj_list in obj_lists:
_UpperCamelCase : List[Any] = obj_list.rstrip('''\n''' ).split(''' ''' )
_UpperCamelCase : Tuple = float(obj[1] ) - float(obj[3] ) / 2
_UpperCamelCase : Any = float(obj[2] ) - float(obj[4] ) / 2
_UpperCamelCase : Tuple = float(obj[1] ) + float(obj[3] ) / 2
_UpperCamelCase : List[Any] = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(UpperCamelCase )
labels.append(UpperCamelCase )
return img_paths, labels
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = 0.0 ,) -> tuple[list, list, str]:
_UpperCamelCase : Optional[int] = np.zeros([output_size[0], output_size[1], 3] ,dtype=np.uinta )
_UpperCamelCase : str = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_UpperCamelCase : Dict = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_UpperCamelCase : Dict = int(scale_x * output_size[1] )
_UpperCamelCase : Dict = int(scale_y * output_size[0] )
_UpperCamelCase : int = []
_UpperCamelCase : Union[str, Any] = []
for i, index in enumerate(UpperCamelCase ):
_UpperCamelCase : Optional[int] = all_img_list[index]
path_list.append(UpperCamelCase )
_UpperCamelCase : str = all_annos[index]
_UpperCamelCase : Tuple = cva.imread(UpperCamelCase )
if i == 0: # top-left
_UpperCamelCase : Any = cva.resize(UpperCamelCase ,(divid_point_x, divid_point_y) )
_UpperCamelCase : Any = img
for bbox in img_annos:
_UpperCamelCase : List[Any] = bbox[1] * scale_x
_UpperCamelCase : Dict = bbox[2] * scale_y
_UpperCamelCase : Any = bbox[3] * scale_x
_UpperCamelCase : Any = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
_UpperCamelCase : Union[str, Any] = cva.resize(UpperCamelCase ,(output_size[1] - divid_point_x, divid_point_y) )
_UpperCamelCase : List[Any] = img
for bbox in img_annos:
_UpperCamelCase : Any = scale_x + bbox[1] * (1 - scale_x)
_UpperCamelCase : Optional[Any] = bbox[2] * scale_y
_UpperCamelCase : Any = scale_x + bbox[3] * (1 - scale_x)
_UpperCamelCase : Optional[int] = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
_UpperCamelCase : Dict = cva.resize(UpperCamelCase ,(divid_point_x, output_size[0] - divid_point_y) )
_UpperCamelCase : List[str] = img
for bbox in img_annos:
_UpperCamelCase : int = bbox[1] * scale_x
_UpperCamelCase : Optional[Any] = scale_y + bbox[2] * (1 - scale_y)
_UpperCamelCase : int = bbox[3] * scale_x
_UpperCamelCase : Any = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
_UpperCamelCase : Dict = cva.resize(
UpperCamelCase ,(output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
_UpperCamelCase : Union[str, Any] = img
for bbox in img_annos:
_UpperCamelCase : Optional[int] = scale_x + bbox[1] * (1 - scale_x)
_UpperCamelCase : Union[str, Any] = scale_y + bbox[2] * (1 - scale_y)
_UpperCamelCase : Optional[Any] = scale_x + bbox[3] * (1 - scale_x)
_UpperCamelCase : List[str] = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
_UpperCamelCase : Optional[Any] = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def snake_case__ ( UpperCamelCase ) -> str:
assert number_char > 1, "The number of character should greater than 1"
_UpperCamelCase : Tuple = ascii_lowercase + digits
return "".join(random.choice(UpperCamelCase ) for _ in range(UpperCamelCase ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 683
|
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class UpperCAmelCase ( a_ ):
"""simple docstring"""
@slow
@require_torch
def _lowercase ( self ) -> List[Any]:
_UpperCamelCase : Union[str, Any] = EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' , '''prajjwal1/bert-tiny''' )
_UpperCamelCase : Optional[int] = BertTokenizer.from_pretrained('''bert-base-uncased''' )
_UpperCamelCase : Optional[Any] = bertabert.config.encoder.vocab_size
_UpperCamelCase : List[str] = tokenizer.sep_token_id
_UpperCamelCase : List[str] = tokenizer.cls_token_id
_UpperCamelCase : Optional[Any] = 128
_UpperCamelCase : int = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''train[:1%]''' )
_UpperCamelCase : Dict = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''validation[:1%]''' )
_UpperCamelCase : Dict = train_dataset.select(range(32 ) )
_UpperCamelCase : Tuple = val_dataset.select(range(16 ) )
_UpperCamelCase : Union[str, Any] = 4
def _map_to_encoder_decoder_inputs(_snake_case ):
# Tokenizer will automatically set [BOS] <text> [EOS]
_UpperCamelCase : Optional[Any] = tokenizer(batch['''article'''] , padding='''max_length''' , truncation=_snake_case , max_length=512 )
_UpperCamelCase : Optional[int] = tokenizer(batch['''highlights'''] , padding='''max_length''' , truncation=_snake_case , max_length=128 )
_UpperCamelCase : str = inputs.input_ids
_UpperCamelCase : Union[str, Any] = inputs.attention_mask
_UpperCamelCase : str = outputs.input_ids
_UpperCamelCase : str = outputs.input_ids.copy()
_UpperCamelCase : Tuple = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
]
_UpperCamelCase : Union[str, Any] = outputs.attention_mask
assert all(len(_snake_case ) == 512 for x in inputs.input_ids )
assert all(len(_snake_case ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(_snake_case ):
_UpperCamelCase : Dict = pred.label_ids
_UpperCamelCase : Optional[int] = pred.predictions
# all unnecessary tokens are removed
_UpperCamelCase : Any = tokenizer.batch_decode(_snake_case , skip_special_tokens=_snake_case )
_UpperCamelCase : Dict = tokenizer.batch_decode(_snake_case , skip_special_tokens=_snake_case )
_UpperCamelCase : int = sum([int(pred_str[i] == label_str[i] ) for i in range(len(_snake_case ) )] ) / len(_snake_case )
return {"accuracy": accuracy}
# map train dataset
_UpperCamelCase : Optional[Any] = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=_snake_case , batch_size=_snake_case , remove_columns=['''article''', '''highlights'''] , )
train_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
# same for validation dataset
_UpperCamelCase : List[Any] = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=_snake_case , batch_size=_snake_case , remove_columns=['''article''', '''highlights'''] , )
val_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
_UpperCamelCase : Optional[int] = self.get_auto_remove_tmp_dir()
_UpperCamelCase : Union[str, Any] = SeqaSeqTrainingArguments(
output_dir=_snake_case , per_device_train_batch_size=_snake_case , per_device_eval_batch_size=_snake_case , predict_with_generate=_snake_case , evaluation_strategy='''steps''' , do_train=_snake_case , do_eval=_snake_case , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
_UpperCamelCase : Optional[int] = SeqaSeqTrainer(
model=_snake_case , args=_snake_case , compute_metrics=_compute_metrics , train_dataset=_snake_case , eval_dataset=_snake_case , tokenizer=_snake_case , )
# start training
trainer.train()
| 683
| 1
|
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
lowerCAmelCase__ = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["""memory_attention""", """encoder_attn"""],
["""attention""", """attn"""],
["""/""", """."""],
[""".LayerNorm.gamma""", """_layer_norm.weight"""],
[""".LayerNorm.beta""", """_layer_norm.bias"""],
["""r.layer_""", """r.layers."""],
["""output_proj""", """out_proj"""],
["""ffn.dense_1.""", """fc2."""],
["""ffn.dense.""", """fc1."""],
["""ffn_layer_norm""", """final_layer_norm"""],
["""kernel""", """weight"""],
["""encoder_layer_norm.""", """encoder.layer_norm."""],
["""decoder_layer_norm.""", """decoder.layer_norm."""],
["""embeddings.weights""", """shared.weight"""],
]
def lowerCamelCase_ ( UpperCAmelCase_ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
_UpperCamelCase : str = k.replace(UpperCAmelCase_ , UpperCAmelCase_ )
return k
def lowerCamelCase_ ( UpperCAmelCase_ : dict , UpperCAmelCase_ : dict ) -> PegasusForConditionalGeneration:
'''simple docstring'''
_UpperCamelCase : List[str] = DEFAULTS.copy()
cfg_kwargs.update(UpperCAmelCase_ )
_UpperCamelCase : int = PegasusConfig(**UpperCAmelCase_ )
_UpperCamelCase : int = PegasusForConditionalGeneration(UpperCAmelCase_ )
_UpperCamelCase : str = torch_model.model.state_dict()
_UpperCamelCase : List[Any] = {}
for k, v in tf_weights.items():
_UpperCamelCase : List[Any] = rename_state_dict_key(UpperCAmelCase_ )
if new_k not in sd:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if "dense" in k or "proj" in new_k:
_UpperCamelCase : int = v.T
_UpperCamelCase : Union[str, Any] = torch.tensor(UpperCAmelCase_ , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
# make sure embedding.padding_idx is respected
_UpperCamelCase : List[Any] = torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1] )
_UpperCamelCase : List[str] = mapping['shared.weight']
_UpperCamelCase : List[Any] = mapping['shared.weight']
_UpperCamelCase : Union[str, Any] = {k: torch.zeros_like(UpperCAmelCase_ ) for k, v in sd.items() if k.endswith('bias' ) and k not in mapping}
mapping.update(**UpperCAmelCase_ )
_UpperCamelCase , _UpperCamelCase : List[str] = torch_model.model.load_state_dict(UpperCAmelCase_ , strict=UpperCAmelCase_ )
_UpperCamelCase : Optional[int] = [
k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
]
assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], F'''no matches found for the following tf keys {extra}'''
return torch_model
def lowerCamelCase_ ( UpperCAmelCase_ : Dict="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
'''simple docstring'''
_UpperCamelCase : str = tf.train.list_variables(UpperCAmelCase_ )
_UpperCamelCase : Tuple = {}
_UpperCamelCase : List[Any] = ['Adafactor', 'global_step']
for name, shape in tqdm(UpperCAmelCase_ , desc='converting tf checkpoint to dict' ):
_UpperCamelCase : List[str] = any(pat in name for pat in ignore_name )
if skip_key:
continue
_UpperCamelCase : Tuple = tf.train.load_variable(UpperCAmelCase_ , UpperCAmelCase_ )
_UpperCamelCase : Optional[int] = array
return tf_weights
def lowerCamelCase_ ( UpperCAmelCase_ : str , UpperCAmelCase_ : str ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase : Optional[Any] = Path(UpperCAmelCase_ ).parent.name
_UpperCamelCase : Tuple = task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
_UpperCamelCase : List[Any] = PegasusTokenizer.from_pretrained('sshleifer/pegasus' , model_max_length=UpperCAmelCase_ )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(UpperCAmelCase_ )
# convert model
_UpperCamelCase : Union[str, Any] = get_tf_weights_as_numpy(UpperCAmelCase_ )
_UpperCamelCase : str = task_specific_params[F'''summarization_{dataset}''']
if dataset == "large":
_UpperCamelCase : Optional[Any] = task_specific_params
_UpperCamelCase : List[str] = convert_pegasus(UpperCAmelCase_ , UpperCAmelCase_ )
torch_model.save_pretrained(UpperCAmelCase_ )
_UpperCamelCase : List[str] = torch_model.state_dict()
sd.pop('model.decoder.embed_positions.weight' )
sd.pop('model.encoder.embed_positions.weight' )
torch.save(UpperCAmelCase_ , Path(UpperCAmelCase_ ) / 'pytorch_model.bin' )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
lowerCAmelCase__ = parser.parse_args()
if args.save_dir is None:
lowerCAmelCase__ = Path(args.tf_ckpt_path).parent.name
lowerCAmelCase__ = os.path.join("""pegasus""", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 648
|
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 648
| 1
|
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class UpperCAmelCase__ ( __snake_case , unittest.TestCase ):
__snake_case : int = BarthezTokenizer
__snake_case : Optional[int] = BarthezTokenizerFast
__snake_case : str = True
__snake_case : str = True
def A__ ( self ):
super().setUp()
_A : Union[str, Any] = BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname ,legacy_format=A__ )
_A : str = tokenizer
def A__ ( self ):
_A : Optional[int] = '''<pad>'''
_A : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A__ ) ,A__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A__ ) ,A__ )
def A__ ( self ):
_A : int = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,'''<s>''' )
self.assertEqual(vocab_keys[1] ,'''<pad>''' )
self.assertEqual(vocab_keys[-1] ,'''<mask>''' )
self.assertEqual(len(A__ ) ,101122 )
def A__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size ,101122 )
@require_torch
def A__ ( self ):
_A : Any = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
_A : Union[str, Any] = [0, 57, 3018, 70307, 91, 2]
_A : List[str] = self.tokenizer(
A__ ,max_length=len(A__ ) ,padding=A__ ,truncation=A__ ,return_tensors='''pt''' )
self.assertIsInstance(A__ ,A__ )
self.assertEqual((2, 6) ,batch.input_ids.shape )
self.assertEqual((2, 6) ,batch.attention_mask.shape )
_A : Dict = batch.input_ids.tolist()[0]
self.assertListEqual(A__ ,A__ )
def A__ ( self ):
if not self.test_rust_tokenizer:
return
_A : Any = self.get_tokenizer()
_A : int = self.get_rust_tokenizer()
_A : str = '''I was born in 92000, and this is falsé.'''
_A : Union[str, Any] = tokenizer.tokenize(A__ )
_A : Tuple = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ ,A__ )
_A : List[Any] = tokenizer.encode(A__ ,add_special_tokens=A__ )
_A : Tuple = rust_tokenizer.encode(A__ ,add_special_tokens=A__ )
self.assertListEqual(A__ ,A__ )
_A : Optional[int] = self.get_rust_tokenizer()
_A : Optional[int] = tokenizer.encode(A__ )
_A : Optional[int] = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ ,A__ )
@slow
def A__ ( self ):
# fmt: off
_A : Dict = {'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_A : int = [
'''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
'''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
'''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
'''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
'''telles que la traduction et la synthèse de texte.''',
]
self.tokenizer_integration_test_util(
expected_encoding=A__ ,model_name='''moussaKam/mbarthez''' ,revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' ,sequences=A__ ,)
| 206
|
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCamelCase : Union[str, Any] ={'UserAgent': UserAgent().random}
def a__ (__lowercase :Optional[Any] ) -> dict:
_A : str = script.contents[0]
_A : Dict = json.loads(data[data.find('''{"config"''' ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class UpperCAmelCase__ :
def __init__( self ,A__ ):
_A : Any = f"""https://www.instagram.com/{username}/"""
_A : Optional[Any] = self.get_json()
def A__ ( self ):
_A : str = requests.get(self.url ,headers=A__ ).text
_A : Dict = BeautifulSoup(A__ ,'''html.parser''' ).find_all('''script''' )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self ):
return f"""{self.__class__.__name__}('{self.username}')"""
def __str__( self ):
return f"""{self.fullname} ({self.username}) is {self.biography}"""
@property
def A__ ( self ):
return self.user_data["username"]
@property
def A__ ( self ):
return self.user_data["full_name"]
@property
def A__ ( self ):
return self.user_data["biography"]
@property
def A__ ( self ):
return self.user_data["business_email"]
@property
def A__ ( self ):
return self.user_data["external_url"]
@property
def A__ ( self ):
return self.user_data["edge_followed_by"]["count"]
@property
def A__ ( self ):
return self.user_data["edge_follow"]["count"]
@property
def A__ ( self ):
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def A__ ( self ):
return self.user_data["profile_pic_url_hd"]
@property
def A__ ( self ):
return self.user_data["is_verified"]
@property
def A__ ( self ):
return self.user_data["is_private"]
def a__ (__lowercase :str = "github" ) -> None:
import os
if os.environ.get('''CI''' ):
return # test failing on GitHub Actions
_A : Optional[int] = InstagramUser(__lowercase )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , __lowercase )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 150
assert instagram_user.number_of_followers > 12_0000
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith('''https://instagram.''' )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
_UpperCamelCase : List[Any] =InstagramUser('github')
print(instagram_user)
print(f'''{instagram_user.number_of_posts = }''')
print(f'''{instagram_user.number_of_followers = }''')
print(f'''{instagram_user.number_of_followings = }''')
print(f'''{instagram_user.email = }''')
print(f'''{instagram_user.website = }''')
print(f'''{instagram_user.profile_picture_url = }''')
print(f'''{instagram_user.is_verified = }''')
print(f'''{instagram_user.is_private = }''')
| 206
| 1
|
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowercase__ ( unittest.TestCase ):
def _UpperCAmelCase ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(_lowercase ) )
def _UpperCAmelCase ( self : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(_lowercase ) )
def _UpperCAmelCase ( self : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(_lowercase ) )
def _UpperCAmelCase ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
self.assertTrue(is_safetensors_compatible(_lowercase ) )
def _UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
# Removed: 'text_encoder/model.safetensors',
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertFalse(is_safetensors_compatible(_lowercase ) )
def _UpperCAmelCase ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
UpperCAmelCase__ = "fp16"
self.assertTrue(is_safetensors_compatible(_lowercase , variant=_lowercase ) )
def _UpperCAmelCase ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = [
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
UpperCAmelCase__ = "fp16"
self.assertTrue(is_safetensors_compatible(_lowercase , variant=_lowercase ) )
def _UpperCAmelCase ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
UpperCAmelCase__ = "fp16"
self.assertTrue(is_safetensors_compatible(_lowercase , variant=_lowercase ) )
def _UpperCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
UpperCAmelCase__ = "fp16"
self.assertFalse(is_safetensors_compatible(_lowercase , variant=_lowercase ) )
def _UpperCAmelCase ( self : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ = [
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
]
UpperCAmelCase__ = "fp16"
self.assertTrue(is_safetensors_compatible(_lowercase , variant=_lowercase ) )
def _UpperCAmelCase ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
UpperCAmelCase__ = "fp16"
self.assertTrue(is_safetensors_compatible(_lowercase , variant=_lowercase ) )
def _UpperCAmelCase ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
# 'text_encoder/model.fp16.safetensors',
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
UpperCAmelCase__ = "fp16"
self.assertFalse(is_safetensors_compatible(_lowercase , variant=_lowercase ) )
| 277
|
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
A = logging.get_logger(__name__)
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self : Union[str, Any] , *_lowercase : Any , **_lowercase : Optional[int] ):
"""simple docstring"""
warnings.warn(
"The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use DPTImageProcessor instead." , _lowercase , )
super().__init__(*_lowercase , **_lowercase )
| 277
| 1
|
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return base * power(__lowercase , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("Raise base to the power of exponent using recursion...")
UpperCAmelCase_ : Dict = int(input("Enter the base: ").strip())
UpperCAmelCase_ : Dict = int(input("Enter the exponent: ").strip())
UpperCAmelCase_ : List[Any] = power(base, abs(exponent))
if exponent < 0: # power() does not properly deal w/ negative exponents
UpperCAmelCase_ : Optional[int] = 1 / result
print(F"""{base} to the power of {exponent} is {result}""")
| 21
|
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""post_extract_proj""": """feature_projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.upsample.0""": """encoder.upsample.projection""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def UpperCamelCase ( __lowercase : int ,__lowercase : List[str] ,__lowercase : str ,__lowercase : Optional[Any] ,__lowercase : Any ):
'''simple docstring'''
for attribute in key.split('.' ):
A_ : Dict = getattr(__lowercase ,__lowercase )
if weight_type is not None:
A_ : Any = getattr(__lowercase ,__lowercase ).shape
else:
A_ : Optional[Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
A_ : int = value
elif weight_type == "weight_g":
A_ : Tuple = value
elif weight_type == "weight_v":
A_ : Union[str, Any] = value
elif weight_type == "bias":
A_ : Any = value
else:
A_ : str = value
logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def UpperCamelCase ( __lowercase : str ,__lowercase : Dict ,__lowercase : Tuple ):
'''simple docstring'''
A_ : Optional[Any] = []
A_ : Tuple = fairseq_model.state_dict()
A_ : Any = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
A_ : Union[str, Any] = False
if "conv_layers" in name:
load_conv_layer(
__lowercase ,__lowercase ,__lowercase ,__lowercase ,hf_model.config.feat_extract_norm == 'group' ,)
A_ : List[str] = True
else:
for key, mapped_key in MAPPING.items():
A_ : str = 'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
A_ : int = True
if "*" in mapped_key:
A_ : str = name.split(__lowercase )[0].split('.' )[-2]
A_ : Optional[Any] = mapped_key.replace('*' ,__lowercase )
if "weight_g" in name:
A_ : Dict = 'weight_g'
elif "weight_v" in name:
A_ : Tuple = 'weight_v'
elif "weight" in name:
A_ : Union[str, Any] = 'weight'
elif "bias" in name:
A_ : Optional[Any] = 'bias'
else:
A_ : Union[str, Any] = None
set_recursively(__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase )
continue
if not is_used:
unused_weights.append(__lowercase )
logger.warning(f'''Unused weights: {unused_weights}''' )
def UpperCamelCase ( __lowercase : Optional[Any] ,__lowercase : Union[str, Any] ,__lowercase : Any ,__lowercase : List[Any] ,__lowercase : Union[str, Any] ):
'''simple docstring'''
A_ : Optional[int] = full_name.split('conv_layers.' )[-1]
A_ : Any = name.split('.' )
A_ : Dict = int(items[0] )
A_ : Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
A_ : Optional[int] = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
A_ : Union[str, Any] = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
A_ : Any = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
A_ : Tuple = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__lowercase )
def UpperCamelCase ( __lowercase : List[str] ,__lowercase : str ):
'''simple docstring'''
A_ : Union[str, Any] = SEWConfig()
if is_finetuned:
A_ : Any = model.wav_encoder.wav_model.cfg
else:
A_ : int = model.cfg
A_ : Any = fs_config.conv_bias
A_ : Dict = eval(fs_config.conv_feature_layers )
A_ : List[Any] = [x[0] for x in conv_layers]
A_ : Optional[Any] = [x[1] for x in conv_layers]
A_ : List[Any] = [x[2] for x in conv_layers]
A_ : Optional[int] = 'gelu'
A_ : Union[str, Any] = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
A_ : Tuple = 0.0
A_ : Dict = fs_config.activation_fn.name
A_ : List[Any] = fs_config.encoder_embed_dim
A_ : int = 0.02
A_ : List[str] = fs_config.encoder_ffn_embed_dim
A_ : Any = 1e-5
A_ : Optional[Any] = fs_config.encoder_layerdrop
A_ : Optional[int] = fs_config.encoder_attention_heads
A_ : Any = fs_config.conv_pos_groups
A_ : int = fs_config.conv_pos
A_ : Tuple = len(__lowercase )
A_ : List[Any] = fs_config.encoder_layers
A_ : Any = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
A_ : Union[str, Any] = model.cfg
A_ : str = fs_config.final_dropout
A_ : Any = fs_config.layerdrop
A_ : str = fs_config.activation_dropout
A_ : Any = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
A_ : str = fs_config.attention_dropout
A_ : Any = fs_config.dropout_input
A_ : Dict = fs_config.dropout
A_ : Optional[Any] = fs_config.mask_channel_length
A_ : List[str] = fs_config.mask_channel_prob
A_ : Tuple = fs_config.mask_length
A_ : Dict = fs_config.mask_prob
A_ : Any = 'Wav2Vec2FeatureExtractor'
A_ : Union[str, Any] = 'Wav2Vec2CTCTokenizer'
return config
@torch.no_grad()
def UpperCamelCase ( __lowercase : List[Any] ,__lowercase : int ,__lowercase : Optional[int]=None ,__lowercase : Optional[Any]=None ,__lowercase : str=True ):
'''simple docstring'''
if is_finetuned:
A_ , A_ , A_ : List[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
A_ , A_ , A_ : Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
A_ : Union[str, Any] = SEWConfig.from_pretrained(__lowercase )
else:
A_ : Dict = convert_config(model[0] ,__lowercase )
A_ : Union[str, Any] = model[0].eval()
A_ : Optional[int] = True if config.feat_extract_norm == 'layer' else False
A_ : List[Any] = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=1_60_00 ,padding_value=0 ,do_normalize=__lowercase ,return_attention_mask=__lowercase ,)
if is_finetuned:
if dict_path:
A_ : Optional[int] = Dictionary.load(__lowercase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
A_ : int = target_dict.pad_index
A_ : List[Any] = target_dict.bos_index
A_ : Optional[Any] = target_dict.pad_index
A_ : str = target_dict.bos_index
A_ : str = target_dict.eos_index
A_ : str = len(target_dict.symbols )
A_ : Union[str, Any] = os.path.join(__lowercase ,'vocab.json' )
if not os.path.isdir(__lowercase ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__lowercase ) )
return
os.makedirs(__lowercase ,exist_ok=__lowercase )
with open(__lowercase ,'w' ,encoding='utf-8' ) as vocab_handle:
json.dump(target_dict.indices ,__lowercase )
A_ : Any = WavaVecaCTCTokenizer(
__lowercase ,unk_token=target_dict.unk_word ,pad_token=target_dict.pad_word ,bos_token=target_dict.bos_word ,eos_token=target_dict.eos_word ,word_delimiter_token='|' ,do_lower_case=__lowercase ,)
A_ : Tuple = WavaVecaProcessor(feature_extractor=__lowercase ,tokenizer=__lowercase )
processor.save_pretrained(__lowercase )
A_ : Dict = SEWForCTC(__lowercase )
else:
A_ : Tuple = SEWModel(__lowercase )
feature_extractor.save_pretrained(__lowercase )
recursively_load_weights(__lowercase ,__lowercase ,__lowercase )
hf_model.save_pretrained(__lowercase )
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
_UpperCAmelCase = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 558
| 0
|
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _SCREAMING_SNAKE_CASE ( ):
print("""Making key files...""" )
make_key_files("""rsa""" , 1024 )
print("""Key files generation successful.""" )
def _SCREAMING_SNAKE_CASE ( snake_case_ ):
print("""Generating prime p...""" )
_lowercase = rabinMiller.generate_large_prime(snake_case_ )
print("""Generating prime q...""" )
_lowercase = rabinMiller.generate_large_prime(snake_case_ )
_lowercase = p * q
print("""Generating e that is relatively prime to (p - 1) * (q - 1)...""" )
while True:
_lowercase = random.randrange(2 ** (key_size - 1) , 2 ** (key_size) )
if cryptoMath.gcd(snake_case_ , (p - 1) * (q - 1) ) == 1:
break
print("""Calculating d that is mod inverse of e...""" )
_lowercase = cryptoMath.find_mod_inverse(snake_case_ , (p - 1) * (q - 1) )
_lowercase = (n, e)
_lowercase = (n, d)
return (public_key, private_key)
def _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
if os.path.exists(F"""{name}_pubkey.txt""" ) or os.path.exists(F"""{name}_privkey.txt""" ):
print("""\nWARNING:""" )
print(
F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
"""Use a different name or delete these files and re-run this program.""" )
sys.exit()
_lowercase , _lowercase = generate_key(snake_case_ )
print(F"""\nWriting public key to file {name}_pubkey.txt...""" )
with open(F"""{name}_pubkey.txt""" , """w""" ) as out_file:
out_file.write(F"""{key_size},{public_key[0]},{public_key[1]}""" )
print(F"""Writing private key to file {name}_privkey.txt...""" )
with open(F"""{name}_privkey.txt""" , """w""" ) as out_file:
out_file.write(F"""{key_size},{private_key[0]},{private_key[1]}""" )
if __name__ == "__main__":
main()
| 572
|
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( snake_case_ ):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_lowercase = 1
_lowercase = 1
while repunit:
_lowercase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _SCREAMING_SNAKE_CASE ( snake_case_ = 1000000 ):
_lowercase = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(snake_case_ ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(F"""{solution() = }""")
| 572
| 1
|
'''simple docstring'''
from manim import *
class lowerCAmelCase ( UpperCamelCase_ ):
def _A ( self : Dict ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase__ : List[Any] = Rectangle(height=0.25 , width=0.25 )
lowerCAmelCase__ : Any = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
lowerCAmelCase__ : Dict = [mem.copy() for i in range(6 )]
lowerCAmelCase__ : str = [mem.copy() for i in range(6 )]
lowerCAmelCase__ : Union[str, Any] = VGroup(*a__ ).arrange(a__ , buff=0 )
lowerCAmelCase__ : List[Any] = VGroup(*a__ ).arrange(a__ , buff=0 )
lowerCAmelCase__ : Union[str, Any] = VGroup(a__ , a__ ).arrange(a__ , buff=0 )
lowerCAmelCase__ : Any = Text("CPU" , font_size=24 )
lowerCAmelCase__ : Tuple = Group(a__ , a__ ).arrange(a__ , buff=0.5 , aligned_edge=a__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(a__ )
lowerCAmelCase__ : int = [mem.copy() for i in range(4 )]
lowerCAmelCase__ : Dict = VGroup(*a__ ).arrange(a__ , buff=0 )
lowerCAmelCase__ : Tuple = Text("GPU" , font_size=24 )
lowerCAmelCase__ : List[str] = Group(a__ , a__ ).arrange(a__ , buff=0.5 , aligned_edge=a__ )
gpu.move_to([-1, -1, 0] )
self.add(a__ )
lowerCAmelCase__ : Optional[Any] = [mem.copy() for i in range(6 )]
lowerCAmelCase__ : List[str] = VGroup(*a__ ).arrange(a__ , buff=0 )
lowerCAmelCase__ : Optional[int] = Text("Model" , font_size=24 )
lowerCAmelCase__ : List[str] = Group(a__ , a__ ).arrange(a__ , buff=0.5 , aligned_edge=a__ )
model.move_to([3, -1.0, 0] )
self.add(a__ )
lowerCAmelCase__ : List[Any] = []
lowerCAmelCase__ : int = []
lowerCAmelCase__ : Optional[Any] = []
for i, rect in enumerate(a__ ):
rect.set_stroke(a__ )
lowerCAmelCase__ : Optional[Any] = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(a__ , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=a__ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=a__ , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=a__ , buff=0.0 )
self.add(a__ )
model_cpu_arr.append(a__ )
self.add(*a__ , *a__ , *a__ )
lowerCAmelCase__ : List[str] = [mem.copy() for i in range(6 )]
lowerCAmelCase__ : List[str] = VGroup(*a__ ).arrange(a__ , buff=0 )
lowerCAmelCase__ : str = Text("Loaded Checkpoint" , font_size=24 )
lowerCAmelCase__ : Any = Group(a__ , a__ ).arrange(a__ , buff=0.5 , aligned_edge=a__ )
checkpoint.move_to([3, 0.5, 0] )
self.add(a__ )
lowerCAmelCase__ : Any = []
lowerCAmelCase__ : List[str] = []
for i, rect in enumerate(a__ ):
lowerCAmelCase__ : Any = fill.copy().set_fill(a__ , opacity=0.7 )
target.move_to(a__ )
ckpt_arr.append(a__ )
lowerCAmelCase__ : Union[str, Any] = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(a__ )
self.add(*a__ , *a__ )
lowerCAmelCase__ : Any = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowerCAmelCase__ : List[Any] = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(a__ , a__ )
lowerCAmelCase__ : Tuple = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(a__ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(a__ )
lowerCAmelCase__ : Dict = MarkupText(
F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
lowerCAmelCase__ : int = [meta_mem.copy() for i in range(6 )]
lowerCAmelCase__ : str = [meta_mem.copy() for i in range(6 )]
lowerCAmelCase__ : Dict = VGroup(*a__ ).arrange(a__ , buff=0 )
lowerCAmelCase__ : Union[str, Any] = VGroup(*a__ ).arrange(a__ , buff=0 )
lowerCAmelCase__ : str = VGroup(a__ , a__ ).arrange(a__ , buff=0 )
lowerCAmelCase__ : Optional[int] = Text("Disk" , font_size=24 )
lowerCAmelCase__ : Any = Group(a__ , a__ ).arrange(a__ , buff=0.5 , aligned_edge=a__ )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(a__ , run_time=3 ) , Write(a__ , run_time=1 ) , Create(a__ , run_time=1 ) )
lowerCAmelCase__ : int = []
for i, rect in enumerate(a__ ):
lowerCAmelCase__ : List[str] = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(a__ , run_time=1.5 ) )
self.play(*a__ )
self.play(FadeOut(a__ ) )
lowerCAmelCase__ : int = MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(a__ , run_time=3 ) )
self.play(
FadeOut(a__ , a__ , *a__ , *a__ ) , )
self.wait()
| 378
|
'''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
snake_case = """."""
if __name__ == "__main__":
snake_case = os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
snake_case = []
snake_case = []
with open(doctest_file_path) as fp:
for line in fp:
snake_case = line.strip()
snake_case = os.path.join(REPO_PATH, line)
if not (os.path.isfile(path) or os.path.isdir(path)):
non_existent_paths.append(line)
all_paths.append(path)
if len(non_existent_paths) > 0:
snake_case = """\n""".join(non_existent_paths)
raise ValueError(f'`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}')
if all_paths != sorted(all_paths):
raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")
| 378
| 1
|
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _UpperCAmelCase ( lowerCAmelCase__ ,unittest.TestCase ):
"""simple docstring"""
a_ = CodeGenTokenizer
a_ = CodeGenTokenizerFast
a_ = True
a_ = {"add_prefix_space": True}
a_ = False
def _lowerCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
a_ : Optional[int] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
"""<|endoftext|>""",
]
a_ : Dict = dict(zip(lowerCAmelCase_ , range(len(lowerCAmelCase_ ) ) ) )
a_ : Optional[Any] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
a_ : Any = {"""unk_token""": """<unk>"""}
a_ : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
a_ : Any = 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(lowerCAmelCase_ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowerCAmelCase_ ) )
def _lowerCAmelCase ( self , **lowerCAmelCase_ ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase_ )
def _lowerCAmelCase ( self , **lowerCAmelCase_ ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase_ )
def _lowerCAmelCase ( self , lowerCAmelCase_ ):
'''simple docstring'''
a_ : str = """lower newer"""
a_ : str = """lower newer"""
return input_text, output_text
def _lowerCAmelCase ( self ):
'''simple docstring'''
a_ : Any = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
a_ : Dict = """lower newer"""
a_ : Optional[int] = ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
a_ : Any = tokenizer.tokenize(lowerCAmelCase_ , add_prefix_space=lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
a_ : Optional[Any] = tokens + [tokenizer.unk_token]
a_ : Any = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , lowerCAmelCase_ )
def _lowerCAmelCase ( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
a_ : Optional[int] = self.get_tokenizer()
a_ : List[Any] = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_ )
a_ : Tuple = """lower newer"""
# Testing tokenization
a_ : List[Any] = tokenizer.tokenize(lowerCAmelCase_ , add_prefix_space=lowerCAmelCase_ )
a_ : Dict = rust_tokenizer.tokenize(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
# Testing conversion to ids without special tokens
a_ : Union[str, Any] = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ , add_prefix_space=lowerCAmelCase_ )
a_ : List[str] = rust_tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
# Testing conversion to ids with special tokens
a_ : Dict = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_ )
a_ : Optional[int] = tokenizer.encode(lowerCAmelCase_ , add_prefix_space=lowerCAmelCase_ )
a_ : Dict = rust_tokenizer.encode(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
# Testing the unknown token
a_ : List[str] = tokens + [rust_tokenizer.unk_token]
a_ : str = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , lowerCAmelCase_ )
def _lowerCAmelCase ( self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
'''simple docstring'''
pass
def _lowerCAmelCase ( self , lowerCAmelCase_=15 ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
a_ : List[str] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
# Simple input
a_ : List[Any] = """This is a simple input"""
a_ : Union[str, Any] = ["""This is a simple input 1""", """This is a simple input 2"""]
a_ : List[Any] = ("""This is a simple input""", """This is a pair""")
a_ : int = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
self.assertRaises(lowerCAmelCase_ , tokenizer_r.encode , lowerCAmelCase_ , max_length=lowerCAmelCase_ , padding="""max_length""" )
# Simple input
self.assertRaises(lowerCAmelCase_ , tokenizer_r.encode_plus , lowerCAmelCase_ , max_length=lowerCAmelCase_ , padding="""max_length""" )
# Simple input
self.assertRaises(
lowerCAmelCase_ , tokenizer_r.batch_encode_plus , lowerCAmelCase_ , max_length=lowerCAmelCase_ , padding="""max_length""" , )
# Pair input
self.assertRaises(lowerCAmelCase_ , tokenizer_r.encode , lowerCAmelCase_ , max_length=lowerCAmelCase_ , padding="""max_length""" )
# Pair input
self.assertRaises(lowerCAmelCase_ , tokenizer_r.encode_plus , lowerCAmelCase_ , max_length=lowerCAmelCase_ , padding="""max_length""" )
# Pair input
self.assertRaises(
lowerCAmelCase_ , tokenizer_r.batch_encode_plus , lowerCAmelCase_ , max_length=lowerCAmelCase_ , padding="""max_length""" , )
def _lowerCAmelCase ( self ):
'''simple docstring'''
a_ : Dict = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token="""<pad>""" )
# Simple input
a_ : Optional[int] = """This is a simple input"""
a_ : Union[str, Any] = ["""This is a simple input looooooooong""", """This is a simple input"""]
a_ : List[str] = ("""This is a simple input""", """This is a pair""")
a_ : int = [
("""This is a simple input loooooong""", """This is a simple input"""),
("""This is a simple pair loooooong""", """This is a simple pair"""),
]
a_ : Optional[int] = tokenizer.pad_token_id
a_ : Optional[int] = tokenizer(lowerCAmelCase_ , padding="""max_length""" , max_length=30 , return_tensors="""np""" )
a_ : str = tokenizer(lowerCAmelCase_ , padding=lowerCAmelCase_ , truncate=lowerCAmelCase_ , return_tensors="""np""" )
a_ : List[Any] = tokenizer(*lowerCAmelCase_ , padding="""max_length""" , max_length=60 , return_tensors="""np""" )
a_ : str = tokenizer(lowerCAmelCase_ , padding=lowerCAmelCase_ , truncate=lowerCAmelCase_ , return_tensors="""np""" )
# s
# test single string max_length padding
self.assertEqual(out_s["""input_ids"""].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s["""input_ids"""] )
self.assertTrue(0 in out_s["""attention_mask"""] )
# s2
# test automatic padding
self.assertEqual(out_sa["""input_ids"""].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["""input_ids"""][0] )
self.assertFalse(0 in out_sa["""attention_mask"""][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["""input_ids"""][1] )
self.assertTrue(0 in out_sa["""attention_mask"""][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["""input_ids"""].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p["""input_ids"""] )
self.assertTrue(0 in out_p["""attention_mask"""] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["""input_ids"""].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["""input_ids"""][0] )
self.assertFalse(0 in out_pa["""attention_mask"""][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["""input_ids"""][1] )
self.assertTrue(0 in out_pa["""attention_mask"""][1] )
def _lowerCAmelCase ( self ):
'''simple docstring'''
a_ : Any = """$$$"""
a_ : Optional[Any] = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=lowerCAmelCase_ , add_bos_token=lowerCAmelCase_ )
a_ : Any = """This is a simple input"""
a_ : Optional[int] = ["""This is a simple input 1""", """This is a simple input 2"""]
a_ : Union[str, Any] = tokenizer.bos_token_id
a_ : int = tokenizer(lowerCAmelCase_ )
a_ : Union[str, Any] = tokenizer(lowerCAmelCase_ )
self.assertEqual(out_s.input_ids[0] , lowerCAmelCase_ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
a_ : Optional[Any] = tokenizer.decode(out_s.input_ids )
a_ : Dict = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , lowerCAmelCase_ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def _lowerCAmelCase ( self ):
'''simple docstring'''
a_ : List[Any] = CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono""" )
a_ : Union[str, Any] = """\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"""
a_ : Tuple = """\nif len_a > len_b: result = a\nelse: result = b"""
a_ : int = tokenizer.encode(lowerCAmelCase_ )
a_ : Any = ["""^#""", re.escape("""<|endoftext|>""" ), """^'''""", """^\"\"\"""", """\n\n\n"""]
a_ : Union[str, Any] = tokenizer.decode(lowerCAmelCase_ , truncate_before_pattern=lowerCAmelCase_ )
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
def _lowerCAmelCase ( self ):
'''simple docstring'''
pass
| 460
|
'''simple docstring'''
def _snake_case ( A_ : list ):
"""simple docstring"""
if len(A_ ) <= 1:
return lst
a_ : Any = 1
while i < len(A_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
a_ , a_ : int = lst[i], lst[i - 1]
i -= 1
if i == 0:
a_ : List[str] = 1
return lst
if __name__ == "__main__":
__snake_case: List[Any] = input("Enter numbers separated by a comma:\n").strip()
__snake_case: Optional[int] = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| 460
| 1
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class __lowercase ( unittest.TestCase ):
def __init__( self : Union[str, Any] ,A : Optional[int] ,A : int=13 ,A : Tuple=7 ,A : Dict=True ,A : Optional[int]=True ,A : Tuple=True ,A : str=True ,A : Any=99 ,A : Tuple=32 ,A : Dict=5 ,A : Optional[int]=4 ,A : Dict=37 ,A : Any="gelu" ,A : Any=0.1 ,A : Optional[int]=0.1 ,A : Union[str, Any]=512 ,A : Any=16 ,A : List[str]=2 ,A : List[Any]=0.0_2 ,A : Optional[int]=4 ,):
'''simple docstring'''
UpperCAmelCase__ : Dict = parent
UpperCAmelCase__ : Any = batch_size
UpperCAmelCase__ : List[Any] = seq_length
UpperCAmelCase__ : Optional[int] = is_training
UpperCAmelCase__ : Optional[Any] = use_attention_mask
UpperCAmelCase__ : int = use_token_type_ids
UpperCAmelCase__ : int = use_labels
UpperCAmelCase__ : Any = vocab_size
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : int = num_hidden_layers
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : Dict = intermediate_size
UpperCAmelCase__ : Any = hidden_act
UpperCAmelCase__ : Union[str, Any] = hidden_dropout_prob
UpperCAmelCase__ : Any = attention_probs_dropout_prob
UpperCAmelCase__ : str = max_position_embeddings
UpperCAmelCase__ : List[Any] = type_vocab_size
UpperCAmelCase__ : List[str] = type_sequence_label_size
UpperCAmelCase__ : List[Any] = initializer_range
UpperCAmelCase__ : List[Any] = num_choices
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ : List[str] = None
if self.use_attention_mask:
UpperCAmelCase__ : str = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ : int = DistilBertConfig(
vocab_size=self.vocab_size ,dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,hidden_dim=self.intermediate_size ,hidden_act=self.hidden_act ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,tie_weights_=A ,)
return config, input_ids, attention_mask
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Any = config_and_inputs
UpperCAmelCase__ : str = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = FlaxDistilBertModelTester(self )
@slow
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase__ : Union[str, Any] = model_class_name.from_pretrained("""distilbert-base-uncased""" )
UpperCAmelCase__ : List[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class __lowercase ( unittest.TestCase ):
@slow
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
UpperCAmelCase__ : List[Any] = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
UpperCAmelCase__ : str = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
UpperCAmelCase__ : Dict = model(A ,attention_mask=A )[0]
UpperCAmelCase__ : List[Any] = (1, 11, 768)
self.assertEqual(output.shape ,A )
UpperCAmelCase__ : Any = np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,A ,atol=1e-4 ) )
| 65
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ =logging.get_logger(__name__)
lowercase__ ={
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class UpperCamelCase__ ( __lowercase ):
_SCREAMING_SNAKE_CASE : Optional[Any] = "roformer"
def __init__(self : Dict , snake_case_ : Optional[Any]=5_0_0_0_0 , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=7_6_8 , snake_case_ : Dict=1_2 , snake_case_ : Optional[int]=1_2 , snake_case_ : Optional[Any]=3_0_7_2 , snake_case_ : Union[str, Any]="gelu" , snake_case_ : List[Any]=0.1 , snake_case_ : Optional[int]=0.1 , snake_case_ : Optional[Any]=1_5_3_6 , snake_case_ : Any=2 , snake_case_ : Optional[int]=0.02 , snake_case_ : int=1E-12 , snake_case_ : Union[str, Any]=0 , snake_case_ : Any=False , snake_case_ : Dict=True , **snake_case_ : Union[str, Any] , ):
super().__init__(pad_token_id=snake_case_ , **snake_case_ )
__a : Dict = vocab_size
__a : Optional[Any] = hidden_size if embedding_size is None else embedding_size
__a : Optional[Any] = hidden_size
__a : Any = num_hidden_layers
__a : Optional[Any] = num_attention_heads
__a : List[Any] = hidden_act
__a : List[Any] = intermediate_size
__a : List[str] = hidden_dropout_prob
__a : List[Any] = attention_probs_dropout_prob
__a : Dict = max_position_embeddings
__a : List[Any] = type_vocab_size
__a : List[Any] = initializer_range
__a : Dict = layer_norm_eps
__a : List[str] = rotary_value
__a : Optional[Any] = use_cache
class UpperCamelCase__ ( __lowercase ):
@property
def lowerCAmelCase (self : Union[str, Any] ):
if self.task == "multiple-choice":
__a : str = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
__a : Optional[int] = {0: '''batch''', 1: '''sequence'''}
__a : Optional[Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 521
| 0
|
# 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
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase_ = {
'''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = ['''VivitImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''VivitModel''',
'''VivitPreTrainedModel''',
'''VivitForVideoClassification''',
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 86
|
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class __A:
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , 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_=5_12 , 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 , ):
UpperCamelCase__ = parent
UpperCamelCase__ = batch_size
UpperCamelCase__ = seq_length
UpperCamelCase__ = is_training
UpperCamelCase__ = use_input_mask
UpperCamelCase__ = use_token_type_ids
UpperCamelCase__ = use_labels
UpperCamelCase__ = vocab_size
UpperCamelCase__ = hidden_size
UpperCamelCase__ = num_hidden_layers
UpperCamelCase__ = num_attention_heads
UpperCamelCase__ = intermediate_size
UpperCamelCase__ = hidden_act
UpperCamelCase__ = hidden_dropout_prob
UpperCamelCase__ = attention_probs_dropout_prob
UpperCamelCase__ = max_position_embeddings
UpperCamelCase__ = type_vocab_size
UpperCamelCase__ = type_sequence_label_size
UpperCamelCase__ = initializer_range
UpperCamelCase__ = num_labels
UpperCamelCase__ = num_choices
UpperCamelCase__ = scope
def UpperCAmelCase_ (self ):
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase__ = None
if self.use_input_mask:
UpperCamelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase__ = None
if self.use_token_type_ids:
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = None
if self.use_labels:
UpperCamelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase__ = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase__ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase_ (self ):
return BioGptConfig(
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=SCREAMING_SNAKE_CASE_ , initializer_range=self.initializer_range , )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = BioGptModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase__ = BioGptForCausalLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = BioGptModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
# create attention mask
UpperCamelCase__ = torch.ones(input_ids.shape , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = self.seq_length // 2
UpperCamelCase__ = 0
# first forward pass
UpperCamelCase__ , UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ ).to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCamelCase__ = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
UpperCamelCase__ = ids_tensor((1,) , SCREAMING_SNAKE_CASE_ ).item() + 1
UpperCamelCase__ = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
UpperCamelCase__ = random_other_next_tokens
# append to next input_ids and attn_mask
UpperCamelCase__ = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCamelCase__ = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )] , dim=1 , )
# get two different outputs
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )["""last_hidden_state"""]
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , past_key_values=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )["""last_hidden_state"""]
# select random slice
UpperCamelCase__ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCamelCase__ = output_from_no_past[:, -1, random_slice_idx].detach()
UpperCamelCase__ = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = BioGptModel(config=SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ ).eval()
UpperCamelCase__ = torch.ones(input_ids.shape , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
# first forward pass
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ , UpperCamelCase__ = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
UpperCamelCase__ = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCamelCase__ = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
UpperCamelCase__ = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCamelCase__ = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )["""last_hidden_state"""]
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , past_key_values=SCREAMING_SNAKE_CASE_ )[
"""last_hidden_state"""
]
# select random slice
UpperCamelCase__ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCamelCase__ = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCamelCase__ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase__ = BioGptForCausalLM(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = BioGptModel(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = self.num_labels
UpperCamelCase__ = BioGptForTokenClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.prepare_config_and_inputs()
(
(
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) ,
) = config_and_inputs
UpperCamelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __A( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE__ = (BioGptForCausalLM,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE__ = (
{
"""feature-extraction""": BioGptModel,
"""text-classification""": BioGptForSequenceClassification,
"""text-generation""": BioGptForCausalLM,
"""token-classification""": BioGptForTokenClassification,
"""zero-shot""": BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE__ = False
def UpperCAmelCase_ (self ):
UpperCamelCase__ = BioGptModelTester(self )
UpperCamelCase__ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def UpperCAmelCase_ (self ):
self.config_tester.run_common_tests()
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase__ = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*SCREAMING_SNAKE_CASE_ , gradient_checkpointing=SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def UpperCAmelCase_ (self ):
UpperCamelCase__ = BioGptForCausalLM.from_pretrained("""microsoft/biogpt""" )
model.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
UpperCamelCase__ = """left"""
# Define PAD Token = EOS Token = 50256
UpperCamelCase__ = tokenizer.eos_token
UpperCamelCase__ = model.config.eos_token_id
# use different length sentences to test batching
UpperCamelCase__ = [
"""Hello, my dog is a little""",
"""Today, I""",
]
UpperCamelCase__ = tokenizer(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , padding=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model.generate(
input_ids=SCREAMING_SNAKE_CASE_ , attention_mask=inputs["""attention_mask"""].to(SCREAMING_SNAKE_CASE_ ) , )
UpperCamelCase__ = tokenizer(sentences[0] , return_tensors="""pt""" ).input_ids.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model.generate(input_ids=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].long().sum().cpu().item()
UpperCamelCase__ = tokenizer(sentences[1] , return_tensors="""pt""" ).input_ids.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model.generate(input_ids=SCREAMING_SNAKE_CASE_ , max_length=model.config.max_length - num_paddings )
UpperCamelCase__ = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = tokenizer.decode(output_non_padded[0] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = tokenizer.decode(output_padded[0] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = [
"""Hello, my dog is a little bit bigger than a little bit.""",
"""Today, I have a good idea of how to use the information""",
]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , [non_padded_sentence, padded_sentence] )
@slow
def UpperCAmelCase_ (self ):
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ = BioGptModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ = 3
UpperCamelCase__ = input_dict["""input_ids"""]
UpperCamelCase__ = input_ids.ne(1 ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCamelCase__ = BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCAmelCase_ (self ):
UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ = 3
UpperCamelCase__ = """multi_label_classification"""
UpperCamelCase__ = input_dict["""input_ids"""]
UpperCamelCase__ = input_ids.ne(1 ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
UpperCamelCase__ = BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class __A( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase_ (self ):
UpperCamelCase__ = BioGptForCausalLM.from_pretrained("""microsoft/biogpt""" )
UpperCamelCase__ = torch.tensor([[2, 48_05, 9, 6_56, 21]] )
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase__ = 4_23_84
UpperCamelCase__ = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = torch.tensor(
[[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
@slow
def UpperCAmelCase_ (self ):
UpperCamelCase__ = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
UpperCamelCase__ = BioGptForCausalLM.from_pretrained("""microsoft/biogpt""" )
model.to(SCREAMING_SNAKE_CASE_ )
torch.manual_seed(0 )
UpperCamelCase__ = tokenizer("""COVID-19 is""" , return_tensors="""pt""" ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model.generate(
**SCREAMING_SNAKE_CASE_ , min_length=1_00 , max_length=10_24 , num_beams=5 , early_stopping=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase__ = tokenizer.decode(output_ids[0] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = (
"""COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"""
""" causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"""
""" territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"""
""" and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"""
""" more than 800,000 deaths."""
)
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 86
| 1
|
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 27
|
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
__A : List[Any] = "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append("importlib_metadata")
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> Union[str, Any]:
"""simple docstring"""
require_version(deps[pkg] , _SCREAMING_SNAKE_CASE )
| 27
| 1
|
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class _UpperCamelCase ( _snake_case ,_snake_case ):
"""simple docstring"""
__a : str = """pixel_values"""
__a : Tuple = False
__a : Optional[int] = TimmBackboneConfig
def __init__( self , lowerCAmelCase__ , **lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , '''timm''' )
super().__init__(snake_case_ )
__lowercase = config
if config.backbone is None:
raise ValueError('''backbone is not set in the config. Please set it to a timm model name.''' )
if config.backbone not in timm.list_models():
raise ValueError(F"backbone {config.backbone} is not supported by timm." )
if hasattr(snake_case_ , '''out_features''' ) and config.out_features is not None:
raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.''' )
__lowercase = getattr(snake_case_ , '''use_pretrained_backbone''' , snake_case_ )
if pretrained is None:
raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.''' )
# We just take the final layer by default. This matches the default for the transformers models.
__lowercase = config.out_indices if getattr(snake_case_ , '''out_indices''' , snake_case_ ) is not None else (-1,)
__lowercase = timm.create_model(
config.backbone , pretrained=snake_case_ , features_only=config.features_only , in_chans=config.num_channels , out_indices=snake_case_ , **snake_case_ , )
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
__lowercase = self._backbone.return_layers
__lowercase = {layer["module"]: str(snake_case_ ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(snake_case_ )
@classmethod
def _SCREAMING_SNAKE_CASE ( cls , lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''vision''', '''timm'''] )
from ...models.timm_backbone import TimmBackboneConfig
__lowercase = kwargs.pop('''config''' , TimmBackboneConfig() )
__lowercase = kwargs.pop('''use_timm_backbone''' , snake_case_ )
if not use_timm:
raise ValueError('''use_timm_backbone must be True for timm backbones''' )
__lowercase = kwargs.pop('''num_channels''' , config.num_channels )
__lowercase = kwargs.pop('''features_only''' , config.features_only )
__lowercase = kwargs.pop('''use_pretrained_backbone''' , config.use_pretrained_backbone )
__lowercase = kwargs.pop('''out_indices''' , config.out_indices )
__lowercase = TimmBackboneConfig(
backbone=snake_case_ , num_channels=snake_case_ , features_only=snake_case_ , use_pretrained_backbone=snake_case_ , out_indices=snake_case_ , )
return super()._from_config(snake_case_ , **snake_case_ )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ ) -> Any:
'''simple docstring'''
pass
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , **lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
__lowercase = return_dict if return_dict is not None else self.config.use_return_dict
__lowercase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__lowercase = output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError('''Cannot output attentions for timm backbones at the moment''' )
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
__lowercase = self._all_layers
__lowercase = self._backbone(snake_case_ , **snake_case_ )
__lowercase = self._return_layers
__lowercase = tuple(hidden_states[i] for i in self.out_indices )
else:
__lowercase = self._backbone(snake_case_ , **snake_case_ )
__lowercase = None
__lowercase = tuple(snake_case_ )
__lowercase = tuple(snake_case_ ) if hidden_states is not None else None
if not return_dict:
__lowercase = (feature_maps,)
if output_hidden_states:
__lowercase = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=snake_case_ , hidden_states=snake_case_ , attentions=snake_case_ )
| 721
|
from __future__ import annotations
def UpperCAmelCase ( lowercase , lowercase ):
"""simple docstring"""
if b == 0:
return (1, 0)
((__lowercase) , (__lowercase)) = extended_euclid(lowercase , a % b )
__lowercase = a // b
return (y, x - k * y)
def UpperCAmelCase ( lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
((__lowercase) , (__lowercase)) = extended_euclid(lowercase , lowercase )
__lowercase = na * na
__lowercase = ra * x * na + ra * y * na
return (n % m + m) % m
def UpperCAmelCase ( lowercase , lowercase ):
"""simple docstring"""
((__lowercase) , (__lowercase)) = extended_euclid(lowercase , lowercase )
if b < 0:
__lowercase = (b % n + n) % n
return b
def UpperCAmelCase ( lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
__lowercase , __lowercase = invert_modulo(lowercase , lowercase ), invert_modulo(lowercase , lowercase )
__lowercase = na * na
__lowercase = ra * x * na + ra * y * na
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name="""chinese_remainder_theorem""", verbose=True)
testmod(name="""chinese_remainder_theorem2""", verbose=True)
testmod(name="""invert_modulo""", verbose=True)
testmod(name="""extended_euclid""", verbose=True)
| 522
| 0
|
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
__A : Optional[Any] = get_tests_dir('''fixtures''')
class __A ( unittest.TestCase ):
def lowercase__ ( self : Union[str, Any] ):
# A mock response for an HTTP head request to emulate server down
lowerCAmelCase : Tuple = mock.Mock()
lowerCAmelCase : Any = 500
lowerCAmelCase : Optional[int] = {}
lowerCAmelCase : Optional[Any] = HTTPError
lowerCAmelCase : Dict = {}
# Download this model to make sure it's in the cache.
lowerCAmelCase : Optional[Any] = ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=UpperCAmelCase_ ) as mock_head:
lowerCAmelCase : int = ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit' )
# This check we did call the fake head request
mock_head.assert_called()
def lowercase__ ( self : Dict ):
# This test is for deprecated behavior and can be removed in v5
lowerCAmelCase : Union[str, Any] = ViTImageProcessor.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json' )
def lowercase__ ( self : Any ):
with self.assertRaises(UpperCAmelCase_ ):
# config is in subfolder, the following should not work without specifying the subfolder
lowerCAmelCase : int = AutoImageProcessor.from_pretrained('hf-internal-testing/stable-diffusion-all-variants' )
lowerCAmelCase : str = AutoImageProcessor.from_pretrained(
'hf-internal-testing/stable-diffusion-all-variants' , subfolder='feature_extractor' )
self.assertIsNotNone(UpperCAmelCase_ )
@is_staging_test
class __A ( unittest.TestCase ):
@classmethod
def lowercase__ ( cls : int ):
lowerCAmelCase : Tuple = TOKEN
HfFolder.save_token(UpperCAmelCase_ )
@classmethod
def lowercase__ ( cls : Tuple ):
try:
delete_repo(token=cls._token , repo_id='test-image-processor' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-image-processor-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-image-processor' )
except HTTPError:
pass
def lowercase__ ( self : Union[str, Any] ):
lowerCAmelCase : str = ViTImageProcessor.from_pretrained(UpperCAmelCase_ )
image_processor.push_to_hub('test-image-processor' , use_auth_token=self._token )
lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor" )
for k, v in image_processor.__dict__.items():
self.assertEqual(UpperCAmelCase_ , getattr(UpperCAmelCase_ , UpperCAmelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-image-processor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
UpperCAmelCase_ , repo_id='test-image-processor' , push_to_hub=UpperCAmelCase_ , use_auth_token=self._token )
lowerCAmelCase : Union[str, Any] = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor" )
for k, v in image_processor.__dict__.items():
self.assertEqual(UpperCAmelCase_ , getattr(UpperCAmelCase_ , UpperCAmelCase_ ) )
def lowercase__ ( self : List[Any] ):
lowerCAmelCase : int = ViTImageProcessor.from_pretrained(UpperCAmelCase_ )
image_processor.push_to_hub('valid_org/test-image-processor' , use_auth_token=self._token )
lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained('valid_org/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(UpperCAmelCase_ , getattr(UpperCAmelCase_ , UpperCAmelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-image-processor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
UpperCAmelCase_ , repo_id='valid_org/test-image-processor-org' , push_to_hub=UpperCAmelCase_ , use_auth_token=self._token )
lowerCAmelCase : List[str] = ViTImageProcessor.from_pretrained('valid_org/test-image-processor-org' )
for k, v in image_processor.__dict__.items():
self.assertEqual(UpperCAmelCase_ , getattr(UpperCAmelCase_ , UpperCAmelCase_ ) )
def lowercase__ ( self : str ):
CustomImageProcessor.register_for_auto_class()
lowerCAmelCase : str = CustomImageProcessor.from_pretrained(UpperCAmelCase_ )
image_processor.push_to_hub('test-dynamic-image-processor' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'AutoImageProcessor': 'custom_image_processing.CustomImageProcessor'} , )
lowerCAmelCase : int = AutoImageProcessor.from_pretrained(
f"{USER}/test-dynamic-image-processor" , trust_remote_code=UpperCAmelCase_ )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , 'CustomImageProcessor' )
| 343
|
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> Dict:
'''simple docstring'''
if index == r:
for j in range(_UpperCAmelCase ):
print(data[j], end=' ' )
print(' ' )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
lowerCAmelCase : List[Any] = arr[i]
combination_util(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, index + 1, _UpperCAmelCase, i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> int:
'''simple docstring'''
lowerCAmelCase : Tuple = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, 0, _UpperCAmelCase, 0 )
if __name__ == "__main__":
# Driver code to check the function above
__A : Optional[Any] = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 343
| 1
|
from timeit import timeit
def __UpperCamelCase ( lowerCAmelCase__ : int ):
if number < 0:
raise ValueError('''the value of input must not be negative''' )
__a : int = 0
while number:
number &= number - 1
result += 1
return result
def __UpperCamelCase ( lowerCAmelCase__ : int ):
if number < 0:
raise ValueError('''the value of input must not be negative''' )
__a : str = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def __UpperCamelCase ( ):
def do_benchmark(lowerCAmelCase__ : int ) -> None:
__a : Optional[Any] = '''import __main__ as z'''
print(f"Benchmark when {number = }:" )
print(f"{get_set_bits_count_using_modulo_operator(lowerCAmelCase__ ) = }" )
__a : Union[str, Any] = timeit('''z.get_set_bits_count_using_modulo_operator(25)''' , setup=lowerCAmelCase__ )
print(f"timeit() runs in {timing} seconds" )
print(f"{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase__ ) = }" )
__a : Dict = timeit(
'''z.get_set_bits_count_using_brian_kernighans_algorithm(25)''' , setup=lowerCAmelCase__ , )
print(f"timeit() runs in {timing} seconds" )
for number in (2_5, 3_7, 5_8, 0):
do_benchmark(lowerCAmelCase__ )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 326
|
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def __UpperCamelCase ( lowerCAmelCase__ : int ):
random.seed(lowerCAmelCase__ )
np.random.seed(lowerCAmelCase__ )
torch.manual_seed(lowerCAmelCase__ )
torch.cuda.manual_seed_all(lowerCAmelCase__ )
# ^^ safe to call this function even if cuda is not available
class UpperCamelCase__ :
def __init__(self : Any , snake_case_ : Iterable[torch.nn.Parameter] , snake_case_ : float = 0.9999 , snake_case_ : float = 0.0 , snake_case_ : int = 0 , snake_case_ : bool = False , snake_case_ : Union[float, int] = 1.0 , snake_case_ : Union[float, int] = 2 / 3 , snake_case_ : Optional[Any] = None , snake_case_ : Dict[str, Any] = None , **snake_case_ : int , ):
if isinstance(snake_case_ , torch.nn.Module ):
__a : Optional[Any] = (
'''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
'''Please pass the parameters of the module instead.'''
)
deprecate(
'''passing a `torch.nn.Module` to `ExponentialMovingAverage`''' , '''1.0.0''' , snake_case_ , standard_warn=snake_case_ , )
__a : Optional[int] = parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
__a : str = True
if kwargs.get('''max_value''' , snake_case_ ) is not None:
__a : List[Any] = '''The `max_value` argument is deprecated. Please use `decay` instead.'''
deprecate('''max_value''' , '''1.0.0''' , snake_case_ , standard_warn=snake_case_ )
__a : Optional[Any] = kwargs['''max_value''']
if kwargs.get('''min_value''' , snake_case_ ) is not None:
__a : Optional[int] = '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
deprecate('''min_value''' , '''1.0.0''' , snake_case_ , standard_warn=snake_case_ )
__a : int = kwargs['''min_value''']
__a : Any = list(snake_case_ )
__a : Optional[int] = [p.clone().detach() for p in parameters]
if kwargs.get('''device''' , snake_case_ ) is not None:
__a : Optional[Any] = '''The `device` argument is deprecated. Please use `to` instead.'''
deprecate('''device''' , '''1.0.0''' , snake_case_ , standard_warn=snake_case_ )
self.to(device=kwargs['''device'''] )
__a : List[str] = None
__a : Tuple = decay
__a : str = min_decay
__a : Any = update_after_step
__a : List[str] = use_ema_warmup
__a : Any = inv_gamma
__a : Any = power
__a : Union[str, Any] = 0
__a : Dict = None # set in `step()`
__a : Any = model_cls
__a : Any = model_config
@classmethod
def lowerCAmelCase (cls : List[str] , snake_case_ : Dict , snake_case_ : Dict ):
__a , __a : Optional[int] = model_cls.load_config(snake_case_ , return_unused_kwargs=snake_case_ )
__a : Dict = model_cls.from_pretrained(snake_case_ )
__a : List[Any] = cls(model.parameters() , model_cls=snake_case_ , model_config=model.config )
ema_model.load_state_dict(snake_case_ )
return ema_model
def lowerCAmelCase (self : Optional[Any] , snake_case_ : Dict ):
if self.model_cls is None:
raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.''' )
if self.model_config is None:
raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.''' )
__a : int = self.model_cls.from_config(self.model_config )
__a : List[Any] = self.state_dict()
state_dict.pop('''shadow_params''' , snake_case_ )
model.register_to_config(**snake_case_ )
self.copy_to(model.parameters() )
model.save_pretrained(snake_case_ )
def lowerCAmelCase (self : Optional[int] , snake_case_ : int ):
__a : Tuple = max(0 , optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
__a : Tuple = 1 - (1 + step / self.inv_gamma) ** -self.power
else:
__a : List[str] = (1 + step) / (1_0 + step)
__a : Dict = min(snake_case_ , self.decay )
# make sure decay is not smaller than min_decay
__a : int = max(snake_case_ , self.min_decay )
return cur_decay_value
@torch.no_grad()
def lowerCAmelCase (self : Optional[int] , snake_case_ : Iterable[torch.nn.Parameter] ):
if isinstance(snake_case_ , torch.nn.Module ):
__a : List[Any] = (
'''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
'''Please pass the parameters of the module instead.'''
)
deprecate(
'''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`''' , '''1.0.0''' , snake_case_ , standard_warn=snake_case_ , )
__a : Union[str, Any] = parameters.parameters()
__a : Optional[Any] = list(snake_case_ )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
__a : str = self.get_decay(self.optimization_step )
__a : List[str] = decay
__a : Dict = 1 - decay
__a : Optional[int] = contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params , snake_case_ ):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
__a : Dict = deepspeed.zero.GatheredParameters(snake_case_ , modifier_rank=snake_case_ )
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param) )
else:
s_param.copy_(snake_case_ )
def lowerCAmelCase (self : int , snake_case_ : Iterable[torch.nn.Parameter] ):
__a : str = list(snake_case_ )
for s_param, param in zip(self.shadow_params , snake_case_ ):
param.data.copy_(s_param.to(param.device ).data )
def lowerCAmelCase (self : int , snake_case_ : int=None , snake_case_ : int=None ):
__a : str = [
p.to(device=snake_case_ , dtype=snake_case_ ) if p.is_floating_point() else p.to(device=snake_case_ )
for p in self.shadow_params
]
def lowerCAmelCase (self : Dict ):
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def lowerCAmelCase (self : Tuple , snake_case_ : Iterable[torch.nn.Parameter] ):
__a : str = [param.detach().cpu().clone() for param in parameters]
def lowerCAmelCase (self : Optional[int] , snake_case_ : Iterable[torch.nn.Parameter] ):
if self.temp_stored_params is None:
raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`''' )
for c_param, param in zip(self.temp_stored_params , snake_case_ ):
param.data.copy_(c_param.data )
# Better memory-wise.
__a : Optional[Any] = None
def lowerCAmelCase (self : Optional[int] , snake_case_ : dict ):
__a : Dict = copy.deepcopy(snake_case_ )
__a : int = state_dict.get('''decay''' , self.decay )
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError('''Decay must be between 0 and 1''' )
__a : List[str] = state_dict.get('''min_decay''' , self.min_decay )
if not isinstance(self.min_decay , snake_case_ ):
raise ValueError('''Invalid min_decay''' )
__a : Dict = state_dict.get('''optimization_step''' , self.optimization_step )
if not isinstance(self.optimization_step , snake_case_ ):
raise ValueError('''Invalid optimization_step''' )
__a : Optional[int] = state_dict.get('''update_after_step''' , self.update_after_step )
if not isinstance(self.update_after_step , snake_case_ ):
raise ValueError('''Invalid update_after_step''' )
__a : Any = state_dict.get('''use_ema_warmup''' , self.use_ema_warmup )
if not isinstance(self.use_ema_warmup , snake_case_ ):
raise ValueError('''Invalid use_ema_warmup''' )
__a : Any = state_dict.get('''inv_gamma''' , self.inv_gamma )
if not isinstance(self.inv_gamma , (float, int) ):
raise ValueError('''Invalid inv_gamma''' )
__a : Tuple = state_dict.get('''power''' , self.power )
if not isinstance(self.power , (float, int) ):
raise ValueError('''Invalid power''' )
__a : Dict = state_dict.get('''shadow_params''' , snake_case_ )
if shadow_params is not None:
__a : Tuple = shadow_params
if not isinstance(self.shadow_params , snake_case_ ):
raise ValueError('''shadow_params must be a list''' )
if not all(isinstance(snake_case_ , torch.Tensor ) for p in self.shadow_params ):
raise ValueError('''shadow_params must all be Tensors''' )
| 326
| 1
|
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> List[Any]:
_snake_case = int(lowerCAmelCase_ )
assert noofclusters < len(lowerCAmelCase_ )
# Find out the dimensionality
_snake_case = len(vectors[0] )
# Will help select random centroids from among the available vectors
_snake_case = list(range(len(lowerCAmelCase_ ) ) )
shuffle(lowerCAmelCase_ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
_snake_case = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
_snake_case = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
_snake_case = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(lowerCAmelCase_ )
]
##These nodes will assign the centroid Variables the appropriate
##values
_snake_case = tf.placeholder('''float64''' , [dim] )
_snake_case = []
for centroid in centroids:
cent_assigns.append(tf.assign(lowerCAmelCase_ , lowerCAmelCase_ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
_snake_case = [tf.Variable(0 ) for i in range(len(lowerCAmelCase_ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
_snake_case = tf.placeholder('''int32''' )
_snake_case = []
for assignment in assignments:
cluster_assigns.append(tf.assign(lowerCAmelCase_ , lowerCAmelCase_ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
_snake_case = tf.placeholder('''float''' , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
_snake_case = tf.reduce_mean(lowerCAmelCase_ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
_snake_case = tf.placeholder('''float''' , [dim] )
_snake_case = tf.placeholder('''float''' , [dim] )
_snake_case = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowerCAmelCase_ , lowerCAmelCase_ ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
_snake_case = tf.placeholder('''float''' , [noofclusters] )
_snake_case = tf.argmin(lowerCAmelCase_ , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
_snake_case = tf.initialize_all_variables()
# Initialize all variables
sess.run(lowerCAmelCase_ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
_snake_case = 100
for _ in range(lowerCAmelCase_ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(lowerCAmelCase_ ) ):
_snake_case = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
_snake_case = [
sess.run(lowerCAmelCase_ , feed_dict={va: vect, va: sess.run(lowerCAmelCase_ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
_snake_case = sess.run(
lowerCAmelCase_ , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(lowerCAmelCase_ ):
# Collect all the vectors assigned to this cluster
_snake_case = [
vectors[i]
for i in range(len(lowerCAmelCase_ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
_snake_case = sess.run(
lowerCAmelCase_ , feed_dict={mean_input: array(lowerCAmelCase_ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
_snake_case = sess.run(lowerCAmelCase_ )
_snake_case = sess.run(lowerCAmelCase_ )
return centroids, assignments
| 103
|
from collections.abc import Iterable
from typing import Any
class A :
def __init__( self : Dict , lowercase_ : int | None = None ) -> int:
"""simple docstring"""
_lowerCamelCase : List[Any] =value
_lowerCamelCase : Node | None =None # Added in order to delete a node easier
_lowerCamelCase : Node | None =None
_lowerCamelCase : Node | None =None
def __repr__( self : Dict ) -> str:
"""simple docstring"""
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({F'''{self.value}''': (self.left, self.right)} , indent=1 )
class A :
def __init__( self : Union[str, Any] , lowercase_ : Node | None = None ) -> int:
"""simple docstring"""
_lowerCamelCase : Optional[int] =root
def __str__( self : Tuple ) -> str:
"""simple docstring"""
return str(self.root )
def lowerCamelCase ( self : Optional[Any] , lowercase_ : Node , lowercase_ : Node | None ) -> None:
"""simple docstring"""
if new_children is not None: # reset its kids
_lowerCamelCase : Optional[int] =node.parent
if node.parent is not None: # reset its parent
if self.is_right(lowercase_ ): # If it is the right children
_lowerCamelCase : int =new_children
else:
_lowerCamelCase : Dict =new_children
else:
_lowerCamelCase : Tuple =new_children
def lowerCamelCase ( self : Optional[Any] , lowercase_ : Node ) -> bool:
"""simple docstring"""
if node.parent and node.parent.right:
return node == node.parent.right
return False
def lowerCamelCase ( self : int ) -> bool:
"""simple docstring"""
return self.root is None
def lowerCamelCase ( self : List[Any] , lowercase_ : Union[str, Any] ) -> None:
"""simple docstring"""
_lowerCamelCase : Optional[Any] =Node(lowercase_ ) # create a new Node
if self.empty(): # if Tree is empty
_lowerCamelCase : Union[str, Any] =new_node # set its root
else: # Tree is not empty
_lowerCamelCase : Optional[int] =self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
_lowerCamelCase : Optional[int] =new_node # We insert the new node in a leaf
break
else:
_lowerCamelCase : Optional[Any] =parent_node.left
else:
if parent_node.right is None:
_lowerCamelCase : Optional[Any] =new_node
break
else:
_lowerCamelCase : Optional[int] =parent_node.right
_lowerCamelCase : Optional[Any] =parent_node
def lowerCamelCase ( self : Any , *lowercase_ : Union[str, Any] ) -> None:
"""simple docstring"""
for value in values:
self.__insert(lowercase_ )
def lowerCamelCase ( self : Optional[int] , lowercase_ : List[Any] ) -> Node | None:
"""simple docstring"""
if self.empty():
raise IndexError('Warning: Tree is empty! please use another.' )
else:
_lowerCamelCase : int =self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
_lowerCamelCase : Dict =node.left if value < node.value else node.right
return node
def lowerCamelCase ( self : Tuple , lowercase_ : Node | None = None ) -> Node | None:
"""simple docstring"""
if node is None:
if self.root is None:
return None
_lowerCamelCase : Union[str, Any] =self.root
if not self.empty():
while node.right is not None:
_lowerCamelCase : Optional[int] =node.right
return node
def lowerCamelCase ( self : Optional[Any] , lowercase_ : Node | None = None ) -> Node | None:
"""simple docstring"""
if node is None:
_lowerCamelCase : Union[str, Any] =self.root
if self.root is None:
return None
if not self.empty():
_lowerCamelCase : Optional[int] =self.root
while node.left is not None:
_lowerCamelCase : List[Any] =node.left
return node
def lowerCamelCase ( self : Tuple , lowercase_ : int ) -> None:
"""simple docstring"""
_lowerCamelCase : List[str] =self.search(lowercase_ ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(lowercase_ , lowercase_ )
elif node.left is None: # Has only right children
self.__reassign_nodes(lowercase_ , node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(lowercase_ , node.left )
else:
_lowerCamelCase : List[str] =self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
_lowerCamelCase : Union[str, Any] =(
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def lowerCamelCase ( self : str , lowercase_ : Node | None ) -> Iterable:
"""simple docstring"""
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def lowerCamelCase ( self : str , lowercase_ : Dict=None ) -> Any:
"""simple docstring"""
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def lowerCamelCase ( self : Union[str, Any] , lowercase_ : list , lowercase_ : Node | None ) -> None:
"""simple docstring"""
if node:
self.inorder(lowercase_ , node.left )
arr.append(node.value )
self.inorder(lowercase_ , node.right )
def lowerCamelCase ( self : Union[str, Any] , lowercase_ : int , lowercase_ : Node ) -> int:
"""simple docstring"""
_lowerCamelCase : list[int] =[]
self.inorder(lowercase_ , lowercase_ ) # append all values to list using inorder traversal
return arr[k - 1]
def a_ ( SCREAMING_SNAKE_CASE__ : Node | None ):
'''simple docstring'''
_lowerCamelCase : int =[]
if curr_node is not None:
_lowerCamelCase : List[Any] =postorder(curr_node.left ) + postorder(curr_node.right ) + [curr_node]
return node_list
def a_ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] =(8, 3, 6, 1, 10, 14, 13, 4, 7)
_lowerCamelCase : int =BinarySearchTree()
for i in testlist:
t.insert(SCREAMING_SNAKE_CASE__ )
# Prints all the elements of the list in order traversal
print(SCREAMING_SNAKE_CASE__ )
if t.search(6 ) is not None:
print('The value 6 exists' )
else:
print('The value 6 doesn\'t exist' )
if t.search(-1 ) is not None:
print('The value -1 exists' )
else:
print('The value -1 doesn\'t exist' )
if not t.empty():
print('Max Value: ' , t.get_max().value ) # type: ignore
print('Min Value: ' , t.get_min().value ) # type: ignore
for i in testlist:
t.remove(SCREAMING_SNAKE_CASE__ )
print(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 464
| 0
|
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 609
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : List[Any] = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenization_lxmert": ["LxmertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = ["LxmertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : str = [
"LxmertEncoder",
"LxmertForPreTraining",
"LxmertForQuestionAnswering",
"LxmertModel",
"LxmertPreTrainedModel",
"LxmertVisualFeatureEncoder",
"LxmertXLayer",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : int = [
"TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLxmertForPreTraining",
"TFLxmertMainLayer",
"TFLxmertModel",
"TFLxmertPreTrainedModel",
"TFLxmertVisualFeatureEncoder",
]
if TYPE_CHECKING:
from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
from .tokenization_lxmert import LxmertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_lxmert_fast import LxmertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lxmert import (
LxmertEncoder,
LxmertForPreTraining,
LxmertForQuestionAnswering,
LxmertModel,
LxmertPreTrainedModel,
LxmertVisualFeatureEncoder,
LxmertXLayer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_lxmert import (
TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLxmertForPreTraining,
TFLxmertMainLayer,
TFLxmertModel,
TFLxmertPreTrainedModel,
TFLxmertVisualFeatureEncoder,
)
else:
import sys
a : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 609
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ : Dict =logging.get_logger(__name__)
lowerCAmelCase__ : Any ={
'''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}
class UpperCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = '''convnextv2'''
def __init__( self , _A=3 , _A=4 , _A=4 , _A=None , _A=None , _A="gelu" , _A=0.0_2 , _A=1e-12 , _A=0.0 , _A=224 , _A=None , _A=None , **_A , ):
'''simple docstring'''
super().__init__(**_A )
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = num_stages
__SCREAMING_SNAKE_CASE = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
__SCREAMING_SNAKE_CASE = [3, 3, 9, 3] if depths is None else depths
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = drop_path_rate
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = ['stem'] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = get_aligned_output_features_output_indices(
out_features=_A , out_indices=_A , stage_names=self.stage_names )
| 148
|
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : List[str] =logging.get_logger(__name__)
lowerCAmelCase__ : List[Any] ={
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
lowerCAmelCase__ : Dict ={
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
lowerCAmelCase__ : Dict ={'''facebook/blenderbot_small-90M''': 512}
def __lowercase ( a__ ) -> str:
__SCREAMING_SNAKE_CASE = set()
__SCREAMING_SNAKE_CASE = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__SCREAMING_SNAKE_CASE = char
__SCREAMING_SNAKE_CASE = set(a__ )
return pairs
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : int = VOCAB_FILES_NAMES
UpperCamelCase__ : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ : Any = ['''input_ids''', '''attention_mask''']
def __init__( self , _A , _A , _A="__start__" , _A="__end__" , _A="__unk__" , _A="__null__" , **_A , ):
'''simple docstring'''
super().__init__(unk_token=_A , bos_token=_A , eos_token=_A , pad_token=_A , **_A )
with open(_A , encoding='utf-8' ) as vocab_handle:
__SCREAMING_SNAKE_CASE = json.load(_A )
__SCREAMING_SNAKE_CASE = {v: k for k, v in self.encoder.items()}
with open(_A , encoding='utf-8' ) as merges_handle:
__SCREAMING_SNAKE_CASE = merges_handle.read().split('\n' )[1:-1]
__SCREAMING_SNAKE_CASE = [tuple(merge.split() ) for merge in merges]
__SCREAMING_SNAKE_CASE = dict(zip(_A , range(len(_A ) ) ) )
__SCREAMING_SNAKE_CASE = {}
@property
def _A ( self ):
'''simple docstring'''
return len(self.encoder )
def _A ( self ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def _A ( self , _A ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__SCREAMING_SNAKE_CASE = re.sub('([.,!?()])' , r' \1' , _A )
__SCREAMING_SNAKE_CASE = re.sub('(\')' , r' \1 ' , _A )
__SCREAMING_SNAKE_CASE = re.sub(r'\s{2,}' , ' ' , _A )
if "\n" in token:
__SCREAMING_SNAKE_CASE = token.replace('\n' , ' __newln__' )
__SCREAMING_SNAKE_CASE = token.split(' ' )
__SCREAMING_SNAKE_CASE = []
for token in tokens:
if not len(_A ):
continue
__SCREAMING_SNAKE_CASE = token.lower()
__SCREAMING_SNAKE_CASE = tuple(_A )
__SCREAMING_SNAKE_CASE = tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
__SCREAMING_SNAKE_CASE = get_pairs(_A )
if not pairs:
words.append(_A )
continue
while True:
__SCREAMING_SNAKE_CASE = min(_A , key=lambda _A : self.bpe_ranks.get(_A , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = bigram
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = 0
while i < len(_A ):
try:
__SCREAMING_SNAKE_CASE = word.index(_A , _A )
new_word.extend(word[i:j] )
__SCREAMING_SNAKE_CASE = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(_A ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__SCREAMING_SNAKE_CASE = tuple(_A )
__SCREAMING_SNAKE_CASE = new_word
if len(_A ) == 1:
break
else:
__SCREAMING_SNAKE_CASE = get_pairs(_A )
__SCREAMING_SNAKE_CASE = '@@ '.join(_A )
__SCREAMING_SNAKE_CASE = word[:-4]
__SCREAMING_SNAKE_CASE = word
words.append(_A )
return " ".join(_A )
def _A ( self , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = re.findall(r'\S+\n?' , _A )
for token in words:
split_tokens.extend(list(self.bpe(_A ).split(' ' ) ) )
return split_tokens
def _A ( self , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = token.lower()
return self.encoder.get(_A , self.encoder.get(self.unk_token ) )
def _A ( self , _A ):
'''simple docstring'''
return self.decoder.get(_A , self.unk_token )
def _A ( self , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = ' '.join(_A ).replace('@@ ' , '' ).strip()
return out_string
def _A ( self , _A , _A = None ):
'''simple docstring'''
if not os.path.isdir(_A ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__SCREAMING_SNAKE_CASE = os.path.join(
_A , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
__SCREAMING_SNAKE_CASE = os.path.join(
_A , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(_A , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_A , ensure_ascii=_A ) + '\n' )
__SCREAMING_SNAKE_CASE = 0
with open(_A , '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 _A : 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!' )
__SCREAMING_SNAKE_CASE = token_index
writer.write(' '.join(_A ) + '\n' )
index += 1
return vocab_file, merge_file
| 148
| 1
|
import pytest
__A : Any = """__dummy_dataset1__"""
__A : List[str] = """
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
\"tokens\": datasets.Sequence(datasets.Value(\"string\")),
\"ner_tags\": datasets.Sequence(
datasets.features.ClassLabel(
names=[
\"O\",
\"B-PER\",
\"I-PER\",
\"B-ORG\",
\"I-ORG\",
\"B-LOC\",
\"I-LOC\",
]
)
),
\"langs\": datasets.Sequence(datasets.Value(\"string\")),
\"spans\": datasets.Sequence(datasets.Value(\"string\")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),
]
def _generate_examples(self, filepath):
with open(filepath, \"r\", encoding=\"utf-8\") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
"""
@pytest.fixture
def lowerCamelCase_ ( ):
'''simple docstring'''
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def lowerCamelCase_ ( ):
'''simple docstring'''
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = dataset_loading_script_name
SCREAMING_SNAKE_CASE = tmp_path / """datasets""" / script_name
script_dir.mkdir(parents=SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = script_dir / f"""{script_name}.py"""
with open(SCREAMING_SNAKE_CASE , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE )
return str(SCREAMING_SNAKE_CASE )
| 450
|
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__A : Tuple = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
__A : List[compression.BaseCompressedFileFileSystem] = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if "://" in dataset_path:
SCREAMING_SNAKE_CASE = dataset_path.split("""://""" )[1]
return dataset_path
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if fs is not None and fs.protocol != "file":
return True
else:
return False
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = not is_remote_filesystem(SCREAMING_SNAKE_CASE )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(SCREAMING_SNAKE_CASE ) , fs._strip_protocol(SCREAMING_SNAKE_CASE ) )
else:
fs.mv(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , recursive=SCREAMING_SNAKE_CASE )
def lowerCamelCase_ ( ):
'''simple docstring'''
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = threading.Lock()
| 450
| 1
|
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCamelCase__ ( A__ ):
@staticmethod
@abstractmethod
def lowerCamelCase_ ( __a : ArgumentParser ):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
raise NotImplementedError()
| 306
|
def __lowerCAmelCase ( _UpperCamelCase = 2000000 ) -> int:
'''simple docstring'''
lowerCamelCase__: Tuple = [0 for i in range(n + 1 )]
lowerCamelCase__: Optional[Any] = 1
lowerCamelCase__: List[str] = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , _UpperCamelCase ):
lowerCamelCase__: Dict = 1
lowerCamelCase__: List[str] = 0
for i in range(_UpperCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F"""{solution() = }""")
| 306
| 1
|
from __future__ import annotations
from collections import Counter
from random import random
class _SCREAMING_SNAKE_CASE :
def __init__(self):
'''simple docstring'''
__UpperCAmelCase ={}
def A__ (self , UpperCAmelCase):
'''simple docstring'''
__UpperCAmelCase ={}
def A__ (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase):
'''simple docstring'''
if nodea not in self.connections:
self.add_node(lowerCamelCase__)
if nodea not in self.connections:
self.add_node(lowerCamelCase__)
__UpperCAmelCase =probability
def A__ (self):
'''simple docstring'''
return list(self.connections)
def A__ (self , UpperCAmelCase):
'''simple docstring'''
__UpperCAmelCase =0
__UpperCAmelCase =random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def SCREAMING_SNAKE_CASE ( snake_case__ , snake_case__ , snake_case__ ) -> Any:
__UpperCAmelCase =MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
__UpperCAmelCase =Counter(graph.get_nodes() )
__UpperCAmelCase =start
for _ in range(__lowerCAmelCase ):
__UpperCAmelCase =graph.transition(__lowerCAmelCase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 718
|
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,
)
UpperCamelCase_ = {
'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ['AlbertTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ['AlbertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'AlbertForMaskedLM',
'AlbertForMultipleChoice',
'AlbertForPreTraining',
'AlbertForQuestionAnswering',
'AlbertForSequenceClassification',
'AlbertForTokenClassification',
'AlbertModel',
'AlbertPreTrainedModel',
'load_tf_weights_in_albert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFAlbertForMaskedLM',
'TFAlbertForMultipleChoice',
'TFAlbertForPreTraining',
'TFAlbertForQuestionAnswering',
'TFAlbertForSequenceClassification',
'TFAlbertForTokenClassification',
'TFAlbertMainLayer',
'TFAlbertModel',
'TFAlbertPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
'FlaxAlbertForMaskedLM',
'FlaxAlbertForMultipleChoice',
'FlaxAlbertForPreTraining',
'FlaxAlbertForQuestionAnswering',
'FlaxAlbertForSequenceClassification',
'FlaxAlbertForTokenClassification',
'FlaxAlbertModel',
'FlaxAlbertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert import AlbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert_fast import AlbertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
AlbertPreTrainedModel,
load_tf_weights_in_albert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertMainLayer,
TFAlbertModel,
TFAlbertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
FlaxAlbertPreTrainedModel,
)
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 142
| 0
|
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger('''transformers.models.speecht5''')
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Union[str, Any]:
hf_model.apply_weight_norm()
UpperCAmelCase__ : Tuple = checkpoint['''input_conv.weight_g''']
UpperCAmelCase__ : Any = checkpoint['''input_conv.weight_v''']
UpperCAmelCase__ : Union[str, Any] = checkpoint['''input_conv.bias''']
for i in range(len(config.upsample_rates ) ):
UpperCAmelCase__ : int = checkpoint[F"""upsamples.{i}.1.weight_g"""]
UpperCAmelCase__ : str = checkpoint[F"""upsamples.{i}.1.weight_v"""]
UpperCAmelCase__ : List[Any] = checkpoint[F"""upsamples.{i}.1.bias"""]
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
UpperCAmelCase__ : int = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
UpperCAmelCase__ : List[Any] = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
UpperCAmelCase__ : Any = checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]
UpperCAmelCase__ : Tuple = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
UpperCAmelCase__ : Tuple = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
UpperCAmelCase__ : List[Any] = checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]
UpperCAmelCase__ : List[Any] = checkpoint['''output_conv.1.weight_g''']
UpperCAmelCase__ : Union[str, Any] = checkpoint['''output_conv.1.weight_v''']
UpperCAmelCase__ : List[Any] = checkpoint['''output_conv.1.bias''']
hf_model.remove_weight_norm()
@torch.no_grad()
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=None , ) -> List[str]:
if config_path is not None:
UpperCAmelCase__ : Tuple = SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase__ )
else:
UpperCAmelCase__ : Union[str, Any] = SpeechTaHifiGanConfig()
UpperCAmelCase__ : Optional[int] = SpeechTaHifiGan(lowerCAmelCase__ )
UpperCAmelCase__ : Any = torch.load(lowerCAmelCase__ )
load_weights(orig_checkpoint['''model''']['''generator'''] , lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase__ : Optional[int] = np.load(lowerCAmelCase__ )
UpperCAmelCase__ : str = stats[0].reshape(-1 )
UpperCAmelCase__ : Optional[int] = stats[1].reshape(-1 )
UpperCAmelCase__ : Tuple = torch.from_numpy(lowerCAmelCase__ ).float()
UpperCAmelCase__ : Union[str, Any] = torch.from_numpy(lowerCAmelCase__ ).float()
model.save_pretrained(lowerCAmelCase__ )
if repo_id:
print('''Pushing to the hub...''' )
model.push_to_hub(lowerCAmelCase__ )
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
parser.add_argument('''--stats_path''', required=True, default=None, type=str, help='''Path to stats.npy file''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
UpperCamelCase__ = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 75
|
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""openai/imagegpt-small""": """""",
"""openai/imagegpt-medium""": """""",
"""openai/imagegpt-large""": """""",
}
class _snake_case ( lowerCamelCase ):
"""simple docstring"""
lowerCamelCase_ = '''imagegpt'''
lowerCamelCase_ = ['''past_key_values''']
lowerCamelCase_ = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , a=5_1_2 + 1 , a=3_2 * 3_2 , a=5_1_2 , a=2_4 , a=8 , a=None , a="quick_gelu" , a=0.1 , a=0.1 , a=0.1 , a=1e-5 , a=0.02 , a=True , a=True , a=False , a=False , a=False , **a , ) -> Optional[int]:
"""simple docstring"""
_A = vocab_size
_A = n_positions
_A = n_embd
_A = n_layer
_A = n_head
_A = n_inner
_A = activation_function
_A = resid_pdrop
_A = embd_pdrop
_A = attn_pdrop
_A = layer_norm_epsilon
_A = initializer_range
_A = scale_attn_weights
_A = use_cache
_A = scale_attn_by_inverse_layer_idx
_A = reorder_and_upcast_attn
_A = tie_word_embeddings
super().__init__(tie_word_embeddings=a , **a )
class _snake_case ( lowerCamelCase ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
] )
def lowercase_ ( self , a , a = 1 , a = -1 , a = False , a = None , a = 3 , a = 3_2 , a = 3_2 , ) -> Mapping[str, Any]:
"""simple docstring"""
_A = self._generate_dummy_images(a , a , a , a )
_A = dict(preprocessor(images=a , return_tensors=a ) )
return inputs
| 317
| 0
|
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
class __magic_name__ (__lowercase ):
def __init__( self , _a ) -> Union[str, Any]:
super().__init__()
lowerCAmelCase_ = nn.ModuleList(_a )
def __a ( self , _a , _a , _a , _a , _a , _a = None , _a = None , _a = None , _a = None , _a = False , _a = True , ) -> Union[ControlNetOutput, Tuple]:
for i, (image, scale, controlnet) in enumerate(zip(_a , _a , self.nets ) ):
lowerCAmelCase_ , lowerCAmelCase_ = controlnet(
_a , _a , _a , _a , _a , _a , _a , _a , _a , _a , _a , )
# merge samples
if i == 0:
lowerCAmelCase_ , lowerCAmelCase_ = down_samples, mid_sample
else:
lowerCAmelCase_ = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(_a , _a )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def __a ( self , _a , _a = True , _a = None , _a = False , _a = None , ) -> int:
lowerCAmelCase_ = 0
lowerCAmelCase_ = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
_a , is_main_process=_a , save_function=_a , safe_serialization=_a , variant=_a , )
idx += 1
lowerCAmelCase_ = model_path_to_save + f"_{idx}"
@classmethod
def __a ( cls , _a , **_a ) -> List[str]:
lowerCAmelCase_ = 0
lowerCAmelCase_ = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
lowerCAmelCase_ = pretrained_model_path
while os.path.isdir(_a ):
lowerCAmelCase_ = ControlNetModel.from_pretrained(_a , **_a )
controlnets.append(_a )
idx += 1
lowerCAmelCase_ = pretrained_model_path + f"_{idx}"
logger.info(f"{len(_a )} controlnets loaded from {pretrained_model_path}." )
if len(_a ) == 0:
raise ValueError(
f"No ControlNets found under {os.path.dirname(_a )}. Expected at least {pretrained_model_path + '_0'}." )
return cls(_a )
| 226
|
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __magic_name__ :
def __init__( self , _a , _a=13 , _a=10 , _a=3 , _a=2 , _a=2 , _a=2 , _a=True , _a=True , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=10 , _a=0.0_2 , _a=0.9 , _a=None , ) -> Tuple:
lowerCAmelCase_ = parent
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = image_size
lowerCAmelCase_ = num_channels
lowerCAmelCase_ = patch_size
lowerCAmelCase_ = tubelet_size
lowerCAmelCase_ = num_frames
lowerCAmelCase_ = is_training
lowerCAmelCase_ = use_labels
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = hidden_dropout_prob
lowerCAmelCase_ = attention_probs_dropout_prob
lowerCAmelCase_ = type_sequence_label_size
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = mask_ratio
lowerCAmelCase_ = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
lowerCAmelCase_ = (image_size // patch_size) ** 2
lowerCAmelCase_ = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
lowerCAmelCase_ = int(mask_ratio * self.seq_length )
def __a ( self ) -> List[Any]:
lowerCAmelCase_ = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ = None
if self.use_labels:
lowerCAmelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ = self.get_config()
return config, pixel_values, labels
def __a ( self ) -> List[Any]:
return VideoMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , tubelet_size=self.tubelet_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 , is_decoder=_a , initializer_range=self.initializer_range , )
def __a ( self , _a , _a , _a ) -> Optional[Any]:
lowerCAmelCase_ = VideoMAEModel(config=_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self , _a , _a , _a ) -> Any:
lowerCAmelCase_ = VideoMAEForPreTraining(_a )
model.to(_a )
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
lowerCAmelCase_ = torch.ones((self.num_masks,) )
lowerCAmelCase_ = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
lowerCAmelCase_ = mask.expand(self.batch_size , -1 ).bool()
lowerCAmelCase_ = model(_a , _a )
# model only returns predictions for masked patches
lowerCAmelCase_ = mask.sum().item()
lowerCAmelCase_ = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def __a ( self ) -> str:
lowerCAmelCase_ = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = config_and_inputs
lowerCAmelCase_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ (__lowercase , __lowercase , unittest.TestCase ):
lowerCamelCase__ = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
lowerCamelCase__ = (
{'''feature-extraction''': VideoMAEModel, '''video-classification''': VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = VideoMAEModelTester(self )
lowerCAmelCase_ = ConfigTester(self , config_class=_a , has_text_modality=_a , hidden_size=37 )
def __a ( self , _a , _a , _a=False ) -> Optional[Any]:
lowerCAmelCase_ = copy.deepcopy(_a )
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
lowerCAmelCase_ = torch.ones((self.model_tester.num_masks,) )
lowerCAmelCase_ = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
lowerCAmelCase_ = mask.expand(self.model_tester.batch_size , -1 ).bool()
lowerCAmelCase_ = bool_masked_pos.to(_a )
if return_labels:
if model_class in [
*get_values(_a ),
]:
lowerCAmelCase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_a )
return inputs_dict
def __a ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason="VideoMAE does not use inputs_embeds" )
def __a ( self ) -> List[str]:
pass
def __a ( self ) -> List[str]:
lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ = model_class(_a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowerCAmelCase_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def __a ( self ) -> List[Any]:
lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ = model_class(_a )
lowerCAmelCase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ = [*signature.parameters.keys()]
lowerCAmelCase_ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _a )
def __a ( self ) -> Dict:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def __a ( self ) -> Dict:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_a )
@slow
def __a ( self ) -> Optional[int]:
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ = VideoMAEModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def __a ( self ) -> Optional[int]:
if not self.has_attentions:
pass
else:
lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ = True
for model_class in self.all_model_classes:
lowerCAmelCase_ = self.model_tester.seq_length - self.model_tester.num_masks
lowerCAmelCase_ = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
lowerCAmelCase_ = True
lowerCAmelCase_ = False
lowerCAmelCase_ = True
lowerCAmelCase_ = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
lowerCAmelCase_ = model(**self._prepare_for_class(_a , _a ) )
lowerCAmelCase_ = outputs.attentions
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCAmelCase_ = True
lowerCAmelCase_ = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
lowerCAmelCase_ = model(**self._prepare_for_class(_a , _a ) )
lowerCAmelCase_ = outputs.attentions
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowerCAmelCase_ = len(_a )
# Check attention is always last and order is fine
lowerCAmelCase_ = True
lowerCAmelCase_ = True
lowerCAmelCase_ = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
lowerCAmelCase_ = model(**self._prepare_for_class(_a , _a ) )
self.assertEqual(out_len + 1 , len(_a ) )
lowerCAmelCase_ = outputs.attentions
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def __a ( self ) -> List[str]:
def check_hidden_states_output(_a , _a , _a ):
lowerCAmelCase_ = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
lowerCAmelCase_ = model(**self._prepare_for_class(_a , _a ) )
lowerCAmelCase_ = outputs.hidden_states
lowerCAmelCase_ = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(_a ) , _a )
lowerCAmelCase_ = self.model_tester.seq_length - self.model_tester.num_masks
lowerCAmelCase_ = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ = True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ = True
check_hidden_states_output(_a , _a , _a )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def __a ( self ) -> List[Any]:
pass
def A():
lowerCAmelCase_ = hf_hub_download(
repo_id="hf-internal-testing/spaghetti-video" , filename="eating_spaghetti.npy" , repo_type="dataset" )
lowerCAmelCase_ = np.load(__a )
return list(__a )
@require_torch
@require_vision
class __magic_name__ (unittest.TestCase ):
@cached_property
def __a ( self ) -> Optional[Any]:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __a ( self ) -> Any:
lowerCAmelCase_ = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics" ).to(
_a )
lowerCAmelCase_ = self.default_image_processor
lowerCAmelCase_ = prepare_video()
lowerCAmelCase_ = image_processor(_a , return_tensors="pt" ).to(_a )
# forward pass
with torch.no_grad():
lowerCAmelCase_ = model(**_a )
# verify the logits
lowerCAmelCase_ = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , _a )
lowerCAmelCase_ = torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1E-4 ) )
@slow
def __a ( self ) -> List[str]:
lowerCAmelCase_ = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short" ).to(_a )
lowerCAmelCase_ = self.default_image_processor
lowerCAmelCase_ = prepare_video()
lowerCAmelCase_ = image_processor(_a , return_tensors="pt" ).to(_a )
# add boolean mask, indicating which patches to mask
lowerCAmelCase_ = hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos" , filename="bool_masked_pos.pt" )
lowerCAmelCase_ = torch.load(_a )
# forward pass
with torch.no_grad():
lowerCAmelCase_ = model(**_a )
# verify the logits
lowerCAmelCase_ = torch.Size([1, 1408, 1536] )
lowerCAmelCase_ = torch.tensor(
[[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]] , device=_a )
self.assertEqual(outputs.logits.shape , _a )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , _a , atol=1E-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
lowerCAmelCase_ = torch.tensor([0.5_1_4_2] , device=_a )
self.assertTrue(torch.allclose(outputs.loss , _a , atol=1E-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
lowerCAmelCase_ = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short" , norm_pix_loss=_a ).to(
_a )
with torch.no_grad():
lowerCAmelCase_ = model(**_a )
lowerCAmelCase_ = torch.tensor(torch.tensor([0.6_4_6_9] ) , device=_a )
self.assertTrue(torch.allclose(outputs.loss , _a , atol=1E-4 ) )
| 226
| 1
|
"""simple docstring"""
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
__lowerCamelCase : str =[]
if isinstance(__snake_case , __snake_case ):
for v in tree.values():
shapes.extend(_fetch_dims(__snake_case ) )
elif isinstance(__snake_case , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(__snake_case ) )
elif isinstance(__snake_case , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError('''Not supported''' )
return shapes
@torch.jit.ignore
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] =[]
for d in reversed(__snake_case ):
idx.append(flat_idx % d )
__lowerCamelCase : Optional[Any] =flat_idx // d
return tuple(reversed(__snake_case ) )
@torch.jit.ignore
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Tuple = None , SCREAMING_SNAKE_CASE : List[str] = None , ):
'''simple docstring'''
def reduce_edge_list(SCREAMING_SNAKE_CASE : Tuple ) -> None:
__lowerCamelCase : int =True
for i in range(len(__snake_case ) ):
__lowerCamelCase : str =-1 * (i + 1)
l[reversed_idx] &= tally
__lowerCamelCase : Optional[int] =l[reversed_idx]
if start_edges is None:
__lowerCamelCase : Union[str, Any] =[s == 0 for s in start]
reduce_edge_list(__snake_case )
if end_edges is None:
__lowerCamelCase : Union[str, Any] =[e == (d - 1) for e, d in zip(__snake_case , __snake_case )]
reduce_edge_list(__snake_case )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(__snake_case ) == 0:
return [()]
elif len(__snake_case ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
__lowerCamelCase : List[Tuple[slice, ...]] =[]
__lowerCamelCase : List[slice] =[]
# Dimensions common to start and end can be selected directly
for s, e in zip(__snake_case , __snake_case ):
if s == e:
path_list.append(slice(__snake_case , s + 1 ) )
else:
break
__lowerCamelCase : Tuple[slice, ...] =tuple(__snake_case )
__lowerCamelCase : Optional[int] =len(__snake_case )
# start == end, and we're done
if divergence_idx == len(__snake_case ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
__lowerCamelCase : Optional[int] =start[divergence_idx]
return tuple(
path + (slice(__snake_case , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
__lowerCamelCase : Any =end[divergence_idx]
return tuple(
path + (slice(__snake_case , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
__lowerCamelCase : str =end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
__lowerCamelCase : str =t.shape[:no_batch_dims]
__lowerCamelCase : Optional[int] =list(_flat_idx_to_idx(__snake_case , __snake_case ) )
# _get_minimal_slice_set is inclusive
__lowerCamelCase : int =list(_flat_idx_to_idx(flat_end - 1 , __snake_case ) )
# Get an ordered list of slices to perform
__lowerCamelCase : Optional[int] =_get_minimal_slice_set(
__snake_case , __snake_case , __snake_case , )
__lowerCamelCase : Union[str, Any] =[t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int = False , SCREAMING_SNAKE_CASE : Optional[int] = None , SCREAMING_SNAKE_CASE : Optional[int] = False , ):
'''simple docstring'''
if not (len(__snake_case ) > 0):
raise ValueError('''Must provide at least one input''' )
__lowerCamelCase : List[Any] =[shape[:no_batch_dims] for shape in _fetch_dims(__snake_case )]
__lowerCamelCase : Any =tuple([max(__snake_case ) for s in zip(*__snake_case )] )
def _prep_inputs(SCREAMING_SNAKE_CASE : str ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
__lowerCamelCase : int =t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
__lowerCamelCase : Tuple =t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
__lowerCamelCase : Optional[Any] =t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
__lowerCamelCase : Dict[str, Any] =tensor_tree_map(_prep_inputs , __snake_case )
__lowerCamelCase : Optional[Any] =None
if _out is not None:
__lowerCamelCase : Optional[int] =tensor_tree_map(lambda SCREAMING_SNAKE_CASE : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
__lowerCamelCase : Optional[Any] =1
for d in orig_batch_dims:
flat_batch_dim *= d
__lowerCamelCase : Tuple =flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(SCREAMING_SNAKE_CASE : Any ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
__lowerCamelCase : int =0
__lowerCamelCase : List[str] =prepped_outputs
for _ in range(__snake_case ):
# Chunk the input
if not low_mem:
__lowerCamelCase : Union[str, Any] =_select_chunk
else:
__lowerCamelCase : Union[str, Any] =partial(
_chunk_slice , flat_start=__snake_case , flat_end=min(__snake_case , i + chunk_size ) , no_batch_dims=len(__snake_case ) , )
__lowerCamelCase : Dict[str, Any] =tensor_tree_map(__snake_case , __snake_case )
# Run the layer on the chunk
__lowerCamelCase : List[str] =layer(**__snake_case )
# Allocate space for the output
if out is None:
__lowerCamelCase : Tuple =tensor_tree_map(lambda SCREAMING_SNAKE_CASE : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , __snake_case )
# Put the chunk in its pre-allocated space
if isinstance(__snake_case , __snake_case ):
def assign(SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : str ) -> None:
for k, v in da.items():
if isinstance(__snake_case , __snake_case ):
assign(__snake_case , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
__lowerCamelCase : Union[str, Any] =da[k]
assign(__snake_case , __snake_case )
elif isinstance(__snake_case , __snake_case ):
for xa, xa in zip(__snake_case , __snake_case ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
__lowerCamelCase : Optional[Any] =xa
elif isinstance(__snake_case , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
__lowerCamelCase : List[Any] =output_chunk
else:
raise ValueError('''Not supported''' )
i += chunk_size
__lowerCamelCase : Tuple =tensor_tree_map(lambda SCREAMING_SNAKE_CASE : t.view(orig_batch_dims + t.shape[1:] ) , __snake_case )
return out
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self :Dict , __lowercase :int = 512 , ):
__lowerCamelCase : List[Any] =max_chunk_size
__lowerCamelCase : Optional[int] =None
__lowerCamelCase : Optional[tuple] =None
def __lowercase ( self :Dict , __lowercase :Callable , __lowercase :tuple , __lowercase :int ):
logging.info('''Tuning chunk size...''' )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
__lowerCamelCase : List[int] =[2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
__lowerCamelCase : Any =[c for c in candidates if c > min_chunk_size]
__lowerCamelCase : int =[min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(__lowercase :int ) -> bool:
try:
with torch.no_grad():
fn(*_SCREAMING_SNAKE_CASE , chunk_size=_SCREAMING_SNAKE_CASE )
return True
except RuntimeError:
return False
__lowerCamelCase : int =0
__lowerCamelCase : Optional[Any] =len(_SCREAMING_SNAKE_CASE ) - 1
while i > min_viable_chunk_size_index:
__lowerCamelCase : List[Any] =test_chunk_size(candidates[i] )
if not viable:
__lowerCamelCase : List[str] =(min_viable_chunk_size_index + i) // 2
else:
__lowerCamelCase : str =i
__lowerCamelCase : Tuple =(i + len(_SCREAMING_SNAKE_CASE ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def __lowercase ( self :str , __lowercase :Iterable , __lowercase :Iterable ):
__lowerCamelCase : Optional[Any] =True
for aa, aa in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
assert type(_SCREAMING_SNAKE_CASE ) == type(_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ):
consistent &= self._compare_arg_caches(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCamelCase : Optional[int] =[v for _, v in sorted(aa.items() , key=lambda __lowercase : x[0] )]
__lowerCamelCase : Optional[int] =[v for _, v in sorted(aa.items() , key=lambda __lowercase : x[0] )]
consistent &= self._compare_arg_caches(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
consistent &= aa == aa
return consistent
def __lowercase ( self :int , __lowercase :Callable , __lowercase :tuple , __lowercase :int , ):
__lowerCamelCase : Any =True
__lowerCamelCase : tuple =tree_map(lambda __lowercase : a.shape if isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ) else a , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(_SCREAMING_SNAKE_CASE )
__lowerCamelCase : Union[str, Any] =self._compare_arg_caches(self.cached_arg_data , _SCREAMING_SNAKE_CASE )
else:
# Otherwise, we can reuse the precomputed value
__lowerCamelCase : Tuple =False
if not consistent:
__lowerCamelCase : List[Any] =self._determine_favorable_chunk_size(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
__lowerCamelCase : Tuple =arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 179
|
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'stable diffusion controlnet',
'0.22.0',
'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
standard_warn=False,
stacklevel=3,
)
| 293
| 0
|
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCamelCase ( ):
lowercase__ : Union[str, Any] = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch """
"""helper utility that will spawn up """
"""multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=__SCREAMING_SNAKE_CASE , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=__SCREAMING_SNAKE_CASE , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=__SCREAMING_SNAKE_CASE )
return parser.parse_args()
def _lowerCamelCase ( ):
lowercase__ : Any = parse_args()
# Import training_script as a module.
lowercase__ : Optional[int] = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
lowercase__ : Tuple = script_fpath.stem
lowercase__ : Union[str, Any] = importlib.import_module(__SCREAMING_SNAKE_CASE )
# Patch sys.argv
lowercase__ : int = [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 704
|
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
__snake_case = [
'small',
'small-base',
'medium',
'medium-base',
'intermediate',
'intermediate-base',
'large',
'large-base',
'xlarge',
'xlarge-base',
]
__snake_case = {
'vocab_file': {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt',
'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt',
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt',
'funnel-transformer/medium-base': (
'https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'
),
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt',
'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt',
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt',
'funnel-transformer/xlarge-base': (
'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json',
'funnel-transformer/small-base': (
'https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'
),
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json',
'funnel-transformer/medium-base': (
'https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'
),
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json',
'funnel-transformer/large-base': (
'https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'
),
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json',
'funnel-transformer/xlarge-base': (
'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'
),
},
}
__snake_case = {F"funnel-transformer/{name}": 512 for name in _model_names}
__snake_case = {F"funnel-transformer/{name}": {'do_lower_case': True} for name in _model_names}
class _SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
"""simple docstring"""
_a : Union[str, Any] = VOCAB_FILES_NAMES
_a : Any = PRETRAINED_VOCAB_FILES_MAP
_a : List[Any] = PRETRAINED_INIT_CONFIGURATION
_a : List[Any] = FunnelTokenizer
_a : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a : int = 2
def __init__( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=True , lowerCamelCase__="<unk>" , lowerCamelCase__="<sep>" , lowerCamelCase__="<pad>" , lowerCamelCase__="<cls>" , lowerCamelCase__="<mask>" , lowerCamelCase__="<s>" , lowerCamelCase__="</s>" , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=None , lowerCamelCase__="##" , **lowerCamelCase__ , ) -> Union[str, Any]:
super().__init__(
lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , do_lower_case=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , bos_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , clean_text=lowerCamelCase__ , tokenize_chinese_chars=lowerCamelCase__ , strip_accents=lowerCamelCase__ , wordpieces_prefix=lowerCamelCase__ , **lowerCamelCase__ , )
lowercase__ : Any = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , lowerCamelCase__ ) != do_lower_case
or normalizer_state.get("""strip_accents""" , lowerCamelCase__ ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , lowerCamelCase__ ) != tokenize_chinese_chars
):
lowercase__ : List[str] = getattr(lowerCamelCase__ , normalizer_state.pop("""type""" ) )
lowercase__ : Optional[Any] = do_lower_case
lowercase__ : Union[str, Any] = strip_accents
lowercase__ : Optional[Any] = tokenize_chinese_chars
lowercase__ : Union[str, Any] = normalizer_class(**lowerCamelCase__ )
lowercase__ : Union[str, Any] = do_lower_case
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__=None ) -> Tuple:
lowercase__ : Union[str, Any] = [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 UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> List[int]:
lowercase__ : Optional[int] = [self.sep_token_id]
lowercase__ : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> Tuple[str]:
lowercase__ : Optional[Any] = self._tokenizer.model.save(lowerCamelCase__ , name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
| 128
| 0
|
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
A_ = random.Random()
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase=1.0 ,__UpperCamelCase=None ,__UpperCamelCase=None ) -> Union[str, Any]:
if rng is None:
lowerCamelCase_ = global_rng
lowerCamelCase_ = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=400 , SCREAMING_SNAKE_CASE_=2000 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=160 , SCREAMING_SNAKE_CASE_=8 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=4000 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = min_seq_length
lowerCamelCase_ = max_seq_length
lowerCamelCase_ = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowerCamelCase_ = padding_value
lowerCamelCase_ = sampling_rate
lowerCamelCase_ = return_attention_mask
lowerCamelCase_ = do_normalize
lowerCamelCase_ = feature_size
lowerCamelCase_ = chunk_length
lowerCamelCase_ = hop_length
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False ) -> Any:
'''simple docstring'''
def _flatten(SCREAMING_SNAKE_CASE_ ):
return list(itertools.chain(*SCREAMING_SNAKE_CASE_ ) )
if equal_length:
lowerCamelCase_ = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
lowerCamelCase_ = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
lowerCamelCase_ = [np.asarray(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = WhisperFeatureExtractor if is_speech_available() else None
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = WhisperFeatureExtractionTester(self )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ = feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE_ )[0]
check_json_file_has_correct_format(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = feat_extract_first.to_dict()
lowerCamelCase_ = feat_extract_second.to_dict()
lowerCamelCase_ = feat_extract_first.mel_filters
lowerCamelCase_ = feat_extract_second.mel_filters
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ = os.path.join(SCREAMING_SNAKE_CASE_ , 'feat_extract.json' )
feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = feat_extract_first.to_dict()
lowerCamelCase_ = feat_extract_second.to_dict()
lowerCamelCase_ = feat_extract_first.mel_filters
lowerCamelCase_ = feat_extract_second.mel_filters
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowerCamelCase_ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
lowerCamelCase_ = [np.asarray(SCREAMING_SNAKE_CASE_ ) for speech_input in speech_inputs]
# Test feature size
lowerCamelCase_ = feature_extractor(SCREAMING_SNAKE_CASE_ , padding='max_length' , return_tensors='np' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
lowerCamelCase_ = feature_extractor(speech_inputs[0] , return_tensors='np' ).input_features
lowerCamelCase_ = feature_extractor(np_speech_inputs[0] , return_tensors='np' ).input_features
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
# Test batched
lowerCamelCase_ = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors='np' ).input_features
lowerCamelCase_ = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
lowerCamelCase_ = [floats_list((1, x) )[0] for x in (800, 800, 800)]
lowerCamelCase_ = np.asarray(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors='np' ).input_features
lowerCamelCase_ = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
# Test truncation required
lowerCamelCase_ = [floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
lowerCamelCase_ = [np.asarray(SCREAMING_SNAKE_CASE_ ) for speech_input in speech_inputs]
lowerCamelCase_ = [x[: feature_extractor.n_samples] for x in speech_inputs]
lowerCamelCase_ = [np.asarray(SCREAMING_SNAKE_CASE_ ) for speech_input in speech_inputs_truncated]
lowerCamelCase_ = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors='np' ).input_features
lowerCamelCase_ = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
import torch
lowerCamelCase_ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ = np.random.rand(100 , 32 ).astype(np.floataa )
lowerCamelCase_ = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowerCamelCase_ = feature_extractor.pad([{'input_features': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
lowerCamelCase_ = feature_extractor.pad([{'input_features': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
lowerCamelCase_ = ds.sort('id' ).select(range(SCREAMING_SNAKE_CASE_ ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = torch.tensor(
[
0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
-0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
] )
# fmt: on
lowerCamelCase_ = self._load_datasamples(1 )
lowerCamelCase_ = WhisperFeatureExtractor()
lowerCamelCase_ = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors='pt' ).input_features
self.assertEqual(input_features.shape , (1, 80, 3000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ = self._load_datasamples(1 )[0]
lowerCamelCase_ = ((audio - audio.min()) / (audio.max() - audio.min())) * 65535 # Rescale to [0, 65535] to show issue
lowerCamelCase_ = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=SCREAMING_SNAKE_CASE_ )[0]
self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE_ ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE_ ) - 1 ) < 1E-3 ) )
| 42
|
import pprint
import requests
lowerCamelCase__ = "https://zenquotes.io/api"
def __A() -> list:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __A() -> list:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
lowerCamelCase__ = random_quotes()
pprint.pprint(response)
| 612
| 0
|
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 _snake_case ( __snake_case ):
_UpperCamelCase = filter(lambda __snake_case : p.requires_grad , model.parameters() )
_UpperCamelCase = sum([np.prod(p.size() ) for p in model_parameters] )
return params
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case , __snake_case ):
if metric == "rouge2":
_UpperCamelCase = '''{val_avg_rouge2:.4f}-{step_count}'''
elif metric == "bleu":
_UpperCamelCase = '''{val_avg_bleu:.4f}-{step_count}'''
elif metric == "em":
_UpperCamelCase = '''{val_avg_em:.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.''' )
_UpperCamelCase = ModelCheckpoint(
dirpath=__snake_case , filename=__snake_case , monitor=f"""val_{metric}""" , mode='''max''' , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def _snake_case ( __snake_case , __snake_case ):
return EarlyStopping(
monitor=f"""val_{metric}""" , mode='''min''' if '''loss''' in metric else '''max''' , patience=__snake_case , verbose=__snake_case , )
class lowerCAmelCase_ ( pl.Callback ):
def UpperCamelCase_ ( self : int , _A : Optional[int] , _A : Dict ):
_UpperCamelCase = {F"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_A )
@rank_zero_only
def UpperCamelCase_ ( self : Any , _A : pl.Trainer , _A : pl.LightningModule , _A : str , _A : Tuple=True ):
logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
_UpperCamelCase = 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
_UpperCamelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
_UpperCamelCase = od / '''test_results.txt'''
_UpperCamelCase = 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.
_UpperCamelCase = od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
_UpperCamelCase = od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=_A )
generations_file.parent.mkdir(exist_ok=_A )
with open(_A , '''a+''' ) as writer:
for key in sorted(_A ):
if key in ["log", "progress_bar", "preds"]:
continue
_UpperCamelCase = metrics[key]
if isinstance(_A , torch.Tensor ):
_UpperCamelCase = val.item()
_UpperCamelCase = F"""{key}: {val:.6f}\n"""
writer.write(_A )
if not save_generations:
return
if "preds" in metrics:
_UpperCamelCase = '''\n'''.join(metrics['''preds'''] )
generations_file.open('''w+''' ).write(_A )
@rank_zero_only
def UpperCamelCase_ ( self : Union[str, Any] , _A : Dict , _A : Tuple ):
try:
_UpperCamelCase = pl_module.model.model.num_parameters()
except AttributeError:
_UpperCamelCase = pl_module.model.num_parameters()
_UpperCamelCase = count_trainable_parameters(_A )
# 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 UpperCamelCase_ ( self : str , _A : pl.Trainer , _A : pl.LightningModule ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_A , _A , '''test''' )
@rank_zero_only
def UpperCamelCase_ ( self : Optional[Any] , _A : pl.Trainer , _A : str ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 71
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCAmelCase = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
"UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
"UniSpeechForCTC",
"UniSpeechForPreTraining",
"UniSpeechForSequenceClassification",
"UniSpeechModel",
"UniSpeechPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
_lowerCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 71
| 1
|
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_UpperCAmelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_UpperCAmelCase : list[int] = [ord(letter) for letter in string.ascii_lowercase]
_UpperCAmelCase : set[int] = {ord(char) for char in VALID_CHARS}
_UpperCAmelCase : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def _SCREAMING_SNAKE_CASE ( __snake_case : list[int] , __snake_case : tuple[int, ...] ):
_A = ""
_A = 42
_A = 42
_A = 42
for keychar, cipherchar in zip(cycle(__snake_case ) , __snake_case ):
_A = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(__snake_case )
return decoded
def _SCREAMING_SNAKE_CASE ( __snake_case : list[int] ):
_A = []
for key in product(__snake_case , repeat=3 ):
_A = try_key(__snake_case , __snake_case )
if encoded is not None:
possibles.append(__snake_case )
return possibles
def _SCREAMING_SNAKE_CASE ( __snake_case : list[str] , __snake_case : str ):
return [possible for possible in possibles if common_word in possible.lower()]
def _SCREAMING_SNAKE_CASE ( __snake_case : str = "p059_cipher.txt" ):
_A = 42
_A = 42
_A = 42
_A = 42
_A = Path(__snake_case ).parent.joinpath(__snake_case ).read_text(encoding='utf-8' )
_A = [int(__snake_case ) for number in data.strip().split(',' )]
_A = filter_valid_chars(__snake_case )
for common_word in COMMON_WORDS:
_A = filter_common_word(__snake_case , __snake_case )
if len(__snake_case ) == 1:
break
_A = possibles[0]
return sum(ord(__snake_case ) for char in decoded_text )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 107
|
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__lowerCAmelCase , int(b / 2 ) ) * actual_power(__lowerCAmelCase , int(b / 2 ) )
else:
return a * actual_power(__lowerCAmelCase , int(b / 2 ) ) * actual_power(__lowerCAmelCase , int(b / 2 ) )
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> float:
"""simple docstring"""
if b < 0:
return 1 / actual_power(__lowerCAmelCase , __lowerCAmelCase )
return actual_power(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
print(power(-2, -3))
| 252
| 0
|
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def a ( A__ ) -> Optional[Any]:
'''simple docstring'''
return {key.lstrip('''-''' ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def a ( ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = ArgumentParser(
'''HuggingFace Datasets CLI tool''' , usage='''datasets-cli <command> [<args>]''' , allow_abbrev=A__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = parser.add_subparsers(help='''datasets-cli command helpers''' )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(A__ )
EnvironmentCommand.register_subcommand(A__ )
TestCommand.register_subcommand(A__ )
RunBeamCommand.register_subcommand(A__ )
DummyDataCommand.register_subcommand(A__ )
# Parse args
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = parser.parse_known_args()
if not hasattr(A__ , '''func''' ):
parser.print_help()
exit(1 )
SCREAMING_SNAKE_CASE__ : Any = parse_unknown_args(A__ )
# Run
SCREAMING_SNAKE_CASE__ : Optional[Any] = args.func(A__ , **A__ )
service.run()
if __name__ == "__main__":
main()
| 250
|
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
a_ :Optional[Any] = logging.get_logger(__name__)
class lowercase :
def __init__( self : Dict , _lowercase : str = None , _lowercase : uuid.UUID = None , _lowercase : List[str]=None , _lowercase : List[Any]=None ):
if not conversation_id:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = uuid.uuida()
if past_user_inputs is None:
SCREAMING_SNAKE_CASE__ : List[str] = []
if generated_responses is None:
SCREAMING_SNAKE_CASE__ : int = []
SCREAMING_SNAKE_CASE__ : uuid.UUID = conversation_id
SCREAMING_SNAKE_CASE__ : List[str] = past_user_inputs
SCREAMING_SNAKE_CASE__ : List[str] = generated_responses
SCREAMING_SNAKE_CASE__ : Optional[str] = text
def __eq__( self : Optional[Any] , _lowercase : List[str] ):
if not isinstance(_lowercase , _lowercase ):
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 lowercase__ ( self : int , _lowercase : str , _lowercase : bool = False ):
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}\".""" )
SCREAMING_SNAKE_CASE__ : Union[str, 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:
SCREAMING_SNAKE_CASE__ : Optional[int] = text
def lowercase__ ( self : Union[str, Any] ):
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
SCREAMING_SNAKE_CASE__ : List[Any] = None
def lowercase__ ( self : Optional[int] , _lowercase : str ):
self.generated_responses.append(_lowercase )
def lowercase__ ( 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 : Any ):
SCREAMING_SNAKE_CASE__ : Dict = f"""Conversation id: {self.uuid} \n"""
for is_user, text in self.iter_texts():
SCREAMING_SNAKE_CASE__ : Dict = '''user''' if is_user else '''bot'''
output += f"""{name} >> {text} \n"""
return output
@add_end_docstrings(
_UpperCAmelCase , r'''
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
''' , )
class lowercase ( _UpperCAmelCase ):
def __init__( self : str , *_lowercase : List[Any] , **_lowercase : Union[str, Any] ):
super().__init__(*_lowercase , **_lowercase )
if self.tokenizer.pad_token_id is None:
SCREAMING_SNAKE_CASE__ : int = self.tokenizer.eos_token
def lowercase__ ( self : List[Any] , _lowercase : Union[str, Any]=None , _lowercase : Dict=None , _lowercase : Tuple=None , **_lowercase : List[Any] ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = {}
SCREAMING_SNAKE_CASE__ : List[Any] = {}
SCREAMING_SNAKE_CASE__ : Tuple = {}
if min_length_for_response is not None:
SCREAMING_SNAKE_CASE__ : List[str] = min_length_for_response
if minimum_tokens is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = minimum_tokens
if "max_length" in generate_kwargs:
SCREAMING_SNAKE_CASE__ : List[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:
SCREAMING_SNAKE_CASE__ : str = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(_lowercase )
return preprocess_params, forward_params, postprocess_params
def __call__( self : Union[str, Any] , _lowercase : Union[Conversation, List[Conversation]] , _lowercase : Dict=0 , **_lowercase : Optional[int] ):
SCREAMING_SNAKE_CASE__ : Tuple = super().__call__(_lowercase , num_workers=_lowercase , **_lowercase )
if isinstance(_lowercase , _lowercase ) and len(_lowercase ) == 1:
return outputs[0]
return outputs
def lowercase__ ( self : str , _lowercase : Conversation , _lowercase : Optional[int]=32 ):
if not isinstance(_lowercase , _lowercase ):
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''' ):
SCREAMING_SNAKE_CASE__ : Dict = self.tokenizer._build_conversation_input_ids(_lowercase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
SCREAMING_SNAKE_CASE__ : Optional[int] = self._legacy_parse_and_tokenize(_lowercase )
if self.framework == "pt":
SCREAMING_SNAKE_CASE__ : List[Any] = torch.LongTensor([input_ids] )
elif self.framework == "tf":
SCREAMING_SNAKE_CASE__ : Optional[Any] = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def lowercase__ ( self : int , _lowercase : Optional[int] , _lowercase : Dict=10 , **_lowercase : Any ):
SCREAMING_SNAKE_CASE__ : List[str] = generate_kwargs.get('''max_length''' , self.model.config.max_length )
SCREAMING_SNAKE_CASE__ : Any = 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})""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = max_length - minimum_tokens
SCREAMING_SNAKE_CASE__ : Tuple = model_inputs['''input_ids'''][:, -trim:]
if "attention_mask" in model_inputs:
SCREAMING_SNAKE_CASE__ : Optional[Any] = model_inputs['''attention_mask'''][:, -trim:]
SCREAMING_SNAKE_CASE__ : Dict = model_inputs.pop('''conversation''' )
SCREAMING_SNAKE_CASE__ : Any = max_length
SCREAMING_SNAKE_CASE__ : Tuple = self.model.generate(**_lowercase , **_lowercase )
if self.model.config.is_encoder_decoder:
SCREAMING_SNAKE_CASE__ : List[str] = 1
else:
SCREAMING_SNAKE_CASE__ : List[str] = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def lowercase__ ( self : Union[str, Any] , _lowercase : List[str] , _lowercase : Dict=True ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = model_outputs['''output_ids''']
SCREAMING_SNAKE_CASE__ : List[str] = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , )
SCREAMING_SNAKE_CASE__ : Optional[int] = model_outputs['''conversation''']
conversation.mark_processed()
conversation.append_response(_lowercase )
return conversation
def lowercase__ ( self : Any , _lowercase : Conversation ):
SCREAMING_SNAKE_CASE__ : int = self.tokenizer.eos_token_id
SCREAMING_SNAKE_CASE__ : int = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(_lowercase , add_special_tokens=_lowercase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(_lowercase , add_special_tokens=_lowercase ) )
if len(_lowercase ) > self.tokenizer.model_max_length:
SCREAMING_SNAKE_CASE__ : Optional[int] = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 250
| 1
|
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : str , lowercase_ : str ) -> float:
def get_matched_characters(lowercase_ : str , lowercase_ : str ) -> str:
_lowerCamelCase = []
_lowerCamelCase = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
_lowerCamelCase = int(max(0 , i - limit ) )
_lowerCamelCase = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowercase_ )
_lowerCamelCase = F"""{_stra[0:_stra.index(lowercase_ )]} {_stra[_stra.index(lowercase_ ) + 1:]}"""
return "".join(lowercase_ )
# matching characters
_lowerCamelCase = get_matched_characters(lowercase_ , lowercase_ )
_lowerCamelCase = get_matched_characters(lowercase_ , lowercase_ )
_lowerCamelCase = len(lowercase_ )
# transposition
_lowerCamelCase = (
len([(ca, ca) for ca, ca in zip(lowercase_ , lowercase_ ) if ca != ca] ) // 2
)
if not match_count:
_lowerCamelCase = 0.0
else:
_lowerCamelCase = (
1
/ 3
* (
match_count / len(lowercase_ )
+ match_count / len(lowercase_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
_lowerCamelCase = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('''hello''', '''world'''))
| 661
|
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : int = 10 ) -> str:
if not isinstance(lowercase_ , lowercase_ ) or n < 0:
raise ValueError('''Invalid input''' )
_lowerCamelCase = 10**n
_lowerCamelCase = 2_84_33 * (pow(2 , 7_83_04_57 , lowercase_ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(1_0) = }""")
| 661
| 1
|
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowerCamelCase ( _UpperCamelCase : List[str] ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = args.pruning_method
__UpperCAmelCase : Union[str, Any] = args.threshold
__UpperCAmelCase : List[Any] = args.model_name_or_path.rstrip("""/""" )
__UpperCAmelCase : List[Any] = args.target_model_path
print(f'''Load fine-pruned model from {model_name_or_path}''' )
__UpperCAmelCase : Union[str, Any] = torch.load(os.path.join(_UpperCamelCase , """pytorch_model.bin""" ) )
__UpperCAmelCase : Optional[int] = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
__UpperCAmelCase : int = tensor
print(f'''Copied layer {name}''' )
elif "classifier" in name or "qa_output" in name:
__UpperCAmelCase : List[str] = tensor
print(f'''Copied layer {name}''' )
elif "bias" in name:
__UpperCAmelCase : Any = tensor
print(f'''Copied layer {name}''' )
else:
if pruning_method == "magnitude":
__UpperCAmelCase : List[str] = MagnitudeBinarizer.apply(inputs=_UpperCamelCase , threshold=_UpperCamelCase )
__UpperCAmelCase : int = tensor * mask
print(f'''Pruned layer {name}''' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
__UpperCAmelCase : Union[str, Any] = name[:-6]
__UpperCAmelCase : int = model[f'''{prefix_}mask_scores''']
__UpperCAmelCase : Tuple = TopKBinarizer.apply(_UpperCamelCase , _UpperCamelCase )
__UpperCAmelCase : List[str] = tensor * mask
print(f'''Pruned layer {name}''' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
__UpperCAmelCase : Union[str, Any] = name[:-6]
__UpperCAmelCase : int = model[f'''{prefix_}mask_scores''']
__UpperCAmelCase : Tuple = ThresholdBinarizer.apply(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
__UpperCAmelCase : Optional[int] = tensor * mask
print(f'''Pruned layer {name}''' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
__UpperCAmelCase : Optional[Any] = name[:-6]
__UpperCAmelCase : str = model[f'''{prefix_}mask_scores''']
__UpperCAmelCase ,__UpperCAmelCase : int = -0.1, 1.1
__UpperCAmelCase : Tuple = torch.sigmoid(_UpperCamelCase )
__UpperCAmelCase : Optional[int] = s * (r - l) + l
__UpperCAmelCase : Optional[int] = s_bar.clamp(min=0.0 , max=1.0 )
__UpperCAmelCase : int = tensor * mask
print(f'''Pruned layer {name}''' )
else:
raise ValueError("""Unknown pruning method""" )
if target_model_path is None:
__UpperCAmelCase : Optional[Any] = os.path.join(
os.path.dirname(_UpperCamelCase ) , f'''bertarized_{os.path.basename(_UpperCamelCase )}''' )
if not os.path.isdir(_UpperCamelCase ):
shutil.copytree(_UpperCamelCase , _UpperCamelCase )
print(f'''\nCreated folder {target_model_path}''' )
torch.save(_UpperCamelCase , os.path.join(_UpperCamelCase , """pytorch_model.bin""" ) )
print("""\nPruned model saved! See you later!""" )
if __name__ == "__main__":
UpperCAmelCase : Dict = argparse.ArgumentParser()
parser.add_argument(
'--pruning_method',
choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
type=str,
required=True,
help=(
'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
' sigmoied_threshold = Soft movement pruning)'
),
)
parser.add_argument(
'--threshold',
type=float,
required=False,
help=(
'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
'Not needed for `l0`'
),
)
parser.add_argument(
'--model_name_or_path',
type=str,
required=True,
help='Folder containing the model that was previously fine-pruned',
)
parser.add_argument(
'--target_model_path',
default=None,
type=str,
required=False,
help='Folder containing the model that was previously fine-pruned',
)
UpperCAmelCase : str = parser.parse_args()
main(args)
| 299
|
"""simple docstring"""
from __future__ import annotations
import queue
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : str , UpperCamelCase : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Any = data
__UpperCAmelCase : List[str] = None
__UpperCAmelCase : Any = None
def lowerCamelCase ( ) -> TreeNode:
'''simple docstring'''
print("""\n********Press N to stop entering at any point of time********\n""" )
__UpperCAmelCase : Optional[int] = input("""Enter the value of the root node: """ ).strip().lower()
__UpperCAmelCase : queue.Queue = queue.Queue()
__UpperCAmelCase : int = TreeNode(int(_UpperCamelCase ) )
q.put(_UpperCamelCase )
while not q.empty():
__UpperCAmelCase : List[str] = q.get()
__UpperCAmelCase : List[str] = f'''Enter the left node of {node_found.data}: '''
__UpperCAmelCase : Tuple = input(_UpperCamelCase ).strip().lower() or """n"""
if check == "n":
return tree_node
__UpperCAmelCase : str = TreeNode(int(_UpperCamelCase ) )
__UpperCAmelCase : List[Any] = left_node
q.put(_UpperCamelCase )
__UpperCAmelCase : List[str] = f'''Enter the right node of {node_found.data}: '''
__UpperCAmelCase : Tuple = input(_UpperCamelCase ).strip().lower() or """n"""
if check == "n":
return tree_node
__UpperCAmelCase : List[str] = TreeNode(int(_UpperCamelCase ) )
__UpperCAmelCase : Tuple = right_node
q.put(_UpperCamelCase )
raise
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
print(node.data , end=""",""" )
pre_order(node.left )
pre_order(node.right )
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
in_order(node.left )
print(node.data , end=""",""" )
in_order(node.right )
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=""",""" )
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
__UpperCAmelCase : queue.Queue = queue.Queue()
q.put(_UpperCamelCase )
while not q.empty():
__UpperCAmelCase : str = 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 ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
__UpperCAmelCase : queue.Queue = queue.Queue()
q.put(_UpperCamelCase )
while not q.empty():
__UpperCAmelCase : Union[str, Any] = []
while not q.empty():
__UpperCAmelCase : Optional[int] = 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(_UpperCamelCase )
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
__UpperCAmelCase : list[TreeNode] = []
__UpperCAmelCase : Optional[Any] = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=""",""" )
stack.append(_UpperCamelCase )
__UpperCAmelCase : Dict = n.left
# end of while means current node doesn't have left child
__UpperCAmelCase : List[str] = stack.pop()
# start to traverse its right child
__UpperCAmelCase : List[str] = n.right
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
__UpperCAmelCase : list[TreeNode] = []
__UpperCAmelCase : Dict = node
while n or stack:
while n:
stack.append(_UpperCamelCase )
__UpperCAmelCase : Tuple = n.left
__UpperCAmelCase : Any = stack.pop()
print(n.data , end=""",""" )
__UpperCAmelCase : List[Any] = n.right
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
__UpperCAmelCase ,__UpperCAmelCase : Optional[int] = [], []
__UpperCAmelCase : Optional[Any] = node
stacka.append(_UpperCamelCase )
while stacka: # to find the reversed order of post order, store it in stack2
__UpperCAmelCase : Tuple = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(_UpperCamelCase )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=""",""" )
def lowerCamelCase ( _UpperCamelCase : str = "" , _UpperCamelCase : int=5_0 , _UpperCamelCase : Tuple="*" ) -> str:
'''simple docstring'''
if not s:
return "\n" + width * char
__UpperCAmelCase ,__UpperCAmelCase : Tuple = divmod(width - len(_UpperCamelCase ) - 2 , 2 )
return f'''{left * char} {s} {(left + extra) * char}'''
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('Binary Tree Traversals'))
UpperCAmelCase : TreeNode = 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())
| 299
| 1
|
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class _snake_case ( A__ ):
'''simple docstring'''
UpperCamelCase__ ="""perceiver"""
def __init__( self : Optional[Any] , snake_case : Dict=256 , snake_case : Dict=1_280 , snake_case : str=768 , snake_case : Optional[int]=1 , snake_case : List[Any]=26 , snake_case : List[Any]=8 , snake_case : int=8 , snake_case : Any=None , snake_case : List[Any]=None , snake_case : Union[str, Any]="kv" , snake_case : List[str]=1 , snake_case : int=1 , snake_case : Dict="gelu" , snake_case : str=0.1 , snake_case : Any=0.02 , snake_case : Optional[Any]=1e-12 , snake_case : Tuple=True , snake_case : Optional[int]=262 , snake_case : List[str]=2_048 , snake_case : str=56 , snake_case : Dict=[368, 496] , snake_case : str=16 , snake_case : Optional[int]=1_920 , snake_case : Optional[Any]=16 , snake_case : Tuple=[1, 16, 224, 224] , **snake_case : List[Any] , ):
super().__init__(**snake_case )
UpperCAmelCase_ :List[Any] = num_latents
UpperCAmelCase_ :Union[str, Any] = d_latents
UpperCAmelCase_ :Union[str, Any] = d_model
UpperCAmelCase_ :int = num_blocks
UpperCAmelCase_ :Optional[int] = num_self_attends_per_block
UpperCAmelCase_ :int = num_self_attention_heads
UpperCAmelCase_ :List[str] = num_cross_attention_heads
UpperCAmelCase_ :List[Any] = qk_channels
UpperCAmelCase_ :str = v_channels
UpperCAmelCase_ :List[Any] = cross_attention_shape_for_attention
UpperCAmelCase_ :str = self_attention_widening_factor
UpperCAmelCase_ :Union[str, Any] = cross_attention_widening_factor
UpperCAmelCase_ :List[str] = hidden_act
UpperCAmelCase_ :str = attention_probs_dropout_prob
UpperCAmelCase_ :str = initializer_range
UpperCAmelCase_ :List[Any] = layer_norm_eps
UpperCAmelCase_ :Tuple = use_query_residual
# masked language modeling attributes
UpperCAmelCase_ :Optional[int] = vocab_size
UpperCAmelCase_ :Dict = max_position_embeddings
# image classification attributes
UpperCAmelCase_ :Tuple = image_size
# flow attributes
UpperCAmelCase_ :Optional[Any] = train_size
# multimodal autoencoding attributes
UpperCAmelCase_ :Dict = num_frames
UpperCAmelCase_ :List[str] = audio_samples_per_frame
UpperCAmelCase_ :Optional[int] = samples_per_patch
UpperCAmelCase_ :Union[str, Any] = output_shape
class _snake_case ( A__ ):
'''simple docstring'''
@property
def snake_case_ ( self : List[Any] ):
if self.task == "multiple-choice":
UpperCAmelCase_ :List[str] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCAmelCase_ :int = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''inputs''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
@property
def snake_case_ ( self : Any ):
return 1e-4
def snake_case_ ( self : Optional[Any] , snake_case : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , snake_case : int = -1 , snake_case : int = -1 , snake_case : int = -1 , snake_case : bool = False , snake_case : Optional[TensorType] = None , snake_case : int = 3 , snake_case : int = 40 , snake_case : int = 40 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(snake_case , snake_case ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
UpperCAmelCase_ :List[Any] = compute_effective_axis_dimension(
snake_case , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCAmelCase_ :Optional[int] = preprocessor.num_special_tokens_to_add(snake_case )
UpperCAmelCase_ :Union[str, Any] = compute_effective_axis_dimension(
snake_case , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case )
# Generate dummy inputs according to compute batch and sequence
UpperCAmelCase_ :List[Any] = [''' '''.join(['''a'''] ) * seq_length] * batch_size
UpperCAmelCase_ :List[str] = dict(preprocessor(snake_case , return_tensors=snake_case ) )
UpperCAmelCase_ :List[str] = inputs.pop('''input_ids''' )
return inputs
elif isinstance(snake_case , snake_case ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
UpperCAmelCase_ :Dict = compute_effective_axis_dimension(snake_case , fixed_dimension=OnnxConfig.default_fixed_batch )
UpperCAmelCase_ :List[Any] = self._generate_dummy_images(snake_case , snake_case , snake_case , snake_case )
UpperCAmelCase_ :str = dict(preprocessor(images=snake_case , return_tensors=snake_case ) )
UpperCAmelCase_ :Optional[Any] = inputs.pop('''pixel_values''' )
return inputs
else:
raise ValueError(
'''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.''' )
| 608
|
"""simple docstring"""
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__lowerCamelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
("beta", "bias"),
("gamma", "weight"),
("pegasus", "model"),
]
__lowerCamelCase = [
(".output.dense", ".fc2"),
("intermediate.LayerNorm", "final_layer_norm"),
("intermediate.dense", "fc1"),
]
__lowerCamelCase = (
INIT_COMMON
+ [
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.out_proj"),
("attention.self", "self_attn"),
("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
("attention.encdec_output.dense", "encoder_attn.out_proj"),
("attention.encdec", "encoder_attn"),
("key", "k_proj"),
("value", "v_proj"),
("query", "q_proj"),
("decoder.LayerNorm", "decoder.layernorm_embedding"),
]
+ END_COMMON
)
__lowerCamelCase = (
INIT_COMMON
+ [
("embeddings.word_embeddings", "shared.weight"),
("embeddings.position_embeddings", "embed_positions.weight"),
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.output"),
("attention.self", "self_attn.self"),
("encoder.LayerNorm", "encoder.layernorm_embedding"),
]
+ END_COMMON
)
__lowerCamelCase = [
"encdec/key/bias",
"encdec/query/bias",
"encdec/value/bias",
"self/key/bias",
"self/query/bias",
"self/value/bias",
"encdec_output/dense/bias",
"attention/output/dense/bias",
]
def a ( __snake_case : Tuple, __snake_case : str ):
'''simple docstring'''
for tf_name, hf_name in patterns:
UpperCAmelCase_ :Optional[int] = k.replace(__snake_case, __snake_case )
return k
def a ( __snake_case : dict, __snake_case : dict ):
'''simple docstring'''
UpperCAmelCase_ :str = BigBirdPegasusConfig(**__snake_case )
UpperCAmelCase_ :Optional[Any] = BigBirdPegasusForConditionalGeneration(__snake_case )
UpperCAmelCase_ :Dict = torch_model.state_dict()
UpperCAmelCase_ :List[Any] = {}
# separating decoder weights
UpperCAmelCase_ :Tuple = {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
UpperCAmelCase_ :Dict = {k: tf_weights[k] for k in tf_weights if not k.startswith('''pegasus/decoder''' )}
for k, v in tqdm(decoder_weights.items(), '''tf -> hf conversion''' ):
UpperCAmelCase_ :int = [k.endswith(__snake_case ) for ending in KEYS_TO_IGNORE]
if any(__snake_case ):
continue
UpperCAmelCase_ :Union[str, Any] = DECODER_PATTERNS
UpperCAmelCase_ :Any = rename_state_dict_key(__snake_case, __snake_case )
if new_k not in state_dict:
raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})' )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
UpperCAmelCase_ :Tuple = v.T
UpperCAmelCase_ :str = torch.from_numpy(__snake_case )
assert v.shape == state_dict[new_k].shape, f'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'
for k, v in tqdm(remaining_weights.items(), '''tf -> hf conversion''' ):
UpperCAmelCase_ :Any = [k.endswith(__snake_case ) for ending in KEYS_TO_IGNORE]
if any(__snake_case ):
continue
UpperCAmelCase_ :str = REMAINING_PATTERNS
UpperCAmelCase_ :Dict = rename_state_dict_key(__snake_case, __snake_case )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})' )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
UpperCAmelCase_ :Tuple = v.T
UpperCAmelCase_ :Any = torch.from_numpy(__snake_case )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'
UpperCAmelCase_ :Optional[int] = mapping['''model.embed_positions.weight''']
UpperCAmelCase_ :Tuple = mapping.pop('''model.embed_positions.weight''' )
UpperCAmelCase_ ,UpperCAmelCase_ :List[str] = torch_model.load_state_dict(__snake_case, strict=__snake_case )
UpperCAmelCase_ :List[Any] = [
k
for k in missing
if k
not in [
'''final_logits_bias''',
'''model.encoder.embed_tokens.weight''',
'''model.decoder.embed_tokens.weight''',
'''lm_head.weight''',
]
]
assert unexpected_missing == [], f'no matches found for the following torch keys {unexpected_missing}'
assert extra == [], f'no matches found for the following tf keys {extra}'
return torch_model
def a ( __snake_case : str ):
'''simple docstring'''
UpperCAmelCase_ :Tuple = tf.train.list_variables(__snake_case )
UpperCAmelCase_ :Optional[int] = {}
UpperCAmelCase_ :Optional[Any] = ['''global_step''']
for name, shape in tqdm(__snake_case, desc='''converting tf checkpoint to dict''' ):
UpperCAmelCase_ :int = any(pat in name for pat in ignore_name )
if skip_key:
continue
UpperCAmelCase_ :List[str] = tf.train.load_variable(__snake_case, __snake_case )
UpperCAmelCase_ :str = array
return tf_weights
def a ( __snake_case : str, __snake_case : str, __snake_case : dict ):
'''simple docstring'''
UpperCAmelCase_ :Any = get_tf_weights_as_numpy(__snake_case )
UpperCAmelCase_ :Union[str, Any] = convert_bigbird_pegasus(__snake_case, __snake_case )
torch_model.save_pretrained(__snake_case )
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument("--tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("--save_dir", default=None, type=str, help="Path to the output PyTorch model.")
__lowerCamelCase = parser.parse_args()
__lowerCamelCase = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 608
| 1
|
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : str ,_SCREAMING_SNAKE_CASE : Dict ) -> List[Any]:
'''simple docstring'''
A = parent
def A( self : Optional[Any] ) -> Any:
'''simple docstring'''
return {}
def snake_case ( ):
A = '<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR="FFFFFF">\n <HR>\n <a href="http://google.com">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style="color:#0000FF">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>'
A = '\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n '
return [html_string_a, html_string_a]
@require_bsa
class UpperCamelCase ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
snake_case = MarkupLMFeatureExtractor if is_bsa_available() else None
def A( self : Tuple ) -> str:
'''simple docstring'''
A = MarkupLMFeatureExtractionTester(self )
@property
def A( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return self.feature_extract_tester.prepare_feat_extract_dict()
def A( self : int ) -> Union[str, Any]:
'''simple docstring'''
# Initialize feature_extractor
A = self.feature_extraction_class()
# Test not batched input
A = get_html_strings()[0]
A = feature_extractor(_SCREAMING_SNAKE_CASE )
# fmt: off
A = [['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
A = [['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
# fmt: on
self.assertEqual(encoding.nodes ,_SCREAMING_SNAKE_CASE )
self.assertEqual(encoding.xpaths ,_SCREAMING_SNAKE_CASE )
# Test batched
A = get_html_strings()
A = feature_extractor(_SCREAMING_SNAKE_CASE )
# fmt: off
A = expected_nodes + [['My First Heading', 'My first paragraph.']]
A = expected_xpaths + [['/html/body/h1', '/html/body/p']]
self.assertEqual(len(encoding.nodes ) ,2 )
self.assertEqual(len(encoding.xpaths ) ,2 )
self.assertEqual(encoding.nodes ,_SCREAMING_SNAKE_CASE )
self.assertEqual(encoding.xpaths ,_SCREAMING_SNAKE_CASE )
| 110
|
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
snake_case = JukeboxTokenizer
snake_case = {
"artist": "Zac Brown Band",
"genres": "Country",
"lyrics": "I met a traveller from an antique land,\n Who said \"Two vast and trunkless legs of stone\n Stand in the desert. . . . Near them, on the sand,\n Half sunk a shattered visage lies, whose frown,\n And wrinkled lip, and sneer of cold command,\n Tell that its sculptor well those passions read\n Which yet survive, stamped on these lifeless things,\n The hand that mocked them, and the heart that fed;\n And on the pedestal, these words appear:\n My name is Ozymandias, King of Kings;\n Look on my Works, ye Mighty, and despair!\n Nothing beside remains. Round the decay\n Of that colossal Wreck, boundless and bare\n The lone and level sands stretch far away\n ",
}
@require_torch
def A( self : Optional[int] ) -> Any:
'''simple docstring'''
import torch
A = JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics' )
A = tokenizer(**self.metas )['input_ids']
# fmt: off
A = [
torch.tensor([[
0, 0, 0, 7_1_6_9, 5_0_7, 9, 7_6, 3_9, 3_1, 4_6, 7_6, 2_7,
7_6, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8, 3_1, 4_4, 7_6, 3_2,
4_4, 4_1, 3_9, 7_6, 2_7, 4_0, 7_6, 2_7, 4_0, 4_6, 3_5, 4_3,
4_7, 3_1, 7_6, 3_8, 2_7, 4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 4_1, 7_6, 4_5, 2_7, 3_5,
3_0, 7_6, 7_1, 2_0, 4_9, 4_1, 7_6, 4_8, 2_7, 4_5, 4_6, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5,
4_5, 7_6, 3_8, 3_1, 3_3, 4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6,
4_1, 4_0, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
1_9, 4_6, 2_7, 4_0, 3_0, 7_6, 3_5, 4_0, 7_6, 4_6, 3_4, 3_1,
7_6, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3, 7_6, 6_3, 7_6, 6_3,
7_6, 6_3, 7_6, 1_4, 3_1, 2_7, 4_4, 7_6, 4_6, 3_4, 3_1, 3_9,
6_4, 7_6, 4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_5, 2_7, 4_0,
3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 8,
2_7, 3_8, 3_2, 7_6, 4_5, 4_7, 4_0, 3_7, 7_6, 2_7, 7_6, 4_5,
3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0, 7_6, 4_8, 3_5, 4_5,
2_7, 3_3, 3_1, 7_6, 3_8, 3_5, 3_1, 4_5, 6_4, 7_6, 4_9, 3_4,
4_1, 4_5, 3_1, 7_6, 3_2, 4_4, 4_1, 4_9, 4_0, 6_4, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6, 4_9,
4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_6, 3_8, 3_5, 4_2, 6_4,
7_6, 2_7, 4_0, 3_0, 7_6, 4_5, 4_0, 3_1, 3_1, 4_4, 7_6, 4_1,
3_2, 7_6, 2_9, 4_1, 3_8, 3_0, 7_6, 2_9, 4_1, 3_9, 3_9, 2_7,
4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_5, 4_6,
4_5, 7_6, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6, 4_1, 4_4, 7_6, 4_9,
3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 4_1, 4_5, 3_1, 7_6, 4_2, 2_7,
4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_6, 4_4, 3_1, 2_7, 3_0, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 3_5, 2_9,
3_4, 7_6, 5_1, 3_1, 4_6, 7_6, 4_5, 4_7, 4_4, 4_8, 3_5, 4_8,
3_1, 6_4, 7_6, 4_5, 4_6, 2_7, 3_9, 4_2, 3_1, 3_0, 7_6, 4_1,
4_0, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 3_8, 3_5, 3_2, 3_1,
3_8, 3_1, 4_5, 4_5, 7_6, 4_6, 3_4, 3_5, 4_0, 3_3, 4_5, 6_4,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_0, 3_4, 3_1,
7_6, 3_4, 2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_9,
4_1, 2_9, 3_7, 3_1, 3_0, 7_6, 4_6, 3_4, 3_1, 3_9, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_4, 3_1, 2_7, 4_4,
4_6, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_2, 3_1, 3_0, 6_6, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6,
4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_2, 3_1, 3_0, 3_1, 4_5,
4_6, 2_7, 3_8, 6_4, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 4_9,
4_1, 4_4, 3_0, 4_5, 7_6, 2_7, 4_2, 4_2, 3_1, 2_7, 4_4, 6_5,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_3, 5_1, 7_6,
4_0, 2_7, 3_9, 3_1, 7_6, 3_5, 4_5, 7_6, 1_5, 5_2, 5_1, 3_9,
2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_6, 1_1, 3_5, 4_0, 3_3,
7_6, 4_1, 3_2, 7_6, 1_1, 3_5, 4_0, 3_3, 4_5, 6_6, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_2, 4_1, 4_1, 3_7, 7_6,
4_1, 4_0, 7_6, 3_9, 5_1, 7_6, 2_3, 4_1, 4_4, 3_7, 4_5, 6_4,
7_6, 5_1, 3_1, 7_6, 1_3, 3_5, 3_3, 3_4, 4_6, 5_1, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 3_0, 3_1, 4_5, 4_2, 2_7, 3_5, 4_4, 6_7,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_4, 4_1, 4_6,
3_4, 3_5, 4_0, 3_3, 7_6, 2_8, 3_1, 4_5, 3_5, 3_0, 3_1, 7_6,
4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3, 7_6, 1_8, 4_1, 4_7,
4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_0, 3_1, 2_9, 2_7, 5_1,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_5, 3_2, 7_6,
4_6, 3_4, 2_7, 4_6, 7_6, 2_9, 4_1, 3_8, 4_1, 4_5, 4_5, 2_7,
3_8, 7_6, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4, 7_6, 2_8, 4_1, 4_7,
4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_6, 2_7, 4_0, 3_0, 7_6, 2_8,
2_7, 4_4, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_4, 3_1, 7_6, 3_8, 4_1, 4_0, 3_1, 7_6, 2_7, 4_0, 3_0,
7_6, 3_8, 3_1, 4_8, 3_1, 3_8, 7_6, 4_5, 2_7, 4_0, 3_0, 4_5,
7_6, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4, 7_6, 3_2, 2_7, 4_4,
7_6, 2_7, 4_9, 2_7, 5_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
7_6, 7_6]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] ,EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] ,EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] ,EXPECTED_OUTPUT[2] ) )
@require_torch
def A( self : Tuple ) -> List[Any]:
'''simple docstring'''
import torch
A = JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics' )
A = tokenizer(**self.metas )['input_ids']
# fmt: off
A = [
torch.tensor([[
0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1, 9, 7_7, 3_9,
3_1, 4_6, 7_7, 2_7, 7_7, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8,
3_1, 4_4, 7_7, 3_2, 4_4, 4_1, 3_9, 7_7, 2_7, 4_0, 7_7, 2_7,
4_0, 4_6, 3_5, 4_3, 4_7, 3_1, 7_7, 3_8, 2_7, 4_0, 3_0, 6_4,
7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 2_3, 3_4, 4_1,
7_7, 4_5, 2_7, 3_5, 3_0, 7_7, 7_2, 2_0, 4_9, 4_1, 7_7, 4_8,
2_7, 4_5, 4_6, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 4_4, 4_7, 4_0,
3_7, 3_8, 3_1, 4_5, 4_5, 7_7, 3_8, 3_1, 3_3, 4_5, 7_7, 4_1,
3_2, 7_7, 4_5, 4_6, 4_1, 4_0, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 1_9, 4_6, 2_7, 4_0, 3_0, 7_7, 3_5, 4_0,
7_7, 4_6, 3_4, 3_1, 7_7, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3,
7_7, 6_3, 7_7, 6_3, 7_7, 6_3, 7_7, 1_4, 3_1, 2_7, 4_4, 7_7,
4_6, 3_4, 3_1, 3_9, 6_4, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1,
7_7, 4_5, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 8, 2_7, 3_8, 3_2, 7_7, 4_5, 4_7, 4_0, 3_7,
7_7, 2_7, 7_7, 4_5, 3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0,
7_7, 4_8, 3_5, 4_5, 2_7, 3_3, 3_1, 7_7, 3_8, 3_5, 3_1, 4_5,
6_4, 7_7, 4_9, 3_4, 4_1, 4_5, 3_1, 7_7, 3_2, 4_4, 4_1, 4_9,
4_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1,
4_0, 3_0, 7_7, 4_9, 4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_7,
3_8, 3_5, 4_2, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_5, 4_0, 3_1,
3_1, 4_4, 7_7, 4_1, 3_2, 7_7, 2_9, 4_1, 3_8, 3_0, 7_7, 2_9,
4_1, 3_9, 3_9, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 2_7,
4_6, 7_7, 3_5, 4_6, 4_5, 7_7, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6,
4_1, 4_4, 7_7, 4_9, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 4_1, 4_5,
3_1, 7_7, 4_2, 2_7, 4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_7, 4_4,
3_1, 2_7, 3_0, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
2_3, 3_4, 3_5, 2_9, 3_4, 7_7, 5_1, 3_1, 4_6, 7_7, 4_5, 4_7,
4_4, 4_8, 3_5, 4_8, 3_1, 6_4, 7_7, 4_5, 4_6, 2_7, 3_9, 4_2,
3_1, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 4_5, 3_1, 7_7,
3_8, 3_5, 3_2, 3_1, 3_8, 3_1, 4_5, 4_5, 7_7, 4_6, 3_4, 3_5,
4_0, 3_3, 4_5, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 2_0, 3_4, 3_1, 7_7, 3_4, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4,
2_7, 4_6, 7_7, 3_9, 4_1, 2_9, 3_7, 3_1, 3_0, 7_7, 4_6, 3_4,
3_1, 3_9, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7,
3_4, 3_1, 2_7, 4_4, 4_6, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 3_2,
3_1, 3_0, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
1, 4_0, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 7_7, 4_2,
3_1, 3_0, 3_1, 4_5, 4_6, 2_7, 3_8, 6_4, 7_7, 4_6, 3_4, 3_1,
4_5, 3_1, 7_7, 4_9, 4_1, 4_4, 3_0, 4_5, 7_7, 2_7, 4_2, 4_2,
3_1, 2_7, 4_4, 6_5, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_3, 5_1, 7_7, 4_0, 2_7, 3_9, 3_1, 7_7, 3_5, 4_5, 7_7,
1_5, 5_2, 5_1, 3_9, 2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_7,
1_1, 3_5, 4_0, 3_3, 7_7, 4_1, 3_2, 7_7, 1_1, 3_5, 4_0, 3_3,
4_5, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1_2,
4_1, 4_1, 3_7, 7_7, 4_1, 4_0, 7_7, 3_9, 5_1, 7_7, 2_3, 4_1,
4_4, 3_7, 4_5, 6_4, 7_7, 5_1, 3_1, 7_7, 1_3, 3_5, 3_3, 3_4,
4_6, 5_1, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 3_0, 3_1, 4_5, 4_2,
2_7, 3_5, 4_4, 6_7, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_4, 4_1, 4_6, 3_4, 3_5, 4_0, 3_3, 7_7, 2_8, 3_1, 4_5,
3_5, 3_0, 3_1, 7_7, 4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3,
7_7, 1_8, 4_1, 4_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7, 3_0,
3_1, 2_9, 2_7, 5_1, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_5, 3_2, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 2_9, 4_1, 3_8,
4_1, 4_5, 4_5, 2_7, 3_8, 7_7, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4,
7_7, 2_8, 4_1, 4_7, 4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_7, 2_7,
4_0, 3_0, 7_7, 2_8, 2_7, 4_4, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_4, 3_1, 7_7, 3_8, 4_1, 4_0, 3_1,
7_7, 2_7, 4_0, 3_0, 7_7, 3_8, 3_1, 4_8, 3_1, 3_8, 7_7, 4_5,
2_7, 4_0, 3_0, 4_5, 7_7, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4,
7_7, 3_2, 2_7, 4_4, 7_7, 2_7, 4_9, 2_7, 5_1, 7_9, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 7_7, 7_7]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] ,EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] ,EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] ,EXPECTED_OUTPUT[2] ) )
| 110
| 1
|
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
_lowerCAmelCase : Tuple = (3, 9, -11, 0, 7, 5, 1, -1)
_lowerCAmelCase : Dict = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
class __magic_name__ :
def __init__( self , __snake_case ) -> None:
'''simple docstring'''
__a =None
for i in sorted(__snake_case , reverse=__snake_case ):
__a =Node(__snake_case , self.head )
def __iter__( self ) -> Iterator[int]:
'''simple docstring'''
__a =self.head
while node:
yield node.data
__a =node.next_node
def __len__( self ) -> int:
'''simple docstring'''
return sum(1 for _ in self )
def __str__( self ) -> str:
'''simple docstring'''
return " -> ".join([str(__snake_case ) for node in self] )
def UpperCamelCase_( _snake_case : SortedLinkedList , _snake_case : SortedLinkedList ):
"""simple docstring"""
return SortedLinkedList(list(_snake_case ) + list(_snake_case ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase : Any = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 242
|
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = DistilBertTokenizer
SCREAMING_SNAKE_CASE = DistilBertTokenizerFast
SCREAMING_SNAKE_CASE = True
@slow
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =DistilBertTokenizer.from_pretrained('distilbert-base-uncased' )
__a =tokenizer.encode('sequence builders' , add_special_tokens=__snake_case )
__a =tokenizer.encode('multi-sequence build' , add_special_tokens=__snake_case )
__a =tokenizer.build_inputs_with_special_tokens(__snake_case )
__a =tokenizer.build_inputs_with_special_tokens(__snake_case , __snake_case )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 242
| 1
|
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase__ ( lowercase__ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[int]:
super().__init__()
self.register_modules(unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = 50 , SCREAMING_SNAKE_CASE = "pil" , SCREAMING_SNAKE_CASE = True , **SCREAMING_SNAKE_CASE , ) -> Any:
__lowerCAmelCase : str = torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=UpperCAmelCase__ , )
__lowerCAmelCase : Dict = image.to(self.device )
# set step values
self.scheduler.set_timesteps(UpperCAmelCase__ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
__lowerCAmelCase : Tuple = self.unet(UpperCAmelCase__ , UpperCAmelCase__ ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
__lowerCAmelCase : Union[str, Any] = self.scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ).prev_sample
__lowerCAmelCase : List[Any] = (image / 2 + 0.5).clamp(0 , 1 )
__lowerCAmelCase : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__lowerCAmelCase : Optional[Any] = self.numpy_to_pil(UpperCAmelCase__ )
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=UpperCAmelCase__ ), "This is a local test"
| 700
|
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
A_ = 1_00
A_ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A_ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_0_0 )
def A ( _UpperCAmelCase : int ) -> set[int]:
'''simple docstring'''
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
__lowerCAmelCase : set[int] = set()
__lowerCAmelCase : int
__lowerCAmelCase : int
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def A ( _UpperCAmelCase : int = 5_0_0_0 ) -> int | None:
'''simple docstring'''
for number_to_partition in range(1 ,_UpperCAmelCase ):
if len(partition(_UpperCAmelCase ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F'''{solution() = }''')
| 123
| 0
|
'''simple docstring'''
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class UpperCAmelCase ( lowercase_):
"""simple docstring"""
lowerCAmelCase_ = CLIPConfig
lowerCAmelCase_ = ["""CLIPEncoderLayer"""]
def __init__( self : Any , UpperCamelCase__ : CLIPConfig ) -> Optional[int]:
super().__init__(UpperCamelCase__ )
_UpperCamelCase =CLIPVisionModelWithProjection(config.vision_config )
_UpperCamelCase =nn.Linear(config.vision_config.projection_dim , 1 )
_UpperCamelCase =nn.Linear(config.vision_config.projection_dim , 1 )
@torch.no_grad()
def UpperCamelCase__ ( self : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int]=0.5 , UpperCamelCase__ : Optional[Any]=0.5 ) -> Optional[Any]:
_UpperCamelCase =self.vision_model(UpperCamelCase__ )[0]
_UpperCamelCase =self.p_head(UpperCamelCase__ )
_UpperCamelCase =nsfw_detected.flatten()
_UpperCamelCase =nsfw_detected > p_threshold
_UpperCamelCase =nsfw_detected.tolist()
if any(UpperCamelCase__ ):
logger.warning(
'''Potential NSFW content was detected in one or more images. A black image will be returned instead.'''
''' Try again with a different prompt and/or seed.''' )
for idx, nsfw_detected_ in enumerate(UpperCamelCase__ ):
if nsfw_detected_:
_UpperCamelCase =np.zeros(images[idx].shape )
_UpperCamelCase =self.w_head(UpperCamelCase__ )
_UpperCamelCase =watermark_detected.flatten()
_UpperCamelCase =watermark_detected > w_threshold
_UpperCamelCase =watermark_detected.tolist()
if any(UpperCamelCase__ ):
logger.warning(
'''Potential watermarked content was detected in one or more images. A black image will be returned instead.'''
''' Try again with a different prompt and/or seed.''' )
for idx, watermark_detected_ in enumerate(UpperCamelCase__ ):
if watermark_detected_:
_UpperCamelCase =np.zeros(images[idx].shape )
return images, nsfw_detected, watermark_detected
| 404
|
'''simple docstring'''
class UpperCAmelCase :
"""simple docstring"""
def __init__( self : Tuple ) -> List[Any]:
_UpperCamelCase =''''''
_UpperCamelCase =''''''
_UpperCamelCase =[]
def UpperCamelCase__ ( self : str , UpperCamelCase__ : int , UpperCamelCase__ : int ) -> int:
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
_UpperCamelCase =self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
_UpperCamelCase =self.__min_dist_top_down_dp(UpperCamelCase__ , n - 1 )
_UpperCamelCase =self.__min_dist_top_down_dp(m - 1 , UpperCamelCase__ )
_UpperCamelCase =self.__min_dist_top_down_dp(m - 1 , n - 1 )
_UpperCamelCase =1 + min(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return self.dp[m][n]
def UpperCamelCase__ ( self : Union[str, Any] , UpperCamelCase__ : str , UpperCamelCase__ : str ) -> int:
_UpperCamelCase =worda
_UpperCamelCase =worda
_UpperCamelCase =[[-1 for _ in range(len(UpperCamelCase__ ) )] for _ in range(len(UpperCamelCase__ ) )]
return self.__min_dist_top_down_dp(len(UpperCamelCase__ ) - 1 , len(UpperCamelCase__ ) - 1 )
def UpperCamelCase__ ( self : int , UpperCamelCase__ : str , UpperCamelCase__ : str ) -> int:
_UpperCamelCase =worda
_UpperCamelCase =worda
_UpperCamelCase =len(UpperCamelCase__ )
_UpperCamelCase =len(UpperCamelCase__ )
_UpperCamelCase =[[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
_UpperCamelCase =j
elif j == 0: # second string is empty
_UpperCamelCase =i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
_UpperCamelCase =self.dp[i - 1][j - 1]
else:
_UpperCamelCase =self.dp[i][j - 1]
_UpperCamelCase =self.dp[i - 1][j]
_UpperCamelCase =self.dp[i - 1][j - 1]
_UpperCamelCase =1 + min(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return self.dp[m][n]
if __name__ == "__main__":
__lowerCamelCase : int = EditDistance()
print('****************** Testing Edit Distance DP Algorithm ******************')
print()
__lowerCamelCase : Optional[int] = input('Enter the first string: ').strip()
__lowerCamelCase : Optional[int] = input('Enter the second string: ').strip()
print()
print(F"""The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}""")
print(F"""The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}""")
print()
print('*************** End of Testing Edit Distance DP Algorithm ***************')
| 404
| 1
|
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCamelCase__ = logging.getLogger(__name__)
def A(__a: List[str] , __a: Tuple ):
lowerCAmelCase_ = np.argmax(__A , axis=1 )
return np.sum(outputs == labels )
def A(__a: int ):
with open(__A , encoding="utf_8" ) as f:
lowerCAmelCase_ = csv.reader(__A )
lowerCAmelCase_ = []
next(__A ) # skip the first line
for line in tqdm(__A ):
output.append((" ".join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def A(__a: List[str] , __a: Dict , __a: List[str] , __a: int , __a: Dict , __a: Any ):
lowerCAmelCase_ = []
for dataset in encoded_datasets:
lowerCAmelCase_ = len(__A )
lowerCAmelCase_ = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
lowerCAmelCase_ = np.zeros((n_batch, 2) , dtype=np.intaa )
lowerCAmelCase_ = np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
lowerCAmelCase_ = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(__A ):
lowerCAmelCase_ = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCAmelCase_ = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCAmelCase_ = with_conta
lowerCAmelCase_ = with_conta
lowerCAmelCase_ = len(__A ) - 1
lowerCAmelCase_ = len(__A ) - 1
lowerCAmelCase_ = with_conta
lowerCAmelCase_ = with_conta
lowerCAmelCase_ = mc_label
lowerCAmelCase_ = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(__A ) for t in all_inputs ) )
return tensor_datasets
def A():
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument("--model_name" , type=__A , default="openai-gpt" , help="pretrained model name" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_eval" , action="store_true" , help="Whether to run eval on the dev set." )
parser.add_argument(
"--output_dir" , default=__A , type=__A , required=__A , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument("--train_dataset" , type=__A , default="" )
parser.add_argument("--eval_dataset" , type=__A , default="" )
parser.add_argument("--seed" , type=__A , default=42 )
parser.add_argument("--num_train_epochs" , type=__A , default=3 )
parser.add_argument("--train_batch_size" , type=__A , default=8 )
parser.add_argument("--eval_batch_size" , type=__A , default=16 )
parser.add_argument("--adam_epsilon" , default=1E-8 , type=__A , help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm" , type=__A , default=1 )
parser.add_argument(
"--max_steps" , default=-1 , type=__A , help=(
"If > 0: set total number of training steps to perform. Override num_train_epochs."
) , )
parser.add_argument(
"--gradient_accumulation_steps" , type=__A , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--learning_rate" , type=__A , default=6.25E-5 )
parser.add_argument("--warmup_steps" , default=0 , type=__A , help="Linear warmup over warmup_steps." )
parser.add_argument("--lr_schedule" , type=__A , default="warmup_linear" )
parser.add_argument("--weight_decay" , type=__A , default=0.01 )
parser.add_argument("--lm_coef" , type=__A , default=0.9 )
parser.add_argument("--n_valid" , type=__A , default=374 )
parser.add_argument("--server_ip" , type=__A , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=__A , default="" , help="Can be used for distant debugging." )
lowerCAmelCase_ = parser.parse_args()
print(__A )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__A )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
lowerCAmelCase_ = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
lowerCAmelCase_ = torch.cuda.device_count()
logger.info("device: {}, n_gpu {}".format(__A , __A ) )
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True." )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
lowerCAmelCase_ = ['''_start_''', '''_delimiter_''', '''_classify_''']
lowerCAmelCase_ = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(__A )
lowerCAmelCase_ = tokenizer.convert_tokens_to_ids(__A )
lowerCAmelCase_ = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(__A ) )
model.to(__A )
# Load and encode the datasets
def tokenize_and_encode(__a: Tuple ):
if isinstance(__A , __A ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__A ) )
elif isinstance(__A , __A ):
return obj
return [tokenize_and_encode(__A ) for o in obj]
logger.info("Encoding dataset..." )
lowerCAmelCase_ = load_rocstories_dataset(args.train_dataset )
lowerCAmelCase_ = load_rocstories_dataset(args.eval_dataset )
lowerCAmelCase_ = (train_dataset, eval_dataset)
lowerCAmelCase_ = tokenize_and_encode(__A )
# Compute the max input length for the Transformer
lowerCAmelCase_ = model.config.n_positions // 2 - 2
lowerCAmelCase_ = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
lowerCAmelCase_ = min(__A , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
lowerCAmelCase_ = pre_process_datasets(__A , __A , __A , *__A )
lowerCAmelCase_ = tensor_datasets[0], tensor_datasets[1]
lowerCAmelCase_ = TensorDataset(*__A )
lowerCAmelCase_ = RandomSampler(__A )
lowerCAmelCase_ = DataLoader(__A , sampler=__A , batch_size=args.train_batch_size )
lowerCAmelCase_ = TensorDataset(*__A )
lowerCAmelCase_ = SequentialSampler(__A )
lowerCAmelCase_ = DataLoader(__A , sampler=__A , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
lowerCAmelCase_ = args.max_steps
lowerCAmelCase_ = args.max_steps // (len(__A ) // args.gradient_accumulation_steps) + 1
else:
lowerCAmelCase_ = len(__A ) // args.gradient_accumulation_steps * args.num_train_epochs
lowerCAmelCase_ = list(model.named_parameters() )
lowerCAmelCase_ = ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
lowerCAmelCase_ = [
{
'''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'''weight_decay''': args.weight_decay,
},
{'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], '''weight_decay''': 0.0},
]
lowerCAmelCase_ = AdamW(__A , lr=args.learning_rate , eps=args.adam_epsilon )
lowerCAmelCase_ = get_linear_schedule_with_warmup(
__A , num_warmup_steps=args.warmup_steps , num_training_steps=__A )
if args.do_train:
lowerCAmelCase_ = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc="Epoch" ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
lowerCAmelCase_ = tqdm(__A , desc="Training" )
for step, batch in enumerate(__A ):
lowerCAmelCase_ = tuple(t.to(__A ) for t in batch )
lowerCAmelCase_ = batch
lowerCAmelCase_ = model(__A , mc_token_ids=__A , lm_labels=__A , mc_labels=__A )
lowerCAmelCase_ = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
lowerCAmelCase_ = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
lowerCAmelCase_ = '''Training loss: {:.2e} lr: {:.2e}'''.format(__A , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
lowerCAmelCase_ = model.module if hasattr(__A , "module" ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
lowerCAmelCase_ = os.path.join(args.output_dir , __A )
lowerCAmelCase_ = os.path.join(args.output_dir , __A )
torch.save(model_to_save.state_dict() , __A )
model_to_save.config.to_json_file(__A )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
lowerCAmelCase_ = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
lowerCAmelCase_ = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(__A )
if args.do_eval:
model.eval()
lowerCAmelCase_ = 0, 0
lowerCAmelCase_ = 0, 0
for batch in tqdm(__A , desc="Evaluating" ):
lowerCAmelCase_ = tuple(t.to(__A ) for t in batch )
lowerCAmelCase_ = batch
with torch.no_grad():
lowerCAmelCase_ = model(
__A , mc_token_ids=__A , lm_labels=__A , mc_labels=__A )
lowerCAmelCase_ = mc_logits.detach().cpu().numpy()
lowerCAmelCase_ = mc_labels.to("cpu" ).numpy()
lowerCAmelCase_ = accuracy(__A , __A )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
lowerCAmelCase_ = eval_loss / nb_eval_steps
lowerCAmelCase_ = eval_accuracy / nb_eval_examples
lowerCAmelCase_ = tr_loss / nb_tr_steps if args.do_train else None
lowerCAmelCase_ = {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}
lowerCAmelCase_ = os.path.join(args.output_dir , "eval_results.txt" )
with open(__A , "w" ) as writer:
logger.info("***** Eval results *****" )
for key in sorted(result.keys() ):
logger.info(" %s = %s" , __A , str(result[key] ) )
writer.write("%s = %s\n" % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 708
|
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowerCamelCase__ = 1_00
lowerCamelCase__ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCamelCase__ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def A(__a: int ):
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
lowerCAmelCase_ = set()
lowerCAmelCase_ = 42
lowerCAmelCase_ = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def A(__a: int = 5000 ):
for number_to_partition in range(1 , __a ):
if len(partition(__a ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F'''{solution() = }''')
| 226
| 0
|
'''simple docstring'''
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 UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
__lowercase = BertTokenizer
__lowercase = BertTokenizerFast
__lowercase = True
__lowercase = True
__lowercase = filter_non_english
def UpperCAmelCase_ ( self :Dict )-> int:
super().setUp()
A__ = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
A__ = 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 :Optional[int] , lowercase_ :Optional[int] )-> Optional[Any]:
A__ = "UNwant\u00E9d,running"
A__ = "unwanted, running"
return input_text, output_text
def UpperCAmelCase_ ( self :int )-> int:
A__ = self.tokenizer_class(self.vocab_file )
A__ = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(_a , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , [9, 6, 7, 12, 10, 11] )
def UpperCAmelCase_ ( self :Tuple )-> Optional[Any]:
if not self.test_rust_tokenizer:
return
A__ = self.get_tokenizer()
A__ = self.get_rust_tokenizer()
A__ = "UNwant\u00E9d,running"
A__ = tokenizer.tokenize(_a )
A__ = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
A__ = tokenizer.encode(_a , add_special_tokens=_a )
A__ = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
A__ = self.get_rust_tokenizer()
A__ = tokenizer.encode(_a )
A__ = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
# With lower casing
A__ = self.get_tokenizer(do_lower_case=_a )
A__ = self.get_rust_tokenizer(do_lower_case=_a )
A__ = "UNwant\u00E9d,running"
A__ = tokenizer.tokenize(_a )
A__ = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
A__ = tokenizer.encode(_a , add_special_tokens=_a )
A__ = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
A__ = self.get_rust_tokenizer()
A__ = tokenizer.encode(_a )
A__ = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
def UpperCAmelCase_ ( self :List[str] )-> Union[str, Any]:
A__ = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def UpperCAmelCase_ ( self :str )-> Union[str, Any]:
A__ = BasicTokenizer(do_lower_case=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCAmelCase_ ( self :Union[str, Any] )-> int:
A__ = BasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def UpperCAmelCase_ ( self :List[str] )-> Optional[int]:
A__ = BasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCAmelCase_ ( self :int )-> Tuple:
A__ = BasicTokenizer(do_lower_case=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCAmelCase_ ( self :str )-> Union[str, Any]:
A__ = BasicTokenizer(do_lower_case=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCAmelCase_ ( self :Tuple )-> str:
A__ = BasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCAmelCase_ ( self :str )-> Tuple:
A__ = BasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCAmelCase_ ( self :Dict )-> List[Any]:
A__ = BasicTokenizer(do_lower_case=_a , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def UpperCAmelCase_ ( self :int )-> List[str]:
A__ = BasicTokenizer()
A__ = "a\n\'ll !!to?\'d of, can\'t."
A__ = ["a", "\'", "ll", "!", "!", "to", "?", "\'", "d", "of", ",", "can", "\'", "t", "."]
self.assertListEqual(tokenizer.tokenize(_a ) , _a )
def UpperCAmelCase_ ( self :int )-> Optional[int]:
A__ = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
A__ = {}
for i, token in enumerate(_a ):
A__ = i
A__ = WordpieceTokenizer(vocab=_a , 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 UpperCAmelCase_ ( self :List[str] )-> List[str]:
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 UpperCAmelCase_ ( self :List[Any] )-> List[str]:
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 UpperCAmelCase_ ( self :int )-> List[str]:
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 UpperCAmelCase_ ( self :Optional[Any] )-> Optional[int]:
A__ = self.get_tokenizer()
A__ = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(_a ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
self.assertListEqual(
[rust_tokenizer.tokenize(_a ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
@slow
def UpperCAmelCase_ ( self :Optional[Any] )-> Optional[int]:
A__ = self.tokenizer_class.from_pretrained("bert-base-uncased" )
A__ = tokenizer.encode("sequence builders" , add_special_tokens=_a )
A__ = tokenizer.encode("multi-sequence build" , add_special_tokens=_a )
A__ = tokenizer.build_inputs_with_special_tokens(_a )
A__ = tokenizer.build_inputs_with_special_tokens(_a , _a )
assert encoded_sentence == [1_01] + text + [1_02]
assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
def UpperCAmelCase_ ( self :Dict )-> Optional[Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
A__ = self.rust_tokenizer_class.from_pretrained(_a , **_a )
A__ = F"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
A__ = tokenizer_r.encode_plus(
_a , return_attention_mask=_a , return_token_type_ids=_a , return_offsets_mapping=_a , add_special_tokens=_a , )
A__ = tokenizer_r.do_lower_case if hasattr(_a , "do_lower_case" ) else False
A__ = (
[
((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 UpperCAmelCase_ ( self :Dict )-> int:
A__ = ["的", "人", "有"]
A__ = "".join(_a )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
A__ = True
A__ = self.tokenizer_class.from_pretrained(_a , **_a )
A__ = self.rust_tokenizer_class.from_pretrained(_a , **_a )
A__ = tokenizer_p.encode(_a , add_special_tokens=_a )
A__ = tokenizer_r.encode(_a , add_special_tokens=_a )
A__ = tokenizer_r.convert_ids_to_tokens(_a )
A__ = tokenizer_p.convert_ids_to_tokens(_a )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(_a , _a )
self.assertListEqual(_a , _a )
A__ = False
A__ = self.rust_tokenizer_class.from_pretrained(_a , **_a )
A__ = self.tokenizer_class.from_pretrained(_a , **_a )
A__ = tokenizer_r.encode(_a , add_special_tokens=_a )
A__ = tokenizer_p.encode(_a , add_special_tokens=_a )
A__ = tokenizer_r.convert_ids_to_tokens(_a )
A__ = tokenizer_p.convert_ids_to_tokens(_a )
# it is expected that only the first Chinese character is not preceded by "##".
A__ = [
F"##{token}" if idx != 0 else token for idx, token in enumerate(_a )
]
self.assertListEqual(_a , _a )
self.assertListEqual(_a , _a )
| 440
|
"""simple docstring"""
from typing import Any
def lowercase ( lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : dict , lowerCAmelCase__ : dict , lowerCAmelCase__ : dict , ) -> list:
_validation(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , )
# Creates data structures and fill initial step
__a = {}
__a = {}
for state in states_space:
__a = observations_space[0]
__a = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
__a = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(lowerCAmelCase__ ) ):
__a = observations_space[o]
__a = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
__a = ''''''
__a = -1
for k_state in states_space:
__a = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
__a = probability
__a = k_state
# Update probabilities and pointers dicts
__a = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
__a = arg_max
# The final observation
__a = observations_space[len(lowerCAmelCase__ ) - 1]
# argmax for given final observation
__a = ''''''
__a = -1
for k_state in states_space:
__a = probabilities[(k_state, final_observation)]
if probability > max_probability:
__a = probability
__a = k_state
__a = arg_max
# Process pointers backwards
__a = last_state
__a = []
for o in range(len(lowerCAmelCase__ ) - 1 , -1 , -1 ):
result.append(lowerCAmelCase__ )
__a = pointers[previous, observations_space[o]]
result.reverse()
return result
def lowercase ( lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , ) -> None:
_validate_not_empty(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , )
_validate_lists(lowerCAmelCase__ , lowerCAmelCase__ )
_validate_dicts(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def lowercase ( lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , ) -> None:
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError('''There\'s an empty parameter''' )
def lowercase ( lowerCAmelCase__ : Any , lowerCAmelCase__ : Any ) -> None:
_validate_list(lowerCAmelCase__ , '''observations_space''' )
_validate_list(lowerCAmelCase__ , '''states_space''' )
def lowercase ( lowerCAmelCase__ : Any , lowerCAmelCase__ : str ) -> None:
if not isinstance(_object , lowerCAmelCase__ ):
__a = f'''{var_name} must be a list'''
raise ValueError(lowerCAmelCase__ )
else:
for x in _object:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__a = f'''{var_name} must be a list of strings'''
raise ValueError(lowerCAmelCase__ )
def lowercase ( lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , ) -> None:
_validate_dict(lowerCAmelCase__ , '''initial_probabilities''' , lowerCAmelCase__ )
_validate_nested_dict(lowerCAmelCase__ , '''transition_probabilities''' )
_validate_nested_dict(lowerCAmelCase__ , '''emission_probabilities''' )
def lowercase ( lowerCAmelCase__ : Any , lowerCAmelCase__ : str ) -> None:
_validate_dict(_object , lowerCAmelCase__ , lowerCAmelCase__ )
for x in _object.values():
_validate_dict(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def lowercase ( lowerCAmelCase__ : Any , lowerCAmelCase__ : str , lowerCAmelCase__ : type , lowerCAmelCase__ : bool = False ) -> None:
if not isinstance(_object , lowerCAmelCase__ ):
__a = f'''{var_name} must be a dict'''
raise ValueError(lowerCAmelCase__ )
if not all(isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) for x in _object ):
__a = f'''{var_name} all keys must be strings'''
raise ValueError(lowerCAmelCase__ )
if not all(isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) for x in _object.values() ):
__a = '''nested dictionary ''' if nested else ''''''
__a = f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
raise ValueError(lowerCAmelCase__ )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 695
| 0
|
from collections import Counter
from timeit import timeit
def lowerCamelCase_ ( _lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def lowerCamelCase_ ( _lowercase = "" ) -> bool:
if len(_lowercase ) == 0:
return True
__A : List[Any] = input_str.replace(" " , "" ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
__A : dict[str, int] = {}
for character in lower_case_input_str:
__A : str = character_freq_dict.get(_lowercase , 0 ) + 1
__A : Any = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def lowerCamelCase_ ( _lowercase = "" ) -> None:
print("\nFor string = " , _lowercase , ":" )
print(
"> can_string_be_rearranged_as_palindrome_counter()" , "\tans =" , can_string_be_rearranged_as_palindrome_counter(_lowercase ) , "\ttime =" , timeit(
"z.can_string_be_rearranged_as_palindrome_counter(z.check_str)" , setup="import __main__ as z" , ) , "seconds" , )
print(
"> can_string_be_rearranged_as_palindrome()" , "\tans =" , can_string_be_rearranged_as_palindrome(_lowercase ) , "\ttime =" , timeit(
"z.can_string_be_rearranged_as_palindrome(z.check_str)" , setup="import __main__ as z" , ) , "seconds" , )
if __name__ == "__main__":
UpperCamelCase = input(
'Enter string to determine if it can be rearranged as a palindrome or not: '
).strip()
benchmark(check_str)
UpperCamelCase = can_string_be_rearranged_as_palindrome_counter(check_str)
print(F'''{check_str} can {"" if status else "not "}be rearranged as a palindrome''')
| 704
|
from __future__ import annotations
def lowerCamelCase_ ( _lowercase , _lowercase , _lowercase ) -> float:
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0" )
if daily_interest_rate < 0:
raise ValueError("daily_interest_rate must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return principal * daily_interest_rate * days_between_payments
def lowerCamelCase_ ( _lowercase , _lowercase , _lowercase , ) -> float:
if number_of_compounding_periods <= 0:
raise ValueError("number_of_compounding_periods must be > 0" )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError("nominal_annual_interest_rate_percentage must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def lowerCamelCase_ ( _lowercase , _lowercase , _lowercase , ) -> float:
if number_of_years <= 0:
raise ValueError("number_of_years must be > 0" )
if nominal_annual_percentage_rate < 0:
raise ValueError("nominal_annual_percentage_rate must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return compound_interest(
_lowercase , nominal_annual_percentage_rate / 365 , number_of_years * 365 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 387
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase = {
'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
'tokenization_electra': ['ElectraTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = ['ElectraTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
'ElectraForCausalLM',
'ElectraForMaskedLM',
'ElectraForMultipleChoice',
'ElectraForPreTraining',
'ElectraForQuestionAnswering',
'ElectraForSequenceClassification',
'ElectraForTokenClassification',
'ElectraModel',
'ElectraPreTrainedModel',
'load_tf_weights_in_electra',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFElectraForMaskedLM',
'TFElectraForMultipleChoice',
'TFElectraForPreTraining',
'TFElectraForQuestionAnswering',
'TFElectraForSequenceClassification',
'TFElectraForTokenClassification',
'TFElectraModel',
'TFElectraPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
'FlaxElectraForCausalLM',
'FlaxElectraForMaskedLM',
'FlaxElectraForMultipleChoice',
'FlaxElectraForPreTraining',
'FlaxElectraForQuestionAnswering',
'FlaxElectraForSequenceClassification',
'FlaxElectraForTokenClassification',
'FlaxElectraModel',
'FlaxElectraPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
from .tokenization_electra import ElectraTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_electra_fast import ElectraTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_electra import (
ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
ElectraForCausalLM,
ElectraForMaskedLM,
ElectraForMultipleChoice,
ElectraForPreTraining,
ElectraForQuestionAnswering,
ElectraForSequenceClassification,
ElectraForTokenClassification,
ElectraModel,
ElectraPreTrainedModel,
load_tf_weights_in_electra,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_electra import (
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFElectraForMaskedLM,
TFElectraForMultipleChoice,
TFElectraForPreTraining,
TFElectraForQuestionAnswering,
TFElectraForSequenceClassification,
TFElectraForTokenClassification,
TFElectraModel,
TFElectraPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_electra import (
FlaxElectraForCausalLM,
FlaxElectraForMaskedLM,
FlaxElectraForMultipleChoice,
FlaxElectraForPreTraining,
FlaxElectraForQuestionAnswering,
FlaxElectraForSequenceClassification,
FlaxElectraForTokenClassification,
FlaxElectraModel,
FlaxElectraPreTrainedModel,
)
else:
import sys
__lowerCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 466
|
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__lowerCAmelCase = {
'sample_size': 32,
'in_channels': 3,
'out_channels': 3,
'layers_per_block': 2,
'num_class_embeds': 1_000,
'block_out_channels': [32, 64],
'attention_head_dim': 8,
'down_block_types': [
'ResnetDownsampleBlock2D',
'AttnDownBlock2D',
],
'up_block_types': [
'AttnUpBlock2D',
'ResnetUpsampleBlock2D',
],
'resnet_time_scale_shift': 'scale_shift',
'upsample_type': 'resnet',
'downsample_type': 'resnet',
}
__lowerCAmelCase = {
'sample_size': 64,
'in_channels': 3,
'out_channels': 3,
'layers_per_block': 3,
'num_class_embeds': 1_000,
'block_out_channels': [192, 192 * 2, 192 * 3, 192 * 4],
'attention_head_dim': 64,
'down_block_types': [
'ResnetDownsampleBlock2D',
'AttnDownBlock2D',
'AttnDownBlock2D',
'AttnDownBlock2D',
],
'up_block_types': [
'AttnUpBlock2D',
'AttnUpBlock2D',
'AttnUpBlock2D',
'ResnetUpsampleBlock2D',
],
'resnet_time_scale_shift': 'scale_shift',
'upsample_type': 'resnet',
'downsample_type': 'resnet',
}
__lowerCAmelCase = {
'sample_size': 256,
'in_channels': 3,
'out_channels': 3,
'layers_per_block': 2,
'num_class_embeds': None,
'block_out_channels': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
'attention_head_dim': 64,
'down_block_types': [
'ResnetDownsampleBlock2D',
'ResnetDownsampleBlock2D',
'ResnetDownsampleBlock2D',
'AttnDownBlock2D',
'AttnDownBlock2D',
'AttnDownBlock2D',
],
'up_block_types': [
'AttnUpBlock2D',
'AttnUpBlock2D',
'AttnUpBlock2D',
'ResnetUpsampleBlock2D',
'ResnetUpsampleBlock2D',
'ResnetUpsampleBlock2D',
],
'resnet_time_scale_shift': 'default',
'upsample_type': 'resnet',
'downsample_type': 'resnet',
}
__lowerCAmelCase = {
'num_train_timesteps': 40,
'sigma_min': 0.002,
'sigma_max': 80.0,
}
__lowerCAmelCase = {
'num_train_timesteps': 201,
'sigma_min': 0.002,
'sigma_max': 80.0,
}
__lowerCAmelCase = {
'num_train_timesteps': 151,
'sigma_min': 0.002,
'sigma_max': 80.0,
}
def _UpperCAmelCase ( __A : Tuple ):
if isinstance(__A , __A ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError('''boolean value expected''' )
def _UpperCAmelCase ( __A : str , __A : Tuple , __A : Optional[int] , __A : int , __A : Optional[Any]=False ):
a_ : str = checkpoint[f'{old_prefix}.in_layers.0.weight']
a_ : Dict = checkpoint[f'{old_prefix}.in_layers.0.bias']
a_ : Optional[Any] = checkpoint[f'{old_prefix}.in_layers.2.weight']
a_ : Any = checkpoint[f'{old_prefix}.in_layers.2.bias']
a_ : Any = checkpoint[f'{old_prefix}.emb_layers.1.weight']
a_ : int = checkpoint[f'{old_prefix}.emb_layers.1.bias']
a_ : Union[str, Any] = checkpoint[f'{old_prefix}.out_layers.0.weight']
a_ : Optional[Any] = checkpoint[f'{old_prefix}.out_layers.0.bias']
a_ : Optional[Any] = checkpoint[f'{old_prefix}.out_layers.3.weight']
a_ : List[Any] = checkpoint[f'{old_prefix}.out_layers.3.bias']
if has_skip:
a_ : int = checkpoint[f'{old_prefix}.skip_connection.weight']
a_ : List[Any] = checkpoint[f'{old_prefix}.skip_connection.bias']
return new_checkpoint
def _UpperCAmelCase ( __A : int , __A : int , __A : Union[str, Any] , __A : Optional[int] , __A : List[str]=None ):
a_ , a_ , a_ : Any = checkpoint[f'{old_prefix}.qkv.weight'].chunk(3 , dim=0 )
a_ , a_ , a_ : List[Any] = checkpoint[f'{old_prefix}.qkv.bias'].chunk(3 , dim=0 )
a_ : List[str] = checkpoint[f'{old_prefix}.norm.weight']
a_ : Optional[Any] = checkpoint[f'{old_prefix}.norm.bias']
a_ : Any = weight_q.squeeze(-1 ).squeeze(-1 )
a_ : int = bias_q.squeeze(-1 ).squeeze(-1 )
a_ : Any = weight_k.squeeze(-1 ).squeeze(-1 )
a_ : Optional[int] = bias_k.squeeze(-1 ).squeeze(-1 )
a_ : str = weight_v.squeeze(-1 ).squeeze(-1 )
a_ : Optional[Any] = bias_v.squeeze(-1 ).squeeze(-1 )
a_ : Union[str, Any] = (
checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1 ).squeeze(-1 )
)
a_ : Any = checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def _UpperCAmelCase ( __A : str , __A : str ):
a_ : int = torch.load(__A , map_location='''cpu''' )
a_ : Union[str, Any] = {}
a_ : int = checkpoint['''time_embed.0.weight''']
a_ : Tuple = checkpoint['''time_embed.0.bias''']
a_ : List[Any] = checkpoint['''time_embed.2.weight''']
a_ : Union[str, Any] = checkpoint['''time_embed.2.bias''']
if unet_config["num_class_embeds"] is not None:
a_ : List[Any] = checkpoint['''label_emb.weight''']
a_ : Optional[int] = checkpoint['''input_blocks.0.0.weight''']
a_ : int = checkpoint['''input_blocks.0.0.bias''']
a_ : Optional[int] = unet_config['''down_block_types''']
a_ : Union[str, Any] = unet_config['''layers_per_block''']
a_ : Optional[Any] = unet_config['''attention_head_dim''']
a_ : str = unet_config['''block_out_channels''']
a_ : Any = 1
a_ : Union[str, Any] = channels_list[0]
for i, layer_type in enumerate(__A ):
a_ : Any = channels_list[i]
a_ : Tuple = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(__A ):
a_ : Union[str, Any] = f'down_blocks.{i}.resnets.{j}'
a_ : int = f'input_blocks.{current_layer}.0'
a_ : List[Any] = True if j == 0 and downsample_block_has_skip else False
a_ : List[str] = convert_resnet(__A , __A , __A , __A , has_skip=__A )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(__A ):
a_ : Union[str, Any] = f'down_blocks.{i}.resnets.{j}'
a_ : Optional[int] = f'input_blocks.{current_layer}.0'
a_ : Optional[int] = True if j == 0 and downsample_block_has_skip else False
a_ : Any = convert_resnet(__A , __A , __A , __A , has_skip=__A )
a_ : Optional[int] = f'down_blocks.{i}.attentions.{j}'
a_ : List[Any] = f'input_blocks.{current_layer}.1'
a_ : int = convert_attention(
__A , __A , __A , __A , __A )
current_layer += 1
if i != len(__A ) - 1:
a_ : Dict = f'down_blocks.{i}.downsamplers.0'
a_ : Optional[int] = f'input_blocks.{current_layer}.0'
a_ : Union[str, Any] = convert_resnet(__A , __A , __A , __A )
current_layer += 1
a_ : Any = current_channels
# hardcoded the mid-block for now
a_ : Tuple = '''mid_block.resnets.0'''
a_ : int = '''middle_block.0'''
a_ : Tuple = convert_resnet(__A , __A , __A , __A )
a_ : List[Any] = '''mid_block.attentions.0'''
a_ : Optional[int] = '''middle_block.1'''
a_ : Optional[int] = convert_attention(__A , __A , __A , __A , __A )
a_ : Any = '''mid_block.resnets.1'''
a_ : Tuple = '''middle_block.2'''
a_ : Tuple = convert_resnet(__A , __A , __A , __A )
a_ : str = 0
a_ : Optional[int] = unet_config['''up_block_types''']
for i, layer_type in enumerate(__A ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
a_ : Tuple = f'up_blocks.{i}.resnets.{j}'
a_ : Optional[int] = f'output_blocks.{current_layer}.0'
a_ : Optional[Any] = convert_resnet(__A , __A , __A , __A , has_skip=__A )
current_layer += 1
if i != len(__A ) - 1:
a_ : int = f'up_blocks.{i}.upsamplers.0'
a_ : Union[str, Any] = f'output_blocks.{current_layer-1}.1'
a_ : int = convert_resnet(__A , __A , __A , __A )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
a_ : Optional[Any] = f'up_blocks.{i}.resnets.{j}'
a_ : str = f'output_blocks.{current_layer}.0'
a_ : List[Any] = convert_resnet(__A , __A , __A , __A , has_skip=__A )
a_ : List[Any] = f'up_blocks.{i}.attentions.{j}'
a_ : List[str] = f'output_blocks.{current_layer}.1'
a_ : str = convert_attention(
__A , __A , __A , __A , __A )
current_layer += 1
if i != len(__A ) - 1:
a_ : Dict = f'up_blocks.{i}.upsamplers.0'
a_ : Any = f'output_blocks.{current_layer-1}.2'
a_ : int = convert_resnet(__A , __A , __A , __A )
a_ : Dict = checkpoint['''out.0.weight''']
a_ : int = checkpoint['''out.0.bias''']
a_ : int = checkpoint['''out.2.weight''']
a_ : str = checkpoint['''out.2.bias''']
return new_checkpoint
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
parser.add_argument(
'--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
)
parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = strabool(args.class_cond)
__lowerCAmelCase = os.path.basename(args.unet_path)
print(F"""Checkpoint: {ckpt_name}""")
# Get U-Net config
if "imagenet64" in ckpt_name:
__lowerCAmelCase = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__lowerCAmelCase = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
__lowerCAmelCase = TEST_UNET_CONFIG
else:
raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")
if not args.class_cond:
__lowerCAmelCase = None
__lowerCAmelCase = con_pt_to_diffuser(args.unet_path, unet_config)
__lowerCAmelCase = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
__lowerCAmelCase = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
__lowerCAmelCase = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__lowerCAmelCase = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")
__lowerCAmelCase = CMStochasticIterativeScheduler(**scheduler_config)
__lowerCAmelCase = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 466
| 1
|
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( __A):
'''simple docstring'''
UpperCamelCase__ : Any = """ClapFeatureExtractor"""
UpperCamelCase__ : Any = ("""RobertaTokenizer""", """RobertaTokenizerFast""")
def __init__( self , a_ , a_ ):
super().__init__(UpperCamelCase__ , UpperCamelCase__ )
def __call__( self , a_=None , a_=None , a_=None , **a_ ):
a__ = kwargs.pop("""sampling_rate""" , UpperCamelCase__ )
if text is None and audios is None:
raise ValueError("""You have to specify either text or audios. Both cannot be none.""" )
if text is not None:
a__ = self.tokenizer(UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ )
if audios is not None:
a__ = self.feature_extractor(
UpperCamelCase__ , sampling_rate=UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ )
if text is not None and audios is not None:
a__ = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCamelCase__ ) , tensor_type=UpperCamelCase__ )
def _a ( self , *a_ , **a_ ):
return self.tokenizer.batch_decode(*UpperCamelCase__ , **UpperCamelCase__ )
def _a ( self , *a_ , **a_ ):
return self.tokenizer.decode(*UpperCamelCase__ , **UpperCamelCase__ )
@property
def _a ( self ):
a__ = self.tokenizer.model_input_names
a__ = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
| 709
|
import os
from datetime import datetime as dt
from github import Github
UpperCAmelCase = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def A_ ( ):
"""simple docstring"""
a__ = Github(os.environ["""GITHUB_TOKEN"""] )
a__ = g.get_repo("""huggingface/diffusers""" )
a__ = repo.get_issues(state="""open""" )
for issue in open_issues:
a__ = sorted(issue.get_comments() , key=lambda __a : i.created_at , reverse=__a )
a__ = comments[0] if len(__a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state="""closed""" )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state="""open""" )
issue.remove_from_labels("""stale""" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
issue.add_to_labels("""stale""" )
if __name__ == "__main__":
main()
| 351
| 0
|
"""simple docstring"""
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
__A : Any = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt''',
'''tiny''': '''https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt''',
'''base.en''': '''https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt''',
'''base''': '''https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt''',
'''small.en''': '''https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt''',
'''small''': '''https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt''',
'''medium.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt''',
'''medium''': '''https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt''',
'''large''': '''https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt''',
'''large-v2''': '''https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt''',
}
def lowercase ( __snake_case : str ):
lowercase_ : List[Any] = ['''layers''', '''blocks''']
for k in ignore_keys:
state_dict.pop(__snake_case , __snake_case )
__A : List[str] = {
'''blocks''': '''layers''',
'''mlp.0''': '''fc1''',
'''mlp.2''': '''fc2''',
'''mlp_ln''': '''final_layer_norm''',
'''.attn.query''': '''.self_attn.q_proj''',
'''.attn.key''': '''.self_attn.k_proj''',
'''.attn.value''': '''.self_attn.v_proj''',
'''.attn_ln''': '''.self_attn_layer_norm''',
'''.attn.out''': '''.self_attn.out_proj''',
'''.cross_attn.query''': '''.encoder_attn.q_proj''',
'''.cross_attn.key''': '''.encoder_attn.k_proj''',
'''.cross_attn.value''': '''.encoder_attn.v_proj''',
'''.cross_attn_ln''': '''.encoder_attn_layer_norm''',
'''.cross_attn.out''': '''.encoder_attn.out_proj''',
'''decoder.ln.''': '''decoder.layer_norm.''',
'''encoder.ln.''': '''encoder.layer_norm.''',
'''token_embedding''': '''embed_tokens''',
'''encoder.positional_embedding''': '''encoder.embed_positions.weight''',
'''decoder.positional_embedding''': '''decoder.embed_positions.weight''',
'''ln_post''': '''layer_norm''',
}
def lowercase ( __snake_case : Optional[int] ):
lowercase_ : List[str] = list(s_dict.keys() )
for key in keys:
lowercase_ : Any = key
for k, v in WHISPER_MAPPING.items():
if k in key:
lowercase_ : Optional[Any] = new_key.replace(__snake_case , __snake_case )
print(F'''{key} -> {new_key}''' )
lowercase_ : Any = s_dict.pop(__snake_case )
return s_dict
def lowercase ( __snake_case : List[Any] ):
lowercase_ , lowercase_ : List[Any] = emb.weight.shape
lowercase_ : int = nn.Linear(__snake_case , __snake_case , bias=__snake_case )
lowercase_ : List[Any] = emb.weight.data
return lin_layer
def lowercase ( __snake_case : str , __snake_case : str ):
os.makedirs(__snake_case , exist_ok=__snake_case )
lowercase_ : Union[str, Any] = os.path.basename(__snake_case )
lowercase_ : Any = url.split('''/''' )[-2]
lowercase_ : Tuple = os.path.join(__snake_case , __snake_case )
if os.path.exists(__snake_case ) and not os.path.isfile(__snake_case ):
raise RuntimeError(F'''{download_target} exists and is not a regular file''' )
if os.path.isfile(__snake_case ):
lowercase_ : List[Any] = open(__snake_case , '''rb''' ).read()
if hashlib.shaaaa(__snake_case ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F'''{download_target} exists, but the SHA256 checksum does not match; re-downloading the file''' )
with urllib.request.urlopen(__snake_case ) as source, open(__snake_case , '''wb''' ) as output:
with tqdm(
total=int(source.info().get('''Content-Length''' ) ) , ncols=8_0 , unit='''iB''' , unit_scale=__snake_case , unit_divisor=1_0_2_4 ) as loop:
while True:
lowercase_ : Optional[Any] = source.read(8_1_9_2 )
if not buffer:
break
output.write(__snake_case )
loop.update(len(__snake_case ) )
lowercase_ : Tuple = open(__snake_case , '''rb''' ).read()
if hashlib.shaaaa(__snake_case ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'''Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.''' )
return model_bytes
def lowercase ( __snake_case : List[str] , __snake_case : List[str] ):
if ".pt" not in checkpoint_path:
lowercase_ : Union[str, Any] = _download(_MODELS[checkpoint_path] )
else:
lowercase_ : Union[str, Any] = torch.load(__snake_case , map_location='''cpu''' )
lowercase_ : str = original_checkpoint['''dims''']
lowercase_ : List[Any] = original_checkpoint['''model_state_dict''']
lowercase_ : Dict = state_dict['''decoder.token_embedding.weight''']
remove_ignore_keys_(__snake_case )
rename_keys(__snake_case )
lowercase_ : str = True
lowercase_ : Tuple = state_dict['''decoder.layers.0.fc1.weight'''].shape[0]
lowercase_ : List[Any] = WhisperConfig(
vocab_size=dimensions['''n_vocab'''] , encoder_ffn_dim=__snake_case , decoder_ffn_dim=__snake_case , num_mel_bins=dimensions['''n_mels'''] , d_model=dimensions['''n_audio_state'''] , max_target_positions=dimensions['''n_text_ctx'''] , encoder_layers=dimensions['''n_audio_layer'''] , encoder_attention_heads=dimensions['''n_audio_head'''] , decoder_layers=dimensions['''n_text_layer'''] , decoder_attention_heads=dimensions['''n_text_state'''] , max_source_positions=dimensions['''n_audio_ctx'''] , )
lowercase_ : Optional[int] = WhisperForConditionalGeneration(__snake_case )
lowercase_ , lowercase_ : List[Any] = model.model.load_state_dict(__snake_case , strict=__snake_case )
if len(__snake_case ) > 0 and not set(__snake_case ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
F''' but all the following weights are missing {missing}''' )
if tie_embeds:
lowercase_ : List[str] = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
lowercase_ : Dict = proj_out_weights
model.save_pretrained(__snake_case )
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
# # Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Patht to the downloaded checkpoints''')
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
__A : List[Any] = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 231
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert import AlbertTokenizer
else:
__A : Optional[int] = None
__A : str = logging.get_logger(__name__)
__A : str = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__A : str = {
'''vocab_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
},
'''tokenizer_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
},
}
__A : Tuple = {
'''albert-base-v1''': 512,
'''albert-large-v1''': 512,
'''albert-xlarge-v1''': 512,
'''albert-xxlarge-v1''': 512,
'''albert-base-v2''': 512,
'''albert-large-v2''': 512,
'''albert-xlarge-v2''': 512,
'''albert-xxlarge-v2''': 512,
}
__A : str = '''▁'''
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Tuple = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ : Any = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ : str = AlbertTokenizer
def __init__( self : Optional[int] , A : Dict=None , A : Tuple=None , A : int=True , A : List[str]=True , A : int=False , A : List[Any]="[CLS]" , A : Dict="[SEP]" , A : Tuple="<unk>" , A : Tuple="[SEP]" , A : Optional[Any]="<pad>" , A : List[str]="[CLS]" , A : Optional[int]="[MASK]" , **A : int , ) -> Optional[Any]:
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
lowercase_ : Union[str, Any] = (
AddedToken(A , lstrip=A , rstrip=A , normalized=A )
if isinstance(A , A )
else mask_token
)
super().__init__(
A , tokenizer_file=A , do_lower_case=A , remove_space=A , keep_accents=A , bos_token=A , eos_token=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , **A , )
lowercase_ : int = do_lower_case
lowercase_ : str = remove_space
lowercase_ : Tuple = keep_accents
lowercase_ : Optional[Any] = vocab_file
lowercase_ : List[str] = False if not self.vocab_file else True
def A ( self : str , A : List[int] , A : Optional[List[int]] = None ) -> List[int]:
lowercase_ : Optional[Any] = [self.sep_token_id]
lowercase_ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def A ( self : List[Any] , A : List[int] , A : Optional[List[int]] = None ) -> List[int]:
lowercase_ : Tuple = [self.sep_token_id]
lowercase_ : List[Any] = [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 A ( self : List[str] , A : str , A : Optional[str] = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(A ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowercase_ : List[Any] = 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 ):
copyfile(self.vocab_file , A )
return (out_vocab_file,)
| 231
| 1
|
"""simple docstring"""
def _lowerCAmelCase ( ):
'''simple docstring'''
for n in range(1 , 1_0_0_0_0_0_0 ):
yield n * (n + 1) // 2
def _lowerCAmelCase ( __lowerCamelCase:Dict ):
'''simple docstring'''
__magic_name__ = 1
__magic_name__ = 2
while i * i <= n:
__magic_name__ = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def _lowerCAmelCase ( ):
'''simple docstring'''
return next(i for i in triangle_number_generator() if count_divisors(__lowerCamelCase ) > 5_0_0 )
if __name__ == "__main__":
print(solution())
| 468
|
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, 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 .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class A_ ( snake_case_ ):
UpperCAmelCase__ = 42
class A_ ( snake_case_ , snake_case_ ):
UpperCAmelCase__ = True
@register_to_config
def __init__( self : Any , __lowerCamelCase : int = 3 , __lowerCamelCase : int = 3 , __lowerCamelCase : Tuple[str] = ("DownEncoderBlock2D",) , __lowerCamelCase : Tuple[str] = ("UpDecoderBlock2D",) , __lowerCamelCase : Tuple[int] = (6_4,) , __lowerCamelCase : int = 1 , __lowerCamelCase : str = "silu" , __lowerCamelCase : int = 4 , __lowerCamelCase : int = 3_2 , __lowerCamelCase : int = 3_2 , __lowerCamelCase : float = 0.1_8215 , ) -> Any:
super().__init__()
# pass init params to Encoder
__magic_name__ = Encoder(
in_channels=__lowerCamelCase , out_channels=__lowerCamelCase , down_block_types=__lowerCamelCase , block_out_channels=__lowerCamelCase , layers_per_block=__lowerCamelCase , act_fn=__lowerCamelCase , norm_num_groups=__lowerCamelCase , double_z=__lowerCamelCase , )
# pass init params to Decoder
__magic_name__ = Decoder(
in_channels=__lowerCamelCase , out_channels=__lowerCamelCase , up_block_types=__lowerCamelCase , block_out_channels=__lowerCamelCase , layers_per_block=__lowerCamelCase , norm_num_groups=__lowerCamelCase , act_fn=__lowerCamelCase , )
__magic_name__ = nn.Convad(2 * latent_channels , 2 * latent_channels , 1 )
__magic_name__ = nn.Convad(__lowerCamelCase , __lowerCamelCase , 1 )
__magic_name__ = False
__magic_name__ = False
# only relevant if vae tiling is enabled
__magic_name__ = self.config.sample_size
__magic_name__ = (
self.config.sample_size[0]
if isinstance(self.config.sample_size , (list, tuple) )
else self.config.sample_size
)
__magic_name__ = int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
__magic_name__ = 0.25
def _snake_case ( self : List[str] , __lowerCamelCase : Tuple , __lowerCamelCase : Dict=False ) -> Optional[int]:
if isinstance(__lowerCamelCase , (Encoder, Decoder) ):
__magic_name__ = value
def _snake_case ( self : Dict , __lowerCamelCase : bool = True ) -> int:
__magic_name__ = use_tiling
def _snake_case ( self : Dict ) -> Optional[int]:
self.enable_tiling(__lowerCamelCase )
def _snake_case ( self : int ) -> str:
__magic_name__ = True
def _snake_case ( self : Optional[Any] ) -> Tuple:
__magic_name__ = False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def _snake_case ( self : Optional[int] ) -> Dict[str, AttentionProcessor]:
__magic_name__ = {}
def fn_recursive_add_processors(__lowerCamelCase : str , __lowerCamelCase : torch.nn.Module , __lowerCamelCase : Dict[str, AttentionProcessor] ):
if hasattr(__lowerCamelCase , "set_processor" ):
__magic_name__ = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f'''{name}.{sub_name}''' , __lowerCamelCase , __lowerCamelCase )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return processors
def _snake_case ( self : Dict , __lowerCamelCase : Union[AttentionProcessor, Dict[str, AttentionProcessor]] ) -> int:
__magic_name__ = len(self.attn_processors.keys() )
if isinstance(__lowerCamelCase , __lowerCamelCase ) and len(__lowerCamelCase ) != count:
raise ValueError(
f'''A dict of processors was passed, but the number of processors {len(__lowerCamelCase )} does not match the'''
f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''' )
def fn_recursive_attn_processor(__lowerCamelCase : str , __lowerCamelCase : torch.nn.Module , __lowerCamelCase : List[str] ):
if hasattr(__lowerCamelCase , "set_processor" ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
module.set_processor(__lowerCamelCase )
else:
module.set_processor(processor.pop(f'''{name}.processor''' ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f'''{name}.{sub_name}''' , __lowerCamelCase , __lowerCamelCase )
for name, module in self.named_children():
fn_recursive_attn_processor(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def _snake_case ( self : List[Any] ) -> Optional[Any]:
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def _snake_case ( self : str , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : bool = True ) -> AutoencoderKLOutput:
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(__lowerCamelCase , return_dict=__lowerCamelCase )
if self.use_slicing and x.shape[0] > 1:
__magic_name__ = [self.encoder(__lowerCamelCase ) for x_slice in x.split(1 )]
__magic_name__ = torch.cat(__lowerCamelCase )
else:
__magic_name__ = self.encoder(__lowerCamelCase )
__magic_name__ = self.quant_conv(__lowerCamelCase )
__magic_name__ = DiagonalGaussianDistribution(__lowerCamelCase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__lowerCamelCase )
def _snake_case ( self : int , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : bool = True ) -> Union[DecoderOutput, torch.FloatTensor]:
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.tiled_decode(__lowerCamelCase , return_dict=__lowerCamelCase )
__magic_name__ = self.post_quant_conv(__lowerCamelCase )
__magic_name__ = self.decoder(__lowerCamelCase )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__lowerCamelCase )
@apply_forward_hook
def _snake_case ( self : Optional[Any] , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : bool = True ) -> Union[DecoderOutput, torch.FloatTensor]:
if self.use_slicing and z.shape[0] > 1:
__magic_name__ = [self._decode(__lowerCamelCase ).sample for z_slice in z.split(1 )]
__magic_name__ = torch.cat(__lowerCamelCase )
else:
__magic_name__ = self._decode(__lowerCamelCase ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=__lowerCamelCase )
def _snake_case ( self : Optional[int] , __lowerCamelCase : str , __lowerCamelCase : List[str] , __lowerCamelCase : Dict ) -> Optional[int]:
__magic_name__ = min(a.shape[2] , b.shape[2] , __lowerCamelCase )
for y in range(__lowerCamelCase ):
__magic_name__ = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def _snake_case ( self : List[str] , __lowerCamelCase : Any , __lowerCamelCase : Tuple , __lowerCamelCase : Tuple ) -> Optional[int]:
__magic_name__ = min(a.shape[3] , b.shape[3] , __lowerCamelCase )
for x in range(__lowerCamelCase ):
__magic_name__ = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def _snake_case ( self : Optional[int] , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : bool = True ) -> AutoencoderKLOutput:
__magic_name__ = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
__magic_name__ = int(self.tile_latent_min_size * self.tile_overlap_factor )
__magic_name__ = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
__magic_name__ = []
for i in range(0 , x.shape[2] , __lowerCamelCase ):
__magic_name__ = []
for j in range(0 , x.shape[3] , __lowerCamelCase ):
__magic_name__ = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
__magic_name__ = self.encoder(__lowerCamelCase )
__magic_name__ = self.quant_conv(__lowerCamelCase )
row.append(__lowerCamelCase )
rows.append(__lowerCamelCase )
__magic_name__ = []
for i, row in enumerate(__lowerCamelCase ):
__magic_name__ = []
for j, tile in enumerate(__lowerCamelCase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
__magic_name__ = self.blend_v(rows[i - 1][j] , __lowerCamelCase , __lowerCamelCase )
if j > 0:
__magic_name__ = self.blend_h(row[j - 1] , __lowerCamelCase , __lowerCamelCase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__lowerCamelCase , dim=3 ) )
__magic_name__ = torch.cat(__lowerCamelCase , dim=2 )
__magic_name__ = DiagonalGaussianDistribution(__lowerCamelCase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__lowerCamelCase )
def _snake_case ( self : str , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : bool = True ) -> Union[DecoderOutput, torch.FloatTensor]:
__magic_name__ = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
__magic_name__ = int(self.tile_sample_min_size * self.tile_overlap_factor )
__magic_name__ = self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
__magic_name__ = []
for i in range(0 , z.shape[2] , __lowerCamelCase ):
__magic_name__ = []
for j in range(0 , z.shape[3] , __lowerCamelCase ):
__magic_name__ = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
__magic_name__ = self.post_quant_conv(__lowerCamelCase )
__magic_name__ = self.decoder(__lowerCamelCase )
row.append(__lowerCamelCase )
rows.append(__lowerCamelCase )
__magic_name__ = []
for i, row in enumerate(__lowerCamelCase ):
__magic_name__ = []
for j, tile in enumerate(__lowerCamelCase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
__magic_name__ = self.blend_v(rows[i - 1][j] , __lowerCamelCase , __lowerCamelCase )
if j > 0:
__magic_name__ = self.blend_h(row[j - 1] , __lowerCamelCase , __lowerCamelCase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__lowerCamelCase , dim=3 ) )
__magic_name__ = torch.cat(__lowerCamelCase , dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__lowerCamelCase )
def _snake_case ( self : List[str] , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : bool = False , __lowerCamelCase : bool = True , __lowerCamelCase : Optional[torch.Generator] = None , ) -> Union[DecoderOutput, torch.FloatTensor]:
__magic_name__ = sample
__magic_name__ = self.encode(__lowerCamelCase ).latent_dist
if sample_posterior:
__magic_name__ = posterior.sample(generator=__lowerCamelCase )
else:
__magic_name__ = posterior.mode()
__magic_name__ = self.decode(__lowerCamelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=__lowerCamelCase )
| 468
| 1
|
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _snake_case ( snake_case__ : BertModel , snake_case__ : str , snake_case__ : str ):
A = ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')
A = (
('layer.', 'layer_'),
('word_embeddings.weight', 'word_embeddings'),
('position_embeddings.weight', 'position_embeddings'),
('token_type_embeddings.weight', 'token_type_embeddings'),
('.', '/'),
('LayerNorm/weight', 'LayerNorm/gamma'),
('LayerNorm/bias', 'LayerNorm/beta'),
('weight', 'kernel'),
)
if not os.path.isdir(snake_case__ ):
os.makedirs(snake_case__ )
A = model.state_dict()
def to_tf_var_name(snake_case__ : str ):
for patt, repl in iter(snake_case__ ):
A = name.replace(snake_case__ , snake_case__ )
return F'bert/{name}'
def create_tf_var(snake_case__ : np.ndarray , snake_case__ : str , snake_case__ : tf.Session ):
A = tf.dtypes.as_dtype(tensor.dtype )
A = tf.get_variable(dtype=snake_case__ , shape=tensor.shape , name=snake_case__ , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(snake_case__ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
A = to_tf_var_name(snake_case__ )
A = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
A = torch_tensor.T
A = create_tf_var(tensor=snake_case__ , name=snake_case__ , session=snake_case__ )
tf.keras.backend.set_value(snake_case__ , snake_case__ )
A = session.run(snake_case__ )
print(F'Successfully created {tf_name}: {np.allclose(snake_case__ , snake_case__ )}' )
A = tf.train.Saver(tf.trainable_variables() )
saver.save(snake_case__ , os.path.join(snake_case__ , model_name.replace('-' , '_' ) + '.ckpt' ) )
def _snake_case ( snake_case__ : Tuple=None ):
A = argparse.ArgumentParser()
parser.add_argument('--model_name' , type=snake_case__ , required=snake_case__ , help='model name e.g. bert-base-uncased' )
parser.add_argument(
'--cache_dir' , type=snake_case__ , default=snake_case__ , required=snake_case__ , help='Directory containing pytorch model' )
parser.add_argument('--pytorch_model_path' , type=snake_case__ , required=snake_case__ , help='/path/to/<pytorch-model-name>.bin' )
parser.add_argument('--tf_cache_dir' , type=snake_case__ , required=snake_case__ , help='Directory in which to save tensorflow model' )
A = parser.parse_args(snake_case__ )
A = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=snake_case__ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 91
|
'''simple docstring'''
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 snake_case :
"""simple docstring"""
@staticmethod
def a__ ( *_lowercase, **_lowercase ) -> Tuple:
pass
@is_pipeline_test
@require_vision
@require_torch
class snake_case ( unittest.TestCase ):
"""simple docstring"""
_a = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def a__ ( self, _lowercase, _lowercase, _lowercase ) -> Tuple:
SCREAMING_SNAKE_CASE_ = pipeline(
'zero-shot-object-detection', model='hf-internal-testing/tiny-random-owlvit-object-detection' )
SCREAMING_SNAKE_CASE_ = [
{
'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
'candidate_labels': ['cat', 'remote', 'couch'],
}
]
return object_detector, examples
def a__ ( self, _lowercase, _lowercase ) -> str:
SCREAMING_SNAKE_CASE_ = object_detector(examples[0], threshold=0.0 )
SCREAMING_SNAKE_CASE_ = len(_lowercase )
self.assertGreater(_lowercase, 0 )
self.assertEqual(
_lowercase, [
{
'score': ANY(_lowercase ),
'label': ANY(_lowercase ),
'box': {'xmin': ANY(_lowercase ), 'ymin': ANY(_lowercase ), 'xmax': ANY(_lowercase ), 'ymax': ANY(_lowercase )},
}
for i in range(_lowercase )
], )
@require_tf
@unittest.skip('Zero Shot Object Detection not implemented in TF' )
def a__ ( self ) -> Union[str, Any]:
pass
@require_torch
def a__ ( self ) -> str:
SCREAMING_SNAKE_CASE_ = pipeline(
'zero-shot-object-detection', model='hf-internal-testing/tiny-random-owlvit-object-detection' )
SCREAMING_SNAKE_CASE_ = object_detector(
'./tests/fixtures/tests_samples/COCO/000000039769.png', candidate_labels=['cat', 'remote', 'couch'], threshold=0.64, )
self.assertEqual(
nested_simplify(_lowercase, decimals=4 ), [
{'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
{'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
{'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
{'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
{'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
{'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
{'score': 0.6_456, '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': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
], )
SCREAMING_SNAKE_CASE_ = object_detector(
[
{
'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
'candidate_labels': ['cat', 'remote', 'couch'],
}
], threshold=0.64, )
self.assertEqual(
nested_simplify(_lowercase, decimals=4 ), [
[
{'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
{'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
{'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
{'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
{'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
{'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
{'score': 0.6_456, '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': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
]
], )
@require_torch
@slow
def a__ ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = pipeline('zero-shot-object-detection' )
SCREAMING_SNAKE_CASE_ = object_detector(
'http://images.cocodataset.org/val2017/000000039769.jpg', candidate_labels=['cat', 'remote', 'couch'], )
self.assertEqual(
nested_simplify(_lowercase, decimals=4 ), [
{'score': 0.2_868, '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': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
{'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
{'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
], )
SCREAMING_SNAKE_CASE_ = 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(_lowercase, decimals=4 ), [
[
{'score': 0.2_868, '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': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
{'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
{'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
],
[
{'score': 0.2_868, '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': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
{'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
{'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
],
], )
@require_tf
@unittest.skip('Zero Shot Object Detection not implemented in TF' )
def a__ ( self ) -> Optional[Any]:
pass
@require_torch
@slow
def a__ ( self ) -> Any:
SCREAMING_SNAKE_CASE_ = 0.2
SCREAMING_SNAKE_CASE_ = pipeline('zero-shot-object-detection' )
SCREAMING_SNAKE_CASE_ = object_detector(
'http://images.cocodataset.org/val2017/000000039769.jpg', candidate_labels=['cat', 'remote', 'couch'], threshold=_lowercase, )
self.assertEqual(
nested_simplify(_lowercase, decimals=4 ), [
{'score': 0.2_868, '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': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
], )
@require_torch
@slow
def a__ ( self ) -> int:
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = pipeline('zero-shot-object-detection' )
SCREAMING_SNAKE_CASE_ = object_detector(
'http://images.cocodataset.org/val2017/000000039769.jpg', candidate_labels=['cat', 'remote', 'couch'], top_k=_lowercase, )
self.assertEqual(
nested_simplify(_lowercase, decimals=4 ), [
{'score': 0.2_868, '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}},
], )
| 294
| 0
|
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class a ( unittest.TestCase ):
@slow
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : Tuple = TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )
_UpperCAmelCase : List[str] = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
_UpperCAmelCase : Tuple = model(A_ )["last_hidden_state"]
_UpperCAmelCase : str = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , A_ )
# compare the actual values for a slice.
_UpperCAmelCase : int = tf.convert_to_tensor(
[[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 467
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class a ( UpperCAmelCase ):
_lowercase = "megatron-bert"
def __init__( self , A_=29056 , A_=1024 , A_=24 , A_=16 , A_=4096 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=2 , A_=0.02 , A_=1e-12 , A_=0 , A_="absolute" , A_=True , **A_ , ):
'''simple docstring'''
super().__init__(pad_token_id=A_ , **A_ )
_UpperCAmelCase : Optional[Any] = vocab_size
_UpperCAmelCase : List[str] = hidden_size
_UpperCAmelCase : List[str] = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Any = hidden_act
_UpperCAmelCase : Optional[Any] = intermediate_size
_UpperCAmelCase : int = hidden_dropout_prob
_UpperCAmelCase : Optional[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Any = max_position_embeddings
_UpperCAmelCase : str = type_vocab_size
_UpperCAmelCase : Optional[int] = initializer_range
_UpperCAmelCase : Dict = layer_norm_eps
_UpperCAmelCase : Tuple = position_embedding_type
_UpperCAmelCase : Tuple = use_cache
| 467
| 1
|
"""simple docstring"""
from __future__ import annotations
import time
UpperCAmelCase = list[tuple[int, int]]
UpperCAmelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
UpperCAmelCase = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class UpperCAmelCase_ :
def __init__( self : List[str] , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : Node | None ) -> Union[str, Any]:
_UpperCamelCase = pos_x
_UpperCamelCase = pos_y
_UpperCamelCase = (pos_y, pos_x)
_UpperCamelCase = goal_x
_UpperCamelCase = goal_y
_UpperCamelCase = parent
class UpperCAmelCase_ :
def __init__( self : Union[str, Any] , __UpperCamelCase : tuple[int, int] , __UpperCamelCase : tuple[int, int] ) -> Any:
_UpperCamelCase = Node(start[1] , start[0] , goal[1] , goal[0] , __UpperCamelCase )
_UpperCamelCase = Node(goal[1] , goal[0] , goal[1] , goal[0] , __UpperCamelCase )
_UpperCamelCase = [self.start]
_UpperCamelCase = False
def _UpperCamelCase ( self : int ) -> Path | None:
while self.node_queue:
_UpperCamelCase = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
_UpperCamelCase = True
return self.retrace_path(__UpperCamelCase )
_UpperCamelCase = self.get_successors(__UpperCamelCase )
for node in successors:
self.node_queue.append(__UpperCamelCase )
if not self.reached:
return [self.start.pos]
return None
def _UpperCamelCase ( self : Dict , __UpperCamelCase : Node ) -> list[Node]:
_UpperCamelCase = []
for action in delta:
_UpperCamelCase = parent.pos_x + action[1]
_UpperCamelCase = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__UpperCamelCase ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(__UpperCamelCase , __UpperCamelCase , self.target.pos_y , self.target.pos_x , __UpperCamelCase ) )
return successors
def _UpperCamelCase ( self : Any , __UpperCamelCase : Node | None ) -> Path:
_UpperCamelCase = node
_UpperCamelCase = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
_UpperCamelCase = current_node.parent
path.reverse()
return path
class UpperCAmelCase_ :
def __init__( self : Optional[int] , __UpperCamelCase : str , __UpperCamelCase : Any ) -> Tuple:
_UpperCamelCase = BreadthFirstSearch(__UpperCamelCase , __UpperCamelCase )
_UpperCamelCase = BreadthFirstSearch(__UpperCamelCase , __UpperCamelCase )
_UpperCamelCase = False
def _UpperCamelCase ( self : Dict ) -> Path | None:
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
_UpperCamelCase = self.fwd_bfs.node_queue.pop(0 )
_UpperCamelCase = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
_UpperCamelCase = True
return self.retrace_bidirectional_path(
__UpperCamelCase , __UpperCamelCase )
_UpperCamelCase = current_bwd_node
_UpperCamelCase = current_fwd_node
_UpperCamelCase = {
self.fwd_bfs: self.fwd_bfs.get_successors(__UpperCamelCase ),
self.bwd_bfs: self.bwd_bfs.get_successors(__UpperCamelCase ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(__UpperCamelCase )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def _UpperCamelCase ( self : List[Any] , __UpperCamelCase : Node , __UpperCamelCase : Node ) -> Path:
_UpperCamelCase = self.fwd_bfs.retrace_path(__UpperCamelCase )
_UpperCamelCase = self.bwd_bfs.retrace_path(__UpperCamelCase )
bwd_path.pop()
bwd_path.reverse()
_UpperCamelCase = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
UpperCAmelCase = (0, 0)
UpperCAmelCase = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
UpperCAmelCase = time.time()
UpperCAmelCase = BreadthFirstSearch(init, goal)
UpperCAmelCase = bfs.search()
UpperCAmelCase = time.time() - start_bfs_time
print("""Unidirectional BFS computation time : """, bfs_time)
UpperCAmelCase = time.time()
UpperCAmelCase = BidirectionalBreadthFirstSearch(init, goal)
UpperCAmelCase = bd_bfs.search()
UpperCAmelCase = time.time() - start_bd_bfs_time
print("""Bidirectional BFS computation time : """, bd_bfs_time)
| 420
|
"""simple docstring"""
def lowercase ( a__ : float , a__ : int ) -> float:
if digit_amount > 0:
return round(number - int(a__ ) , a__ )
return number - int(a__ )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.3_45, 1))
print(decimal_isolate(35.3_45, 2))
print(decimal_isolate(35.3_45, 3))
print(decimal_isolate(-14.7_89, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.1_23, 1))
print(decimal_isolate(-14.1_23, 2))
print(decimal_isolate(-14.1_23, 3))
| 420
| 1
|
def lowerCamelCase__ ( ) -> List[Any]:
"""simple docstring"""
a__ :Any = []
a__ :Union[str, Any] = 1
while len(a ) < 1e6:
constant.append(str(a ) )
i += 1
a__ :str = "".join(a )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9_999] )
* int(constant[99_999] )
* int(constant[999_999] )
)
if __name__ == "__main__":
print(solution())
| 702
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
# See all SEW models at https://huggingface.co/models?filter=sew
}
class lowerCAmelCase_ ( _a):
lowerCamelCase_ = 'sew'
def __init__( self : Any , __A : str=32 , __A : Dict=768 , __A : int=12 , __A : Dict=12 , __A : Dict=3072 , __A : int=2 , __A : Union[str, Any]="gelu" , __A : Union[str, Any]=0.1 , __A : Optional[Any]=0.1 , __A : Union[str, Any]=0.1 , __A : str=0.0 , __A : Union[str, Any]=0.1 , __A : Optional[Any]=0.1 , __A : Tuple=0.02 , __A : Any=1E-5 , __A : Optional[Any]="group" , __A : str="gelu" , __A : Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , __A : Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , __A : int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , __A : List[str]=False , __A : Tuple=128 , __A : Tuple=16 , __A : Optional[int]=True , __A : Union[str, Any]=0.05 , __A : List[str]=10 , __A : Optional[int]=2 , __A : List[Any]=0.0 , __A : Optional[Any]=10 , __A : Tuple=0 , __A : Tuple="mean" , __A : Any=False , __A : str=False , __A : Dict=256 , __A : Union[str, Any]=0 , __A : Optional[int]=1 , __A : Optional[Any]=2 , **__A : Tuple , ) ->Tuple:
"""simple docstring"""
super().__init__(**__A , pad_token_id=__A , bos_token_id=__A , eos_token_id=__A )
a__ :List[Any] = hidden_size
a__ :List[Any] = feat_extract_norm
a__ :List[str] = feat_extract_activation
a__ :Any = list(__A )
a__ :Dict = list(__A )
a__ :Optional[int] = list(__A )
a__ :Any = conv_bias
a__ :List[str] = num_conv_pos_embeddings
a__ :str = num_conv_pos_embedding_groups
a__ :Optional[int] = len(self.conv_dim )
a__ :List[Any] = num_hidden_layers
a__ :str = intermediate_size
a__ :Dict = squeeze_factor
a__ :List[Any] = hidden_act
a__ :Optional[Any] = num_attention_heads
a__ :Tuple = hidden_dropout
a__ :Tuple = attention_dropout
a__ :List[Any] = activation_dropout
a__ :str = feat_proj_dropout
a__ :Any = final_dropout
a__ :Dict = layerdrop
a__ :List[str] = layer_norm_eps
a__ :Tuple = initializer_range
a__ :Dict = 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
a__ :int = apply_spec_augment
a__ :Optional[Any] = mask_time_prob
a__ :List[Any] = mask_time_length
a__ :Any = mask_time_min_masks
a__ :Any = mask_feature_prob
a__ :Dict = mask_feature_length
a__ :Union[str, Any] = mask_feature_min_masks
# ctc loss
a__ :Dict = ctc_loss_reduction
a__ :Any = ctc_zero_infinity
# sequence classification
a__ :Optional[int] = use_weighted_layer_sum
a__ :Tuple = classifier_proj_size
@property
def _snake_case ( self : Dict ) ->Union[str, Any]:
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 373
| 0
|
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class a_ ( _UpperCAmelCase , unittest.TestCase ):
a : Optional[Any] = 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
def _snake_case ( self : Union[str, Any] , __UpperCamelCase : Any=0 ) ->Dict:
'''simple docstring'''
_UpperCAmelCase = floats_tensor((1, 3, 1_28, 1_28) , rng=random.Random(__UpperCamelCase ) )
_UpperCAmelCase = np.random.RandomState(__UpperCamelCase )
_UpperCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""strength""": 0.7_5,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _snake_case ( self : Tuple ) ->Optional[int]:
'''simple docstring'''
_UpperCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = pipe(**__UpperCamelCase ).images
_UpperCAmelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 1_28, 1_28, 3)
_UpperCAmelCase = np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def _snake_case ( self : Tuple ) ->Optional[int]:
'''simple docstring'''
_UpperCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
_UpperCAmelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = pipe(**__UpperCamelCase ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_UpperCAmelCase = np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _snake_case ( self : Dict ) ->int:
'''simple docstring'''
_UpperCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
_UpperCAmelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
# warmup pass to apply optimizations
_UpperCAmelCase = pipe(**self.get_dummy_inputs() )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = pipe(**__UpperCamelCase ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_UpperCAmelCase = np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _snake_case ( self : Dict ) ->Optional[int]:
'''simple docstring'''
_UpperCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
_UpperCAmelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = pipe(**__UpperCamelCase ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_UpperCAmelCase = np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _snake_case ( self : str ) ->Tuple:
'''simple docstring'''
_UpperCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
_UpperCAmelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = pipe(**__UpperCamelCase ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_UpperCAmelCase = np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _snake_case ( self : Any ) ->int:
'''simple docstring'''
_UpperCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = pipe(**__UpperCamelCase ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_UpperCAmelCase = np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class a_ ( unittest.TestCase ):
@property
def _snake_case ( self : Any ) ->Optional[int]:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _snake_case ( self : Dict ) ->Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = ort.SessionOptions()
_UpperCAmelCase = False
return options
def _snake_case ( self : Tuple ) ->Any:
'''simple docstring'''
_UpperCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
_UpperCAmelCase = init_image.resize((7_68, 5_12) )
# using the PNDM scheduler by default
_UpperCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCAmelCase = """A fantasy landscape, trending on artstation"""
_UpperCAmelCase = np.random.RandomState(0 )
_UpperCAmelCase = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , strength=0.7_5 , guidance_scale=7.5 , num_inference_steps=10 , generator=__UpperCamelCase , output_type="""np""" , )
_UpperCAmelCase = output.images
_UpperCAmelCase = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 7_68, 3)
_UpperCAmelCase = np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def _snake_case ( self : Optional[Any] ) ->Optional[int]:
'''simple docstring'''
_UpperCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
_UpperCAmelCase = init_image.resize((7_68, 5_12) )
_UpperCAmelCase = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
_UpperCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=__UpperCamelCase , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCAmelCase = """A fantasy landscape, trending on artstation"""
_UpperCAmelCase = np.random.RandomState(0 )
_UpperCAmelCase = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , strength=0.7_5 , guidance_scale=7.5 , num_inference_steps=20 , generator=__UpperCamelCase , output_type="""np""" , )
_UpperCAmelCase = output.images
_UpperCAmelCase = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 7_68, 3)
_UpperCAmelCase = np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 555
|
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
a : int = 5_0_0_0_0_0
a , a : Union[str, Any] = os.path.split(__file__)
a : Dict = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def _UpperCamelCase ( _A , **_A ) -> Any:
"""simple docstring"""
_UpperCAmelCase = dataset.map(**_A )
@get_duration
def _UpperCamelCase ( _A , **_A ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = dataset.filter(**_A )
def _UpperCamelCase ( ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = {"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
_UpperCAmelCase = generate_example_dataset(
os.path.join(_A , """dataset.arrow""" ) , _A , num_examples=_A )
_UpperCAmelCase = transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=_A )
def tokenize(_A ):
return tokenizer(examples["""text"""] )
_UpperCAmelCase = map(_A )
_UpperCAmelCase = map(_A , batched=_A )
_UpperCAmelCase = map(_A , function=lambda _A : None , batched=_A )
with dataset.formatted_as(type="""numpy""" ):
_UpperCAmelCase = map(_A , function=lambda _A : None , batched=_A )
with dataset.formatted_as(type="""pandas""" ):
_UpperCAmelCase = map(_A , function=lambda _A : None , batched=_A )
with dataset.formatted_as(type="""torch""" , columns="""numbers""" ):
_UpperCAmelCase = map(_A , function=lambda _A : None , batched=_A )
with dataset.formatted_as(type="""tensorflow""" , columns="""numbers""" ):
_UpperCAmelCase = map(_A , function=lambda _A : None , batched=_A )
_UpperCAmelCase = map(_A , function=_A , batched=_A )
_UpperCAmelCase = filter(_A )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(_A , """wb""" ) as f:
f.write(json.dumps(_A ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 555
| 1
|
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
lowercase_ = logging.get_logger(__name__)
lowercase_ = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
lowercase_ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class SCREAMING_SNAKE_CASE :
_UpperCamelCase : str = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Model type selected in the list: ' + ', '.join(__SCREAMING_SNAKE_CASE )} )
_UpperCamelCase : str = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'} )
_UpperCamelCase : int = field(
default=1_28 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
_UpperCamelCase : int = field(
default=1_28 , metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} , )
_UpperCamelCase : int = field(
default=64 , metadata={
'help': (
'The maximum number of tokens for the question. Questions longer than this will '
'be truncated to this length.'
)
} , )
_UpperCamelCase : int = field(
default=30 , metadata={
'help': (
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
)
} , )
_UpperCamelCase : bool = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
_UpperCamelCase : bool = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'} )
_UpperCamelCase : float = field(
default=0.0 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
_UpperCamelCase : int = field(
default=20 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
_UpperCamelCase : int = field(
default=0 , metadata={
'help': (
'language id of input for language-specific xlm models (see'
' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
)
} , )
_UpperCamelCase : int = field(default=1 , metadata={'help': 'multiple threads for converting example to features'} )
class SCREAMING_SNAKE_CASE (__SCREAMING_SNAKE_CASE ):
_UpperCamelCase : Union[str, Any] = 'train'
_UpperCamelCase : List[Any] = 'dev'
class SCREAMING_SNAKE_CASE (__SCREAMING_SNAKE_CASE ):
_UpperCamelCase : SquadDataTrainingArguments
_UpperCamelCase : List[SquadFeatures]
_UpperCamelCase : Split
_UpperCamelCase : bool
def __init__( self : Dict , a : Union[str, Any] , a : int , a : Any = None , a : int = Split.train , a : Optional[Any] = False , a : Optional[Any] = None , a : Any = "pt" , )-> Any:
"""simple docstring"""
lowercase__ = args
lowercase__ = is_language_sensitive
lowercase__ = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_a , _a ):
try:
lowercase__ = Split[mode]
except KeyError:
raise KeyError('mode is not a valid split name' )
lowercase__ = mode
# Load data features from cache or dataset file
lowercase__ = 'v2' if args.version_2_with_negative else 'v1'
lowercase__ = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}""" , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowercase__ = cached_features_file + '.lock'
with FileLock(_a ):
if os.path.exists(_a ) and not args.overwrite_cache:
lowercase__ = time.time()
lowercase__ = torch.load(_a )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
lowercase__ = self.old_features['features']
lowercase__ = self.old_features.get('dataset' , _a )
lowercase__ = self.old_features.get('examples' , _a )
logger.info(
f"""Loading features from cached file {cached_features_file} [took %.3f s]""" , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
' future run' )
else:
if mode == Split.dev:
lowercase__ = self.processor.get_dev_examples(args.data_dir )
else:
lowercase__ = self.processor.get_train_examples(args.data_dir )
lowercase__ , lowercase__ = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_a , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_a , )
lowercase__ = time.time()
torch.save(
{'features': self.features, 'dataset': self.dataset, 'examples': self.examples} , _a , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""" )
def __len__( self : Union[str, Any] )-> Dict:
"""simple docstring"""
return len(self.features )
def __getitem__( self : int , a : List[Any] )-> Any:
"""simple docstring"""
lowercase__ = self.features[i]
lowercase__ = torch.tensor(feature.input_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.attention_mask , dtype=torch.long )
lowercase__ = torch.tensor(feature.token_type_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.cls_index , dtype=torch.long )
lowercase__ = torch.tensor(feature.p_mask , dtype=torch.float )
lowercase__ = torch.tensor(feature.is_impossible , dtype=torch.float )
lowercase__ = {
'input_ids': input_ids,
'attention_mask': attention_mask,
'token_type_ids': token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({'cls_index': cls_index, 'p_mask': p_mask} )
if self.args.version_2_with_negative:
inputs.update({'is_impossible': is_impossible} )
if self.is_language_sensitive:
inputs.update({'langs': (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
lowercase__ = torch.tensor(feature.start_position , dtype=torch.long )
lowercase__ = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({'start_positions': start_positions, 'end_positions': end_positions} )
return inputs
| 720
|
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()
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
lowercase_ = {
"""b0""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 224,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 240,
"""dropout_rate""": 0.2,
"""dw_padding""": [16],
},
"""b2""": {
"""hidden_dim""": 1_408,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 260,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 16],
},
"""b3""": {
"""hidden_dim""": 1_536,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 300,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 18],
},
"""b4""": {
"""hidden_dim""": 1_792,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 380,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 2_048,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 456,
"""dropout_rate""": 0.4,
"""dw_padding""": [13, 27],
},
"""b6""": {
"""hidden_dim""": 2_304,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 528,
"""dropout_rate""": 0.5,
"""dw_padding""": [31],
},
"""b7""": {
"""hidden_dim""": 2_560,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 600,
"""dropout_rate""": 0.5,
"""dw_padding""": [18],
},
}
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> str:
lowercase__ = EfficientNetConfig()
lowercase__ = CONFIG_MAP[model_name]['hidden_dim']
lowercase__ = CONFIG_MAP[model_name]['width_coef']
lowercase__ = CONFIG_MAP[model_name]['depth_coef']
lowercase__ = CONFIG_MAP[model_name]['image_size']
lowercase__ = CONFIG_MAP[model_name]['dropout_rate']
lowercase__ = CONFIG_MAP[model_name]['dw_padding']
lowercase__ = 'huggingface/label-files'
lowercase__ = 'imagenet-1k-id2label.json'
lowercase__ = 1000
lowercase__ = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type='dataset' ) , 'r' ) )
lowercase__ = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
return config
def __UpperCamelCase () -> Tuple:
lowercase__ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowercase__ = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
lowercase__ = CONFIG_MAP[model_name]['image_size']
lowercase__ = EfficientNetImageProcessor(
size={'height': size, 'width': size} , image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3] , do_center_crop=_SCREAMING_SNAKE_CASE , )
return preprocessor
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> Tuple:
lowercase__ = [v.split('_' )[0].split('block' )[1] for v in original_param_names if v.startswith('block' )]
lowercase__ = sorted(set(_SCREAMING_SNAKE_CASE ) )
lowercase__ = len(_SCREAMING_SNAKE_CASE )
lowercase__ = {b: str(_SCREAMING_SNAKE_CASE ) for b, i in zip(_SCREAMING_SNAKE_CASE , range(_SCREAMING_SNAKE_CASE ) )}
lowercase__ = []
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:
lowercase__ = 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') )
lowercase__ = {}
for item in rename_keys:
if item[0] in original_param_names:
lowercase__ = 'efficientnet.' + item[1]
lowercase__ = 'classifier.weight'
lowercase__ = 'classifier.bias'
return key_mapping
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
for key, value in tf_params.items():
if "normalization" in key:
continue
lowercase__ = key_mapping[key]
if "_conv" in key and "kernel" in key:
lowercase__ = torch.from_numpy(_SCREAMING_SNAKE_CASE ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
lowercase__ = torch.from_numpy(_SCREAMING_SNAKE_CASE ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
lowercase__ = torch.from_numpy(np.transpose(_SCREAMING_SNAKE_CASE ) )
else:
lowercase__ = torch.from_numpy(_SCREAMING_SNAKE_CASE )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
lowercase__ = model_classes[model_name](
include_top=_SCREAMING_SNAKE_CASE , weights='imagenet' , input_tensor=_SCREAMING_SNAKE_CASE , input_shape=_SCREAMING_SNAKE_CASE , pooling=_SCREAMING_SNAKE_CASE , classes=1000 , classifier_activation='softmax' , )
lowercase__ = original_model.trainable_variables
lowercase__ = original_model.non_trainable_variables
lowercase__ = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
lowercase__ = param.numpy()
lowercase__ = list(tf_params.keys() )
# Load HuggingFace model
lowercase__ = get_efficientnet_config(_SCREAMING_SNAKE_CASE )
lowercase__ = EfficientNetForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
lowercase__ = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('Converting parameters...' )
lowercase__ = rename_keys(_SCREAMING_SNAKE_CASE )
replace_params(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Initialize preprocessor and preprocess input image
lowercase__ = convert_image_processor(_SCREAMING_SNAKE_CASE )
lowercase__ = preprocessor(images=prepare_img() , return_tensors='pt' )
# HF model inference
hf_model.eval()
with torch.no_grad():
lowercase__ = hf_model(**_SCREAMING_SNAKE_CASE )
lowercase__ = outputs.logits.detach().numpy()
# Original model inference
lowercase__ = False
lowercase__ = CONFIG_MAP[model_name]['image_size']
lowercase__ = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
lowercase__ = image.img_to_array(_SCREAMING_SNAKE_CASE )
lowercase__ = np.expand_dims(_SCREAMING_SNAKE_CASE , axis=0 )
lowercase__ = original_model.predict(_SCREAMING_SNAKE_CASE )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 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(_SCREAMING_SNAKE_CASE ):
os.mkdir(_SCREAMING_SNAKE_CASE )
# Save converted model and image processor
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
preprocessor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
# Push model and image processor to hub
print(F"""Pushing converted {model_name} to the hub...""" )
lowercase__ = F"""efficientnet-{model_name}"""
preprocessor.push_to_hub(_SCREAMING_SNAKE_CASE )
hf_model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase_ = 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""")
lowercase_ = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 45
| 0
|
'''simple docstring'''
import argparse
import os
# New Code #
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 import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A = 16
__A = 32
def _A ( lowercase__ , lowercase__ = 16 ):
lowercase__ = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowercase__ = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowercase__ ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase__ , max_length=lowercase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase__ = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase__ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase__ = 16
elif accelerator.mixed_precision != "no":
lowercase__ = 8
else:
lowercase__ = None
return tokenizer.pad(
lowercase__ , padding="""longest""" , max_length=lowercase__ , pad_to_multiple_of=lowercase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowercase__ = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
lowercase__ = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A = mocked_dataloaders # noqa: F811
def _A ( lowercase__ , lowercase__ ):
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , lowercase__ ) == "1":
lowercase__ = 2
# Initialize accelerator
lowercase__ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ = config["""lr"""]
lowercase__ = int(config["""num_epochs"""] )
lowercase__ = int(config["""seed"""] )
lowercase__ = int(config["""batch_size"""] )
lowercase__ = evaluate.load("""glue""" , """mrpc""" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowercase__ )
def inner_training_loop(lowercase__ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowercase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase__ = model.to(accelerator.device )
# Instantiate optimizer
lowercase__ = AdamW(params=model.parameters() , lr=lowercase__ )
lowercase__ , lowercase__ = get_dataloaders(lowercase__ , lowercase__ )
# Instantiate scheduler
lowercase__ = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=100 , num_training_steps=(len(lowercase__ ) * num_epochs) , )
# 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.
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Now we train the model
for epoch in range(lowercase__ ):
model.train()
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase__ = model(**lowercase__ )
lowercase__ = outputs.loss
accelerator.backward(lowercase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase__ = model(**lowercase__ )
lowercase__ = outputs.logits.argmax(dim=-1 )
lowercase__ , lowercase__ = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
lowercase__ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , lowercase__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def _A ( ):
lowercase__ = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowercase__ , default=lowercase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
lowercase__ = parser.parse_args()
lowercase__ = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main()
| 325
|
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class A ( unittest.TestCase ):
def A__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__ = """hf-internal-testing/tiny-random-t5"""
lowercase__ = AutoTokenizer.from_pretrained(lowerCamelCase__ )
lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__ )
lowercase__ = tokenizer("""This is me""" , return_tensors="""pt""" )
lowercase__ = model.to_bettertransformer()
self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
lowercase__ = model.generate(**lowerCamelCase__ )
lowercase__ = model.reverse_bettertransformer()
self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCamelCase__ )
lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__ )
self.assertFalse(
any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
lowercase__ = model_reloaded.generate(**lowerCamelCase__ )
self.assertTrue(torch.allclose(lowerCamelCase__ , lowerCamelCase__ ) )
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase__ = """hf-internal-testing/tiny-random-t5"""
lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__ )
lowercase__ = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(lowerCamelCase__ ):
model.save_pretrained(lowerCamelCase__ )
lowercase__ = model.reverse_bettertransformer()
model.save_pretrained(lowerCamelCase__ )
| 325
| 1
|
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Optional[int]:
A : int = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
A : Union[str, Any] = sd_pipe.to(__lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )
sd_pipe.set_scheduler("sample_euler" )
A : Dict = "A painting of a squirrel eating a burger"
A : str = torch.manual_seed(0 )
A : int = sd_pipe([prompt] , generator=__lowerCamelCase , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
A : Any = output.images
A : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
A : Any = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Union[str, Any]:
A : Tuple = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
A : Any = sd_pipe.to(__lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )
sd_pipe.set_scheduler("sample_euler" )
A : Dict = "A painting of a squirrel eating a burger"
A : Any = torch.manual_seed(0 )
A : Optional[int] = sd_pipe([prompt] , generator=__lowerCamelCase , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
A : List[Any] = output.images
A : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
A : Optional[int] = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[int]:
A : int = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
A : List[str] = sd_pipe.to(__lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )
sd_pipe.set_scheduler("sample_dpmpp_2m" )
A : str = "A painting of a squirrel eating a burger"
A : str = torch.manual_seed(0 )
A : Dict = sd_pipe(
[prompt] , generator=__lowerCamelCase , guidance_scale=7.5 , num_inference_steps=15 , output_type="np" , use_karras_sigmas=__lowerCamelCase , )
A : str = output.images
A : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
A : str = np.array(
[0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 17
|
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__SCREAMING_SNAKE_CASE = """."""
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE = os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
with open(doctest_file_path) as fp:
for line in fp:
__SCREAMING_SNAKE_CASE = line.strip()
__SCREAMING_SNAKE_CASE = os.path.join(REPO_PATH, line)
if not (os.path.isfile(path) or os.path.isdir(path)):
non_existent_paths.append(line)
all_paths.append(path)
if len(non_existent_paths) > 0:
__SCREAMING_SNAKE_CASE = """\n""".join(non_existent_paths)
raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
if all_paths != sorted(all_paths):
raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")
| 17
| 1
|
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = 42
UpperCAmelCase = None
UpperCAmelCase = None
def _snake_case ( __snake_case ):
# Validation
def is_valid_tree(__snake_case ) -> bool:
if node is None:
return True
if not isinstance(__snake_case , __snake_case ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(__snake_case ):
raise ValueError(
'''Each node should be type of TreeNode and data should be float.''' )
def is_binary_search_tree_recursive_check(
__snake_case , __snake_case , __snake_case ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , __snake_case , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , __snake_case )
)
return is_binary_search_tree_recursive_check(__snake_case , -float('''inf''' ) , float('''inf''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10
|
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")
_UpperCamelCase = logging.getLogger(__name__)
@dataclass
class __a :
"""simple docstring"""
__UpperCamelCase : Optional[int] = field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCamelCase : bool = field(
default=__magic_name__ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
__UpperCamelCase : bool = field(
default=__magic_name__ , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
__UpperCamelCase : Optional[int] = field(
default=__magic_name__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
__UpperCamelCase : Optional[int] = field(
default=__magic_name__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
__UpperCamelCase : Optional[int] = field(
default=__magic_name__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
@dataclass
class __a :
"""simple docstring"""
__UpperCamelCase : str = field(
default=__magic_name__ , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
__UpperCamelCase : str = field(
default=__magic_name__ , metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'} )
__UpperCamelCase : Optional[str] = field(
default=__magic_name__ , metadata={'help': 'Train language if it is different from the evaluation language.'} )
__UpperCamelCase : Optional[str] = field(
default=__magic_name__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__UpperCamelCase : Optional[str] = field(
default=__magic_name__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__UpperCamelCase : Optional[str] = field(
default=__magic_name__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
__UpperCamelCase : Optional[bool] = field(
default=__magic_name__ , metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'} , )
__UpperCamelCase : bool = field(
default=__magic_name__ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
__UpperCamelCase : str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
__UpperCamelCase : bool = field(
default=__magic_name__ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
__UpperCamelCase : bool = field(
default=__magic_name__ , metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} , )
def SCREAMING_SNAKE_CASE ( ) -> Tuple:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCAmelCase__ : List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Optional[Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_xnli" , lowercase__ )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowerCAmelCase__ : Optional[int] = training_args.get_process_log_level()
logger.setLevel(lowercase__ )
datasets.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
lowerCAmelCase__ : str = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCAmelCase__ : List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
"Use --overwrite_output_dir to overcome." )
elif last_checkpoint is not None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
lowerCAmelCase__ : Any = load_dataset(
"xnli" , model_args.language , split="train" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
lowerCAmelCase__ : Optional[Any] = load_dataset(
"xnli" , model_args.train_language , split="train" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase__ : Optional[int] = train_dataset.features["label"].names
if training_args.do_eval:
lowerCAmelCase__ : Tuple = load_dataset(
"xnli" , model_args.language , split="validation" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase__ : Dict = eval_dataset.features["label"].names
if training_args.do_predict:
lowerCAmelCase__ : Any = load_dataset(
"xnli" , model_args.language , split="test" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase__ : Union[str, Any] = predict_dataset.features["label"].names
# Labels
lowerCAmelCase__ : Optional[Any] = len(lowercase__ )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCAmelCase__ : Tuple = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowercase__ , idalabel={str(lowercase__ ): label for i, label in enumerate(lowercase__ )} , labelaid={label: i for i, label in enumerate(lowercase__ )} , finetuning_task="xnli" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase__ : Dict = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase__ : List[str] = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=lowercase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
lowerCAmelCase__ : List[str] = "max_length"
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
lowerCAmelCase__ : List[Any] = False
def preprocess_function(lowercase__ ):
# Tokenize the texts
return tokenizer(
examples["premise"] , examples["hypothesis"] , padding=lowercase__ , max_length=data_args.max_seq_length , truncation=lowercase__ , )
if training_args.do_train:
if data_args.max_train_samples is not None:
lowerCAmelCase__ : Optional[Any] = min(len(lowercase__ ) , data_args.max_train_samples )
lowerCAmelCase__ : Optional[int] = train_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc="train dataset map pre-processing" ):
lowerCAmelCase__ : int = train_dataset.map(
lowercase__ , batched=lowercase__ , load_from_cache_file=not data_args.overwrite_cache , desc="Running tokenizer on train dataset" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(lowercase__ ) ) , 3 ):
logger.info(F"""Sample {index} of the training set: {train_dataset[index]}.""" )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowerCAmelCase__ : Optional[int] = min(len(lowercase__ ) , data_args.max_eval_samples )
lowerCAmelCase__ : Union[str, Any] = eval_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc="validation dataset map pre-processing" ):
lowerCAmelCase__ : List[str] = eval_dataset.map(
lowercase__ , batched=lowercase__ , load_from_cache_file=not data_args.overwrite_cache , desc="Running tokenizer on validation dataset" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
lowerCAmelCase__ : str = min(len(lowercase__ ) , data_args.max_predict_samples )
lowerCAmelCase__ : Union[str, Any] = predict_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc="prediction dataset map pre-processing" ):
lowerCAmelCase__ : Dict = predict_dataset.map(
lowercase__ , batched=lowercase__ , load_from_cache_file=not data_args.overwrite_cache , desc="Running tokenizer on prediction dataset" , )
# Get the metric function
lowerCAmelCase__ : int = evaluate.load("xnli" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowercase__ ):
lowerCAmelCase__ : Union[str, Any] = p.predictions[0] if isinstance(p.predictions , lowercase__ ) else p.predictions
lowerCAmelCase__ : Union[str, Any] = np.argmax(lowercase__ , axis=1 )
return metric.compute(predictions=lowercase__ , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
lowerCAmelCase__ : Tuple = default_data_collator
elif training_args.fpaa:
lowerCAmelCase__ : Tuple = DataCollatorWithPadding(lowercase__ , pad_to_multiple_of=8 )
else:
lowerCAmelCase__ : List[Any] = None
# Initialize our Trainer
lowerCAmelCase__ : int = Trainer(
model=lowercase__ , args=lowercase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowercase__ , tokenizer=lowercase__ , data_collator=lowercase__ , )
# Training
if training_args.do_train:
lowerCAmelCase__ : str = None
if training_args.resume_from_checkpoint is not None:
lowerCAmelCase__ : Optional[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowerCAmelCase__ : List[str] = last_checkpoint
lowerCAmelCase__ : Optional[Any] = trainer.train(resume_from_checkpoint=lowercase__ )
lowerCAmelCase__ : str = train_result.metrics
lowerCAmelCase__ : Union[str, Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase__ )
)
lowerCAmelCase__ : List[Any] = min(lowercase__ , len(lowercase__ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train" , lowercase__ )
trainer.save_metrics("train" , lowercase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
lowerCAmelCase__ : str = trainer.evaluate(eval_dataset=lowercase__ )
lowerCAmelCase__ : Tuple = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase__ )
lowerCAmelCase__ : Any = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics("eval" , lowercase__ )
trainer.save_metrics("eval" , lowercase__ )
# Prediction
if training_args.do_predict:
logger.info("*** Predict ***" )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = trainer.predict(lowercase__ , metric_key_prefix="predict" )
lowerCAmelCase__ : List[str] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowercase__ )
)
lowerCAmelCase__ : str = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics("predict" , lowercase__ )
trainer.save_metrics("predict" , lowercase__ )
lowerCAmelCase__ : Union[str, Any] = np.argmax(lowercase__ , axis=1 )
lowerCAmelCase__ : List[str] = os.path.join(training_args.output_dir , "predictions.txt" )
if trainer.is_world_process_zero():
with open(lowercase__ , "w" ) as writer:
writer.write("index\tprediction\n" )
for index, item in enumerate(lowercase__ ):
lowerCAmelCase__ : List[Any] = label_list[item]
writer.write(F"""{index}\t{item}\n""" )
if __name__ == "__main__":
main()
| 453
| 0
|
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
__lowerCamelCase = logging.get_logger(__name__)
class UpperCamelCase_ :
lowercase = None
@experimental
def UpperCamelCase__ ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> int:
"""simple docstring"""
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
return _map_with_joblib(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCamelCase__ ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Dict:
"""simple docstring"""
_a : int = num_proc if num_proc <= len(UpperCAmelCase ) else len(UpperCAmelCase )
_a : Tuple = [] # We organize the splits ourselve (contiguous splits)
for index in range(UpperCAmelCase ):
_a : List[str] = len(UpperCAmelCase ) // num_proc
_a : Any = len(UpperCAmelCase ) % num_proc
_a : Optional[int] = div * index + min(UpperCAmelCase , UpperCAmelCase )
_a : Optional[int] = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(UpperCAmelCase ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
F'Error dividing inputs iterable among processes. '
F'Total number of objects {len(UpperCAmelCase )}, '
F'length: {sum(len(i[1] ) for i in split_kwds )}' )
logger.info(
F'Spawning {num_proc} processes for {len(UpperCAmelCase )} objects in slices of {[len(i[1] ) for i in split_kwds]}' )
_a , _a : List[Any] = None, None
if not disable_tqdm:
_a , _a : Any = (RLock(),), tqdm.set_lock
with Pool(UpperCAmelCase , initargs=UpperCAmelCase , initializer=UpperCAmelCase ) as pool:
_a : Union[str, Any] = pool.map(UpperCAmelCase , UpperCAmelCase )
logger.info(F'Finished {num_proc} processes' )
_a : int = [obj for proc_res in mapped for obj in proc_res]
logger.info(F'Unpacked {len(UpperCAmelCase )} objects' )
return mapped
def UpperCamelCase__ ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name , n_jobs=UpperCAmelCase ):
return joblib.Parallel()(
joblib.delayed(UpperCAmelCase )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def UpperCamelCase__ ( UpperCAmelCase ) -> Any:
"""simple docstring"""
_a : Optional[Any] = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
_a : Any = None
| 307
|
from maths.prime_factors import prime_factors
def UpperCamelCase__ ( UpperCAmelCase ) -> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
_a : Optional[Any] = F'Input value of [number={number}] must be an integer'
raise TypeError(UpperCAmelCase )
if number < 1:
raise ValueError('''Input must be a positive integer''' )
return -1 if len(prime_factors(UpperCAmelCase ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 307
| 1
|
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
lowerCamelCase_ = [
'openmmlab/upernet-convnext-tiny',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
lowerCamelCase_ = 'UperNetConfig'
class lowercase_ ( nn.Module ):
"""simple docstring"""
def __init__( self : List[str] , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : Union[int, Tuple[int, int]] , __lowerCamelCase : Union[int, Tuple[int, int], str] = 0 , __lowerCamelCase : bool = False , __lowerCamelCase : Union[int, Tuple[int, int]] = 1 , ):
"""simple docstring"""
super().__init__()
_SCREAMING_SNAKE_CASE = nn.Convad(
in_channels=__lowerCamelCase , out_channels=__lowerCamelCase , kernel_size=__lowerCamelCase , padding=__lowerCamelCase , bias=__lowerCamelCase , dilation=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = nn.BatchNormad(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = nn.ReLU()
def lowerCAmelCase_ ( self : List[str] , __lowerCamelCase : torch.Tensor ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = self.conv(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = self.batch_norm(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = self.activation(__lowerCamelCase )
return output
class lowercase_ ( nn.Module ):
"""simple docstring"""
def __init__( self : str , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int ):
"""simple docstring"""
super().__init__()
_SCREAMING_SNAKE_CASE = [
nn.AdaptiveAvgPoolad(__lowerCamelCase ),
UperNetConvModule(__lowerCamelCase , __lowerCamelCase , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(__lowerCamelCase ) , __lowerCamelCase )
def lowerCAmelCase_ ( self : List[str] , __lowerCamelCase : torch.Tensor ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = input
for layer in self.layers:
_SCREAMING_SNAKE_CASE = layer(__lowerCamelCase )
return hidden_state
class lowercase_ ( nn.Module ):
"""simple docstring"""
def __init__( self : str , __lowerCamelCase : Tuple[int, ...] , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool ):
"""simple docstring"""
super().__init__()
_SCREAMING_SNAKE_CASE = pool_scales
_SCREAMING_SNAKE_CASE = align_corners
_SCREAMING_SNAKE_CASE = in_channels
_SCREAMING_SNAKE_CASE = channels
_SCREAMING_SNAKE_CASE = []
for i, pool_scale in enumerate(__lowerCamelCase ):
_SCREAMING_SNAKE_CASE = UperNetPyramidPoolingBlock(pool_scale=__lowerCamelCase , in_channels=__lowerCamelCase , channels=__lowerCamelCase )
self.blocks.append(__lowerCamelCase )
self.add_module(str(__lowerCamelCase ) , __lowerCamelCase )
def lowerCAmelCase_ ( self : int , __lowerCamelCase : torch.Tensor ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
for ppm in self.blocks:
_SCREAMING_SNAKE_CASE = ppm(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = nn.functional.interpolate(
__lowerCamelCase , size=x.size()[2:] , mode="bilinear" , align_corners=self.align_corners )
ppm_outs.append(__lowerCamelCase )
return ppm_outs
class lowercase_ ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[Any] , __lowerCamelCase : Any , __lowerCamelCase : str ):
"""simple docstring"""
super().__init__()
_SCREAMING_SNAKE_CASE = config
_SCREAMING_SNAKE_CASE = config.pool_scales # e.g. (1, 2, 3, 6)
_SCREAMING_SNAKE_CASE = in_channels
_SCREAMING_SNAKE_CASE = config.hidden_size
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
_SCREAMING_SNAKE_CASE = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
_SCREAMING_SNAKE_CASE = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
_SCREAMING_SNAKE_CASE = nn.ModuleList()
_SCREAMING_SNAKE_CASE = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
_SCREAMING_SNAKE_CASE = UperNetConvModule(__lowerCamelCase , self.channels , kernel_size=1 )
_SCREAMING_SNAKE_CASE = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(__lowerCamelCase )
self.fpn_convs.append(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def lowerCAmelCase_ ( self : List[Any] ):
"""simple docstring"""
self.apply(self._init_weights )
def lowerCAmelCase_ ( self : List[str] , __lowerCamelCase : List[Any] ):
"""simple docstring"""
if isinstance(__lowerCamelCase , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def lowerCAmelCase_ ( self : Any , __lowerCamelCase : Any ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = inputs[-1]
_SCREAMING_SNAKE_CASE = [x]
psp_outs.extend(self.psp_modules(__lowerCamelCase ) )
_SCREAMING_SNAKE_CASE = torch.cat(__lowerCamelCase , dim=1 )
_SCREAMING_SNAKE_CASE = self.bottleneck(__lowerCamelCase )
return output
def lowerCAmelCase_ ( self : Tuple , __lowerCamelCase : torch.Tensor ):
"""simple docstring"""
# build laterals
_SCREAMING_SNAKE_CASE = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(__lowerCamelCase ) )
# build top-down path
_SCREAMING_SNAKE_CASE = len(__lowerCamelCase )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
_SCREAMING_SNAKE_CASE = laterals[i - 1].shape[2:]
_SCREAMING_SNAKE_CASE = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=__lowerCamelCase , mode="bilinear" , align_corners=self.align_corners )
# build outputs
_SCREAMING_SNAKE_CASE = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
_SCREAMING_SNAKE_CASE = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode="bilinear" , align_corners=self.align_corners )
_SCREAMING_SNAKE_CASE = torch.cat(__lowerCamelCase , dim=1 )
_SCREAMING_SNAKE_CASE = self.fpn_bottleneck(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = self.classifier(__lowerCamelCase )
return output
class lowercase_ ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : int = 2 , __lowerCamelCase : int = 3 , __lowerCamelCase : Union[int, Tuple[int, int]] = 1 ):
"""simple docstring"""
super().__init__()
_SCREAMING_SNAKE_CASE = config
_SCREAMING_SNAKE_CASE = config.auxiliary_in_channels
_SCREAMING_SNAKE_CASE = config.auxiliary_channels
_SCREAMING_SNAKE_CASE = config.auxiliary_num_convs
_SCREAMING_SNAKE_CASE = config.auxiliary_concat_input
_SCREAMING_SNAKE_CASE = in_index
_SCREAMING_SNAKE_CASE = (kernel_size // 2) * dilation
_SCREAMING_SNAKE_CASE = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=__lowerCamelCase , padding=__lowerCamelCase , dilation=__lowerCamelCase ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=__lowerCamelCase , padding=__lowerCamelCase , dilation=__lowerCamelCase ) )
if self.num_convs == 0:
_SCREAMING_SNAKE_CASE = nn.Identity()
else:
_SCREAMING_SNAKE_CASE = nn.Sequential(*__lowerCamelCase )
if self.concat_input:
_SCREAMING_SNAKE_CASE = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=__lowerCamelCase , padding=kernel_size // 2 )
_SCREAMING_SNAKE_CASE = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def lowerCAmelCase_ ( self : str ):
"""simple docstring"""
self.apply(self._init_weights )
def lowerCAmelCase_ ( self : Tuple , __lowerCamelCase : Optional[int] ):
"""simple docstring"""
if isinstance(__lowerCamelCase , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def lowerCAmelCase_ ( self : str , __lowerCamelCase : torch.Tensor ):
"""simple docstring"""
# just take the relevant feature maps
_SCREAMING_SNAKE_CASE = encoder_hidden_states[self.in_index]
_SCREAMING_SNAKE_CASE = self.convs(__lowerCamelCase )
if self.concat_input:
_SCREAMING_SNAKE_CASE = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
_SCREAMING_SNAKE_CASE = self.classifier(__lowerCamelCase )
return output
class lowercase_ ( A ):
"""simple docstring"""
lowerCamelCase_ = UperNetConfig
lowerCamelCase_ = '''pixel_values'''
lowerCamelCase_ = True
def lowerCAmelCase_ ( self : List[Any] , __lowerCamelCase : Any ):
"""simple docstring"""
if isinstance(__lowerCamelCase , __lowerCamelCase ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def lowerCAmelCase_ ( self : Tuple ):
"""simple docstring"""
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def lowerCAmelCase_ ( self : Optional[Any] , __lowerCamelCase : int , __lowerCamelCase : str=False ):
"""simple docstring"""
if isinstance(__lowerCamelCase , __lowerCamelCase ):
_SCREAMING_SNAKE_CASE = value
lowerCamelCase_ = r'\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowerCamelCase_ = r'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
'''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.''' , A , )
class lowercase_ ( A ):
"""simple docstring"""
def __init__( self : int , __lowerCamelCase : List[str] ):
"""simple docstring"""
super().__init__(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
_SCREAMING_SNAKE_CASE = UperNetHead(__lowerCamelCase , in_channels=self.backbone.channels )
_SCREAMING_SNAKE_CASE = UperNetFCNHead(__lowerCamelCase ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("batch_size, sequence_length" ) )
@replace_return_docstrings(output_type=__lowerCamelCase , config_class=_CONFIG_FOR_DOC )
def lowerCAmelCase_ ( self : List[Any] , __lowerCamelCase : Optional[torch.Tensor] = None , __lowerCamelCase : Optional[bool] = None , __lowerCamelCase : Optional[bool] = None , __lowerCamelCase : Optional[torch.Tensor] = None , __lowerCamelCase : Optional[bool] = None , ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = return_dict if return_dict is not None else self.config.use_return_dict
_SCREAMING_SNAKE_CASE = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_SCREAMING_SNAKE_CASE = output_attentions if output_attentions is not None else self.config.output_attentions
_SCREAMING_SNAKE_CASE = self.backbone.forward_with_filtered_kwargs(
__lowerCamelCase , output_hidden_states=__lowerCamelCase , output_attentions=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = outputs.feature_maps
_SCREAMING_SNAKE_CASE = self.decode_head(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = nn.functional.interpolate(__lowerCamelCase , size=pixel_values.shape[2:] , mode="bilinear" , align_corners=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = None
if self.auxiliary_head is not None:
_SCREAMING_SNAKE_CASE = self.auxiliary_head(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = nn.functional.interpolate(
__lowerCamelCase , size=pixel_values.shape[2:] , mode="bilinear" , align_corners=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError("The number of labels should be greater than one" )
else:
# compute weighted loss
_SCREAMING_SNAKE_CASE = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
_SCREAMING_SNAKE_CASE = loss_fct(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = loss_fct(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
_SCREAMING_SNAKE_CASE = (logits,) + outputs[1:]
else:
_SCREAMING_SNAKE_CASE = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=__lowerCamelCase , logits=__lowerCamelCase , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 418
|
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def SCREAMING_SNAKE_CASE_ ( __A : np.ndarray , __A : tuple[int, int] , __A : tuple[int, int] , __A : bool , ) -> tuple[float | int, list[tuple[int, int]]]:
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = grid.shape
_SCREAMING_SNAKE_CASE = [-1, 1, 0, 0]
_SCREAMING_SNAKE_CASE = [0, 0, -1, 1]
if allow_diagonal:
dx += [-1, -1, 1, 1]
dy += [-1, 1, -1, 1]
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = [(0, source)], set()
_SCREAMING_SNAKE_CASE = np.full((rows, cols) , np.inf )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = np.empty((rows, cols) , dtype=__A )
_SCREAMING_SNAKE_CASE = None
while queue:
((_SCREAMING_SNAKE_CASE), (_SCREAMING_SNAKE_CASE)) = heappop(__A )
if (x, y) in visited:
continue
visited.add((x, y) )
if (x, y) == destination:
_SCREAMING_SNAKE_CASE = []
while (x, y) != source:
path.append((x, y) )
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = predecessors[x, y]
path.append(__A ) # add the source manually
path.reverse()
return matrix[destination], path
for i in range(len(__A ) ):
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = x + dx[i], y + dy[i]
if 0 <= nx < rows and 0 <= ny < cols:
_SCREAMING_SNAKE_CASE = grid[nx][ny]
if next_node == 1 and matrix[nx, ny] > dist + 1:
heappush(__A , (dist + 1, (nx, ny)) )
_SCREAMING_SNAKE_CASE = dist + 1
_SCREAMING_SNAKE_CASE = (x, y)
return np.inf, []
if __name__ == "__main__":
import doctest
doctest.testmod()
| 418
| 1
|
import random
from .binary_exp_mod import bin_exp_mod
def A_ ( _lowerCAmelCase , _lowerCAmelCase=1000 ) -> Optional[int]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
UpperCamelCase : Union[str, Any] = n - 1
UpperCamelCase : Tuple = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
UpperCamelCase : Optional[int] = 0
while count < prec:
UpperCamelCase : Optional[int] = random.randint(2 , n - 1 )
UpperCamelCase : Dict = bin_exp_mod(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
if b != 1:
UpperCamelCase : List[Any] = True
for _ in range(_lowerCAmelCase ):
if b == n - 1:
UpperCamelCase : Optional[Any] = False
break
UpperCamelCase : Optional[Any] = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
__lowerCamelCase : Union[str, Any] = abs(int(input("""Enter bound : """).strip()))
print("""Here's the list of primes:""")
print(""", """.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 38
|
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ( __snake_case ):
_UpperCAmelCase :Optional[int] = ['image_processor', 'tokenizer']
_UpperCAmelCase :Tuple = 'BlipImageProcessor'
_UpperCAmelCase :Optional[int] = 'AutoTokenizer'
def __init__( self , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : str = False
super().__init__(A_ , A_ )
UpperCamelCase : str = self.image_processor
def __call__( self , A_ = None , A_ = None , A_ = True , A_ = False , A_ = None , A_ = None , A_ = 0 , A_ = None , A_ = None , A_ = False , A_ = False , A_ = False , A_ = False , A_ = False , A_ = True , A_ = None , **A_ , ):
'''simple docstring'''
if images is None and text is None:
raise ValueError("You have to specify either images or text." )
# Get only text
if images is None:
UpperCamelCase : int = self.tokenizer
UpperCamelCase : Optional[int] = self.tokenizer(
text=A_ , add_special_tokens=A_ , padding=A_ , truncation=A_ , max_length=A_ , stride=A_ , pad_to_multiple_of=A_ , return_attention_mask=A_ , return_overflowing_tokens=A_ , return_special_tokens_mask=A_ , return_offsets_mapping=A_ , return_token_type_ids=A_ , return_length=A_ , verbose=A_ , return_tensors=A_ , **A_ , )
return text_encoding
# add pixel_values
UpperCamelCase : int = self.image_processor(A_ , return_tensors=A_ )
if text is not None:
UpperCamelCase : Dict = self.tokenizer(
text=A_ , add_special_tokens=A_ , padding=A_ , truncation=A_ , max_length=A_ , stride=A_ , pad_to_multiple_of=A_ , return_attention_mask=A_ , return_overflowing_tokens=A_ , return_special_tokens_mask=A_ , return_offsets_mapping=A_ , return_token_type_ids=A_ , return_length=A_ , verbose=A_ , return_tensors=A_ , **A_ , )
else:
UpperCamelCase : Dict = None
if text_encoding is not None:
encoding_image_processor.update(A_ )
return encoding_image_processor
def __UpperCamelCase( self , *A_ , **A_ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*A_ , **A_ )
def __UpperCamelCase( self , *A_ , **A_ ):
'''simple docstring'''
return self.tokenizer.decode(*A_ , **A_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[str] = self.tokenizer.model_input_names
UpperCamelCase : int = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 38
| 1
|
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ = TypeVar('''T''')
def UpperCAmelCase__ ( lowerCamelCase_ : int ):
return (position - 1) // 2
def UpperCAmelCase__ ( lowerCamelCase_ : int ):
return (2 * position) + 1
def UpperCAmelCase__ ( lowerCamelCase_ : int ):
return (2 * position) + 2
class _UpperCamelCase( Generic[T] ):
def __init__( self : List[str] ):
'''simple docstring'''
__a : list[tuple[T, int]] = []
__a : dict[T, int] = {}
__a : int = 0
def __len__( self : Any ):
'''simple docstring'''
return self.elements
def __repr__( self : Any ):
'''simple docstring'''
return str(self.heap )
def __lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
return self.elements == 0
def __lowerCAmelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : T , SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
self.heap.append((elem, weight) )
__a : List[Any] = self.elements
self.elements += 1
self._bubble_up(SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
__a , __a : Union[str, Any] = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
__a , __a : Dict = self.heap[0]
self._bubble_down(SCREAMING_SNAKE_CASE__ )
return elem
def __lowerCAmelCase ( self : List[str] , SCREAMING_SNAKE_CASE__ : T , SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
__a : List[Any] = self.position_map[elem]
__a : str = (elem, weight)
if position > 0:
__a : Tuple = get_parent_position(SCREAMING_SNAKE_CASE__ )
__a , __a : Dict = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(SCREAMING_SNAKE_CASE__ )
else:
self._bubble_down(SCREAMING_SNAKE_CASE__ )
else:
self._bubble_down(SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Any , SCREAMING_SNAKE_CASE__ : T ):
'''simple docstring'''
__a : List[Any] = self.position_map[elem]
if curr_pos == 0:
return None
__a : List[str] = get_parent_position(SCREAMING_SNAKE_CASE__ )
__a , __a : str = self.heap[curr_pos]
__a , __a : Optional[int] = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return self._bubble_up(SCREAMING_SNAKE_CASE__ )
return None
def __lowerCAmelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : T ):
'''simple docstring'''
__a : int = self.position_map[elem]
__a , __a : Optional[Any] = self.heap[curr_pos]
__a : Tuple = get_child_left_position(SCREAMING_SNAKE_CASE__ )
__a : Optional[Any] = get_child_right_position(SCREAMING_SNAKE_CASE__ )
if child_left_position < self.elements and child_right_position < self.elements:
__a , __a : str = self.heap[child_left_position]
__a , __a : List[str] = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return self._bubble_down(SCREAMING_SNAKE_CASE__ )
if child_left_position < self.elements:
__a , __a : Any = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return self._bubble_down(SCREAMING_SNAKE_CASE__ )
else:
return None
if child_right_position < self.elements:
__a , __a : Union[str, Any] = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return self._bubble_down(SCREAMING_SNAKE_CASE__ )
return None
def __lowerCAmelCase ( self : List[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
__a : Optional[Any] = self.heap[nodea_pos][0]
__a : str = self.heap[nodea_pos][0]
__a , __a : int = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
__a : str = nodea_pos
__a : Optional[int] = nodea_pos
class _UpperCamelCase( Generic[T] ):
def __init__( self : List[Any] ):
'''simple docstring'''
__a : dict[T, dict[T, int]] = {}
__a : int = 0
def __repr__( self : Tuple ):
'''simple docstring'''
return str(self.connections )
def __len__( self : Dict ):
'''simple docstring'''
return self.nodes
def __lowerCAmelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : T ):
'''simple docstring'''
if node not in self.connections:
__a : Tuple = {}
self.nodes += 1
def __lowerCAmelCase ( self : Any , SCREAMING_SNAKE_CASE__ : T , SCREAMING_SNAKE_CASE__ : T , SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
self.add_node(SCREAMING_SNAKE_CASE__ )
self.add_node(SCREAMING_SNAKE_CASE__ )
__a : Optional[Any] = weight
__a : Any = weight
def UpperCAmelCase__ ( lowerCamelCase_ : GraphUndirectedWeighted[T] , ):
__a : dict[T, int] = {node: maxsize for node in graph.connections}
__a : dict[T, T | None] = {node: None for node in graph.connections}
__a : MinPriorityQueue[T] = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(lowerCamelCase_ , lowerCamelCase_ )
if priority_queue.is_empty():
return dist, parent
# initialization
__a : Optional[int] = priority_queue.extract_min()
__a : int = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
__a : str = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(lowerCamelCase_ , dist[neighbour] )
__a : Optional[int] = node
# running prim's algorithm
while not priority_queue.is_empty():
__a : Any = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
__a : Tuple = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(lowerCamelCase_ , dist[neighbour] )
__a : Dict = node
return dist, parent
| 47
|
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Any =logging.get_logger(__name__)
__lowerCAmelCase : int ="https://openaipublic.azureedge.net/jukebox/models/"
__lowerCAmelCase : Any ={
"jukebox-1b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"1b_lyrics/prior_level_2.pth.tar",
],
"jukebox-5b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"5b_lyrics/prior_level_2.pth.tar",
],
}
def UpperCamelCase ( _lowerCamelCase : str ):
if key.endswith(".model.1.bias" ) and len(key.split("." ) ) > 10:
A__ = key.replace(".model.1.bias" , ".conv1d_1.bias" )
elif key.endswith(".model.1.weight" ) and len(key.split("." ) ) > 10:
A__ = key.replace(".model.1.weight" , ".conv1d_1.weight" )
elif key.endswith(".model.3.bias" ) and len(key.split("." ) ) > 10:
A__ = key.replace(".model.3.bias" , ".conv1d_2.bias" )
elif key.endswith(".model.3.weight" ) and len(key.split("." ) ) > 10:
A__ = key.replace(".model.3.weight" , ".conv1d_2.weight" )
if "conditioner_blocks.0." in key:
A__ = key.replace("conditioner_blocks.0" , "conditioner_blocks" )
if "prime_prior" in key:
A__ = key.replace("prime_prior" , "encoder" )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
A__ = key.replace(".emb." , "." )
if key.endswith("k" ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(".k" , ".codebook" )
if "y_emb." in key:
return key.replace("y_emb." , "metadata_embedding." )
if "x_emb.emb." in key:
A__ = key.replace("0.x_emb.emb" , "embed_tokens" )
if "prime_state_ln" in key:
return key.replace("prime_state_ln" , "encoder.final_layer_norm" )
if ".ln" in key:
return key.replace(".ln" , ".layer_norm" )
if "_ln" in key:
return key.replace("_ln" , "_layer_norm" )
if "prime_state_proj" in key:
return key.replace("prime_state_proj" , "encoder.proj_in" )
if "prime_x_out" in key:
return key.replace("prime_x_out" , "encoder.lm_head" )
if "prior.x_out" in key:
return key.replace("x_out" , "fc_proj_out" )
if "x_emb" in key:
return key.replace("x_emb" , "embed_tokens" )
return key
def UpperCamelCase ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : Dict , _lowerCamelCase : str ):
A__ = {}
import re
A__ = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
A__ = re.compile(
r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
A__ = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
A__ = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
A__ = re.compile(
r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
A__ = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
A__ = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)" )
A__ = re.compile(
r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
A__ = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)" )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(_lowerCamelCase ):
A__ = re_encoder_block_conv_in.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = int(groups[2] ) * 2 + int(groups[3] )
A__ = F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
A__ = re_encoder_block_conv_in.sub(_lowerCamelCase , _lowerCamelCase )
elif re_encoder_block_resnet.fullmatch(_lowerCamelCase ):
A__ = re_encoder_block_resnet.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = int(groups[2] ) * 2 + int(groups[3] )
A__ = {"1": 1, "3": 2}[groups[-2]]
A__ = F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
A__ = F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
A__ = prefix + resnet_block
A__ = re_encoder_block_resnet.sub(_lowerCamelCase , _lowerCamelCase )
elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase ):
A__ = re_encoder_block_proj_out.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
A__ = re_encoder_block_proj_out.sub(_lowerCamelCase , _lowerCamelCase )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(_lowerCamelCase ):
A__ = re_decoder_block_conv_out.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = int(groups[2] ) * 2 + int(groups[3] ) - 2
A__ = F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
A__ = re_decoder_block_conv_out.sub(_lowerCamelCase , _lowerCamelCase )
elif re_decoder_block_resnet.fullmatch(_lowerCamelCase ):
A__ = re_decoder_block_resnet.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = int(groups[2] ) * 2 + int(groups[3] ) - 2
A__ = {"1": 1, "3": 2}[groups[-2]]
A__ = F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
A__ = F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
A__ = prefix + resnet_block
A__ = re_decoder_block_resnet.sub(_lowerCamelCase , _lowerCamelCase )
elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase ):
A__ = re_decoder_block_proj_in.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
A__ = re_decoder_block_proj_in.sub(_lowerCamelCase , _lowerCamelCase )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(_lowerCamelCase ):
A__ = re_prior_cond_conv_out.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = int(groups[1] ) * 2 + int(groups[2] ) - 2
A__ = F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
A__ = re_prior_cond_conv_out.sub(_lowerCamelCase , _lowerCamelCase )
elif re_prior_cond_resnet.fullmatch(_lowerCamelCase ):
A__ = re_prior_cond_resnet.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = int(groups[1] ) * 2 + int(groups[2] ) - 2
A__ = {"1": 1, "3": 2}[groups[-2]]
A__ = F"conditioner_blocks.upsampler.upsample_block.{block_index}."
A__ = F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
A__ = prefix + resnet_block
A__ = re_prior_cond_resnet.sub(_lowerCamelCase , _lowerCamelCase )
elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase ):
A__ = re_prior_cond_proj_in.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
A__ = re_prior_cond_proj_in.sub(_lowerCamelCase , _lowerCamelCase )
# keep original key
else:
A__ = original_key
A__ = replace_key(_lowerCamelCase )
if F"{key_prefix}.{key}" not in model_state_dict or key is None:
print(F"failed converting {original_key} to {key}, does not match" )
# handle missmatched shape
elif value.shape != model_state_dict[F"{key_prefix}.{key}"].shape:
A__ = model_state_dict[F"{key_prefix}.{key}"]
print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match" )
A__ = original_key
A__ = original_key
A__ = value
return new_dict
@torch.no_grad()
def UpperCamelCase ( _lowerCamelCase : str=None , _lowerCamelCase : Dict=None ):
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/' )[-1]}" ):
A__ = requests.get(F"{PREFIX}{file}" , allow_redirects=_lowerCamelCase )
os.makedirs(F"{pytorch_dump_folder_path}/" , exist_ok=_lowerCamelCase )
open(F"{pytorch_dump_folder_path}/{file.split('/' )[-1]}" , "wb" ).write(r.content )
A__ = MODEL_MAPPING[model_name.split("/" )[-1]]
A__ = JukeboxConfig.from_pretrained(_lowerCamelCase )
A__ = JukeboxModel(_lowerCamelCase )
A__ = []
A__ = {}
for i, dict_name in enumerate(_lowerCamelCase ):
A__ = torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/' )[-1]}" )["model"]
A__ = {}
for k in old_dic.keys():
if k.endswith(".b" ):
A__ = old_dic[k]
elif k.endswith(".w" ):
A__ = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
A__ = old_dic[k]
else:
A__ = old_dic[k]
A__ = "vqvae" if i == 0 else F"priors.{3 - i}"
A__ = fix_jukebox_keys(_lowerCamelCase , model.state_dict() , _lowerCamelCase , _lowerCamelCase )
weight_dict.append(_lowerCamelCase )
A__ = weight_dict.pop(0 )
model.vqvae.load_state_dict(_lowerCamelCase )
for i in range(len(_lowerCamelCase ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
with open(F"{pytorch_dump_folder_path}/mapping.json" , "w" ) as txtfile:
json.dump(_lowerCamelCase , _lowerCamelCase )
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCamelCase )
return weight_dict
if __name__ == "__main__":
__lowerCAmelCase : Dict =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="jukebox-5b-lyrics",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="jukebox-5b-lyrics-converted",
type=str,
help="Path to the output PyTorch model directory.",
)
__lowerCAmelCase : int =parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 440
| 0
|
def __UpperCAmelCase ( a_):
snake_case_ = [0] * len(a_)
snake_case_ = []
snake_case_ = []
snake_case_ = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_)):
if indegree[i] == 0:
queue.append(a_)
while queue:
snake_case_ = queue.pop(0)
cnt += 1
topo.append(a_)
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(a_)
if cnt != len(a_):
print('Cycle exists')
else:
print(a_)
# Adjacency List of Graph
lowercase = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 711
|
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __UpperCAmelCase ( a_):
if isinstance(a_ , torch.Tensor):
return image
elif isinstance(a_ , PIL.Image.Image):
snake_case_ = [image]
snake_case_ = [trans(img.convert('RGB')) for img in image]
snake_case_ = torch.stack(a_)
return image
class UpperCamelCase_ ( snake_case_ ):
'''simple docstring'''
def __init__( self , a , a ) -> List[Any]:
super().__init__()
# make sure scheduler can always be converted to DDIM
snake_case_ = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=a , scheduler=a )
def _UpperCamelCase ( self , a ) -> List[str]:
if strength < 0 or strength > 1:
raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}''' )
def _UpperCamelCase ( self , a , a , a ) -> Any:
# get the original timestep using init_timestep
snake_case_ = min(int(num_inference_steps * strength ) , a )
snake_case_ = max(num_inference_steps - init_timestep , 0 )
snake_case_ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def _UpperCamelCase ( self , a , a , a , a , a , a=None ) -> List[Any]:
if not isinstance(a , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a )}''' )
snake_case_ = image.to(device=a , dtype=a )
if isinstance(a , a ) and len(a ) != batch_size:
raise ValueError(
F'''You have passed a list of generators of length {len(a )}, but requested an effective batch'''
F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
snake_case_ = init_latents.shape
snake_case_ = randn_tensor(a , generator=a , device=a , dtype=a )
# get latents
print('add noise to latents at timestep' , a )
snake_case_ = self.scheduler.add_noise(a , a , a )
snake_case_ = init_latents
return latents
@torch.no_grad()
def __call__( self , a = None , a = 0.8 , a = 1 , a = None , a = 0.0 , a = 50 , a = None , a = "pil" , a = True , ) -> Union[ImagePipelineOutput, Tuple]:
self.check_inputs(a )
# 2. Preprocess image
snake_case_ = preprocess(a )
# 3. set timesteps
self.scheduler.set_timesteps(a , device=self.device )
snake_case_ , snake_case_ = self.get_timesteps(a , a , self.device )
snake_case_ = timesteps[:1].repeat(a )
# 4. Prepare latent variables
snake_case_ = self.prepare_latents(a , a , a , self.unet.dtype , self.device , a )
snake_case_ = latents
# 5. Denoising loop
for t in self.progress_bar(a ):
# 1. predict noise model_output
snake_case_ = self.unet(a , a ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
snake_case_ = self.scheduler.step(
a , a , a , eta=a , use_clipped_model_output=a , generator=a , ).prev_sample
snake_case_ = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ = self.numpy_to_pil(a )
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=a )
| 607
| 0
|
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
a : Optional[int] = version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def lowercase__(A , A , A , A , A , A , A , A=False , ) ->Any:
"""simple docstring"""
output_path.parent.mkdir(parents=_UpperCamelCase , exist_ok=_UpperCamelCase )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
_UpperCamelCase , _UpperCamelCase , f=output_path.as_posix() , input_names=_UpperCamelCase , output_names=_UpperCamelCase , dynamic_axes=_UpperCamelCase , do_constant_folding=_UpperCamelCase , use_external_data_format=_UpperCamelCase , enable_onnx_checker=_UpperCamelCase , opset_version=_UpperCamelCase , )
else:
export(
_UpperCamelCase , _UpperCamelCase , f=output_path.as_posix() , input_names=_UpperCamelCase , output_names=_UpperCamelCase , dynamic_axes=_UpperCamelCase , do_constant_folding=_UpperCamelCase , opset_version=_UpperCamelCase , )
@torch.no_grad()
def lowercase__(A , A , A , A = False ) ->Optional[int]:
"""simple docstring"""
lowercase__ : Dict= torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
lowercase__ : Union[str, Any]= '''cuda'''
elif fpaa and not torch.cuda.is_available():
raise ValueError("`float16` model export is only supported on GPUs with CUDA" )
else:
lowercase__ : Optional[int]= '''cpu'''
lowercase__ : List[str]= Path(_UpperCamelCase )
# VAE DECODER
lowercase__ : Dict= AutoencoderKL.from_pretrained(model_path + "/vae" )
lowercase__ : str= vae_decoder.config.latent_channels
# forward only through the decoder part
lowercase__ : int= vae_decoder.decode
onnx_export(
_UpperCamelCase , model_args=(
torch.randn(1 , _UpperCamelCase , 25 , 25 ).to(device=_UpperCamelCase , dtype=_UpperCamelCase ),
False,
) , output_path=output_path / "vae_decoder" / "model.onnx" , ordered_input_names=["latent_sample", "return_dict"] , output_names=["sample"] , dynamic_axes={
"latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
} , opset=_UpperCamelCase , )
del vae_decoder
if __name__ == "__main__":
a : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"""--model_path""",
type=str,
required=True,
help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
)
parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
parser.add_argument(
"""--opset""",
default=14,
type=int,
help="""The version of the ONNX operator set to use.""",
)
parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")
a : Optional[Any] = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print("""SD: Done: ONNX""")
| 218
|
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
lowerCAmelCase_ = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowerCAmelCase_ = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowerCAmelCase_ = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCAmelCase_ = [0] * args.vocab_size
for k, v in counter.items():
lowerCAmelCase_ = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 60
| 0
|
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
a = len(UpperCAmelCase__ )
a = sum(UpperCAmelCase__ )
a = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
a = True
for i in range(1 , s + 1 ):
a = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
a = dp[i][j - 1]
if arr[i - 1] <= j:
a = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
a = s - 2 * j
break
return diff
| 708
|
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=13 , __lowerCAmelCase : Any=7 , __lowerCAmelCase : int=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Any=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : str=99 , __lowerCAmelCase : List[str]=64 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=5 , __lowerCAmelCase : int=4 , __lowerCAmelCase : Optional[Any]=37 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : List[Any]=16 , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : Optional[Any]=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : Union[str, Any]=None , ) -> List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = embedding_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 A ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
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 A ( self : int ) -> List[str]:
"""simple docstring"""
return MobileBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = MobileBertModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : Any ) -> str:
"""simple docstring"""
a = MobileBertForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
a = MobileBertForNextSentencePrediction(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A ( self : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
a = MobileBertForPreTraining(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , next_sentence_label=__lowerCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
a = MobileBertForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
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[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
a = self.num_choices
a = MobileBertForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
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(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
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 _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': MobileBertModel,
'''fill-mask''': MobileBertForMaskedLM,
'''question-answering''': MobileBertForQuestionAnswering,
'''text-classification''': MobileBertForSequenceClassification,
'''token-classification''': MobileBertForTokenClassification,
'''zero-shot''': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = True
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=False ) -> Any:
"""simple docstring"""
a = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class in get_values(__lowerCAmelCase ):
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCAmelCase )
a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = MobileBertModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : int ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : str ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase )
def A ( self : str ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase )
def A ( self : int ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase )
def A ( self : List[Any] ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase )
def A ( self : int ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
return torch.tensor(
UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ , )
A_ : Dict = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(__lowerCAmelCase )
a = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[
[
[-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
[-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
[2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
]
] , device=__lowerCAmelCase , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
a = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
a = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 32
| 0
|
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
SCREAMING_SNAKE_CASE_ = 'pytorch_model.bin'
@dataclasses.dataclass
class a :
_lowercase = dataclasses.field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."} )
_lowercase = dataclasses.field(
default=UpperCAmelCase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."} , )
@dataclasses.dataclass
class a :
_lowercase = dataclasses.field(metadata={"help": "A csv or a json file containing the training data."} )
_lowercase = dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."} )
_lowercase = dataclasses.field(
default=UpperCAmelCase , metadata={"help": "A csv or a json file containing the validation data."} )
_lowercase = dataclasses.field(
default=UpperCAmelCase , metadata={"help": "The name of the task to train on."} , )
_lowercase = dataclasses.field(
default=UpperCAmelCase , metadata={"help": "The list of labels for the task."} )
@dataclasses.dataclass
class a :
_lowercase = dataclasses.field(
metadata={"help": "The output directory where the model predictions and checkpoints will be written."} )
_lowercase = dataclasses.field(
default="accuracy" , metadata={"help": "The evaluation metric used for the task."} )
_lowercase = dataclasses.field(
default="no" , metadata={
"help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
} , )
_lowercase = dataclasses.field(
default=1_0 , metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} , )
_lowercase = dataclasses.field(
default=0.0 , metadata={
"help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
} , )
_lowercase = dataclasses.field(
default=UpperCAmelCase , metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."} , )
_lowercase = dataclasses.field(
default=UpperCAmelCase , metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."} , )
_lowercase = dataclasses.field(
default=UpperCAmelCase , metadata={"help": "Whether to fine-tune on labeled data after pseudo training."} , )
_lowercase = dataclasses.field(
default=0.0 , metadata={"help": "Confidence threshold for pseudo-labeled data filtering."} , )
_lowercase = dataclasses.field(
default=1_0_0 , metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} , )
_lowercase = dataclasses.field(
default=UpperCAmelCase , metadata={"help": "Random seed for initialization."} , )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: Dict , lowerCAmelCase: str , lowerCAmelCase: List[str] , lowerCAmelCase: List[Any] , lowerCAmelCase: int ) -> List[str]:
_UpperCAmelCase : int = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
_UpperCAmelCase : List[Any] = dataset.filter(lambda lowerCAmelCase : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
_UpperCAmelCase : Optional[int] = int(eval_result * len(lowerCAmelCase ) )
print(lowerCAmelCase )
_UpperCAmelCase : Tuple = dataset.sort("probability" , reverse=lowerCAmelCase )
_UpperCAmelCase : List[str] = dataset.select(range(lowerCAmelCase ) )
_UpperCAmelCase : Optional[int] = dataset.remove_columns(["label", "probability"] )
_UpperCAmelCase : Tuple = dataset.rename_column("prediction" , "label" )
_UpperCAmelCase : int = dataset.map(lambda lowerCAmelCase : {"label": idalabel[example["label"]]} )
_UpperCAmelCase : Union[str, Any] = dataset.shuffle(seed=args.seed )
_UpperCAmelCase : str = os.path.join(lowerCAmelCase , F'train_pseudo.{args.data_file_extension}' )
if args.data_file_extension == "csv":
dataset.to_csv(lowerCAmelCase , index=lowerCAmelCase )
else:
dataset.to_json(lowerCAmelCase )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: Union[str, Any] , lowerCAmelCase: int , lowerCAmelCase: List[str] , **lowerCAmelCase: List[Any] ) -> Union[str, Any]:
_UpperCAmelCase : List[Any] = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
_UpperCAmelCase : Tuple = STModelArguments(model_name_or_path=lowerCAmelCase )
_UpperCAmelCase : Tuple = STDataArguments(train_file=lowerCAmelCase , infer_file=lowerCAmelCase )
_UpperCAmelCase : List[Any] = STTrainingArguments(output_dir=lowerCAmelCase )
_UpperCAmelCase : Tuple = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(lowerCAmelCase ).items():
setattr(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
for key, value in kwargs.items():
if hasattr(lowerCAmelCase , lowerCAmelCase ):
setattr(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Sanity checks
_UpperCAmelCase : Union[str, Any] = {}
_UpperCAmelCase : Dict = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
_UpperCAmelCase : List[str] = args.train_file
_UpperCAmelCase : Optional[int] = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
_UpperCAmelCase : List[str] = args.eval_file
for key in data_files:
_UpperCAmelCase : Any = data_files[key].split("." )[-1]
assert extension in ["csv", "json"], F'`{key}_file` should be a csv or a json file.'
if args.data_file_extension is None:
_UpperCAmelCase : List[str] = extension
else:
assert extension == args.data_file_extension, F'`{key}_file` should be a {args.data_file_extension} file`.'
assert (
args.eval_metric in datasets.list_metrics()
), F'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("Creating the initial data directory for self-training..." )
_UpperCAmelCase : Optional[int] = F'{args.output_dir}/self-train_iter-{{}}'.format
_UpperCAmelCase : Any = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=lowerCAmelCase )
os.makedirs(lowerCAmelCase , exist_ok=lowerCAmelCase )
accelerator.wait_for_everyone()
_UpperCAmelCase : List[Any] = None
_UpperCAmelCase : List[str] = None
_UpperCAmelCase : str = 0
_UpperCAmelCase : str = False
# Show the progress bar
_UpperCAmelCase : Optional[int] = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
_UpperCAmelCase : List[str] = data_dir_format(lowerCAmelCase )
assert os.path.exists(lowerCAmelCase )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
_UpperCAmelCase : Optional[Any] = os.path.join(lowerCAmelCase , "stage-1" )
_UpperCAmelCase : Dict = {
"accelerator": accelerator,
"model_name_or_path": args.model_name_or_path,
"cache_dir": args.cache_dir,
"do_train": True,
"train_file": data_files["train"] if iteration == 0 else data_files["train_pseudo"],
"do_eval": True if args.eval_file is not None else False,
"eval_file": data_files["eval"],
"do_predict": True,
"infer_file": data_files["infer"],
"task_name": args.task_name,
"label_list": args.label_list,
"output_dir": current_output_dir,
"eval_metric": args.eval_metric,
"evaluation_strategy": args.evaluation_strategy,
"early_stopping_patience": args.early_stopping_patience,
"early_stopping_threshold": args.early_stopping_threshold,
"seed": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(lowerCAmelCase , lowerCAmelCase ):
arguments_dict.update({key: value} )
_UpperCAmelCase : str = os.path.join(lowerCAmelCase , "best-checkpoint" , lowerCAmelCase )
if os.path.exists(lowerCAmelCase ):
logger.info(
"Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1." , lowerCAmelCase , lowerCAmelCase , )
else:
logger.info("***** Running self-training: iteration: %d, stage: 1 *****" , lowerCAmelCase )
finetune(**lowerCAmelCase )
accelerator.wait_for_everyone()
assert os.path.exists(lowerCAmelCase )
logger.info("Self-training job completed: iteration: %d, stage: 1." , lowerCAmelCase )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
_UpperCAmelCase : List[Any] = os.path.join(lowerCAmelCase , "best-checkpoint" )
_UpperCAmelCase : Union[str, Any] = os.path.join(lowerCAmelCase , "stage-2" )
# Update arguments_dict
_UpperCAmelCase : Optional[int] = model_path
_UpperCAmelCase : List[Any] = data_files["train"]
_UpperCAmelCase : List[str] = current_output_dir
_UpperCAmelCase : Any = os.path.join(lowerCAmelCase , "best-checkpoint" , lowerCAmelCase )
if os.path.exists(lowerCAmelCase ):
logger.info(
"Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2." , lowerCAmelCase , lowerCAmelCase , )
else:
logger.info("***** Running self-training: iteration: %d, stage: 2 *****" , lowerCAmelCase )
finetune(**lowerCAmelCase )
accelerator.wait_for_everyone()
assert os.path.exists(lowerCAmelCase )
logger.info("Self-training job completed: iteration: %d, stage: 2." , lowerCAmelCase )
_UpperCAmelCase : Optional[Any] = iteration
_UpperCAmelCase : Dict = data_dir_format(iteration + 1 )
_UpperCAmelCase : int = AutoConfig.from_pretrained(os.path.join(lowerCAmelCase , "best-checkpoint" ) )
_UpperCAmelCase : Optional[Any] = config.idalabel
_UpperCAmelCase : Any = os.path.join(lowerCAmelCase , "eval_results_best-checkpoint.json" )
_UpperCAmelCase : List[str] = os.path.join(lowerCAmelCase , "test_results_best-checkpoint.json" )
assert os.path.exists(lowerCAmelCase )
with open(lowerCAmelCase , "r" ) as f:
_UpperCAmelCase : Any = float(json.load(lowerCAmelCase )[args.eval_metric] )
_UpperCAmelCase : Optional[int] = os.path.join(lowerCAmelCase , "infer_output_best-checkpoint.csv" )
assert os.path.exists(lowerCAmelCase )
# Loading the dataset from local csv or json files.
_UpperCAmelCase : Tuple = load_dataset(args.data_file_extension , data_files={"data": data_files["infer"]} )["data"]
_UpperCAmelCase : str = load_dataset("csv" , data_files={"data": infer_output_file} )["data"]
if accelerator.is_main_process:
os.makedirs(lowerCAmelCase , exist_ok=lowerCAmelCase )
shutil.copy(lowerCAmelCase , os.path.join(lowerCAmelCase , F'eval_results_iter-{iteration}.json' ) )
if os.path.exists(lowerCAmelCase ):
shutil.copy(lowerCAmelCase , os.path.join(lowerCAmelCase , F'test_results_iter-{iteration}.json' ) )
create_pseudo_labeled_data(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
accelerator.wait_for_everyone()
_UpperCAmelCase : Tuple = os.path.join(lowerCAmelCase , F'train_pseudo.{args.data_file_extension}' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
_UpperCAmelCase : str = eval_result
if best_iteration is None:
_UpperCAmelCase : List[Any] = new_iteration
_UpperCAmelCase : Union[str, Any] = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
_UpperCAmelCase : str = new_iteration
_UpperCAmelCase : Optional[int] = new_eval_result
_UpperCAmelCase : Dict = 0
else:
if new_eval_result == best_eval_result:
_UpperCAmelCase : Union[str, Any] = new_iteration
_UpperCAmelCase : Any = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
_UpperCAmelCase : List[Any] = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("Best iteration: %d" , lowerCAmelCase )
logger.info("Best evaluation result: %s = %f" , args.eval_metric , lowerCAmelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(lowerCAmelCase , F'eval_results_iter-{iteration}.json' ) , os.path.join(lowerCAmelCase , "eval_results_best-iteration.json" ) , )
else:
# Assume that the last iteration is the best
logger.info("Best iteration: %d" , args.max_selftrain_iterations - 1 )
logger.info("Best evaluation result: %s = %f" , args.eval_metric , lowerCAmelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(lowerCAmelCase , F'eval_results_iter-{args.max_selftrain_iterations - 1}.json' ) , os.path.join(lowerCAmelCase , "eval_results_best-iteration.json" ) , )
| 300
|
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int ) -> list[list[int]]:
_UpperCAmelCase : list[list[int]] = []
create_all_state(1 , lowerCAmelCase , lowerCAmelCase , [] , lowerCAmelCase )
return result
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: list[int] , lowerCAmelCase: list[list[int]] , ) -> None:
if level == 0:
total_list.append(current_list[:] )
return
for i in range(lowerCAmelCase , total_number - level + 2 ):
current_list.append(lowerCAmelCase )
create_all_state(i + 1 , lowerCAmelCase , level - 1 , lowerCAmelCase , lowerCAmelCase )
current_list.pop()
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: list[list[int]] ) -> None:
for i in total_list:
print(*lowerCAmelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = 4
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = generate_all_combinations(n, k)
print_all_state(total_list)
| 300
| 1
|
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_UpperCAmelCase : List[str] = _symbol_database.Default()
_UpperCAmelCase : List[Any] = _descriptor_pool.Default().AddSerializedFile(
b"""\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"""
)
_UpperCAmelCase : Optional[Any] = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
_UpperCAmelCase : int = None
_UpperCAmelCase : Tuple = b"""H\003"""
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
_UpperCAmelCase : Any = 4_5
_UpperCAmelCase : Tuple = 1_5_8_1
_UpperCAmelCase : Optional[int] = 1_5_1_7
_UpperCAmelCase : Optional[int] = 1_5_7_0
_UpperCAmelCase : Optional[int] = 1_5_8_4
_UpperCAmelCase : Optional[Any] = 1_7_9_3
_UpperCAmelCase : Union[str, Any] = 1_7_9_5
_UpperCAmelCase : Dict = 1_9_1_6
_UpperCAmelCase : List[Any] = 1_8_6_4
_UpperCAmelCase : Optional[Any] = 1_9_0_5
_UpperCAmelCase : Tuple = 1_9_1_9
_UpperCAmelCase : Dict = 2_4_2_9
_UpperCAmelCase : Optional[int] = 2_2_0_8
_UpperCAmelCase : Union[str, Any] = 2_4_1_8
_UpperCAmelCase : List[str] = 2_3_2_3
_UpperCAmelCase : Optional[Any] = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 704
|
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
_UpperCAmelCase : Tuple = random.Random()
if is_torch_available():
import torch
def __magic_name__( lowerCamelCase, lowerCamelCase=1.0, lowerCamelCase=None, lowerCamelCase=None):
if rng is None:
__lowerCAmelCase = global_rng
__lowerCAmelCase = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
class a__ ( unittest.TestCase ):
"""simple docstring"""
def __init__(self , __lowercase , __lowercase=7 , __lowercase=4_00 , __lowercase=20_00 , __lowercase=1 , __lowercase=0.0 , __lowercase=1_60_00 , __lowercase=True , __lowercase=True , ):
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = min_seq_length
__lowerCAmelCase = max_seq_length
__lowerCAmelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
__lowerCAmelCase = feature_size
__lowerCAmelCase = padding_value
__lowerCAmelCase = sampling_rate
__lowerCAmelCase = return_attention_mask
__lowerCAmelCase = do_normalize
def _snake_case (self ):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def _snake_case (self , __lowercase=False , __lowercase=False ):
def _flatten(__lowercase ):
return list(itertools.chain(*__lowercase ) )
if equal_length:
__lowerCAmelCase = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
__lowerCAmelCase = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
__lowerCAmelCase = [np.asarray(__lowercase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class a__ ( __A , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] = ASTFeatureExtractor
def _snake_case (self ):
__lowerCAmelCase = ASTFeatureExtractionTester(self )
def _snake_case (self ):
# Tests that all call wrap to encode_plus and batch_encode_plus
__lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
__lowerCAmelCase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
__lowerCAmelCase = [np.asarray(__lowercase ) for speech_input in speech_inputs]
# Test not batched input
__lowerCAmelCase = feat_extract(speech_inputs[0] , return_tensors='''np''' ).input_values
__lowerCAmelCase = feat_extract(np_speech_inputs[0] , return_tensors='''np''' ).input_values
self.assertTrue(np.allclose(__lowercase , __lowercase , atol=1e-3 ) )
# Test batched
__lowerCAmelCase = feat_extract(__lowercase , padding=__lowercase , return_tensors='''np''' ).input_values
__lowerCAmelCase = feat_extract(__lowercase , padding=__lowercase , return_tensors='''np''' ).input_values
for enc_seq_a, enc_seq_a in zip(__lowercase , __lowercase ):
self.assertTrue(np.allclose(__lowercase , __lowercase , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
__lowerCAmelCase = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
__lowerCAmelCase = np.asarray(__lowercase )
__lowerCAmelCase = feat_extract(__lowercase , return_tensors='''np''' ).input_values
__lowerCAmelCase = feat_extract(__lowercase , return_tensors='''np''' ).input_values
for enc_seq_a, enc_seq_a in zip(__lowercase , __lowercase ):
self.assertTrue(np.allclose(__lowercase , __lowercase , atol=1e-3 ) )
@require_torch
def _snake_case (self ):
import torch
__lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__lowerCAmelCase = np.random.rand(1_00 ).astype(np.floataa )
__lowerCAmelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
__lowerCAmelCase = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''np''' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
__lowerCAmelCase = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''pt''' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def _snake_case (self , __lowercase ):
from datasets import load_dataset
__lowerCAmelCase = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
__lowerCAmelCase = ds.sort('''id''' ).select(range(__lowercase ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
@require_torch
def _snake_case (self ):
# fmt: off
__lowerCAmelCase = torch.tensor(
[-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
-1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
-1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
-0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9] )
# fmt: on
__lowerCAmelCase = self._load_datasamples(1 )
__lowerCAmelCase = ASTFeatureExtractor()
__lowerCAmelCase = feature_extractor(__lowercase , return_tensors='''pt''' ).input_values
self.assertEquals(input_values.shape , (1, 10_24, 1_28) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , __lowercase , atol=1e-4 ) )
| 474
| 0
|
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Dict:
"""simple docstring"""
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
A__ = flax_key_tuple[:-1] + ('''weight''',)
A__ = torch.permute(lowercase_ , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(lowercase_ ):
# linear layer
A__ = flax_key_tuple[:-1] + ('''weight''',)
A__ = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
A__ = flax_key_tuple[:-1] + ('''weight''',)
return flax_key_tuple, flax_tensor
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
if "metadata" in layer:
A__ = layer.split('''metadata''' )
A__ = ''''''.join(split_layer[0] )[:-1]
A__ = [tuple(('''metadata''' + split_layer[1]).split('''/''' ) )]
elif "kvstore" in layer:
A__ = layer.split('''kvstore''' )
A__ = ''''''.join(split_layer[0] )[:-1]
A__ = [tuple(('''kvstore''' + split_layer[1]).split('''/''' ) )]
else:
A__ = layer.split('''/''' )
A__ = '''/'''.join(split_layer[:-1] )
A__ = (split_layer[-1],)
if "kvstore/path" in layer:
A__ = f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
elif "kvstore/driver" in layer:
A__ = '''file'''
else:
A__ = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
A__ = rename_keys(lowercase_ )
A__ = {}
for k, v in current_block.items():
A__ = v
A__ = new_current_block
torch.save(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ = WEIGHTS_NAME ) -> Any:
"""simple docstring"""
A__ = convert_file_size_to_int(lowercase_ )
A__ = []
A__ = {}
A__ = 0
A__ = 0
os.makedirs(lowercase_ , exist_ok=lowercase_ )
with gfile.GFile(switch_checkpoint_path + '''/checkpoint''' , '''rb''' ) as fp:
A__ = serialization.msgpack_restore(fp.read() )['''optimizer''']['''target''']
A__ = flatten_dict(lowercase_ , sep='''/''' )
A__ = {}
for layer in checkpoint_info.keys():
A__ , A__ , A__ = get_key_and_tensorstore_dict(
lowercase_ , lowercase_ , lowercase_ )
if curr_real_layer_name in all_layers:
A__ = content
else:
A__ = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
A__ = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
A__ = torch.tensor(lowercase_ )
A__ = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
A__ , A__ = rename_base_flax_keys(tuple(key.split('''/''' ) ) , lowercase_ )
A__ = '''/'''.join(lowercase_ )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
A__ = os.path.join(
lowercase_ , weights_name.replace('''.bin''' , f"""-{len(lowercase_ )+1:05d}-of-???.bin""" ) )
rename_and_save_block(lowercase_ , lowercase_ )
sharded_state_dicts.append(current_block.keys() )
del current_block
A__ = {}
A__ = 0
A__ = raw_weights.to(getattr(lowercase_ , lowercase_ ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
A__ = os.path.join(lowercase_ , weights_name.replace('''.bin''' , f"""-{len(lowercase_ )+1:05d}-of-???.bin""" ) )
rename_and_save_block(lowercase_ , lowercase_ )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(lowercase_ ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
A__ = {}
A__ = {}
for idx, shard in enumerate(lowercase_ ):
A__ = weights_name.replace(
'''.bin''' , f"""-{idx+1:05d}-of-{len(lowercase_ ):05d}.bin""" ) # len(sharded_state_dicts):05d}
A__ = os.path.join(lowercase_ , weights_name.replace('''.bin''' , f"""-{idx+1:05d}-of-???.bin""" ) )
os.rename(lowercase_ , os.path.join(lowercase_ , lowercase_ ) )
A__ = shard
for key in shard:
A__ = shard_file
# Add the metadata
A__ = {'''total_size''': total_size}
A__ = {'''metadata''': metadata, '''weight_map''': weight_map}
with open(os.path.join(lowercase_ , lowercase_ ) , '''w''' , encoding='''utf-8''' ) as f:
A__ = json.dumps(lowercase_ , indent=2 , sort_keys=lowercase_ ) + '''\n'''
f.write(lowercase_ )
return metadata, index
if __name__ == "__main__":
_lowerCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
type=str,
required=False,
help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
)
parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
type=str,
required=False,
help="""Path to the output pytorch model.""",
)
_lowerCamelCase : int = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def SCREAMING_SNAKE_CASE ( ) -> Any:
"""simple docstring"""
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
A__ = SwitchTransformersConfig.from_pretrained('''google/switch-base-8''' )
config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted''' )
A__ = SwitchTransformersForConditionalGeneration.from_pretrained(
'''/home/arthur_huggingface_co/transformers/switch_converted''' , device_map='''auto''' )
A__ = TaTokenizer.from_pretrained('''t5-small''' )
A__ = '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''
A__ = tokenizer(lowercase_ , return_tensors='''pt''' ).input_ids
A__ = model.generate(lowercase_ , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 87
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Any = {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : int = ["""XLNetTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : int = ["""XLNetTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Union[str, Any] = [
"""XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XLNetForMultipleChoice""",
"""XLNetForQuestionAnswering""",
"""XLNetForQuestionAnsweringSimple""",
"""XLNetForSequenceClassification""",
"""XLNetForTokenClassification""",
"""XLNetLMHeadModel""",
"""XLNetModel""",
"""XLNetPreTrainedModel""",
"""load_tf_weights_in_xlnet""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Dict = [
"""TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFXLNetForMultipleChoice""",
"""TFXLNetForQuestionAnsweringSimple""",
"""TFXLNetForSequenceClassification""",
"""TFXLNetForTokenClassification""",
"""TFXLNetLMHeadModel""",
"""TFXLNetMainLayer""",
"""TFXLNetModel""",
"""TFXLNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
_lowerCamelCase : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87
| 1
|
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowerCamelCase ( __UpperCAmelCase ):
UpperCAmelCase__ : Union[str, Any] = ["""image_processor""", """tokenizer"""]
UpperCAmelCase__ : List[str] = """OwlViTImageProcessor"""
UpperCAmelCase__ : List[Any] = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__(self : Tuple , _A : List[str]=None , _A : Optional[int]=None , **_A : Any ) -> str:
snake_case = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , UpperCAmelCase_ , )
snake_case = kwargs.pop("feature_extractor" )
snake_case = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ )
def __call__(self : List[Any] , _A : Optional[Any]=None , _A : List[Any]=None , _A : Any=None , _A : Tuple="max_length" , _A : Optional[int]="np" , **_A : str ) -> Optional[int]:
if text is None and query_images is None and images is None:
raise ValueError(
"You have to specify at least one text or query image or image. All three cannot be none." )
if text is not None:
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or (isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and not isinstance(text[0] , UpperCAmelCase_ )):
snake_case = [self.tokenizer(UpperCAmelCase_ , padding=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )]
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and isinstance(text[0] , UpperCAmelCase_ ):
snake_case = []
# Maximum number of queries across batch
snake_case = max([len(UpperCAmelCase_ ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(UpperCAmelCase_ ) != max_num_queries:
snake_case = t + [" "] * (max_num_queries - len(UpperCAmelCase_ ))
snake_case = self.tokenizer(UpperCAmelCase_ , padding=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
encodings.append(UpperCAmelCase_ )
else:
raise TypeError("Input text should be a string, a list of strings or a nested list of strings" )
if return_tensors == "np":
snake_case = np.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 )
snake_case = np.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
snake_case = jnp.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 )
snake_case = jnp.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
snake_case = torch.cat([encoding["input_ids"] for encoding in encodings] , dim=0 )
snake_case = torch.cat([encoding["attention_mask"] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
snake_case = tf.stack([encoding["input_ids"] for encoding in encodings] , axis=0 )
snake_case = tf.stack([encoding["attention_mask"] for encoding in encodings] , axis=0 )
else:
raise ValueError("Target return tensor type could not be returned" )
snake_case = BatchEncoding()
snake_case = input_ids
snake_case = attention_mask
if query_images is not None:
snake_case = BatchEncoding()
snake_case = self.image_processor(
UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ ).pixel_values
snake_case = query_pixel_values
if images is not None:
snake_case = self.image_processor(UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
if text is not None and images is not None:
snake_case = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
snake_case = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCAmelCase_ ) , tensor_type=UpperCAmelCase_ )
def UpperCAmelCase(self : Optional[Any] , *_A : Dict , **_A : int ) -> Dict:
return self.image_processor.post_process(*UpperCAmelCase_ , **UpperCAmelCase_ )
def UpperCAmelCase(self : Optional[int] , *_A : Optional[int] , **_A : Optional[Any] ) -> Tuple:
return self.image_processor.post_process_object_detection(*UpperCAmelCase_ , **UpperCAmelCase_ )
def UpperCAmelCase(self : Optional[int] , *_A : Any , **_A : str ) -> Tuple:
return self.image_processor.post_process_image_guided_detection(*UpperCAmelCase_ , **UpperCAmelCase_ )
def UpperCAmelCase(self : str , *_A : Any , **_A : int ) -> int:
return self.tokenizer.batch_decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
def UpperCAmelCase(self : List[str] , *_A : str , **_A : Optional[Any] ) -> List[Any]:
return self.tokenizer.decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
@property
def UpperCAmelCase(self : List[Any] ) -> List[str]:
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , UpperCAmelCase_ , )
return self.image_processor_class
@property
def UpperCAmelCase(self : Dict ) -> Union[str, Any]:
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , UpperCAmelCase_ , )
return self.image_processor
| 711
|
import sys
_A = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def lowercase_ ( A__ ) -> int:
"""simple docstring"""
snake_case = 1
for digit in s:
product *= int(A__ )
return product
def lowercase_ ( A__ = N ) -> int:
"""simple docstring"""
snake_case = -sys.maxsize - 1
snake_case = n[:13]
snake_case = 13
while cur_index < len(A__ ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
snake_case = substr[1:] + n[cur_index]
cur_index += 1
else:
snake_case = max(A__ , str_eval(A__ ) )
snake_case = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f"{solution() = }")
| 294
| 0
|
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class snake_case (UpperCamelCase ):
lowerCAmelCase__ :Dict = ["vqvae"]
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,) -> Dict:
super().__init__()
self.register_modules(unet=UpperCAmelCase_ ,scheduler=UpperCAmelCase_ ,mel=UpperCAmelCase_ ,vqvae=UpperCAmelCase_ )
def _a ( self ) -> int:
return 50 if isinstance(self.scheduler ,UpperCAmelCase_ ) else 1_000
@torch.no_grad()
def __call__( self ,UpperCAmelCase_ = 1 ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = 0 ,UpperCAmelCase_ = 0 ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = 0 ,UpperCAmelCase_ = 0 ,UpperCAmelCase_ = None ,UpperCAmelCase_ = 0 ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_=True ,) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
lowercase__ = steps or self.get_default_steps()
self.scheduler.set_timesteps(UpperCAmelCase_ )
lowercase__ = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
lowercase__ = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
lowercase__ = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) ,generator=UpperCAmelCase_ ,device=self.device ,)
lowercase__ = noise
lowercase__ = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(UpperCAmelCase_ ,UpperCAmelCase_ )
lowercase__ = self.mel.audio_slice_to_image(UpperCAmelCase_ )
lowercase__ = np.frombuffer(input_image.tobytes() ,dtype="uint8" ).reshape(
(input_image.height, input_image.width) )
lowercase__ = (input_image / 255) * 2 - 1
lowercase__ = torch.tensor(input_image[np.newaxis, :, :] ,dtype=torch.float ).to(self.device )
if self.vqvae is not None:
lowercase__ = self.vqvae.encode(torch.unsqueeze(UpperCAmelCase_ ,0 ) ).latent_dist.sample(
generator=UpperCAmelCase_ )[0]
lowercase__ = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
lowercase__ = self.scheduler.add_noise(UpperCAmelCase_ ,UpperCAmelCase_ ,self.scheduler.timesteps[start_step - 1] )
lowercase__ = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
lowercase__ = int(mask_start_secs * pixels_per_second )
lowercase__ = int(mask_end_secs * pixels_per_second )
lowercase__ = self.scheduler.add_noise(UpperCAmelCase_ ,UpperCAmelCase_ ,torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet ,UpperCAmelCase_ ):
lowercase__ = self.unet(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )["sample"]
else:
lowercase__ = self.unet(UpperCAmelCase_ ,UpperCAmelCase_ )["sample"]
if isinstance(self.scheduler ,UpperCAmelCase_ ):
lowercase__ = self.scheduler.step(
model_output=UpperCAmelCase_ ,timestep=UpperCAmelCase_ ,sample=UpperCAmelCase_ ,eta=UpperCAmelCase_ ,generator=UpperCAmelCase_ ,)["prev_sample"]
else:
lowercase__ = self.scheduler.step(
model_output=UpperCAmelCase_ ,timestep=UpperCAmelCase_ ,sample=UpperCAmelCase_ ,generator=UpperCAmelCase_ ,)["prev_sample"]
if mask is not None:
if mask_start > 0:
lowercase__ = mask[:, step, :, :mask_start]
if mask_end > 0:
lowercase__ = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
lowercase__ = 1 / self.vqvae.config.scaling_factor * images
lowercase__ = self.vqvae.decode(UpperCAmelCase_ )["sample"]
lowercase__ = (images / 2 + 0.5).clamp(0 ,1 )
lowercase__ = images.cpu().permute(0 ,2 ,3 ,1 ).numpy()
lowercase__ = (images * 255).round().astype("uint8" )
lowercase__ = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(UpperCAmelCase_ ,mode="RGB" ).convert("L" ) for _ in images) )
lowercase__ = [self.mel.image_to_audio(UpperCAmelCase_ ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(UpperCAmelCase_ )[:, np.newaxis, :] ) ,**ImagePipelineOutput(UpperCAmelCase_ ) )
@torch.no_grad()
def _a ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = 50 ) -> np.ndarray:
assert isinstance(self.scheduler ,UpperCAmelCase_ )
self.scheduler.set_timesteps(UpperCAmelCase_ )
lowercase__ = np.array(
[np.frombuffer(image.tobytes() ,dtype="uint8" ).reshape((1, image.height, image.width) ) for image in images] )
lowercase__ = (sample / 255) * 2 - 1
lowercase__ = torch.Tensor(UpperCAmelCase_ ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps ,(0,) ) ):
lowercase__ = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
lowercase__ = self.scheduler.alphas_cumprod[t]
lowercase__ = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
lowercase__ = 1 - alpha_prod_t
lowercase__ = self.unet(UpperCAmelCase_ ,UpperCAmelCase_ )["sample"]
lowercase__ = (1 - alpha_prod_t_prev) ** 0.5 * model_output
lowercase__ = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
lowercase__ = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def _a ( UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ) -> torch.Tensor:
lowercase__ = acos(torch.dot(torch.flatten(UpperCAmelCase_ ) ,torch.flatten(UpperCAmelCase_ ) ) / torch.norm(UpperCAmelCase_ ) / torch.norm(UpperCAmelCase_ ) )
return sin((1 - alpha) * theta ) * xa / sin(UpperCAmelCase_ ) + sin(alpha * theta ) * xa / sin(UpperCAmelCase_ )
| 267
|
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class snake_case (unittest.TestCase ):
def _a ( self ) -> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def _a ( self ) -> List[Any]:
lowercase__ , lowercase__ = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-canny" ,from_pt=UpperCAmelCase_ ,dtype=jnp.bfloataa )
lowercase__ , lowercase__ = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" ,controlnet=UpperCAmelCase_ ,from_pt=UpperCAmelCase_ ,dtype=jnp.bfloataa )
lowercase__ = controlnet_params
lowercase__ = "bird"
lowercase__ = jax.device_count()
lowercase__ = pipe.prepare_text_inputs([prompts] * num_samples )
lowercase__ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" )
lowercase__ = pipe.prepare_image_inputs([canny_image] * num_samples )
lowercase__ = jax.random.PRNGKey(0 )
lowercase__ = jax.random.split(UpperCAmelCase_ ,jax.device_count() )
lowercase__ = replicate(UpperCAmelCase_ )
lowercase__ = shard(UpperCAmelCase_ )
lowercase__ = shard(UpperCAmelCase_ )
lowercase__ = pipe(
prompt_ids=UpperCAmelCase_ ,image=UpperCAmelCase_ ,params=UpperCAmelCase_ ,prng_seed=UpperCAmelCase_ ,num_inference_steps=50 ,jit=UpperCAmelCase_ ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
lowercase__ = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase__ = images[0, 253:256, 253:256, -1]
lowercase__ = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase__ = jnp.array(
[0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78] )
print(F'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def _a ( self ) -> List[Any]:
lowercase__ , lowercase__ = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-openpose" ,from_pt=UpperCAmelCase_ ,dtype=jnp.bfloataa )
lowercase__ , lowercase__ = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" ,controlnet=UpperCAmelCase_ ,from_pt=UpperCAmelCase_ ,dtype=jnp.bfloataa )
lowercase__ = controlnet_params
lowercase__ = "Chef in the kitchen"
lowercase__ = jax.device_count()
lowercase__ = pipe.prepare_text_inputs([prompts] * num_samples )
lowercase__ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" )
lowercase__ = pipe.prepare_image_inputs([pose_image] * num_samples )
lowercase__ = jax.random.PRNGKey(0 )
lowercase__ = jax.random.split(UpperCAmelCase_ ,jax.device_count() )
lowercase__ = replicate(UpperCAmelCase_ )
lowercase__ = shard(UpperCAmelCase_ )
lowercase__ = shard(UpperCAmelCase_ )
lowercase__ = pipe(
prompt_ids=UpperCAmelCase_ ,image=UpperCAmelCase_ ,params=UpperCAmelCase_ ,prng_seed=UpperCAmelCase_ ,num_inference_steps=50 ,jit=UpperCAmelCase_ ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
lowercase__ = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase__ = images[0, 253:256, 253:256, -1]
lowercase__ = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase__ = jnp.array(
[[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]] )
print(F'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 267
| 1
|
'''simple docstring'''
UpperCAmelCase : Dict = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCAmelCase : Tuple = {
'm': 0,
'km': 3,
'Mm': 6,
'Gm': 9,
'Tm': 1_2,
'Pm': 1_5,
'Em': 1_8,
'Zm': 2_1,
'Ym': 2_4,
}
def _a ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case : Optional[Any] = from_type.lower().strip('''s''' )
_snake_case : int = to_type.lower().strip('''s''' )
_snake_case : Any = UNIT_SYMBOL.get(lowerCAmelCase_ , lowerCAmelCase_ )
_snake_case : Dict = UNIT_SYMBOL.get(lowerCAmelCase_ , lowerCAmelCase_ )
if from_sanitized not in METRIC_CONVERSION:
_snake_case : int = (
f'''Invalid \'from_type\' value: {from_type!r}.\n'''
f'''Conversion abbreviations are: {", ".join(lowerCAmelCase_ )}'''
)
raise ValueError(lowerCAmelCase_ )
if to_sanitized not in METRIC_CONVERSION:
_snake_case : Any = (
f'''Invalid \'to_type\' value: {to_type!r}.\n'''
f'''Conversion abbreviations are: {", ".join(lowerCAmelCase_ )}'''
)
raise ValueError(lowerCAmelCase_ )
_snake_case : Optional[Any] = METRIC_CONVERSION[from_sanitized]
_snake_case : Dict = METRIC_CONVERSION[to_sanitized]
_snake_case : str = 1
if from_exponent > to_exponent:
_snake_case : List[str] = from_exponent - to_exponent
else:
_snake_case : Union[str, Any] = -(to_exponent - from_exponent)
return value * pow(10 , lowerCAmelCase_ )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 47
|
'''simple docstring'''
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
UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase : List[Any] = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCAmelCase : Any = {
'tokenizer_file': {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
},
}
UpperCAmelCase : Optional[Any] = {
'gpt-neox-20b': 2_0_4_8,
}
class lowerCamelCase (a__ ):
_lowercase : Optional[int] = VOCAB_FILES_NAMES
_lowercase : str = PRETRAINED_VOCAB_FILES_MAP
_lowercase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowercase : Optional[int] = ["""input_ids""", """attention_mask"""]
def __init__( self , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__="<|endoftext|>" , lowercase__="<|endoftext|>" , lowercase__="<|endoftext|>" , lowercase__=False , **lowercase__ , ) -> List[Any]:
"""simple docstring"""
super().__init__(
lowercase__ , lowercase__ , tokenizer_file=lowercase__ , unk_token=lowercase__ , bos_token=lowercase__ , eos_token=lowercase__ , add_prefix_space=lowercase__ , **lowercase__ , )
_snake_case : Optional[int] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , lowercase__ ) != add_prefix_space:
_snake_case : int = getattr(lowercase__ , pre_tok_state.pop('''type''' ) )
_snake_case : int = add_prefix_space
_snake_case : Optional[Any] = pre_tok_class(**lowercase__ )
_snake_case : List[str] = add_prefix_space
def UpperCAmelCase_ ( self , lowercase__ , lowercase__ = None ) -> Tuple[str]:
"""simple docstring"""
_snake_case : Optional[int] = self._tokenizer.model.save(lowercase__ , name=lowercase__ )
return tuple(lowercase__ )
def UpperCAmelCase_ ( self , lowercase__ ) -> List[int]:
"""simple docstring"""
_snake_case : List[str] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowercase__ , add_special_tokens=lowercase__ ) + [self.eos_token_id] )
if len(lowercase__ ) > self.model_max_length:
_snake_case : Dict = input_ids[-self.model_max_length :]
return input_ids
| 47
| 1
|
"""simple docstring"""
__UpperCamelCase : str = 2_5_6
# Modulus to hash a string
__UpperCamelCase : Union[str, Any] = 1_0_0_0_0_0_3
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
lowerCAmelCase__ : Dict = len(A_ )
lowerCAmelCase__ : Dict = len(A_ )
if p_len > t_len:
return False
lowerCAmelCase__ : Union[str, Any] = 0
lowerCAmelCase__ : Optional[Any] = 0
lowerCAmelCase__ : Optional[Any] = 1
# Calculating the hash of pattern and substring of text
for i in range(A_ ):
lowerCAmelCase__ : Dict = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
lowerCAmelCase__ : Any = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
lowerCAmelCase__ : List[Any] = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
lowerCAmelCase__ : Any = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def __SCREAMING_SNAKE_CASE ( ):
lowerCAmelCase__ : Optional[Any] = '''abc1abc12'''
lowerCAmelCase__ : List[Any] = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
lowerCAmelCase__ : int = '''alskfjaldsk23adsfabcabc'''
assert rabin_karp(A_ , A_ ) and not rabin_karp(A_ , A_ )
# Test 2)
lowerCAmelCase__ : Optional[Any] = '''ABABX'''
lowerCAmelCase__ : Optional[Any] = '''ABABZABABYABABX'''
assert rabin_karp(A_ , A_ )
# Test 3)
lowerCAmelCase__ : Optional[int] = '''AAAB'''
lowerCAmelCase__ : Dict = '''ABAAAAAB'''
assert rabin_karp(A_ , A_ )
# Test 4)
lowerCAmelCase__ : Union[str, Any] = '''abcdabcy'''
lowerCAmelCase__ : Optional[int] = '''abcxabcdabxabcdabcdabcy'''
assert rabin_karp(A_ , A_ )
# Test 5)
lowerCAmelCase__ : Tuple = '''Lü'''
lowerCAmelCase__ : Optional[Any] = '''Lüsai'''
assert rabin_karp(A_ , A_ )
lowerCAmelCase__ : Dict = '''Lue'''
assert not rabin_karp(A_ , A_ )
print('''Success.''' )
if __name__ == "__main__":
test_rabin_karp()
| 450
|
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__UpperCamelCase : Optional[Any] = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : str ,lowercase_ : Optional[Any] ,lowercase_ : Optional[Any]=1_6 ,lowercase_ : int=1_3 ,lowercase_ : Optional[int]=7 ,lowercase_ : int=1_4 ,lowercase_ : str=1_0 ,lowercase_ : List[Any]=1_9 ,lowercase_ : Any=5 ,lowercase_ : Any=4 ,lowercase_ : List[str]=True ,lowercase_ : Union[str, Any]=1_6 ,lowercase_ : Tuple=2 ,lowercase_ : str=4 ,lowercase_ : Tuple=4 ,lowercase_ : int="gelu" ,lowercase_ : Any=0.1 ,lowercase_ : int=0.1 ,lowercase_ : Optional[int]=[1, 2, 3, 4, 5] ,lowercase_ : List[Any]=2_5 ,lowercase_ : Union[str, Any]=5 ,):
lowerCAmelCase__ : List[str] = d_model
lowerCAmelCase__ : Optional[Any] = parent
lowerCAmelCase__ : Dict = batch_size
lowerCAmelCase__ : Any = prediction_length
lowerCAmelCase__ : str = context_length
lowerCAmelCase__ : int = cardinality
lowerCAmelCase__ : Dict = num_time_features
lowerCAmelCase__ : str = lags_sequence
lowerCAmelCase__ : int = embedding_dimension
lowerCAmelCase__ : Dict = is_training
lowerCAmelCase__ : Optional[Any] = hidden_size
lowerCAmelCase__ : Optional[int] = num_hidden_layers
lowerCAmelCase__ : List[str] = num_attention_heads
lowerCAmelCase__ : int = intermediate_size
lowerCAmelCase__ : List[str] = hidden_act
lowerCAmelCase__ : Dict = hidden_dropout_prob
lowerCAmelCase__ : Optional[int] = attention_probs_dropout_prob
lowerCAmelCase__ : Tuple = context_length
lowerCAmelCase__ : Optional[Any] = prediction_length + label_length
lowerCAmelCase__ : Union[str, Any] = label_length
lowerCAmelCase__ : Optional[int] = moving_average
lowerCAmelCase__ : Dict = autocorrelation_factor
def __lowerCAmelCase ( self : Tuple ):
return AutoformerConfig(
d_model=self.d_model ,encoder_layers=self.num_hidden_layers ,decoder_layers=self.num_hidden_layers ,encoder_attention_heads=self.num_attention_heads ,decoder_attention_heads=self.num_attention_heads ,encoder_ffn_dim=self.intermediate_size ,decoder_ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,prediction_length=self.prediction_length ,context_length=self.context_length ,label_length=self.label_length ,lags_sequence=self.lags_sequence ,num_time_features=self.num_time_features ,num_static_categorical_features=1 ,cardinality=[self.cardinality] ,embedding_dimension=[self.embedding_dimension] ,moving_average=self.moving_average ,)
def __lowerCAmelCase ( self : Union[str, Any] ,lowercase_ : str ):
lowerCAmelCase__ : str = config.context_length + max(config.lags_sequence )
lowerCAmelCase__ : Optional[int] = ids_tensor([self.batch_size, 1] ,config.cardinality[0] )
lowerCAmelCase__ : int = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
lowerCAmelCase__ : Any = floats_tensor([self.batch_size, _past_length] )
lowerCAmelCase__ : List[str] = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
lowerCAmelCase__ : List[str] = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
lowerCAmelCase__ : Tuple = floats_tensor([self.batch_size, config.prediction_length] )
lowerCAmelCase__ : Optional[int] = {
'''past_values''': past_values,
'''static_categorical_features''': static_categorical_features,
'''past_time_features''': past_time_features,
'''past_observed_mask''': past_observed_mask,
'''future_time_features''': future_time_features,
'''future_values''': future_values,
}
return inputs_dict
def __lowerCAmelCase ( self : Optional[Any] ):
lowerCAmelCase__ : Dict = self.get_config()
lowerCAmelCase__ : Dict = self.prepare_autoformer_inputs_dict(lowercase_ )
return config, inputs_dict
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ ,lowerCAmelCase__ : List[Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def __lowerCAmelCase ( self : Dict ,lowercase_ : Optional[int] ,lowercase_ : Union[str, Any] ):
lowerCAmelCase__ : Any = AutoformerModel(config=lowercase_ ).to(lowercase_ ).eval()
lowerCAmelCase__ : Tuple = model(**lowercase_ )
lowerCAmelCase__ : Any = outputs.encoder_last_hidden_state
lowerCAmelCase__ : str = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ : List[Any] = model.get_encoder()
encoder.save_pretrained(lowercase_ )
lowerCAmelCase__ : Union[str, Any] = AutoformerEncoder.from_pretrained(lowercase_ ).to(lowercase_ )
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : str = model.create_network_inputs(**lowercase_ )
lowerCAmelCase__ ,lowerCAmelCase__ : Optional[int] = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
lowerCAmelCase__ : int = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) ,dim=-1 ,)
lowerCAmelCase__ : Dict = encoder(inputs_embeds=lowercase_ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
lowerCAmelCase__ : Optional[int] = (
torch.mean(transformer_inputs[:, : config.context_length, ...] ,dim=1 )
.unsqueeze(1 )
.repeat(1 ,config.prediction_length ,1 )
)
lowerCAmelCase__ : Optional[Any] = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] ,device=enc_input.device ,)
lowerCAmelCase__ : List[Any] = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) ,dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) ,dim=-1 ,)
lowerCAmelCase__ : Any = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) ,dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) ,dim=-1 ,)
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ : Optional[int] = model.get_decoder()
decoder.save_pretrained(lowercase_ )
lowerCAmelCase__ : Tuple = AutoformerDecoder.from_pretrained(lowercase_ ).to(lowercase_ )
lowerCAmelCase__ : Union[str, Any] = decoder(
trend=lowercase_ ,inputs_embeds=lowercase_ ,encoder_hidden_states=lowercase_ ,)[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class SCREAMING_SNAKE_CASE ( a_ , a_ , unittest.TestCase ):
"""simple docstring"""
lowercase__ = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
lowercase__ = (AutoformerForPrediction,) if is_torch_available() else ()
lowercase__ = {"feature-extraction": AutoformerModel} if is_torch_available() else {}
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def __lowerCAmelCase ( self : int ):
lowerCAmelCase__ : int = AutoformerModelTester(self )
lowerCAmelCase__ : Optional[int] = ConfigTester(self ,config_class=lowercase_ ,has_text_modality=lowercase_ )
def __lowerCAmelCase ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : str ):
lowerCAmelCase__ ,lowerCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
lowerCAmelCase__ : Optional[Any] = model_class(lowercase_ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
lowerCAmelCase__ ,lowerCAmelCase__ : List[Any] = model_class.from_pretrained(lowercase_ ,output_loading_info=lowercase_ )
self.assertEqual(info['''missing_keys'''] ,[] )
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*lowercase_ )
@unittest.skip(reason='''Model has no tokens embeddings''' )
def __lowerCAmelCase ( self : Optional[int] ):
pass
def __lowerCAmelCase ( self : Optional[Any] ):
lowerCAmelCase__ : List[str] = inspect.signature(getattr(lowercase_ ,'''forward''' ) )
# The main input is the name of the argument after `self`
lowerCAmelCase__ : Any = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name ,lowercase_ )
def __lowerCAmelCase ( self : Union[str, Any] ):
lowerCAmelCase__ ,lowerCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ : int = model_class(lowercase_ )
lowerCAmelCase__ : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ : int = [*signature.parameters.keys()]
lowerCAmelCase__ : Optional[int] = [
'''past_values''',
'''past_time_features''',
'''past_observed_mask''',
'''static_categorical_features''',
'''static_real_features''',
'''future_values''',
'''future_time_features''',
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('''future_observed_mask''' )
expected_arg_names.extend(
[
'''decoder_attention_mask''',
'''head_mask''',
'''decoder_head_mask''',
'''cross_attn_head_mask''',
'''encoder_outputs''',
'''past_key_values''',
'''output_hidden_states''',
'''output_attentions''',
'''use_cache''',
'''return_dict''',
] )
self.assertListEqual(arg_names[: len(lowercase_ )] ,lowercase_ )
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ ,lowerCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ : List[str] = True
lowerCAmelCase__ : int = getattr(self.model_tester ,'''seq_length''' ,lowercase_ )
lowerCAmelCase__ : Union[str, Any] = getattr(self.model_tester ,'''decoder_seq_length''' ,lowercase_ )
lowerCAmelCase__ : Tuple = getattr(self.model_tester ,'''encoder_seq_length''' ,lowercase_ )
lowerCAmelCase__ : List[Any] = getattr(self.model_tester ,'''d_model''' ,lowercase_ )
lowerCAmelCase__ : Union[str, Any] = getattr(self.model_tester ,'''num_attention_heads''' ,lowercase_ )
lowerCAmelCase__ : str = d_model // num_attention_heads
for model_class in self.all_model_classes:
lowerCAmelCase__ : List[str] = True
lowerCAmelCase__ : Optional[int] = False
lowerCAmelCase__ : Optional[Any] = True
lowerCAmelCase__ : Any = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
lowerCAmelCase__ : str = model(**self._prepare_for_class(lowercase_ ,lowercase_ ) )
lowerCAmelCase__ : Optional[Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowercase_ ) ,self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCAmelCase__ : List[str] = True
lowerCAmelCase__ : Union[str, Any] = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
lowerCAmelCase__ : Dict = model(**self._prepare_for_class(lowercase_ ,lowercase_ ) )
lowerCAmelCase__ : int = outputs.encoder_attentions
self.assertEqual(len(lowercase_ ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, encoder_seq_length, dim] ,)
lowerCAmelCase__ : Tuple = len(lowercase_ )
lowerCAmelCase__ : List[str] = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(lowercase_ ,lowercase_ )
# decoder attentions
lowerCAmelCase__ : str = outputs.decoder_attentions
self.assertIsInstance(lowercase_ ,(list, tuple) )
self.assertEqual(len(lowercase_ ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, decoder_seq_length, dim] ,)
# cross attentions
lowerCAmelCase__ : Any = outputs.cross_attentions
self.assertIsInstance(lowercase_ ,(list, tuple) )
self.assertEqual(len(lowercase_ ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, decoder_seq_length, dim] ,)
# Check attention is always last and order is fine
lowerCAmelCase__ : str = True
lowerCAmelCase__ : Tuple = True
lowerCAmelCase__ : Optional[Any] = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
lowerCAmelCase__ : Optional[Any] = model(**self._prepare_for_class(lowercase_ ,lowercase_ ) )
self.assertEqual(out_len + 2 ,len(lowercase_ ) )
lowerCAmelCase__ : Optional[Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowercase_ ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, encoder_seq_length, dim] ,)
@is_flaky()
def __lowerCAmelCase ( self : str ):
super().test_retain_grad_hidden_states_attentions()
def __SCREAMING_SNAKE_CASE ( A_="train-batch.pt" ):
lowerCAmelCase__ : Any = hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''' , filename=A_ , repo_type='''dataset''' )
lowerCAmelCase__ : Union[str, Any] = torch.load(A_ , map_location=A_ )
return batch
@require_torch
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : int ):
lowerCAmelCase__ : List[Any] = AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(lowercase_ )
lowerCAmelCase__ : int = prepare_batch()
with torch.no_grad():
lowerCAmelCase__ : Any = model(
past_values=batch['''past_values'''] ,past_time_features=batch['''past_time_features'''] ,past_observed_mask=batch['''past_observed_mask'''] ,static_categorical_features=batch['''static_categorical_features'''] ,future_values=batch['''future_values'''] ,future_time_features=batch['''future_time_features'''] ,)[0]
lowerCAmelCase__ : Dict = torch.Size(
(6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape ,lowercase_ )
lowerCAmelCase__ : Optional[int] = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] ,device=lowercase_ )
self.assertTrue(torch.allclose(output[0, :3, :3] ,lowercase_ ,atol=lowercase_ ) )
def __lowerCAmelCase ( self : Dict ):
lowerCAmelCase__ : List[Any] = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(lowercase_ )
lowerCAmelCase__ : Optional[int] = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
lowerCAmelCase__ : str = model(
past_values=batch['''past_values'''] ,past_time_features=batch['''past_time_features'''] ,past_observed_mask=batch['''past_observed_mask'''] ,static_categorical_features=batch['''static_categorical_features'''] ,).encoder_last_hidden_state
lowerCAmelCase__ : Any = torch.Size((6_4, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape ,lowercase_ )
lowerCAmelCase__ : Optional[int] = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] ,device=lowercase_ )
self.assertTrue(torch.allclose(output[0, :3, :3] ,lowercase_ ,atol=lowercase_ ) )
def __lowerCAmelCase ( self : List[str] ):
lowerCAmelCase__ : Union[str, Any] = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(lowercase_ )
lowerCAmelCase__ : Dict = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
lowerCAmelCase__ : int = model.generate(
static_categorical_features=batch['''static_categorical_features'''] ,past_time_features=batch['''past_time_features'''] ,past_values=batch['''past_values'''] ,future_time_features=batch['''future_time_features'''] ,past_observed_mask=batch['''past_observed_mask'''] ,)
lowerCAmelCase__ : List[Any] = torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape ,lowercase_ )
lowerCAmelCase__ : Union[str, Any] = torch.tensor([3130.6763, 4056.5293, 7053.0786] ,device=lowercase_ )
lowerCAmelCase__ : Dict = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] ,lowercase_ ,rtol=1E-1 ) )
| 450
| 1
|
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
snake_case__ : Optional[Any] = 2_0_0
# 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.
snake_case__ : int = 5_0
# 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.
snake_case__ : int = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = len([g for position, g in enumerate(__lowercase) if g == main_target[position]])
return (item, float(__lowercase))
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = random.randint(0 , len(__lowercase) - 1)
UpperCamelCase_ = parent_a[:random_slice] + parent_a[random_slice:]
UpperCamelCase_ = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = list(__lowercase)
if random.uniform(0 , 1) < MUTATION_PROBABILITY:
UpperCamelCase_ = random.choice(__lowercase)
return "".join(__lowercase)
def _snake_case (__lowercase , __lowercase , __lowercase , ):
UpperCamelCase_ = []
# Generate more children proportionally to the fitness score.
UpperCamelCase_ = int(parent_a[1] * 100) + 1
UpperCamelCase_ = 10 if child_n >= 10 else child_n
for _ in range(__lowercase):
UpperCamelCase_ = population_score[random.randint(0 , __lowercase)][0]
UpperCamelCase_ = crossover(parent_a[0] , __lowercase)
# Append new string to the population list.
pop.append(mutate(__lowercase , __lowercase))
pop.append(mutate(__lowercase , __lowercase))
return pop
def _snake_case (__lowercase , __lowercase , __lowercase = True):
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
UpperCamelCase_ = f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
raise ValueError(__lowercase)
# Verify that the target contains no genes besides the ones inside genes variable.
UpperCamelCase_ = sorted({c for c in target if c not in genes})
if not_in_genes_list:
UpperCamelCase_ = f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
raise ValueError(__lowercase)
# Generate random starting population.
UpperCamelCase_ = []
for _ in range(__lowercase):
population.append(''.join([random.choice(__lowercase) for i in range(len(__lowercase))]))
# Just some logs to know what the algorithms is doing.
UpperCamelCase_ = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(__lowercase)
# 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.
UpperCamelCase_ = [evaluate(__lowercase , __lowercase) for item in population]
# Check if there is a matching evolution.
UpperCamelCase_ = sorted(__lowercase , key=lambda __lowercase: x[1] , reverse=__lowercase)
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.
UpperCamelCase_ = population[: int(N_POPULATION / 3)]
population.clear()
population.extend(__lowercase)
# Normalize population score to be between 0 and 1.
UpperCamelCase_ = [
(item, score / len(__lowercase)) for item, score in population_score
]
# This is selection
for i in range(__lowercase):
population.extend(select(population_score[int(__lowercase)] , __lowercase , __lowercase))
# 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(__lowercase) > N_POPULATION:
break
if __name__ == "__main__":
snake_case__ : Dict = (
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
snake_case__ : Any = list(
""" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
"""nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
)
snake_case__ : Union[str, Any] = basic(target_str, genes_list)
print(
f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
)
| 707
|
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
snake_case__ : List[str] = logging.get_logger(__name__)
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def __init__( self , *_UpperCAmelCase , **_UpperCAmelCase ) -> None:
warnings.warn(
'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use VideoMAEImageProcessor instead.' , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 618
| 0
|
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _snake_case( UpperCAmelCase , unittest.TestCase ):
__snake_case: List[str] = DDIMPipeline
__snake_case: Tuple = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
__snake_case: Union[str, Any] = PipelineTesterMixin.required_optional_params - {
'''num_images_per_prompt''',
'''latents''',
'''callback''',
'''callback_steps''',
}
__snake_case: str = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
__snake_case: int = False
def _UpperCamelCase (self : Tuple ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
A__ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
A__ = DDIMScheduler()
A__ = {'unet': unet, 'scheduler': scheduler}
return components
def _UpperCamelCase (self : Any , a : List[str] , a : str=0 ) -> Any:
"""simple docstring"""
if str(a ).startswith('mps' ):
A__ = torch.manual_seed(a )
else:
A__ = torch.Generator(device=a ).manual_seed(a )
A__ = {
'batch_size': 1,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def _UpperCamelCase (self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
A__ = 'cpu'
A__ = self.get_dummy_components()
A__ = self.pipeline_class(**a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
A__ = self.get_dummy_inputs(a )
A__ = pipe(**a ).images
A__ = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
A__ = np.array(
[1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04] )
A__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(a , 1e-3 )
def _UpperCamelCase (self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def _UpperCamelCase (self : Any ) -> int:
"""simple docstring"""
super().test_save_load_local(expected_max_difference=3e-3 )
def _UpperCamelCase (self : Optional[Any] ) -> List[str]:
"""simple docstring"""
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def _UpperCamelCase (self : List[str] ) -> Any:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class _snake_case( unittest.TestCase ):
def _UpperCamelCase (self : Any ) -> List[Any]:
"""simple docstring"""
A__ = 'google/ddpm-cifar10-32'
A__ = UNetaDModel.from_pretrained(a )
A__ = DDIMScheduler()
A__ = DDIMPipeline(unet=a , scheduler=a )
ddim.to(a )
ddim.set_progress_bar_config(disable=a )
A__ = torch.manual_seed(0 )
A__ = ddim(generator=a , eta=0.0 , output_type='numpy' ).images
A__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A__ = np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _UpperCamelCase (self : int ) -> Optional[int]:
"""simple docstring"""
A__ = 'google/ddpm-ema-bedroom-256'
A__ = UNetaDModel.from_pretrained(a )
A__ = DDIMScheduler.from_pretrained(a )
A__ = DDIMPipeline(unet=a , scheduler=a )
ddpm.to(a )
ddpm.set_progress_bar_config(disable=a )
A__ = torch.manual_seed(0 )
A__ = ddpm(generator=a , output_type='numpy' ).images
A__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
A__ = np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 531
|
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class _snake_case:
def __init__(self : List[Any] , a : str , a : Any=13 , a : Optional[Any]=30 , a : Union[str, Any]=2 , a : List[str]=3 , a : List[str]=True , a : List[Any]=True , a : Tuple=32 , a : Optional[Any]=5 , a : str=4 , a : List[str]=37 , a : List[str]="gelu" , a : int=0.1 , a : int=0.1 , a : str=10 , a : Tuple=0.02 , a : Union[str, Any]=3 , a : List[str]=None , a : Any=2 , ) -> Optional[int]:
"""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
A__ = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
A__ = (image_size // patch_size) ** 2
A__ = num_patches + 2
def _UpperCamelCase (self : Optional[Any] ) -> List[str]:
"""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 _UpperCamelCase (self : Union[str, Any] ) -> int:
"""simple docstring"""
return DeiTConfig(
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=a , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _UpperCamelCase (self : Optional[int] , a : Tuple , a : Dict , a : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
A__ = DeiTModel(config=a )
model.to(a )
model.eval()
A__ = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCamelCase (self : Optional[int] , a : Any , a : Optional[Any] , a : Optional[int] ) -> Dict:
"""simple docstring"""
A__ = DeiTForMaskedImageModeling(config=a )
model.to(a )
model.eval()
A__ = model(a )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A__ = 1
A__ = DeiTForMaskedImageModeling(a )
model.to(a )
model.eval()
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ = model(a )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _UpperCamelCase (self : Optional[Any] , a : Optional[Any] , a : Union[str, Any] , a : Union[str, Any] ) -> str:
"""simple docstring"""
A__ = self.type_sequence_label_size
A__ = DeiTForImageClassification(a )
model.to(a )
model.eval()
A__ = model(a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A__ = 1
A__ = DeiTForImageClassification(a )
model.to(a )
model.eval()
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ = model(a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _UpperCamelCase (self : List[Any] ) -> 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_torch
class _snake_case( UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
__snake_case: Optional[Any] = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
__snake_case: int = (
{
'''feature-extraction''': DeiTModel,
'''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
__snake_case: Any = False
__snake_case: Any = False
__snake_case: Any = False
def _UpperCamelCase (self : Any ) -> int:
"""simple docstring"""
A__ = DeiTModelTester(self )
A__ = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=37 )
def _UpperCamelCase (self : Dict ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds' )
def _UpperCamelCase (self : List[str] ) -> List[str]:
"""simple docstring"""
pass
def _UpperCamelCase (self : Optional[Any] ) -> 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(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCamelCase (self : Union[str, Any] ) -> 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(a )
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] , a )
def _UpperCamelCase (self : Union[str, Any] ) -> Dict:
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCamelCase (self : Dict ) -> Any:
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*a )
def _UpperCamelCase (self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a )
def _UpperCamelCase (self : Optional[int] , a : int , a : Union[str, Any] , a : List[Any]=False ) -> Optional[int]:
"""simple docstring"""
A__ = super()._prepare_for_class(a , a , return_labels=a )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _UpperCamelCase (self : Any ) -> Tuple:
"""simple docstring"""
if not self.model_tester.is_training:
return
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(a )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
A__ = model_class(a )
model.to(a )
model.train()
A__ = self._prepare_for_class(a , a , return_labels=a )
A__ = model(**a ).loss
loss.backward()
def _UpperCamelCase (self : Optional[Any] ) -> int:
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
A__ = False
A__ = True
for model_class in self.all_model_classes:
if model_class in get_values(a ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
A__ = model_class(a )
model.gradient_checkpointing_enable()
model.to(a )
model.train()
A__ = self._prepare_for_class(a , a , return_labels=a )
A__ = model(**a ).loss
loss.backward()
def _UpperCamelCase (self : Optional[Any] ) -> List[str]:
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = [
{'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
{'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
{'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(a ),
*get_values(a ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}""" ):
A__ = problem_type['title']
A__ = problem_type['num_labels']
A__ = model_class(a )
model.to(a )
model.train()
A__ = self._prepare_for_class(a , a , return_labels=a )
if problem_type["num_labels"] > 1:
A__ = inputs['labels'].unsqueeze(1 ).repeat(1 , problem_type['num_labels'] )
A__ = inputs['labels'].to(problem_type['dtype'] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=a ) as warning_list:
A__ = model(**a ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f"""Something is going wrong in the regression problem: intercepted {w.message}""" )
loss.backward()
@slow
def _UpperCamelCase (self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = DeiTModel.from_pretrained(a )
self.assertIsNotNone(a )
def _A ( ):
'''simple docstring'''
A__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class _snake_case( unittest.TestCase ):
@cached_property
def _UpperCamelCase (self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224' )
if is_vision_available()
else None
)
@slow
def _UpperCamelCase (self : List[str] ) -> Optional[Any]:
"""simple docstring"""
A__ = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224' ).to(
a )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
A__ = model(**a )
# verify the logits
A__ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , a )
A__ = torch.tensor([-1.0266, 0.1912, -1.2861] ).to(a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def _UpperCamelCase (self : Tuple ) -> str:
"""simple docstring"""
A__ = DeiTModel.from_pretrained(
'facebook/deit-base-distilled-patch16-224' , torch_dtype=torch.floataa , device_map='auto' )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=a , return_tensors='pt' )
A__ = inputs.pixel_values.to(a )
# forward pass to make sure inference works in fp16
with torch.no_grad():
A__ = model(a )
| 531
| 1
|
from __future__ import annotations
def snake_case_ ( __lowercase ):
return [ord(__lowercase ) - 9_6 for elem in plain]
def snake_case_ ( __lowercase ):
return "".join(chr(elem + 9_6 ) for elem in encoded )
def snake_case_ ( ):
UpperCAmelCase_ : List[Any] = encode(input('''-> ''' ).strip().lower() )
print('''Encoded: ''' , __lowercase )
print('''Decoded:''' , decode(__lowercase ) )
if __name__ == "__main__":
main()
| 707
|
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class lowerCAmelCase__:
'''simple docstring'''
A_ : torch.Tensor # [batch_size x 3]
A_ : torch.Tensor # [batch_size x 3]
A_ : torch.Tensor # [batch_size x 3]
A_ : torch.Tensor # [batch_size x 3]
A_ : int
A_ : int
A_ : float
A_ : float
A_ : Tuple[int]
def _lowerCamelCase ( self : List[Any] ):
'''simple docstring'''
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = torch.arange(self.height * self.width )
UpperCAmelCase_ : Any = torch.stack(
[
pixel_indices % self.width,
torch.div(__snake_case , self.width , rounding_mode='''trunc''' ),
] , axis=1 , )
return coords
@property
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase_ , *UpperCAmelCase_ : List[Any] = self.shape
UpperCAmelCase_ : Optional[Any] = int(np.prod(__snake_case ) )
UpperCAmelCase_ : str = self.get_image_coords()
UpperCAmelCase_ : List[str] = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
UpperCAmelCase_ : Tuple = self.get_camera_rays(__snake_case )
UpperCAmelCase_ : Union[str, Any] = rays.view(__snake_case , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def _lowerCamelCase ( self : Dict , __snake_case : torch.Tensor ):
'''simple docstring'''
UpperCAmelCase_ , *UpperCAmelCase_ , UpperCAmelCase_ : int = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
UpperCAmelCase_ : str = coords.view(__snake_case , -1 , 2 )
UpperCAmelCase_ : List[Any] = self.resolution()
UpperCAmelCase_ : Optional[Any] = self.fov()
UpperCAmelCase_ : int = (flat.float() / (res - 1)) * 2 - 1
UpperCAmelCase_ : Optional[Any] = fracs * torch.tan(fov / 2 )
UpperCAmelCase_ : Optional[Any] = fracs.view(__snake_case , -1 , 2 )
UpperCAmelCase_ : List[Any] = (
self.z.view(__snake_case , 1 , 3 )
+ self.x.view(__snake_case , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(__snake_case , 1 , 3 ) * fracs[:, :, 1:]
)
UpperCAmelCase_ : Union[str, Any] = directions / directions.norm(dim=-1 , keepdim=__snake_case )
UpperCAmelCase_ : Optional[int] = torch.stack(
[
torch.broadcast_to(self.origin.view(__snake_case , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(__snake_case , *__snake_case , 2 , 3 )
def _lowerCamelCase ( self : Any , __snake_case : int , __snake_case : int ):
'''simple docstring'''
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=__snake_case , height=__snake_case , x_fov=self.x_fov , y_fov=self.y_fov , )
def snake_case_ ( __lowercase ):
UpperCAmelCase_ : List[Any] = []
UpperCAmelCase_ : int = []
UpperCAmelCase_ : Any = []
UpperCAmelCase_ : Tuple = []
for theta in np.linspace(0 , 2 * np.pi , num=2_0 ):
UpperCAmelCase_ : Tuple = np.array([np.sin(__lowercase ), np.cos(__lowercase ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
UpperCAmelCase_ : str = -z * 4
UpperCAmelCase_ : List[Any] = np.array([np.cos(__lowercase ), -np.sin(__lowercase ), 0.0] )
UpperCAmelCase_ : Tuple = np.cross(__lowercase , __lowercase )
origins.append(__lowercase )
xs.append(__lowercase )
ys.append(__lowercase )
zs.append(__lowercase )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(__lowercase , axis=0 ) ).float() , x=torch.from_numpy(np.stack(__lowercase , axis=0 ) ).float() , y=torch.from_numpy(np.stack(__lowercase , axis=0 ) ).float() , z=torch.from_numpy(np.stack(__lowercase , axis=0 ) ).float() , width=__lowercase , height=__lowercase , x_fov=0.7 , y_fov=0.7 , shape=(1, len(__lowercase )) , )
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