

import os
import re
import shutil
import sys
import tempfile
import unittest

import black


SCREAMING_SNAKE_CASE_        =      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
SCREAMING_SNAKE_CASE_        =      '    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'

class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    tempfile.mkdtemp()
										os.makedirs(os.path.join(self.transformer_dir						,					"models/bert/"	)	)
										_UpperCAmelCase       :		Any 		=    self.transformer_dir
										shutil.copy(
										    os.path.join(A_						,					"src/transformers/models/bert/modeling_bert.py"	)						,					os.path.join(self.transformer_dir						,					"models/bert/modeling_bert.py"	)						,					)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    "src/transformers"
										shutil.rmtree(self.transformer_dir	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_=None	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    comment + f'\nclass {class_name}(nn.Module):\n' + class_code
										if overwrite_result is not None:
														_UpperCAmelCase       :		Optional[Any] 		=    comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
										_UpperCAmelCase       :		Dict 		=    black.Mode(target_versions={black.TargetVersion.PYaa}						,					line_length=119	)
										_UpperCAmelCase       :		Dict 		=    black.format_str(A_						,					mode=A_	)
										_UpperCAmelCase       :		List[str] 		=    os.path.join(self.transformer_dir						,					"new_code.py"	)
										with open(A_						,					"w"						,					newline="\n"	) as f:
														f.write(A_	)
										if overwrite_result is None:
														self.assertTrue(len(check_copies.is_copy_consistent(A_	)	) == 0	)
										else:
														check_copies.is_copy_consistent(f.name						,					overwrite=A_	)
														with open(A_						,					"r"	) as f:
																		self.assertTrue(f.read()						,					A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"	)
										self.assertEqual(A_						,					A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"						,					"BertLMPredictionHead"						,					REFERENCE_CODE + "\n"						,					)

										# With no empty line at the end
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"						,					"BertLMPredictionHead"						,					A_						,					)

										# Copy consistency with rename
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"						,					"TestModelLMPredictionHead"						,					re.sub("Bert"						,					"TestModel"						,					A_	)						,					)

										# Copy consistency with a really long name
										_UpperCAmelCase       :		Optional[int] 		=    "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
										self.check_copy_consistency(
										    f'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'						,					f'{long_class_name}LMPredictionHead'						,					re.sub("Bert"						,					A_						,					A_	)						,					)

										# Copy consistency with overwrite
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"						,					"TestModelLMPredictionHead"						,					A_						,					overwrite_result=re.sub("Bert"						,					"TestModel"						,					A_	)						,					)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    check_copies.LOCALIZED_READMES["README_zh-hans.md"]

										_UpperCAmelCase       :		Dict 		=    (
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
										    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
										    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
										    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
										    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
										    " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
										    " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
										    " method has been applied to compress GPT2 into"
										    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
										    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
										    " Multilingual BERT into"
										    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
										    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
										    " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
										    " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
										    " Luong, Quoc V. Le, Christopher D. Manning."
										)
										_UpperCAmelCase       :		Dict 		=    (
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
										    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
										)
										_UpperCAmelCase       :		Union[str, Any] 		=    (
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
										    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
										    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
										    " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
										    " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
										    " method has been applied to compress GPT2 into"
										    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
										    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
										    " Multilingual BERT into"
										    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
										    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
										    " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
										    " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
										    " Christopher D. Manning 发布。\n"
										)

										_UpperCAmelCase       :		str 		=    check_copies.convert_to_localized_md(
										    A_						,					A_						,					localized_readme["format_model_list"]	)

										self.assertFalse(A_	)
										self.assertEqual(A_						,					A_	)

										_UpperCAmelCase       :		List[str] 		=    check_copies.convert_to_localized_md(
										    A_						,					A_						,					localized_readme["format_model_list"]	)

										# Check whether the number of models is equal to README.md after conversion.
										self.assertTrue(A_	)

										_UpperCAmelCase       :		List[str] 		=    (
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
										    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
										    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
										    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
										)
										_UpperCAmelCase       :		Optional[int] 		=    (
										    "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
										    " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
										)
										_UpperCAmelCase       :		Optional[Any] 		=    (
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
										    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
										)

										_UpperCAmelCase       :		Dict 		=    check_copies.convert_to_localized_md(
										    A_						,					A_						,					localized_readme["format_model_list"]	)

										# Check if the model link is synchronized.
										self.assertEqual(A_						,					A_	)




from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"timesformer"


						def __init__(       self						,					A_=224						,					A_=16						,					A_=3						,					A_=8						,					A_=768						,					A_=12						,					A_=12						,					A_=3072						,					A_="gelu"						,					A_=0.0						,					A_=0.0						,					A_=0.02						,					A_=1e-6						,					A_=True						,					A_="divided_space_time"						,					A_=0						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    image_size
										_UpperCAmelCase       :		Tuple 		=    patch_size
										_UpperCAmelCase       :		Any 		=    num_channels
										_UpperCAmelCase       :		List[Any] 		=    num_frames

										_UpperCAmelCase       :		str 		=    hidden_size
										_UpperCAmelCase       :		Any 		=    num_hidden_layers
										_UpperCAmelCase       :		Dict 		=    num_attention_heads
										_UpperCAmelCase       :		Optional[int] 		=    intermediate_size
										_UpperCAmelCase       :		Union[str, Any] 		=    hidden_act
										_UpperCAmelCase       :		Optional[Any] 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Tuple 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Union[str, Any] 		=    initializer_range
										_UpperCAmelCase       :		Optional[Any] 		=    layer_norm_eps
										_UpperCAmelCase       :		str 		=    qkv_bias

										_UpperCAmelCase       :		Tuple 		=    attention_type
										_UpperCAmelCase       :		List[Any] 		=    drop_path_rate


