



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

import black


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

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
lowerCAmelCase__					  =  '''    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 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   str													=					tempfile.mkdtemp()
									os.makedirs(os.path.join(self.transformer_dir				,       "models/bert/"					)					)
									lowercase__					:   Optional[int]													=					self.transformer_dir
									shutil.copy(
									    os.path.join(SCREAMING_SNAKE_CASE				,       "src/transformers/models/bert/modeling_bert.py"					)				,       os.path.join(self.transformer_dir				,       "models/bert/modeling_bert.py"					)				,       )







					def 			snake_case					(							self :     Union[str, Any]					):
									lowercase__					:   Optional[Any]													=					'src/transformers'
									shutil.rmtree(self.transformer_dir					)







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







					def 			snake_case					(							self :     Any					):
									lowercase__					:   Any													=					check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"					)
									self.assertEqual(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Tuple					):
									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"				,       SCREAMING_SNAKE_CASE				,       )

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

									# Copy consistency with a really long name
									lowercase__					:   List[str]													=					'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"				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)				,       )

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







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   int													=					check_copies.LOCALIZED_READMES['README_zh-hans.md']

									lowercase__					:   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.\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.'
									)
									lowercase__					:   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'
									)
									lowercase__					:   Tuple													=					(
									    '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'
									)

									lowercase__					:   str													=					check_copies.convert_to_localized_md(
									    SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       localized_readme["format_model_list"]					)

									self.assertFalse(SCREAMING_SNAKE_CASE					)
									self.assertEqual(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   Any													=					check_copies.convert_to_localized_md(
									    SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       localized_readme["format_model_list"]					)

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

									lowercase__					:   Optional[int]													=					(
									    '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.'
									)
									lowercase__					:   Any													=					(
									    '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'
									)
									lowercase__					:   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'
									)

									lowercase__					:   str													=					check_copies.convert_to_localized_md(
									    SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       localized_readme["format_model_list"]					)

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__					  =  {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''DebertaTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DebertaForMaskedLM''',
				    '''DebertaForQuestionAnswering''',
				    '''DebertaForSequenceClassification''',
				    '''DebertaForTokenClassification''',
				    '''DebertaModel''',
				    '''DebertaPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFDebertaForMaskedLM''',
				    '''TFDebertaForQuestionAnswering''',
				    '''TFDebertaForSequenceClassification''',
				    '''TFDebertaForTokenClassification''',
				    '''TFDebertaModel''',
				    '''TFDebertaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
				from .tokenization_deberta import DebertaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_deberta_fast import DebertaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deberta import (
								    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DebertaForMaskedLM,
								    DebertaForQuestionAnswering,
								    DebertaForSequenceClassification,
								    DebertaForTokenClassification,
								    DebertaModel,
								    DebertaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deberta import (
								    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDebertaForMaskedLM,
								    TFDebertaForQuestionAnswering,
								    TFDebertaForSequenceClassification,
								    TFDebertaForTokenClassification,
								    TFDebertaModel,
								    TFDebertaPreTrainedModel,
								)


else:
				import sys

				lowerCAmelCase__					  =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




