


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):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""




class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	tempfile.mkdtemp()
							os.makedirs(os.path.join(self.transformer_dir				,     '''models/bert/'''))
							__UpperCAmelCase			:							List[Any]	    =	self.transformer_dir
							shutil.copy(
							    os.path.join(__UpperCAmelCase				,     '''src/transformers/models/bert/modeling_bert.py''')				,     os.path.join(self.transformer_dir				,     '''models/bert/modeling_bert.py''')				,     )


	def  A(    self):
							__UpperCAmelCase			:							int	    =	'src/transformers'
							shutil.rmtree(self.transformer_dir)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__=None):
							__UpperCAmelCase			:							Union[str, Any]	    =	comment + F"\nclass {class_name}(nn.Module):\n" + class_code
							if overwrite_result is not None:
													__UpperCAmelCase			:							List[Any]	    =	comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
							__UpperCAmelCase			:							Any	    =	black.Mode(target_versions={black.TargetVersion.PYaa}				,     line_length=1_1_9)
							__UpperCAmelCase			:							int	    =	black.format_str(__UpperCAmelCase				,     mode=__UpperCAmelCase)
							__UpperCAmelCase			:							Any	    =	os.path.join(self.transformer_dir				,     '''new_code.py''')
							with open(__UpperCAmelCase				,     '''w'''				,     newline='''\n''') as f:
													f.write(__UpperCAmelCase)
							if overwrite_result is None:
													self.assertTrue(len(check_copies.is_copy_consistent(__UpperCAmelCase)) == 0)
							else:
													check_copies.is_copy_consistent(f.name				,     overwrite=__UpperCAmelCase)
													with open(__UpperCAmelCase				,     '''r''') as f:
																			self.assertTrue(f.read()				,     __UpperCAmelCase)


	def  A(    self):
							__UpperCAmelCase			:							str	    =	check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead''')
							self.assertEqual(__UpperCAmelCase				,     __UpperCAmelCase)


	def  A(    self):
							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'''				,     __UpperCAmelCase				,     )

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

							# 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'''				,     __UpperCAmelCase				,     __UpperCAmelCase)				,     )

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


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	check_copies.LOCALIZED_READMES['README_zh-hans.md']

							__UpperCAmelCase			:							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.\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			:							Optional[int]	    =	(
							    '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			:							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			:							Union[str, Any]	    =	check_copies.convert_to_localized_md(
							    __UpperCAmelCase				,     __UpperCAmelCase				,     localized_readme['''format_model_list'''])

							self.assertFalse(__UpperCAmelCase)
							self.assertEqual(__UpperCAmelCase				,     __UpperCAmelCase)

							__UpperCAmelCase			:							Optional[int]	    =	check_copies.convert_to_localized_md(
							    __UpperCAmelCase				,     __UpperCAmelCase				,     localized_readme['''format_model_list'''])

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

							__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			:							Dict	    =	(
							    '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			:							int	    =	check_copies.convert_to_localized_md(
							    __UpperCAmelCase				,     __UpperCAmelCase				,     localized_readme['''format_model_list'''])

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








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A											=						{"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """SEWForCTC""",
      """SEWForSequenceClassification""",
      """SEWModel""",
      """SEWPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  __A											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
