



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

import black


UpperCAmelCase						:		Optional[Any]			= 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.
UpperCAmelCase						:		int			= """    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    SCREAMING_SNAKE_CASE__ (							unittest.TestCase   ):

    def 					_UpperCAmelCase	(				self			:				List[str]):







      """simple docstring"""







      lowercase_								= tempfile.mkdtemp()
      os.makedirs(os.path.join(self.transformer_dir ,  """models/bert/"""))
      lowercase_								= self.transformer_dir
      shutil.copy(
          os.path.join(_lowerCAmelCase ,  """src/transformers/models/bert/modeling_bert.py""") ,  os.path.join(self.transformer_dir ,  """models/bert/modeling_bert.py""") ,  )

    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      lowercase_								= """src/transformers"""
      shutil.rmtree(self.transformer_dir)

    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Optional[Any]=None):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead""")
      self.assertEqual(_lowerCAmelCase ,  _lowerCAmelCase)

    def 					_UpperCAmelCase	(				self			:				List[str]):







      """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""" ,  _lowerCAmelCase ,  )

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

      # Copy consistency with a really long name
      lowercase_								= """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""" ,  _lowerCAmelCase ,  _lowerCAmelCase) ,  )

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




    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      lowercase_								= check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

      lowercase_								= (
          """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_								= (
          """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_								= (
          """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_					,    lowercase_								= check_copies.convert_to_localized_md(
          _lowerCAmelCase ,  _lowerCAmelCase ,  localized_readme["""format_model_list"""])

      self.assertFalse(_lowerCAmelCase)
      self.assertEqual(_lowerCAmelCase ,  _lowerCAmelCase)

      lowercase_					,    lowercase_								= check_copies.convert_to_localized_md(
          _lowerCAmelCase ,  _lowerCAmelCase ,  localized_readme["""format_model_list"""])

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

      lowercase_								= (
          """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_								= (
          """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_								= (
          """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_					,    lowercase_								= check_copies.convert_to_localized_md(
          _lowerCAmelCase ,  _lowerCAmelCase ,  localized_readme["""format_model_list"""])

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






"""simple docstring"""
from typing import TYPE_CHECKING

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


UpperCAmelCase						:		Union[str, Any]			= {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= ["LongformerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Tuple			= [
           "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LongformerForMaskedLM",
           "LongformerForMultipleChoice",
           "LongformerForQuestionAnswering",
           "LongformerForSequenceClassification",
           "LongformerForTokenClassification",
           "LongformerModel",
           "LongformerPreTrainedModel",
           "LongformerSelfAttention",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= [
           "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFLongformerForMaskedLM",
           "TFLongformerForMultipleChoice",
           "TFLongformerForQuestionAnswering",
           "TFLongformerForSequenceClassification",
           "TFLongformerForTokenClassification",
           "TFLongformerModel",
           "TFLongformerPreTrainedModel",
           "TFLongformerSelfAttention",
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

       UpperCAmelCase						:		Optional[Any]			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

