






"""simple docstring"""



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

import black


_lowerCAmelCase	:	str				=      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	:	List[Any]				=      """    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      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir							,			"models/bert/"							)							)
        UpperCAmelCase__						:    Optional[Any]							=							self.transformer_dir
        shutil.copy(
            os.path.join(snake_case__							,			"src/transformers/models/bert/modeling_bert.py"							)							,			os.path.join(self.transformer_dir							,			"models/bert/modeling_bert.py"							)							,			)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							"src/transformers"
        shutil.rmtree(self.transformer_dir							)
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	List[str]							,			snake_case__      :	Any							,			snake_case__      :	Optional[int]							,			snake_case__      :	Any=None							):


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


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"							)
        self.assertEqual(snake_case__							,			snake_case__							)
   def       __a							(		self      :	str							):


        '''simple docstring'''
        # Base copy consistency
        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"							,			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"							,			snake_case__							)							,			)

        # 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"							,			snake_case__							,			snake_case__							)							,			)

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




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


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							check_copies.LOCALIZED_READMES["README_zh-hans.md"]

        UpperCAmelCase__						:    Any							=							(
            "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__						:    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。\n"
        )
        UpperCAmelCase__						:    str							=							(
            "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__							,				UpperCAmelCase__						:    List[Any]							=							check_copies.convert_to_localized_md(
            snake_case__							,			snake_case__							,			localized_readme["format_model_list"]							)

        self.assertFalse(snake_case__							)
        self.assertEqual(snake_case__							,			snake_case__							)

        UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							check_copies.convert_to_localized_md(
            snake_case__							,			snake_case__							,			localized_readme["format_model_list"]							)

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

        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."
        )
        UpperCAmelCase__						:    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__							,				UpperCAmelCase__						:    Optional[Any]							=							check_copies.convert_to_localized_md(
            snake_case__							,			snake_case__							,			localized_readme["format_model_list"]							)

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










"""simple docstring"""



from typing import TYPE_CHECKING

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


_lowerCAmelCase	:	Tuple				=      {
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase	:	int				=      ["""LxmertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase	:	Tuple				=      [
     """LxmertEncoder""",
     """LxmertForPreTraining""",
     """LxmertForQuestionAnswering""",
     """LxmertModel""",
     """LxmertPreTrainedModel""",
     """LxmertVisualFeatureEncoder""",
     """LxmertXLayer""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase	:	Optional[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

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


