'''simple docstring'''

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

import black


__a: List[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.
__a: 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 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    List[Any]  =					tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir	,   '''models/bert/''' ) )
        lowercase__				:    int  =					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 _lowerCAmelCase(						self )  ->  Tuple:
        lowercase__				:    Any  =					'''src/transformers'''
        shutil.rmtree(self.transformer_dir )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase=None )  ->  List[str]:
        lowercase__				:    Dict  =					comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
        if overwrite_result is not None:
         lowercase__				:    Union[str, Any]  =					comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
        lowercase__				:    int  =					black.Mode(target_versions={black.TargetVersion.PYaa}	,   line_length=119 )
        lowercase__				:    List[Any]  =					black.format_str(__lowerCAmelCase	,   mode=__lowerCAmelCase )
        lowercase__				:    List[str]  =					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 _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    Optional[int]  =					check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead''' )
        self.assertEqual(__lowerCAmelCase	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  str:
        # 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'''	,   __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__				:    Any  =					'''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 _lowerCAmelCase(						self )  ->  Tuple:
        lowercase__				:    Optional[int]  =					check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

        lowercase__				:    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.'''
        )
        lowercase__				:    List[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__				:    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。\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__				:    Tuple  =					check_copies.convert_to_localized_md(
            __lowerCAmelCase	,   __lowerCAmelCase	,   localized_readme['''format_model_list'''] )

        self.assertFalse(__lowerCAmelCase )
        self.assertEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__ ,							lowercase__				:    Tuple  =					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__				:    List[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.'''
        )
        lowercase__				:    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'''
        )
        lowercase__				:    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'''
        )

        lowercase__ ,							lowercase__				:    int  =					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_torch_available


__a: Dict          =   {
    """configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
    """tokenization_ctrl""": ["""CTRLTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a: int          =   [
      """CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CTRLForSequenceClassification""",
      """CTRLLMHeadModel""",
      """CTRLModel""",
      """CTRLPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a: int          =   [
      """TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFCTRLForSequenceClassification""",
      """TFCTRLLMHeadModel""",
      """TFCTRLModel""",
      """TFCTRLPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
  from .tokenization_ctrl import CTRLTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ctrl import (
        CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        CTRLForSequenceClassification,
        CTRLLMHeadModel,
        CTRLModel,
        CTRLPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_ctrl import (
        TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFCTRLForSequenceClassification,
        TFCTRLLMHeadModel,
        TFCTRLModel,
        TFCTRLPreTrainedModel,
    )

else:
  import sys

  __a: Dict          =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

