



"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest

import black


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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	tempfile.mkdtemp()
    os.makedirs(os.path.join(self.transformer_dir      ,     """models/bert/"""						)						)
    UpperCAmelCase	:					Dict	=	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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	"""src/transformers"""
    shutil.rmtree(self.transformer_dir						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	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	:					str	=	black.Mode(target_versions={black.TargetVersion.PYaa}      ,     line_length=119						)
    UpperCAmelCase	:					Optional[int]	=	black.format_str(_SCREAMING_SNAKE_CASE      ,     mode=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Tuple	=	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 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""						)
    self.assertEqual(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''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"""      ,     _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
    UpperCAmelCase	:					Dict	=	"""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 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

    UpperCAmelCase	:					Dict	=	(
        """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	:					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"""
    )
    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。\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	:					Tuple	=	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						)

    UpperCAmelCase	,		UpperCAmelCase	:					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						)

    UpperCAmelCase	:					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	:					Tuple	=	(
        """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	:					List[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。\n"""
    )

    UpperCAmelCase	,		UpperCAmelCase	:					List[Any]	=	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						)





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

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


A:   List[str]								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Tuple								=		["DPTFeatureExtractor"]
      A:   int								=		["DPTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[Any]								=		[
          "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPTForDepthEstimation",
          "DPTForSemanticSegmentation",
          "DPTModel",
          "DPTPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_dpt import DPTFeatureExtractor
            from .image_processing_dpt import DPTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpt import (
                DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPTForDepthEstimation,
                DPTForSemanticSegmentation,
                DPTModel,
                DPTPreTrainedModel,
            )


else:
      import sys

      A:   str								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
