






'''simple docstring'''



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

import black


__UpperCamelCase      :       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.
__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      )							->Any:



         '''simple docstring'''
         __a				     =    tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir ,   'models/bert/'      )      )
         __a				     =    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      )							->List[Any]:



         '''simple docstring'''
         __a				     =    'src/transformers'
         shutil.rmtree(self.transformer_dir      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase=None      )							->List[Any]:



         '''simple docstring'''
         __a				     =    comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
         if overwrite_result is not None:
            __a				     =    comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
         __a				     =    black.Mode(target_versions={black.TargetVersion.PYaa} ,   line_length=119      )
         __a				     =    black.format_str(lowerCamelCase ,   mode=lowerCamelCase      )
         __a				     =    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      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'      )
         self.assertEqual(lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Any:



         '''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' ,   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
         __a				     =    '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      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    check_copies.LOCALIZED_READMES['README_zh-hans.md']

         __a				     =    (
             '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.'
         )
         __a				     =    (
             '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'
         )
         __a				     =    (
             '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'
         )

         __a     ,			__a				     =    check_copies.convert_to_localized_md(
             lowerCamelCase ,   lowerCamelCase ,   localized_readme['format_model_list']      )

         self.assertFalse(lowerCamelCase      )
         self.assertEqual(lowerCamelCase ,   lowerCamelCase      )

         __a     ,			__a				     =    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      )

         __a				     =    (
             '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.'
         )
         __a				     =    (
             '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'
         )
         __a				     =    (
             '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'
         )

         __a     ,			__a				     =    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_torch_available


__UpperCamelCase      :       Tuple      			=      {
    """configuration_blip_2""": [
        """BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Blip2Config""",
        """Blip2QFormerConfig""",
        """Blip2VisionConfig""",
    ],
    """processing_blip_2""": ["""Blip2Processor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[int]      			=      [
     """BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Blip2Model""",
     """Blip2QFormerModel""",
     """Blip2PreTrainedModel""",
     """Blip2ForConditionalGeneration""",
     """Blip2VisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_blip_a import (
     BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlipaConfig,
     BlipaQFormerConfig,
     BlipaVisionConfig,
 )
 from .processing_blip_a import BlipaProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blip_a import (
      BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlipaForConditionalGeneration,
      BlipaModel,
      BlipaPreTrainedModel,
      BlipaQFormerModel,
      BlipaVisionModel,
  )

else:
 import sys

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