"""simple docstring"""



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

import black


__lowercase		:		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.
__lowercase		:		List[str]														=							"    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      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     List[str]    )    ->       List[str]:
         __snake_case				=							tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir	,      '''models/bert/'''    )    )
         __snake_case				=							self.transformer_dir
         shutil.copy(
             os.path.join(A_	,      '''src/transformers/models/bert/modeling_bert.py'''    )	,      os.path.join(self.transformer_dir	,      '''models/bert/modeling_bert.py'''    )	,      )
    def  lowercase					(		self							:     Any    )    ->       int:
         __snake_case				=							'''src/transformers'''
         shutil.rmtree(self.transformer_dir    )
    def  lowercase					(		self							:     str	,      A_							:     List[str]	,      A_							:     List[Any]	,      A_							:     Optional[Any]	,      A_							:     str=None    )    ->       int:
         __snake_case				=							comment + f"\nclass {class_name}(nn.Module):\n" + class_code
         if overwrite_result is not None:
              __snake_case				=							comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
         __snake_case				=							black.Mode(target_versions={black.TargetVersion.PYaa}	,      line_length=119    )
         __snake_case				=							black.format_str(A_	,      mode=A_    )
         __snake_case				=							os.path.join(self.transformer_dir	,      '''new_code.py'''    )
         with open(A_	,      '''w'''	,      newline='''\n'''    ) as f:
              f.write(A_    )
         if overwrite_result is None:
              self.assertTrue(len(check_copies.is_copy_consistent(A_    )    ) == 0    )
         else:
              check_copies.is_copy_consistent(f.name	,      overwrite=A_    )
              with open(A_	,      '''r'''    ) as f:
                   self.assertTrue(f.read()	,      A_    )
    def  lowercase					(		self							:     str    )    ->       Optional[int]:
         __snake_case				=							check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''    )
         self.assertEqual(A_	,      A_    )
    def  lowercase					(		self							:     int    )    ->       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'''	,      A_	,      )

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

         # Copy consistency with a really long name
         __snake_case				=							'''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'''	,      A_	,      A_    )	,      )

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







    def  lowercase					(		self							:     Any    )    ->       str:
         __snake_case				=							check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

         __snake_case				=							(
             '''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.'''
         )
         __snake_case				=							(
             '''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'''
         )
         __snake_case				=							(
             '''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'''
         )

         __snake_case		,	__snake_case				=							check_copies.convert_to_localized_md(
             A_	,      A_	,      localized_readme['''format_model_list''']    )

         self.assertFalse(A_    )
         self.assertEqual(A_	,      A_    )

         __snake_case		,	__snake_case				=							check_copies.convert_to_localized_md(
             A_	,      A_	,      localized_readme['''format_model_list''']    )

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

         __snake_case				=							(
             '''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.'''
         )
         __snake_case				=							(
             '''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'''
         )
         __snake_case				=							(
             '''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'''
         )

         __snake_case		,	__snake_case				=							check_copies.convert_to_localized_md(
             A_	,      A_	,      localized_readme['''format_model_list''']    )

         # Check if the model link is synchronized.
         self.assertEqual(A_	,      A_    )
"""simple docstring"""



from typing import TYPE_CHECKING

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


__lowercase		:		Dict														=							{
    "configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
    "processing_vision_text_dual_encoder": ["VisionTextDualEncoderProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Optional[int]														=							["VisionTextDualEncoderModel"]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Optional[int]														=							["FlaxVisionTextDualEncoderModel"]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Any														=							["TFVisionTextDualEncoderModel"]


if TYPE_CHECKING:
       from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
       from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel


else:
       import sys

       __lowercase		:		Tuple														=							_LazyModule(__name__, globals()["__file__"], _import_structure)