


import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
       import numpy as np
       import torch

       from . import (
           AlbertForPreTraining,
           BartForConditionalGeneration,
           BertForPreTraining,
           BertForQuestionAnswering,
           BertForSequenceClassification,
           CamembertForMaskedLM,
           CTRLLMHeadModel,
           DistilBertForMaskedLM,
           DistilBertForQuestionAnswering,
           DPRContextEncoder,
           DPRQuestionEncoder,
           DPRReader,
           ElectraForPreTraining,
           FlaubertWithLMHeadModel,
           GPTaLMHeadModel,
           LayoutLMForMaskedLM,
           LxmertForPreTraining,
           LxmertVisualFeatureEncoder,
           OpenAIGPTLMHeadModel,
           RobertaForMaskedLM,
           RobertaForSequenceClassification,
           TaForConditionalGeneration,
           TransfoXLLMHeadModel,
           XLMRobertaForMaskedLM,
           XLMWithLMHeadModel,
           XLNetLMHeadModel,
       )


logging.set_verbosity_info()

__lowerCamelCase        =     {
    '''bart''': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''bert''': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-base-cased-finetuned-mrpc''': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''dpr''': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''gpt2''': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlnet''': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm''': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm-roberta''': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''transfo-xl''': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''openai-gpt''': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''roberta''': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''layoutlm''': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''roberta-large-mnli''': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''camembert''': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''flaubert''': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert''': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert-base-distilled-squad''': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert''': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert-visual-feature-encoder''': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''ctrl''': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''albert''': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''t5''': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''electra''': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''wav2vec2''': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase=False  ,  __UpperCamelCase=True ):
     if model_type not in MODEL_CLASSES:
          raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.''' )

     a_			,			a_			,			a_			,			a_    :     Optional[int]										=    MODEL_CLASSES[model_type]

     # Initialise TF model
     if config_file in aws_config_map:
          a_    :     List[str]										=    cached_file(__UpperCamelCase  ,  __UpperCamelCase  ,  force_download=not use_cached_models )
     a_    :     Optional[Any]										=    config_class.from_json_file(__UpperCamelCase )
     a_    :     int										=    True
     a_    :     Union[str, Any]										=    True
     print(F'''Building TensorFlow model from configuration: {config}''' )
     a_    :     int										=    model_class(__UpperCamelCase )

     # Load weights from tf checkpoint
     if pytorch_checkpoint_path in aws_config_map.keys():
          a_    :     Any										=    cached_file(
              __UpperCamelCase  ,  __UpperCamelCase  ,  force_download=not use_cached_models )
     # Load PyTorch checkpoint in tf2 model:
     a_    :     int										=    load_pytorch_checkpoint_in_tfa_model(__UpperCamelCase  ,  __UpperCamelCase )

     if compare_with_pt_model:
          a_    :     int										=    tf_model(tf_model.dummy_inputs  ,  training=__UpperCamelCase )  # build the network

          a_    :     Any										=    torch.load(__UpperCamelCase  ,  map_location="""cpu""" )
          a_    :     Optional[int]										=    pt_model_class.from_pretrained(
              pretrained_model_name_or_path=__UpperCamelCase  ,  config=__UpperCamelCase  ,  state_dict=__UpperCamelCase )

          with torch.no_grad():
               a_    :     List[str]										=    pt_model(**pt_model.dummy_inputs )

          a_    :     int										=    pto[0].numpy()
          a_    :     Union[str, Any]										=    tfo[0].numpy()
          a_    :     int										=    np.amax(np.abs(np_pt - np_tf ) )
          print(F'''Max absolute difference between models outputs {diff}''' )
          assert diff <= 2E-2, F'''Error, model absolute difference is >2e-2: {diff}'''

     # Save pytorch-model
     print(F'''Save TensorFlow model to {tf_dump_path}''' )
     tf_model.save_weights(__UpperCamelCase  ,  save_format="""h5""" )



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase=None  ,  __UpperCamelCase=None  ,  __UpperCamelCase=False  ,  __UpperCamelCase=False  ,  __UpperCamelCase=False  ,  __UpperCamelCase=False  ,  ):
     if args_model_type is None:
          a_    :     Any										=    list(MODEL_CLASSES.keys() )
     else:
          a_    :     List[str]										=    [args_model_type]

     for j, model_type in enumerate(__UpperCamelCase  ,  start=1 ):
          print("""=""" * 1_0_0 )
          print(F''' Converting model type {j}/{len(__UpperCamelCase )}: {model_type}''' )
          print("""=""" * 1_0_0 )
          if model_type not in MODEL_CLASSES:
               raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.''' )

          a_			,			a_			,			a_			,			a_			,			a_    :     List[Any]										=    MODEL_CLASSES[model_type]

          if model_shortcut_names_or_path is None:
               a_    :     Optional[Any]										=    list(aws_model_maps.keys() )
          if config_shortcut_names_or_path is None:
               a_    :     str										=    model_shortcut_names_or_path

          for i, (model_shortcut_name, config_shortcut_name) in enumerate(
              zip(__UpperCamelCase  ,  __UpperCamelCase )  ,  start=1 ):
               print("""-""" * 1_0_0 )
               if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                    if not only_convert_finetuned_models:
                         print(F'''    Skipping finetuned checkpoint {model_shortcut_name}''' )
                         continue
                    a_    :     Optional[Any]										=    model_shortcut_name
               elif only_convert_finetuned_models:
                    print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}''' )
                    continue
               print(
                   F'''    Converting checkpoint {i}/{len(__UpperCamelCase )}: {model_shortcut_name} - model_type {model_type}''' )
               print("""-""" * 1_0_0 )

               if config_shortcut_name in aws_config_map:
                    a_    :     Any										=    cached_file(__UpperCamelCase  ,  __UpperCamelCase  ,  force_download=not use_cached_models )
               else:
                    a_    :     Dict										=    config_shortcut_name

               if model_shortcut_name in aws_model_maps:
                    a_    :     int										=    cached_file(__UpperCamelCase  ,  __UpperCamelCase  ,  force_download=not use_cached_models )
               else:
                    a_    :     Optional[Any]										=    model_shortcut_name

               if os.path.isfile(__UpperCamelCase ):
                    a_    :     Optional[int]										=    """converted_model"""

               convert_pt_checkpoint_to_tf(
                   model_type=__UpperCamelCase  ,  pytorch_checkpoint_path=__UpperCamelCase  ,  config_file=__UpperCamelCase  ,  tf_dump_path=os.path.join(__UpperCamelCase  ,  model_shortcut_name + """-tf_model.h5""" )  ,  compare_with_pt_model=__UpperCamelCase  ,  )
               if remove_cached_files:
                    os.remove(__UpperCamelCase )
                    os.remove(__UpperCamelCase )


if __name__ == "__main__":
       __lowerCamelCase        =     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
       )
       parser.add_argument(
           '''--model_type''',
           default=None,
           type=str,
           help=(
               f'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
               '''convert all the models from AWS.'''
           ),
       )
       parser.add_argument(
           '''--pytorch_checkpoint_path''',
           default=None,
           type=str,
           help=(
               '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
               '''If not given, will download and convert all the checkpoints from AWS.'''
           ),
       )
       parser.add_argument(
           '''--config_file''',
           default=None,
           type=str,
           help=(
               '''The config json file corresponding to the pre-trained model. \n'''
               '''This specifies the model architecture. If not given and '''
               '''--pytorch_checkpoint_path is not given or is a shortcut name '''
               '''use the configuration associated to the shortcut name on the AWS'''
           ),
       )
       parser.add_argument(
           '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
       )
       parser.add_argument(
           '''--use_cached_models''',
           action='''store_true''',
           help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
       )
       parser.add_argument(
           '''--remove_cached_files''',
           action='''store_true''',
           help='''Remove pytorch models after conversion (save memory when converting in batches).''',
       )
       parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
       __lowerCamelCase        =     parser.parse_args()

       # if args.pytorch_checkpoint_path is not None:
       #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
       #                                 args.pytorch_checkpoint_path,
       #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
       #                                 args.tf_dump_path,
       #                                 compare_with_pt_model=args.compare_with_pt_model,
       #                                 use_cached_models=args.use_cached_models)
       # else:
       convert_all_pt_checkpoints_to_tf(
           args.model_type.lower() if args.model_type is not None else None,
           args.tf_dump_path,
           model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
           if args.pytorch_checkpoint_path is not None
           else None,
           config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
           compare_with_pt_model=args.compare_with_pt_model,
           use_cached_models=args.use_cached_models,
           remove_cached_files=args.remove_cached_files,
           only_convert_finetuned_models=args.only_convert_finetuned_models,
       )







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

import black


__lowerCamelCase        =     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        =     '''    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   a__	(						unittest.TestCase    ):

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Optional[Any]										=    tempfile.mkdtemp()
            os.makedirs(os.path.join(self.transformer_dir      ,							"""models/bert/"""    )    )
            a_    :     Optional[Any]										=    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      :     Dict    ):
            a_    :     Any										=    """src/transformers"""
            shutil.rmtree(self.transformer_dir    )

       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Tuple=None    ):
            a_    :     Any										=    comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
            if overwrite_result is not None:
                 a_    :     Any										=    comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
            a_    :     Dict										=    black.Mode(target_versions={black.TargetVersion.PYaa}      ,							line_length=1_1_9    )
            a_    :     Optional[Any]										=    black.format_str(lowerCamelCase_      ,							mode=lowerCamelCase_    )
            a_    :     Optional[Any]										=    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      :     Dict    ):
            a_    :     Tuple										=    check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""    )
            self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            # 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_    :     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"""      ,							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      :     int    ):
            a_    :     Optional[Any]										=    check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

            a_    :     Tuple										=    (
                """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_    :     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"""
            )
            a_    :     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。\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_    :     Optional[int]										=    check_copies.convert_to_localized_md(
                lowerCamelCase_      ,							lowerCamelCase_      ,							localized_readme["""format_model_list"""]    )

            self.assertFalse(lowerCamelCase_    )
            self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

            a_			,			a_    :     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_    )

            a_    :     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."""
            )
            a_    :     Union[str, Any]										=    (
                """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_    :     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"""
            )

            a_			,			a_    :     Tuple										=    check_copies.convert_to_localized_md(
                lowerCamelCase_      ,							lowerCamelCase_      ,							localized_readme["""format_model_list"""]    )

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



