


import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__lowerCamelCase        =     logging.getLogger(__name__)
__lowerCamelCase        =     list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__lowerCamelCase        =     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class   a__	:
       lowerCamelCase__:     Optional[str]							    =     field(
           default=_UpperCAmelCase						,       metadata={
               """help""": (
                   """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
               )
           }						,       )
       lowerCamelCase__:     Optional[str]							    =     field(
           default=_UpperCAmelCase						,       metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_UpperCAmelCase    )}						,       )
       lowerCamelCase__:     Optional[str]							    =     field(
           default=_UpperCAmelCase						,       metadata={
               """help""": (
                   """Override some existing default config settings when a model is trained from scratch. Example: """
                   """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
               )
           }						,       )
       lowerCamelCase__:     Optional[str]							    =     field(
           default=_UpperCAmelCase						,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
       lowerCamelCase__:     Optional[str]							    =     field(
           default=_UpperCAmelCase						,       metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
       lowerCamelCase__:     Optional[str]							    =     field(
           default=_UpperCAmelCase						,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=_UpperCAmelCase						,       metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default="""main"""						,       metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=_UpperCAmelCase						,       metadata={
               """help""": (
                   """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                   """with private models)."""
               )
           }						,       )

       def 						UpperCAmelCase(   self      :     List[str]    ):
            if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
                 raise ValueError(
                     """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""    )


@dataclass
class   a__	:
       lowerCamelCase__:     Optional[str]							    =     field(
           default=_UpperCAmelCase						,       metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}    )
       lowerCamelCase__:     Optional[str]							    =     field(
           default=_UpperCAmelCase						,       metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
       lowerCamelCase__:     Optional[str]							    =     field(default=_UpperCAmelCase						,       metadata={"""help""": """The input training data file (a text file)."""}    )
       lowerCamelCase__:     Optional[str]							    =     field(
           default=_UpperCAmelCase						,       metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}						,       )
       lowerCamelCase__:     Optional[str]							    =     field(
           default=_UpperCAmelCase						,       metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}						,       )
       lowerCamelCase__:     Optional[str]							    =     field(
           default=_UpperCAmelCase						,       metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=_UpperCAmelCase						,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )
       lowerCamelCase__:     Optional[int]							    =     field(
           default=5						,       metadata={
               """help""": """The percentage of the train set used as validation set in case there's no validation split"""
           }						,       )
       lowerCamelCase__:     Optional[int]							    =     field(
           default=_UpperCAmelCase						,       metadata={
               """help""": (
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated. Default to the max input length of the model."""
               )
           }						,       )
       lowerCamelCase__:     Optional[int]							    =     field(
           default=_UpperCAmelCase						,       metadata={"""help""": """The number of processes to use for the preprocessing."""}						,       )
       lowerCamelCase__:     float							    =     field(
           default=0.15						,       metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}    )
       lowerCamelCase__:     bool							    =     field(
           default=_UpperCAmelCase						,       metadata={
               """help""": (
                   """Whether to pad all samples to `max_seq_length`. """
                   """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
               )
           }						,       )

       def 						UpperCAmelCase(   self      :     Tuple    ):
            if self.train_file is not None:
                 a_    :     Optional[int]										=    self.train_file.split("""."""    )[-1]
                 assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
            if self.validation_file is not None:
                 a_    :     List[str]										=    self.validation_file.split("""."""    )[-1]
                 assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):





     with open(SCREAMING_SNAKE_CASE__  ,  """r"""  ,  encoding="""utf-8""" ) as f:
          a_    :     Dict										=    [json.loads(SCREAMING_SNAKE_CASE__ ) for line in f.read().splitlines() if (len(SCREAMING_SNAKE_CASE__ ) > 0 and not line.isspace())]
     assert len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ )

     a_    :     List[Any]										=    {c: dataset[c] for c in dataset.column_names}
     a_    :     Union[str, Any]										=    refs
     return Dataset.from_dict(SCREAMING_SNAKE_CASE__ )



def  _a			(						):
     a_    :     List[Any]										=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
     if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          a_    :     List[str]										=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
     else:
          a_    :     List[str]										=    parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     a_    :     List[Any]										=    None
     if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
          a_    :     Dict										=    get_last_checkpoint(training_args.output_dir )
          if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
               raise ValueError(
                   F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                   """Use --overwrite_output_dir to overcome.""" )
          elif last_checkpoint is not None:
               logger.info(
                   F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                   """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )

    # Setup logging
     logging.basicConfig(
         format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""  ,  datefmt="""%m/%d/%Y %H:%M:%S"""  ,  handlers=[logging.StreamHandler(sys.stdout )]  ,  )
     logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )

     # Log on each process the small summary:
     logger.warning(
         F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
         + F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank ):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info("""Training/evaluation parameters %s"""  ,  SCREAMING_SNAKE_CASE__ )

     # Set seed before initializing model.
     set_seed(training_args.seed )

     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
     # (the dataset will be downloaded automatically from the datasets Hub).
     #
     # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
     # 'text' is found. You can easily tweak this behavior (see below).
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
     if data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          a_    :     Tuple										=    load_dataset(data_args.dataset_name  ,  data_args.dataset_config_name )
          if "validation" not in datasets.keys():
               a_    :     Optional[Any]										=    load_dataset(
                   data_args.dataset_name  ,  data_args.dataset_config_name  ,  split=F'''train[:{data_args.validation_split_percentage}%]'''  ,  )
               a_    :     Any										=    load_dataset(
                   data_args.dataset_name  ,  data_args.dataset_config_name  ,  split=F'''train[{data_args.validation_split_percentage}%:]'''  ,  )
     else:
          a_    :     int										=    {}
          if data_args.train_file is not None:
               a_    :     Any										=    data_args.train_file
          if data_args.validation_file is not None:
               a_    :     str										=    data_args.validation_file
          a_    :     Optional[int]										=    data_args.train_file.split(""".""" )[-1]
          if extension == "txt":
               a_    :     Union[str, Any]										=    """text"""
          a_    :     Dict										=    load_dataset(SCREAMING_SNAKE_CASE__  ,  data_files=SCREAMING_SNAKE_CASE__ )
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     a_    :     Any										=    {
         """cache_dir""": model_args.cache_dir,
         """revision""": model_args.model_revision,
         """use_auth_token""": True if model_args.use_auth_token else None,
     }
     if model_args.config_name:
          a_    :     Optional[Any]										=    AutoConfig.from_pretrained(model_args.config_name  ,  **SCREAMING_SNAKE_CASE__ )
     elif model_args.model_name_or_path:
          a_    :     Optional[Any]										=    AutoConfig.from_pretrained(model_args.model_name_or_path  ,  **SCREAMING_SNAKE_CASE__ )
     else:
          a_    :     List[Any]										=    CONFIG_MAPPING[model_args.model_type]()
          logger.warning("""You are instantiating a new config instance from scratch.""" )
          if model_args.config_overrides is not None:
               logger.info(F'''Overriding config: {model_args.config_overrides}''' )
               config.update_from_string(model_args.config_overrides )
               logger.info(F'''New config: {config}''' )

     a_    :     Any										=    {
         """cache_dir""": model_args.cache_dir,
         """use_fast""": model_args.use_fast_tokenizer,
         """revision""": model_args.model_revision,
         """use_auth_token""": True if model_args.use_auth_token else None,
     }
     if model_args.tokenizer_name:
          a_    :     Dict										=    AutoTokenizer.from_pretrained(model_args.tokenizer_name  ,  **SCREAMING_SNAKE_CASE__ )
     elif model_args.model_name_or_path:
          a_    :     Optional[Any]										=    AutoTokenizer.from_pretrained(model_args.model_name_or_path  ,  **SCREAMING_SNAKE_CASE__ )
     else:
          raise ValueError(
              """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
              """You can do it from another script, save it, and load it from here, using --tokenizer_name.""" )

     if model_args.model_name_or_path:
          a_    :     List[str]										=    AutoModelForMaskedLM.from_pretrained(
              model_args.model_name_or_path  ,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path )  ,  config=SCREAMING_SNAKE_CASE__  ,  cache_dir=model_args.cache_dir  ,  revision=model_args.model_revision  ,  use_auth_token=True if model_args.use_auth_token else None  ,  )
     else:
          logger.info("""Training new model from scratch""" )
          a_    :     Optional[Any]										=    AutoModelForMaskedLM.from_config(SCREAMING_SNAKE_CASE__ )

     model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE__ ) )

     # Preprocessing the datasets.
     # First we tokenize all the texts.
     if training_args.do_train:
          a_    :     List[Any]										=    datasets["""train"""].column_names
     else:
          a_    :     Tuple										=    datasets["""validation"""].column_names
     a_    :     Union[str, Any]										=    """text""" if """text""" in column_names else column_names[0]

     a_    :     Optional[Any]										=    """max_length""" if data_args.pad_to_max_length else False

     def tokenize_function(__UpperCamelCase ):
          # Remove empty lines
          a_    :     List[Any]										=    [line for line in examples["""text"""] if len(SCREAMING_SNAKE_CASE__ ) > 0 and not line.isspace()]
          return tokenizer(examples["""text"""]  ,  padding=SCREAMING_SNAKE_CASE__  ,  truncation=SCREAMING_SNAKE_CASE__  ,  max_length=data_args.max_seq_length )

     a_    :     Any										=    datasets.map(
         SCREAMING_SNAKE_CASE__  ,  batched=SCREAMING_SNAKE_CASE__  ,  num_proc=data_args.preprocessing_num_workers  ,  remove_columns=[text_column_name]  ,  load_from_cache_file=not data_args.overwrite_cache  ,  )

     # Add the chinese references if provided
     if data_args.train_ref_file is not None:
          a_    :     List[Any]										=    add_chinese_references(tokenized_datasets["""train"""]  ,  data_args.train_ref_file )
     if data_args.validation_ref_file is not None:
          a_    :     Any										=    add_chinese_references(
              tokenized_datasets["""validation"""]  ,  data_args.validation_ref_file )
     # If we have ref files, need to avoid it removed by trainer
     a_    :     str										=    data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
          a_    :     List[str]										=    False

     # Data collator
     # This one will take care of randomly masking the tokens.
     a_    :     List[Any]										=    DataCollatorForWholeWordMask(tokenizer=SCREAMING_SNAKE_CASE__  ,  mlm_probability=data_args.mlm_probability )

     # Initialize our Trainer
     a_    :     str										=    Trainer(
         model=SCREAMING_SNAKE_CASE__  ,  args=SCREAMING_SNAKE_CASE__  ,  train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None  ,  eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None  ,  tokenizer=SCREAMING_SNAKE_CASE__  ,  data_collator=SCREAMING_SNAKE_CASE__  ,  )

     # Training
     if training_args.do_train:
          if last_checkpoint is not None:
               a_    :     Optional[int]										=    last_checkpoint
          elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
               a_    :     str										=    model_args.model_name_or_path
          else:
               a_    :     Tuple										=    None
          a_    :     int										=    trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE__ )
          trainer.save_model()  # Saves the tokenizer too for easy upload

          a_    :     Optional[Any]										=    os.path.join(training_args.output_dir  ,  """train_results.txt""" )
          if trainer.is_world_process_zero():
               with open(SCREAMING_SNAKE_CASE__  ,  """w""" ) as writer:
                    logger.info("""***** Train results *****""" )
                    for key, value in sorted(train_result.metrics.items() ):
                         logger.info(F'''  {key} = {value}''' )
                         writer.write(F'''{key} = {value}\n''' )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
               trainer.state.save_to_json(os.path.join(training_args.output_dir  ,  """trainer_state.json""" ) )

    # Evaluation
     a_    :     Tuple										=    {}
     if training_args.do_eval:
          logger.info("""*** Evaluate ***""" )

          a_    :     List[Any]										=    trainer.evaluate()

          a_    :     Optional[Any]										=    math.exp(eval_output["""eval_loss"""] )
          a_    :     List[str]										=    perplexity

          a_    :     List[Any]										=    os.path.join(training_args.output_dir  ,  """eval_results_mlm_wwm.txt""" )
          if trainer.is_world_process_zero():
               with open(SCREAMING_SNAKE_CASE__  ,  """w""" ) as writer:
                    logger.info("""***** Eval results *****""" )
                    for key, value in sorted(results.items() ):
                         logger.info(F'''  {key} = {value}''' )
                         writer.write(F'''{key} = {value}\n''' )

     return results



def  _a			(						__UpperCamelCase ):
     main()


if __name__ == "__main__":
       main()







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_    )



