


'''simple docstring'''




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


_a   :   int												=   logging.getLogger(__name__)
_a   :   Optional[Any]												=   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_a   :   Dict												=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 			_lowercase :
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={
           "help": (
               "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
           )
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__lowercase							)}						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					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"
           )
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}						,					)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}						,					)
   _SCREAMING_SNAKE_CASE  :  str          =	field(
       default="main"						,					metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}						,					)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					metadata={
           "help": (
               "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
               "with private models)."
           )
       }						,					)
   def a  (						self       :      List[Any]      ) ->							Tuple:
        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 			_lowercase :
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "The name of the dataset to use (via the datasets library)."}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(default=__lowercase						,					metadata={"help": "The input training data file (a text file)."}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}						,					)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					metadata={"help": "Overwrite the cached training and evaluation sets"}							)
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	field(
       default=5						,					metadata={
           "help": "The percentage of the train set used as validation set in case there's no validation split"
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	field(
       default=__lowercase						,					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."
           )
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	field(
       default=__lowercase						,					metadata={"help": "The number of processes to use for the preprocessing."}						,					)
   _SCREAMING_SNAKE_CASE  :  float          =	field(
       default=0.15						,					metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}							)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					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 a  (						self       :      Optional[Any]      ) ->							Optional[int]:
        if self.train_file is not None:
             __snake_case           =						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:
             __snake_case           =						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		(lowercase__			:   Dict							,   lowercase__			:   Dict						)			->			int:






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

     __snake_case           =						{c: dataset[c] for c in dataset.column_names}
     __snake_case           =						refs
     return Dataset.from_dict(lowercase__						)





def   _a		()			->			Tuple:






     """simple docstring"""
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.

     __snake_case           =						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.
          __snake_case      ,       __snake_case      ,       __snake_case           =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
     else:
          __snake_case      ,       __snake_case      ,       __snake_case           =						parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     __snake_case           =						None
     if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
          __snake_case           =						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'							,   lowercase__						)

     # 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.
          __snake_case           =						load_dataset(data_args.dataset_name							,   data_args.dataset_config_name						)
          if "validation" not in datasets.keys():
               __snake_case           =						load_dataset(
                   data_args.dataset_name							,   data_args.dataset_config_name							,   split=f'train[:{data_args.validation_split_percentage}%]'							,   )
               __snake_case           =						load_dataset(
                   data_args.dataset_name							,   data_args.dataset_config_name							,   split=f'train[{data_args.validation_split_percentage}%:]'							,   )
     else:
          __snake_case           =						{}
          if data_args.train_file is not None:
               __snake_case           =						data_args.train_file
          if data_args.validation_file is not None:
               __snake_case           =						data_args.validation_file
          __snake_case           =						data_args.train_file.split('.'						)[-1]
          if extension == "txt":
               __snake_case           =						'text'
          __snake_case           =						load_dataset(lowercase__							,   data_files=lowercase__						)
     # 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.
     __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,
     }
     if model_args.config_name:
          __snake_case           =						AutoConfig.from_pretrained(model_args.config_name							,   **lowercase__						)
     elif model_args.model_name_or_path:
          __snake_case           =						AutoConfig.from_pretrained(model_args.model_name_or_path							,   **lowercase__						)
     else:
          __snake_case           =						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}'						)

     __snake_case           =						{
         '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:
          __snake_case           =						AutoTokenizer.from_pretrained(model_args.tokenizer_name							,   **lowercase__						)
     elif model_args.model_name_or_path:
          __snake_case           =						AutoTokenizer.from_pretrained(model_args.model_name_or_path							,   **lowercase__						)
     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:
          __snake_case           =						AutoModelForMaskedLM.from_pretrained(
              model_args.model_name_or_path							,   from_tf=bool('.ckpt' in model_args.model_name_or_path						)							,   config=lowercase__							,   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'						)
          __snake_case           =						AutoModelForMaskedLM.from_config(lowercase__						)

     model.resize_token_embeddings(len(lowercase__						)						)

     # Preprocessing the datasets.
     # First we tokenize all the texts.
     if training_args.do_train:
          __snake_case           =						datasets['train'].column_names
     else:
          __snake_case           =						datasets['validation'].column_names
     __snake_case           =						'text' if 'text' in column_names else column_names[0]

     __snake_case           =						'max_length' if data_args.pad_to_max_length else False

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

     __snake_case           =						datasets.map(
         lowercase__							,   batched=lowercase__							,   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:
          __snake_case           =						add_chinese_references(tokenized_datasets['train']							,   data_args.train_ref_file						)
     if data_args.validation_ref_file is not None:
          __snake_case           =						add_chinese_references(
              tokenized_datasets['validation']							,   data_args.validation_ref_file						)
     # If we have ref files, need to avoid it removed by trainer
     __snake_case           =						data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
          __snake_case           =						False

     # Data collator
     # This one will take care of randomly masking the tokens.
     __snake_case           =						DataCollatorForWholeWordMask(tokenizer=lowercase__							,   mlm_probability=data_args.mlm_probability						)

     # Initialize our Trainer
     __snake_case           =						Trainer(
         model=lowercase__							,   args=lowercase__							,   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=lowercase__							,   data_collator=lowercase__							,   )

     # Training
     if training_args.do_train:
          if last_checkpoint is not None:
               __snake_case           =						last_checkpoint
          elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path						):
               __snake_case           =						model_args.model_name_or_path
          else:
               __snake_case           =						None
          __snake_case           =						trainer.train(resume_from_checkpoint=lowercase__						)
          trainer.save_model()  # Saves the tokenizer too for easy upload

          __snake_case           =						os.path.join(training_args.output_dir							,   'train_results.txt'						)
          if trainer.is_world_process_zero():
               with open(lowercase__							,   '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
     __snake_case           =						{}
     if training_args.do_eval:
          logger.info('*** Evaluate ***'						)

          __snake_case           =						trainer.evaluate()

          __snake_case           =						math.exp(eval_output['eval_loss']						)
          __snake_case           =						perplexity

          __snake_case           =						os.path.join(training_args.output_dir							,   'eval_results_mlm_wwm.txt'						)
          if trainer.is_world_process_zero():
               with open(lowercase__							,   '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		(lowercase__			:   int						)			->			Any:






     """simple docstring"""
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
       main()











import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a__: List[Any]							   =						logging.getLogger(__name__)


a__: str							   =						list(MODEL_WITH_LM_HEAD_MAPPING.keys())
a__: Tuple							   =						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class        SCREAMING_SNAKE_CASE__					:
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={
         '''help''': (
             '''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
             ''' scratch.'''
         )
     }							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCamelCase__ )}							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,   )




@dataclass
class        SCREAMING_SNAKE_CASE__					:
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''The input training data file (a text file).'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={
         '''help''': (
             '''The input training data files (multiple files in glob format). '''
             '''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
         )
     }							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''}							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''}							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''}							,   )

 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''} )
 __SCREAMING_SNAKE_CASE							=      field(default=UpperCamelCase__							,   metadata={'''help''': '''Whether ot not to use whole word mask.'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=0.1_5							,   metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=1 / 6							,   metadata={
         '''help''': (
             '''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
             ''' modeling.'''
         )
     }							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=5							,   metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''} )

 __SCREAMING_SNAKE_CASE							=      field(
     default=-1							,   metadata={
         '''help''': (
             '''Optional input sequence length after tokenization.'''
             '''The training dataset will be truncated in block of this size for training.'''
             '''Default to the model max input length for single sentence inputs (take into account special tokens).'''
         )
     }							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )


def       UpperCamelCase__(			UpperCamelCase__     :			DataTrainingArguments					,	UpperCamelCase__     :			PreTrainedTokenizer					,	UpperCamelCase__     :			bool = False					,	UpperCamelCase__     :			Optional[str] = None					,	)->str:
       def _dataset(UpperCamelCase__     :			List[str]					,	UpperCamelCase__     :			Optional[int]=None    ):
              if args.line_by_line:
                     if ref_path is not None:
                            if not args.whole_word_mask or not args.mlm:
                                   raise ValueError('''You need to set world whole masking and mlm to True for Chinese Whole Word Mask'''    )
                            return LineByLineWithRefDataset(
                                tokenizer=UpperCamelCase__					,	file_path=UpperCamelCase__					,	block_size=args.block_size					,	ref_path=UpperCamelCase__					,	)

                     return LineByLineTextDataset(tokenizer=UpperCamelCase__					,	file_path=UpperCamelCase__					,	block_size=args.block_size    )
              else:
                     return TextDataset(
                         tokenizer=UpperCamelCase__					,	file_path=UpperCamelCase__					,	block_size=args.block_size					,	overwrite_cache=args.overwrite_cache					,	cache_dir=UpperCamelCase__					,	)

       if evaluate:
              return _dataset(args.eval_data_file					,	args.eval_ref_file    )
       elif args.train_data_files:
              return ConcatDataset([_dataset(UpperCamelCase__    ) for f in glob(args.train_data_files    )]    )
       else:
              return _dataset(args.train_data_file					,	args.train_ref_file    )


def       UpperCamelCase__(			)->List[Any]:
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.

       A__		   =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
       A__   ,      A__   ,      A__		   =		parser.parse_args_into_dataclasses()

       if data_args.eval_data_file is None and training_args.do_eval:
              raise ValueError(
                  '''Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '''
                  '''or remove the --do_eval argument.'''    )
       if (
           os.path.exists(training_args.output_dir    )
           and os.listdir(training_args.output_dir    )
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
                  ''' --overwrite_output_dir to overcome.'''    )

       # Setup logging
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''					,	datefmt='''%m/%d/%Y %H:%M:%S'''					,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,	)
       logger.warning(
           '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''					,	training_args.local_rank					,	training_args.device					,	training_args.n_gpu					,	bool(training_args.local_rank != -1    )					,	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'''					,	UpperCamelCase__    )

       # Set seed
       set_seed(training_args.seed    )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       if model_args.config_name:
              A__		   =		AutoConfig.from_pretrained(model_args.config_name					,	cache_dir=model_args.cache_dir    )
       elif model_args.model_name_or_path:
              A__		   =		AutoConfig.from_pretrained(model_args.model_name_or_path					,	cache_dir=model_args.cache_dir    )
       else:
              A__		   =		CONFIG_MAPPING[model_args.model_type]()
              logger.warning('''You are instantiating a new config instance from scratch.'''    )

       if model_args.tokenizer_name:
              A__		   =		AutoTokenizer.from_pretrained(model_args.tokenizer_name					,	cache_dir=model_args.cache_dir    )
       elif model_args.model_name_or_path:
              A__		   =		AutoTokenizer.from_pretrained(model_args.model_name_or_path					,	cache_dir=model_args.cache_dir    )
       else:
              raise ValueError(
                  '''You are instantiating a new tokenizer from scratch. This is not supported, but 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__		   =		AutoModelWithLMHead.from_pretrained(
                  model_args.model_name_or_path					,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path    )					,	config=UpperCamelCase__					,	cache_dir=model_args.cache_dir					,	)
       else:
              logger.info('''Training new model from scratch'''    )
              A__		   =		AutoModelWithLMHead.from_config(UpperCamelCase__    )

       model.resize_token_embeddings(len(UpperCamelCase__    )    )

       if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
              raise ValueError(
                  '''BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'''
                  '''--mlm flag (masked language modeling).'''    )

       if data_args.block_size <= 0:
              A__		   =		tokenizer.max_len
              # Our input block size will be the max possible for the model
       else:
              A__		   =		min(data_args.block_size					,	tokenizer.max_len    )

       # Get datasets

       A__		   =		(
           get_dataset(UpperCamelCase__					,	tokenizer=UpperCamelCase__					,	cache_dir=model_args.cache_dir    ) if training_args.do_train else None
       )
       A__		   =		(
           get_dataset(UpperCamelCase__					,	tokenizer=UpperCamelCase__					,	evaluate=UpperCamelCase__					,	cache_dir=model_args.cache_dir    )
           if training_args.do_eval
           else None
       )
       if config.model_type == "xlnet":
              A__		   =		DataCollatorForPermutationLanguageModeling(
                  tokenizer=UpperCamelCase__					,	plm_probability=data_args.plm_probability					,	max_span_length=data_args.max_span_length					,	)
       else:
              if data_args.mlm and data_args.whole_word_mask:
                     A__		   =		DataCollatorForWholeWordMask(
                         tokenizer=UpperCamelCase__					,	mlm_probability=data_args.mlm_probability    )
              else:
                     A__		   =		DataCollatorForLanguageModeling(
                         tokenizer=UpperCamelCase__					,	mlm=data_args.mlm					,	mlm_probability=data_args.mlm_probability    )

    # Initialize our Trainer
       A__		   =		Trainer(
           model=UpperCamelCase__					,	args=UpperCamelCase__					,	data_collator=UpperCamelCase__					,	train_dataset=UpperCamelCase__					,	eval_dataset=UpperCamelCase__					,	prediction_loss_only=UpperCamelCase__					,	)

       # Training
       if training_args.do_train:
              A__		   =		(
                  model_args.model_name_or_path
                  if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path    )
                  else None
              )
              trainer.train(model_path=UpperCamelCase__    )
              trainer.save_model()
              # For convenience, we also re-save the tokenizer to the same directory,
              # so that you can share your model easily on huggingface.co/models =)
              if trainer.is_world_master():
                     tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
       A__		   =		{}
       if training_args.do_eval:
              logger.info('''*** Evaluate ***'''    )

              A__		   =		trainer.evaluate()

              A__		   =		math.exp(eval_output['''eval_loss''']    )
              A__		   =		{'''perplexity''': perplexity}

              A__		   =		os.path.join(training_args.output_dir					,	'''eval_results_lm.txt'''    )
              if trainer.is_world_master():
                     with open(UpperCamelCase__					,	'''w'''    ) as writer:
                            logger.info('''***** Eval results *****'''    )
                            for key in sorted(result.keys()    ):
                                   logger.info('''  %s = %s'''					,	UpperCamelCase__					,	str(result[key]    )    )
                                   writer.write('''%s = %s\n''' % (key, str(result[key]    ))    )

              results.update(UpperCamelCase__    )

       return results


def       UpperCamelCase__(			UpperCamelCase__     :			List[str]    )->List[str]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
       main()





