




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					:
  _UpperCamelCase   :			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.'''
          )
      }			,   )
  _UpperCamelCase   :			Optional[str] =			field(
      default=_UpperCAmelCase			,   metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_UpperCAmelCase      )}			,   )
  _UpperCamelCase   :			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'''
          )
      }			,   )
  _UpperCamelCase   :			Optional[str] =			field(
      default=_UpperCAmelCase			,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}      )
  _UpperCamelCase   :			Optional[str] =			field(
      default=_UpperCAmelCase			,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}      )
  _UpperCamelCase   :			Optional[str] =			field(
      default=_UpperCAmelCase			,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,   )
  _UpperCamelCase   :			bool =			field(
      default=_UpperCAmelCase			,   metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}			,   )
  _UpperCamelCase   :			str =			field(
      default='''main'''			,   metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}			,   )
  _UpperCamelCase   :			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 					__a			(  self		:	Tuple      )		->    List[Any]:






       """simple docstring"""







       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					:
  _UpperCamelCase   :			Optional[str] =			field(
      default=_UpperCAmelCase			,   metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}      )
  _UpperCamelCase   :			Optional[str] =			field(
      default=_UpperCAmelCase			,   metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}      )
  _UpperCamelCase   :			Optional[str] =			field(default=_UpperCAmelCase			,   metadata={'''help''': '''The input training data file (a text file).'''}      )
  _UpperCamelCase   :			Optional[str] =			field(
      default=_UpperCAmelCase			,   metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}			,   )
  _UpperCamelCase   :			Optional[str] =			field(
      default=_UpperCAmelCase			,   metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}			,   )
  _UpperCamelCase   :			Optional[str] =			field(
      default=_UpperCAmelCase			,   metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}			,   )
  _UpperCamelCase   :			bool =			field(
      default=_UpperCAmelCase			,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}      )
  _UpperCamelCase   :			Optional[int] =			field(
      default=5			,   metadata={
          '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
      }			,   )
  _UpperCamelCase   :			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.'''
          )
      }			,   )
  _UpperCamelCase   :			Optional[int] =			field(
      default=_UpperCAmelCase			,   metadata={'''help''': '''The number of processes to use for the preprocessing.'''}			,   )
  _UpperCamelCase   :			float =			field(
      default=0.15			,   metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}      )
  _UpperCamelCase   :			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 					__a			(  self		:	int      )		->    str:






       """simple docstring"""







       if self.train_file is not None:
            lowercase					:   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:
            lowercase					:   Dict								= self.validation_file.split('''.'''      )[-1]
            assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."






def 					snake_case(				__magic_name__ ,		__magic_name__    ) ->							Optional[Any]:





     '''simple docstring'''


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

     lowercase					:   Tuple								= {c: dataset[c] for c in dataset.column_names}
     lowercase					:   Union[str, Any]								= refs
     return Dataset.from_dict(snake_case_    )






def 					snake_case(				) ->							List[str]:





     '''simple docstring'''


     lowercase					:   Optional[int]								= 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.
          lowercase			,       lowercase			,       lowercase					:   Any								= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
     else:
          lowercase			,       lowercase			,       lowercase					:   Optional[Any]								= parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     lowercase					:   Union[str, Any]								= None
     if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
          lowercase					:   List[Any]								= 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''' ,		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.
          lowercase					:   List[str]								= load_dataset(data_args.dataset_name ,		data_args.dataset_config_name    )
          if "validation" not in datasets.keys():
               lowercase					:   int								= load_dataset(
                   data_args.dataset_name ,		data_args.dataset_config_name ,		split=F"""train[:{data_args.validation_split_percentage}%]""" ,		)
               lowercase					:   Dict								= load_dataset(
                   data_args.dataset_name ,		data_args.dataset_config_name ,		split=F"""train[{data_args.validation_split_percentage}%:]""" ,		)
     else:
          lowercase					:   Any								= {}
          if data_args.train_file is not None:
               lowercase					:   Optional[Any]								= data_args.train_file
          if data_args.validation_file is not None:
               lowercase					:   List[str]								= data_args.validation_file
          lowercase					:   List[str]								= data_args.train_file.split('''.'''    )[-1]
          if extension == "txt":
               lowercase					:   Optional[int]								= '''text'''
          lowercase					:   Dict								= load_dataset(snake_case_ ,		data_files=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.
     lowercase					:   Tuple								= {
         '''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:
          lowercase					:   Dict								= AutoConfig.from_pretrained(model_args.config_name ,		**snake_case_    )
     elif model_args.model_name_or_path:
          lowercase					:   Union[str, Any]								= AutoConfig.from_pretrained(model_args.model_name_or_path ,		**snake_case_    )
     else:
          lowercase					:   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}"""    )

     lowercase					:   Optional[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:
          lowercase					:   List[Any]								= AutoTokenizer.from_pretrained(model_args.tokenizer_name ,		**snake_case_    )
     elif model_args.model_name_or_path:
          lowercase					:   List[str]								= AutoTokenizer.from_pretrained(model_args.model_name_or_path ,		**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:
          lowercase					:   Union[str, Any]								= AutoModelForMaskedLM.from_pretrained(
              model_args.model_name_or_path ,		from_tf=bool('''.ckpt''' in model_args.model_name_or_path    ) ,		config=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'''    )
          lowercase					:   Optional[int]								= AutoModelForMaskedLM.from_config(snake_case_    )

     model.resize_token_embeddings(len(snake_case_    )    )

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

     lowercase					:   Optional[Any]								= '''max_length''' if data_args.pad_to_max_length else False

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

     lowercase					:   Any								= datasets.map(
         snake_case_ ,		batched=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:
          lowercase					:   List[Any]								= add_chinese_references(tokenized_datasets['''train'''] ,		data_args.train_ref_file    )
     if data_args.validation_ref_file is not None:
          lowercase					:   Optional[int]								= add_chinese_references(
              tokenized_datasets['''validation'''] ,		data_args.validation_ref_file    )
     # If we have ref files, need to avoid it removed by trainer
     lowercase					:   List[str]								= data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
          lowercase					:   Union[str, Any]								= False

     # Data collator
     # This one will take care of randomly masking the tokens.
     lowercase					:   int								= DataCollatorForWholeWordMask(tokenizer=snake_case_ ,		mlm_probability=data_args.mlm_probability    )

     # Initialize our Trainer
     lowercase					:   Any								= Trainer(
         model=snake_case_ ,		args=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=snake_case_ ,		data_collator=snake_case_ ,		)

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

          lowercase					:   Optional[Any]								= os.path.join(training_args.output_dir ,		'''train_results.txt'''    )
          if trainer.is_world_process_zero():
               with open(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
     lowercase					:   int								= {}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''    )

          lowercase					:   Optional[Any]								= trainer.evaluate()

          lowercase					:   List[str]								= math.exp(eval_output['''eval_loss''']    )
          lowercase					:   Tuple								= perplexity

          lowercase					:   List[str]								= os.path.join(training_args.output_dir ,		'''eval_results_mlm_wwm.txt'''    )
          if trainer.is_world_process_zero():
               with open(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 					snake_case(				__magic_name__    ) ->							Optional[int]:





     '''simple docstring'''


     main()


if __name__ == "__main__":
 main()






import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar

class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   logging.get_logger()

         # the current default level is logging.WARNING
         __snake_case							       =   logging.get_verbosity()

         logging.set_verbosity_error()
         self.assertEqual(logger.getEffectiveLevel()				,			logging.get_verbosity()      )

         logging.set_verbosity_warning()
         self.assertEqual(logger.getEffectiveLevel()				,			logging.get_verbosity()      )

         logging.set_verbosity_info()
         self.assertEqual(logger.getEffectiveLevel()				,			logging.get_verbosity()      )

         logging.set_verbosity_debug()
         self.assertEqual(logger.getEffectiveLevel()				,			logging.get_verbosity()      )

         # restore to the original level
         logging.set_verbosity(a__      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   logging.get_verbosity()

         __snake_case							       =   logging.get_logger('''transformers.models.bart.tokenization_bart'''      )
         __snake_case							       =   '''Testing 1, 2, 3'''

         # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
         if level_origin <= logging.WARNING:
            with CaptureLogger(a__      ) as cl:
               logger.warning(a__      )
            self.assertEqual(cl.out				,			msg + '''\n'''      )

         # this is setting the level for all of `transformers.*` loggers
         logging.set_verbosity_error()

         # should not be able to log warnings
         with CaptureLogger(a__      ) as cl:
            logger.warning(a__      )
         self.assertEqual(cl.out				,			''''''      )

         # should be able to log warnings again
         logging.set_verbosity_warning()
         with CaptureLogger(a__      ) as cl:
            logger.warning(a__      )
         self.assertEqual(cl.out				,			msg + '''\n'''      )

         # restore to the original level
         logging.set_verbosity(a__      )


      @mockenv(TRANSFORMERS_VERBOSITY='''error'''      )
      def    a (self       :      Dict      ):





         """simple docstring"""



         transformers.utils.logging._reset_library_root_logger()
         # this action activates the env var
         __snake_case							       =   logging.get_logger('''transformers.models.bart.tokenization_bart'''      )

         __snake_case							       =   os.getenv('''TRANSFORMERS_VERBOSITY'''				,			a__      )
         __snake_case							       =   logging.log_levels[env_level_str]

         __snake_case							       =   logging.get_verbosity()
         self.assertEqual(
             a__				,			a__				,			f"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"""				,			)

         # restore to the original level
         __snake_case							       =   ''''''
         transformers.utils.logging._reset_library_root_logger()


      @mockenv(TRANSFORMERS_VERBOSITY='''super-error'''      )
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         transformers.utils.logging._reset_library_root_logger()
         __snake_case							       =   logging.logging.getLogger()
         with CaptureLogger(a__      ) as cl:
            # this action activates the env var
            logging.get_logger('''transformers.models.bart.tokenization_bart'''      )
         self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''				,			cl.out      )

         # no need to restore as nothing was changed


      def    a (self       :      Any      ):





         """simple docstring"""



         transformers.utils.logging._reset_library_root_logger()

         __snake_case							       =   logging.get_logger('''transformers.models.bart.tokenization_bart'''      )
         __snake_case							       =   '''Testing 1, 2, 3'''

         with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''      ):
            # nothing should be logged as env var disables this method
            with CaptureLogger(a__      ) as cl:
               logger.warning_advice(a__      )
            self.assertEqual(cl.out				,			''''''      )

         with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''      ):
            # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
            with CaptureLogger(a__      ) as cl:
               logger.warning_advice(a__      )
            self.assertEqual(cl.out				,			msg + '''\n'''      )






def lowerCamelCase__    (							)      ->				str:
   disable_progress_bar()
   assert are_progress_bars_disabled()

   enable_progress_bar()
   assert not are_progress_bars_disabled()

