
"""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


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



@dataclass
class 			lowerCAmelCase_		:


    """simple docstring"""



    __UpperCamelCase   :		Optional[str]       =     field(
        default=a__				,      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=a__				,      metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(a__						)}				,      )
    __UpperCamelCase   :		Optional[str]       =     field(
        default=a__				,      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=a__				,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
    __UpperCamelCase   :		Optional[str]       =     field(
        default=a__				,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}						)
    __UpperCamelCase   :		Optional[str]       =     field(
        default=a__				,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}				,      )
    __UpperCamelCase   :		bool       =     field(
        default=a__				,      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=a__				,      metadata={
            '''help''': (
                '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                '''with private models).'''
            )
        }				,      )



    def 			__magic_name__					(self					)      ->	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 			lowerCAmelCase_		:


    """simple docstring"""



    __UpperCamelCase   :		Optional[str]       =     field(
        default=a__				,      metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}						)
    __UpperCamelCase   :		Optional[str]       =     field(
        default=a__				,      metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}						)
    __UpperCamelCase   :		Optional[str]       =     field(default=a__				,      metadata={'''help''': '''The input training data file (a text file).'''}						)
    __UpperCamelCase   :		Optional[str]       =     field(
        default=a__				,      metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}				,      )
    __UpperCamelCase   :		Optional[str]       =     field(
        default=a__				,      metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}				,      )
    __UpperCamelCase   :		Optional[str]       =     field(
        default=a__				,      metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}				,      )
    __UpperCamelCase   :		bool       =     field(
        default=a__				,      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=a__				,      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=a__				,      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=a__				,      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 			__magic_name__					(self					)      ->	str:



           """simple docstring"""




           if self.train_file is not None:
                  SCREAMING_SNAKE_CASE__		: str     =     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:
                  SCREAMING_SNAKE_CASE__		: 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 lowercase_						(     _snake_case     ,_snake_case		):
       with open(_snake_case     ,"""r"""     ,encoding="""utf-8"""		) as f:
              SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [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		)

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

def lowercase_						(     ):
       # 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.

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

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

       SCREAMING_SNAKE_CASE__		: Tuple     =     {
           """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:
              SCREAMING_SNAKE_CASE__		: str     =     AutoTokenizer.from_pretrained(model_args.tokenizer_name     ,**_snake_case		)
       elif model_args.model_name_or_path:
              SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     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:
              SCREAMING_SNAKE_CASE__		: Optional[int]     =     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"""		)
              SCREAMING_SNAKE_CASE__		: Dict     =     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:
              SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     datasets["""train"""].column_names
       else:
              SCREAMING_SNAKE_CASE__		: int     =     datasets["""validation"""].column_names
       SCREAMING_SNAKE_CASE__		: List[str]     =     """text""" if """text""" in column_names else column_names[0]

       SCREAMING_SNAKE_CASE__		: Any     =     """max_length""" if data_args.pad_to_max_length else False

       def tokenize_function(_snake_case		):
              # Remove empty lines
              SCREAMING_SNAKE_CASE__		: List[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		)

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

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

       # Initialize our Trainer
       SCREAMING_SNAKE_CASE__		: 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:
                     SCREAMING_SNAKE_CASE__		: Dict     =     last_checkpoint
              elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path		):
                     SCREAMING_SNAKE_CASE__		: str     =     model_args.model_name_or_path
              else:
                     SCREAMING_SNAKE_CASE__		: str     =     None
              SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     trainer.train(resume_from_checkpoint=_snake_case		)
              trainer.save_model()  # Saves the tokenizer too for easy upload

              SCREAMING_SNAKE_CASE__		: str     =     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
       SCREAMING_SNAKE_CASE__		: str     =     {}
       if training_args.do_eval:
              logger.info("""*** Evaluate ***"""		)

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

              SCREAMING_SNAKE_CASE__		: str     =     math.exp(eval_output["""eval_loss"""]		)
              SCREAMING_SNAKE_CASE__		: List[str]     =     perplexity

              SCREAMING_SNAKE_CASE__		: 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 lowercase_						(     _snake_case		):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
     main()






"""simple docstring"""




import baseaa

def lowercase_						(     _snake_case		):
							return baseaa.baaencode(string.encode("""utf-8"""		)		)

def lowercase_						(     _snake_case		):
							return baseaa.baadecode(_snake_case		).decode("""utf-8"""		)


if __name__ == "__main__":
					UpperCAmelCase__   :					Dict												=				'Hello World!'
					UpperCAmelCase__   :					Tuple												=				baseaa_encode(test)
					print(encoded)

					UpperCAmelCase__   :					Tuple												=				baseaa_decode(encoded)
					print(decoded)




