"""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


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







@dataclass
class  UpperCamelCase	:
   lowercase							=						field(
       default=UpperCAmelCase__		,   metadata={
           'help': (
               'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
           )
       }		,   )
   lowercase							=						field(
       default=UpperCAmelCase__		,   metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(UpperCAmelCase__						)}		,   )
   lowercase							=						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'
           )
       }		,   )
   lowercase							=						field(
       default=UpperCAmelCase__		,   metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
   lowercase							=						field(
       default=UpperCAmelCase__		,   metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}						)
   lowercase							=						field(
       default=UpperCAmelCase__		,   metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}		,   )
   lowercase							=						field(
       default=UpperCAmelCase__		,   metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}		,   )
   lowercase							=						field(
       default='main'		,   metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}		,   )
   lowercase							=						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]:


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


        '''simple docstring'''


        if self.train_file is not None:
             lowercase_			:       Any									=			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_			:       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     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Tuple   ):
     with open(__SCREAMING_SNAKE_CASE  ,    'r'  ,    encoding='utf-8'   ) as f:
          lowercase_			:       int									=			[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   )

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

     lowercase_			:       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.
          lowercase_			:       Optional[Any]									=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
     else:
          lowercase_			:       Dict									=			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_			:       Tuple									=			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.
          lowercase_			:       List[str]									=			load_dataset(data_args.dataset_name  ,    data_args.dataset_config_name   )
          if "validation" not in datasets.keys():
               lowercase_			:       Optional[Any]									=			load_dataset(
                   data_args.dataset_name  ,    data_args.dataset_config_name  ,    split=F'''train[:{data_args.validation_split_percentage}%]'''  ,    )
               lowercase_			:       Optional[int]									=			load_dataset(
                   data_args.dataset_name  ,    data_args.dataset_config_name  ,    split=F'''train[{data_args.validation_split_percentage}%:]'''  ,    )
     else:
          lowercase_			:       List[Any]									=			{}
          if data_args.train_file is not None:
               lowercase_			:       List[str]									=			data_args.train_file
          if data_args.validation_file is not None:
               lowercase_			:       Union[str, Any]									=			data_args.validation_file
          lowercase_			:       Union[str, Any]									=			data_args.train_file.split('.'   )[-1]
          if extension == "txt":
               lowercase_			:       Union[str, Any]									=			"""text"""
          lowercase_			:       Optional[int]									=			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.
     lowercase_			:       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:
          lowercase_			:       int									=			AutoConfig.from_pretrained(model_args.config_name  ,    **__SCREAMING_SNAKE_CASE   )
     elif model_args.model_name_or_path:
          lowercase_			:       Optional[Any]									=			AutoConfig.from_pretrained(model_args.model_name_or_path  ,    **__SCREAMING_SNAKE_CASE   )
     else:
          lowercase_			:       List[str]									=			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_			:       List[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_			:       Optional[Any]									=			AutoTokenizer.from_pretrained(model_args.tokenizer_name  ,    **__SCREAMING_SNAKE_CASE   )
     elif model_args.model_name_or_path:
          lowercase_			:       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:
          lowercase_			:       Dict									=			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'   )
          lowercase_			:       str									=			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:
          lowercase_			:       str									=			datasets["""train"""].column_names
     else:
          lowercase_			:       List[Any]									=			datasets["""validation"""].column_names
     lowercase_			:       Dict									=			"""text""" if """text""" in column_names else column_names[0]

     lowercase_			:       List[str]									=			"""max_length""" if data_args.pad_to_max_length else False

     def tokenize_function(__SCREAMING_SNAKE_CASE							:  Tuple   ):
          # Remove empty lines
          lowercase_			:       List[str]									=			[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   )

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

     # Data collator
     # This one will take care of randomly masking the tokens.
     lowercase_			:       Tuple									=			DataCollatorForWholeWordMask(tokenizer=__SCREAMING_SNAKE_CASE  ,    mlm_probability=data_args.mlm_probability   )

     # Initialize our Trainer
     lowercase_			:       Optional[Any]									=			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:
               lowercase_			:       Optional[Any]									=			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_			:       str									=			trainer.train(resume_from_checkpoint=__SCREAMING_SNAKE_CASE   )
          trainer.save_model()  # Saves the tokenizer too for easy upload

          lowercase_			:       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
     lowercase_			:       List[str]									=			{}
     if training_args.do_eval:
          logger.info('*** Evaluate ***'   )

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

          lowercase_			:       Dict									=			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(__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     lowercase__( __SCREAMING_SNAKE_CASE							:  Any   ):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
       main()


"""simple docstring"""







import numpy as np



def     lowercase__( __SCREAMING_SNAKE_CASE							:  np.array   ):
     return 1 / (1 + np.exp(-vector   ))


if __name__ == "__main__":
       import doctest

       doctest.testmod()

