




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





@dataclass
class    __lowercase						:
   _a							=      field(
       default=__magic_name__       ,							metadata={
           """help""": (
               """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
           )
       }       ,							)
   _a							=      field(
       default=__magic_name__       ,							metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(__magic_name__ )}       ,							)
   _a							=      field(
       default=__magic_name__       ,							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"""
           )
       }       ,							)
   _a							=      field(
       default=__magic_name__       ,							metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
   _a							=      field(
       default=__magic_name__       ,							metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
   _a							=      field(
       default=__magic_name__       ,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,							)
   _a							=      field(
       default=__magic_name__       ,							metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}       ,							)
   _a							=      field(
       default="""main"""       ,							metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}       ,							)
   _a							=      field(
       default=__magic_name__       ,							metadata={
           """help""": (
               """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
               """with private models)."""
           )
       }       ,							)







   def 		UpperCamelCase__			(	self      )				->    int:
     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						:
   _a							=      field(
       default=__magic_name__       ,							metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
   _a							=      field(
       default=__magic_name__       ,							metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
   _a							=      field(default=__magic_name__       ,							metadata={"""help""": """The input training data file (a text file)."""} )
   _a							=      field(
       default=__magic_name__       ,							metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}       ,							)
   _a							=      field(
       default=__magic_name__       ,							metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}       ,							)
   _a							=      field(
       default=__magic_name__       ,							metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}       ,							)
   _a							=      field(
       default=__magic_name__       ,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
   _a							=      field(
       default=5       ,							metadata={
           """help""": """The percentage of the train set used as validation set in case there's no validation split"""
       }       ,							)
   _a							=      field(
       default=__magic_name__       ,							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."""
           )
       }       ,							)
   _a							=      field(
       default=__magic_name__       ,							metadata={"""help""": """The number of processes to use for the preprocessing."""}       ,							)
   _a							=      field(
       default=0.15       ,							metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""} )
   _a							=      field(
       default=__magic_name__       ,							metadata={
           """help""": (
               """Whether to pad all samples to `max_seq_length`. """
               """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
           )
       }       ,							)







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


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

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


def 						SCREAMING_SNAKE_CASE       (      ):
  # 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)			)
  if len(sys.argv			) == 2 and sys.argv[1].endswith('.json'			):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    __a	,       __a	,       __a			=					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
  else:
    __a	,       __a	,       __a			=					parser.parse_args_into_dataclasses()

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

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

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

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

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

  if model_args.model_name_or_path:
    __a			=					AutoModelForMaskedLM.from_pretrained(
        model_args.model_name_or_path				,					from_tf=bool('.ckpt' in model_args.model_name_or_path			)				,					config=a_				,					cache_dir=model_args.cache_dir				,					revision=model_args.model_revision				,					use_auth_token=True if model_args.use_auth_token else None				,					)
  else:
    logger.info('Training new model from scratch'			)
    __a			=					AutoModelForMaskedLM.from_config(a_			)

  model.resize_token_embeddings(len(a_			)			)

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

  __a			=					'max_length' if data_args.pad_to_max_length else False

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

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

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

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

  # Initialize our Trainer
  __a			=					Trainer(
      model=a_				,					args=a_				,					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=a_				,					data_collator=a_				,					)

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

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

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

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

    __a			=					trainer.evaluate()

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

    __a			=					os.path.join(training_args.output_dir				,					'eval_results_mlm_wwm.txt'			)
    if trainer.is_world_process_zero():
      with open(a_				,					'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 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]			):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
  main()








'''simple docstring'''


from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


UpperCAmelCase_ 		=      logging.get_logger(__name__)


UpperCAmelCase_ 		=      TypeVar("DatasetType", Dataset, IterableDataset)


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[DatasetType]				,					a_	:							Optional[List[float]] = None				,					a_	:							Optional[int] = None				,					a_	:							Optional[DatasetInfo] = None				,					a_	:							Optional[NamedSplit] = None				,					a_	:							Literal["first_exhausted", "all_exhausted"] = "first_exhausted"				,					):
  from .arrow_dataset import Dataset
  from .iterable_dataset import IterableDataset

  if not datasets:
    raise ValueError('Unable to interleave an empty list of datasets.'			)
  for i, dataset in enumerate(a_			):
    if not isinstance(a_				,					(Dataset, IterableDataset)			):
      if isinstance(a_				,					(DatasetDict, IterableDatasetDict)			):
        if not dataset:
          raise ValueError(
              f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
              'is an empty dataset dictionary.'			)
        raise ValueError(
            f"Dataset at position {i} has at least one split: {list(a_			)}\n"
            f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(a_			)			)}']"			)
      raise ValueError(
          f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(a_			).__name__}."			)
    if i == 0:
      __a	,       __a			=					(
          (Dataset, IterableDataset) if isinstance(a_				,					a_			) else (IterableDataset, Dataset)
      )
    elif not isinstance(a_				,					a_			):
      raise ValueError(
          f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."			)
  if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
    raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."			)
  if dataset_type is Dataset:
    return _interleave_map_style_datasets(
        a_				,					a_				,					a_				,					info=a_				,					split=a_				,					stopping_strategy=a_			)
  else:
    return _interleave_iterable_datasets(
        a_				,					a_				,					a_				,					info=a_				,					split=a_				,					stopping_strategy=a_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[DatasetType]				,					a_	:							Optional[DatasetInfo] = None				,					a_	:							Optional[NamedSplit] = None				,					a_	:							int = 0				,					):

  if not dsets:
    raise ValueError('Unable to concatenate an empty list of datasets.'			)
  for i, dataset in enumerate(a_			):
    if not isinstance(a_				,					(Dataset, IterableDataset)			):
      if isinstance(a_				,					(DatasetDict, IterableDatasetDict)			):
        if not dataset:
          raise ValueError(
              f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
              'is an empty dataset dictionary.'			)
        raise ValueError(
            f"Dataset at position {i} has at least one split: {list(a_			)}\n"
            f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(a_			)			)}']"			)
      raise ValueError(
          f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(a_			).__name__}."			)
    if i == 0:
      __a	,       __a			=					(
          (Dataset, IterableDataset) if isinstance(a_				,					a_			) else (IterableDataset, Dataset)
      )
    elif not isinstance(a_				,					a_			):
      raise ValueError(
          f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."			)
  if dataset_type is Dataset:
    return _concatenate_map_style_datasets(a_				,					info=a_				,					split=a_				,					axis=a_			)
  else:
    return _concatenate_iterable_datasets(a_				,					info=a_				,					split=a_				,					axis=a_			)



