



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     :  Optional[Any]					     =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class   lowerCamelCase					:
  """simple docstring"""







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







  def    __A     (							self :       Any   )  ->	Dict:
        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"""







  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''The input training data file (a text file).'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}  ,      )
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}  ,      )
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}  ,      )
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}					)
  lowerCamelCase__     =						field(
      default=5  ,      metadata={
          '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
      }  ,      )
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      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.'''
          )
      }  ,      )
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''The number of processes to use for the preprocessing.'''}  ,      )
  lowerCamelCase__     =						field(
      default=0.15  ,      metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      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[int]   )  ->	Union[str, Any]:
        if self.train_file is not None:
              SCREAMING_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:
              SCREAMING_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__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      with open(__UpperCamelCase     ,      "r"     ,      encoding="utf-8"				) as f:
            SCREAMING_SNAKE_CASE_          =  [json.loads(__UpperCamelCase				) for line in f.read().splitlines() if (len(__UpperCamelCase				) > 0 and not line.isspace())]
      assert len(__UpperCamelCase				) == len(__UpperCamelCase				)

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


def      a__ (		):
      # 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_          =  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_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
      else:
            SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  parser.parse_args_into_dataclasses()

      # Detecting last checkpoint.
      SCREAMING_SNAKE_CASE_          =  None
      if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
            SCREAMING_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"     ,      __UpperCamelCase				)

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

      SCREAMING_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:
            SCREAMING_SNAKE_CASE_          =  AutoTokenizer.from_pretrained(model_args.tokenizer_name     ,      **__UpperCamelCase				)
      elif model_args.model_name_or_path:
            SCREAMING_SNAKE_CASE_          =  AutoTokenizer.from_pretrained(model_args.model_name_or_path     ,      **__UpperCamelCase				)
      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_          =  AutoModelForMaskedLM.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     ,      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_          =  AutoModelForMaskedLM.from_config(__UpperCamelCase				)

      model.resize_token_embeddings(len(__UpperCamelCase				)				)

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

      SCREAMING_SNAKE_CASE_          =  "max_length" if data_args.pad_to_max_length else False

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

      SCREAMING_SNAKE_CASE_          =  datasets.map(
          __UpperCamelCase     ,      batched=__UpperCamelCase     ,      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_          =  add_chinese_references(tokenized_datasets["train"]     ,      data_args.train_ref_file				)
      if data_args.validation_ref_file is not None:
            SCREAMING_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
      SCREAMING_SNAKE_CASE_          =  data_args.train_ref_file or data_args.validation_ref_file
      if has_ref:
            SCREAMING_SNAKE_CASE_          =  False

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

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE_          =  Trainer(
          model=__UpperCamelCase     ,      args=__UpperCamelCase     ,      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=__UpperCamelCase     ,      data_collator=__UpperCamelCase     ,      )

      # Training
      if training_args.do_train:
            if last_checkpoint is not None:
                  SCREAMING_SNAKE_CASE_          =  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_          =  model_args.model_name_or_path
            else:
                  SCREAMING_SNAKE_CASE_          =  None
            SCREAMING_SNAKE_CASE_          =  trainer.train(resume_from_checkpoint=__UpperCamelCase				)
            trainer.save_model()  # Saves the tokenizer too for easy upload

            SCREAMING_SNAKE_CASE_          =  os.path.join(training_args.output_dir     ,      "train_results.txt"				)
            if trainer.is_world_process_zero():
                  with open(__UpperCamelCase     ,      "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_          =  {}
      if training_args.do_eval:
            logger.info("*** Evaluate ***"				)

            SCREAMING_SNAKE_CASE_          =  trainer.evaluate()

            SCREAMING_SNAKE_CASE_          =  math.exp(eval_output["eval_loss"]				)
            SCREAMING_SNAKE_CASE_          =  perplexity

            SCREAMING_SNAKE_CASE_          =  os.path.join(training_args.output_dir     ,      "eval_results_mlm_wwm.txt"				)
            if trainer.is_world_process_zero():
                  with open(__UpperCamelCase     ,      "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__ (		__UpperCamelCase				):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
      main()



from __future__ import annotations


def      a__ (		__UpperCamelCase				):

      # preprocessing the first row
      for i in range(1     ,      len(matrix[0]				)				):
            matrix[0][i] += matrix[0][i - 1]

      # preprocessing the first column
      for i in range(1     ,      len(__UpperCamelCase				)				):
            matrix[i][0] += matrix[i - 1][0]

      # updating the path cost for current position
      for i in range(1     ,      len(__UpperCamelCase				)				):
            for j in range(1     ,      len(matrix[0]				)				):
                  matrix[i][j] += min(matrix[i - 1][j]     ,      matrix[i][j - 1]				)

      return matrix[-1][-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()


