


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


UpperCAmelCase     =       logging.getLogger(__name__)







@dataclass
class lowerCAmelCase_		:

 '''simple docstring'''

 __snake_case				=    field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
 __snake_case				=    field(
     default=_a   ,		metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
 __snake_case				=    field(
     default=_a   ,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
 __snake_case				=    field(default=_a   ,		metadata={"help": "Set this flag to use fast tokenization."}			)
 # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
 # or just modify its tokenizer_config.json.
 __snake_case				=    field(
     default=_a   ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,		)






def 			__lowerCAmelCase       ()->    Dict:




      """simple docstring"""

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

      if (
          os.path.exists(training_args.output_dir   )
          and os.listdir(training_args.output_dir   )
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
                ''' --overwrite_output_dir to overcome.'''   )

      # Setup logging
      logging.basicConfig(
          format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''   ,   datefmt='''%m/%d/%Y %H:%M:%S'''   ,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,   )
      logger.warning(
          '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''   ,   training_args.local_rank   ,   training_args.device   ,   training_args.n_gpu   ,   bool(training_args.local_rank != -1   )   ,   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_   )

      # Prepare Question-Answering task
      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      snake_case_			= AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,   cache_dir=model_args.cache_dir   ,   )
      snake_case_			= AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,   cache_dir=model_args.cache_dir   ,   use_fast=A_   ,   )
      snake_case_			= AutoModelForQuestionAnswering.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   ,   )

      # Get datasets
      snake_case_			= hasattr(model.config   ,   '''lang2id'''   )
      snake_case_			= (
          SquadDataset(
              A_   ,   tokenizer=A_   ,   is_language_sensitive=A_   ,   cache_dir=model_args.cache_dir   )
          if training_args.do_train
          else None
      )
      snake_case_			= (
          SquadDataset(
              A_   ,   tokenizer=A_   ,   mode='''dev'''   ,   is_language_sensitive=A_   ,   cache_dir=model_args.cache_dir   ,   )
          if training_args.do_eval
          else None
      )

      # Data collator
      snake_case_			= DataCollatorWithPadding(A_   ,   pad_to_multiple_of=8   ) if training_args.fpaa else None

      # Initialize our Trainer
      snake_case_			= Trainer(
          model=A_   ,   args=A_   ,   train_dataset=A_   ,   eval_dataset=A_   ,   data_collator=A_   ,   )

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_master():
                  tokenizer.save_pretrained(training_args.output_dir   )






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    int:




      """simple docstring"""

      main()


if __name__ == "__main__":
   main()



"""simple docstring"""







class 		_A    :







    """simple docstring"""




    def __init__(   self							:       Any):
      a		:						Tuple 				=     [0]
      a		:						str 				=     0






    def       __snake_case				(   self							:       Optional[Any]							,					__UpperCAmelCase							:       int):
      a		:						Any 				=     self.__heap[i]
      while i // 2 > 0:
        if self.__heap[i] > self.__heap[i // 2]:
          a		:						Tuple 				=     self.__heap[i // 2]
          a		:						Any 				=     temporary
        i //= 2






    def       __snake_case				(   self							:       Dict							,					__UpperCAmelCase							:       int):
      self.__heap.append(__UpperCAmelCase)
      self.__size += 1
      self.__swap_up(self.__size)






    def       __snake_case				(   self							:       str							,					__UpperCAmelCase							:       int):
      while self.__size >= 2 * i:
        if 2 * i + 1 > self.__size:
          a		:						List[Any] 				=     2 * i
        else:
          if self.__heap[2 * i] > self.__heap[2 * i + 1]:
            a		:						Tuple 				=     2 * i
          else:
            a		:						str 				=     2 * i + 1
        a		:						Optional[int] 				=     self.__heap[i]
        if self.__heap[i] < self.__heap[bigger_child]:
          a		:						Any 				=     self.__heap[bigger_child]
          a		:						Union[str, Any] 				=     temporary
        a		:						Dict 				=     bigger_child






    def       __snake_case				(   self							:       List[str]):
      a		:						int 				=     self.__heap[1]
      a		:						Tuple 				=     self.__heap[self.__size]
      self.__size -= 1
      self.__heap.pop()
      self.__swap_down(1)
      return max_value






    @property
    def       __snake_case				(   self							:       List[str]):
      return self.__heap[1:]






    def __len__(   self							:       Optional[int]):
      return self.__size


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # create an instance of BinaryHeap
 __lowercase          = BinaryHeap()
 binary_heap.insert(6)
 binary_heap.insert(10)
 binary_heap.insert(15)
 binary_heap.insert(12)
 # pop root(max-values because it is max heap)
 print(binary_heap.pop())  # 15
 print(binary_heap.pop())  # 12
 # get the list and size after operations
 print(binary_heap.get_list)
 print(len(binary_heap))






