

'''simple docstring'''
from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar







class 							A__					( A__						):





  @abstractmethod
  def __lt__(	self   :			T							,      _a   :			T				)			->					bool:





       '''simple docstring'''






       pass





  @abstractmethod
  def __gt__(	self   :			T							,      _a   :			T				)			->					bool:





       '''simple docstring'''






       pass





  @abstractmethod
  def __eq__(	self   :			T							,      _a   :			object				)			->					bool:





       '''simple docstring'''






       pass

lowerCamelCase				:   str      				=  TypeVar("T", bound=Comparable)







class 							A__					( Generic[T]						):





  def __init__(	self   :			Tuple				)			->					None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=[]
       _SCREAMING_SNAKE_CASE												=0





  def __repr__(	self   :			Optional[Any]				)			->					str:





       '''simple docstring'''






       return str(self.h				)





  def 		A						(	self   :			List[str]							,      _a   :			int				)			->					int | None:





       '''simple docstring'''






       if child_idx > 0:
            return (child_idx - 1) // 2
       return None





  def 		A						(	self   :			Any							,      _a   :			int				)			->					int | None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=2 * parent_idx + 1
       if left_child_index < self.heap_size:
            return left_child_index
       return None





  def 		A						(	self   :			Tuple							,      _a   :			int				)			->					int | None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=2 * parent_idx + 2
       if right_child_index < self.heap_size:
            return right_child_index
       return None





  def 		A						(	self   :			List[Any]							,      _a   :			int				)			->					None:





       '''simple docstring'''






       if index < self.heap_size:
            _SCREAMING_SNAKE_CASE												=index
            _SCREAMING_SNAKE_CASE												=self.left_child_idx(_a				)
            _SCREAMING_SNAKE_CASE												=self.right_child_idx(_a				)
            # check which child is larger than its parent
            if left_child is not None and self.h[left_child] > self.h[violation]:
                 _SCREAMING_SNAKE_CASE												=left_child
            if right_child is not None and self.h[right_child] > self.h[violation]:
                 _SCREAMING_SNAKE_CASE												=right_child
            # if violation indeed exists
            if violation != index:
                 # swap to fix the violation
                 _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=self.h[index], self.h[violation]
                 # fix the subsequent violation recursively if any
                 self.max_heapify(_a				)





  def 		A						(	self   :			Union[str, Any]							,      _a   :			Iterable[T]				)			->					None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=list(_a				)
       _SCREAMING_SNAKE_CASE												=len(self.h				)
       if self.heap_size > 1:
            # max_heapify from right to left but exclude leaves (last level)
            for i in range(self.heap_size // 2 - 1							,      -1							,      -1				):
                 self.max_heapify(_a				)





  def 		A						(	self   :			List[str]				)			->					T:





       '''simple docstring'''






       if self.heap_size >= 2:
            _SCREAMING_SNAKE_CASE												=self.h[0]
            _SCREAMING_SNAKE_CASE												=self.h.pop(-1				)
            self.heap_size -= 1
            self.max_heapify(0				)
            return me
       elif self.heap_size == 1:
            self.heap_size -= 1
            return self.h.pop(-1				)
       else:
            raise Exception('Empty heap'				)





  def 		A						(	self   :			str							,      _a   :			T				)			->					None:





       '''simple docstring'''






       self.h.append(_a				)
       _SCREAMING_SNAKE_CASE												=(self.heap_size - 1) // 2
       self.heap_size += 1
       while idx >= 0:
            self.max_heapify(_a				)
            _SCREAMING_SNAKE_CASE												=(idx - 1) // 2





  def 		A						(	self   :			List[str]				)			->					None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.heap_size
       for j in range(size - 1							,      0							,      -1				):
            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=self.h[j], self.h[0]
            self.heap_size -= 1
            self.max_heapify(0				)
       _SCREAMING_SNAKE_CASE												=size


if __name__ == "__main__":
     import doctest

     # run doc test
     doctest.testmod()

     # demo
     for unsorted in [
         [0],
         [2],
         [3, 5],
         [5, 3],
         [5, 5],
         [0, 0, 0, 0],
         [1, 1, 1, 1],
         [2, 2, 3, 5],
         [0, 2, 2, 3, 5],
         [2, 5, 3, 0, 2, 3, 0, 3],
         [6, 1, 2, 7, 9, 3, 4, 5, 1_0, 8],
         [1_0_3, 9, 1, 7, 1_1, 1_5, 2_5, 2_0_1, 2_0_9, 1_0_7, 5],
         [-4_5, -2, -5],
     ]:
          print(f'''unsorted array: {unsorted}''')

          lowerCamelCase				:   Heap[int]      				=  Heap()
          heap.build_max_heap(unsorted)
          print(f'''after build heap: {heap}''')

          print(f'''max value: {heap.extract_max()}''')
          print(f'''after max value removed: {heap}''')

          heap.insert(1_0_0)
          print(f'''after new value 100 inserted: {heap}''')

          heap.heap_sort()
          print(f'''heap-sorted array: {heap}\n''')





'''simple docstring'''
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


lowerCamelCase				:   int      				=  logging.getLogger(__name__)







@dataclass
class 							A__					:
  A__					=       field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}						)
  A__					=       field(
      default=A__  ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
  A__					=       field(
      default=A__  ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}						)
  A__					=       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.
  A__					=       field(
      default=A__  ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,     )


def  _lowerCAmelCase      (		)   ->    Union[str, Any]:
     """simple docstring"""




     _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()

     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'							,				_UpperCamelCase       )

     # 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.

     _SCREAMING_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							,				)
     _SCREAMING_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=_UpperCamelCase							,				)
     _SCREAMING_SNAKE_CASE												=AutoModelForQuestionAnswering.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							,				)

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

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

     # Initialize our Trainer
     _SCREAMING_SNAKE_CASE												=Trainer(
         model=_UpperCamelCase							,				args=_UpperCamelCase							,				train_dataset=_UpperCamelCase							,				eval_dataset=_UpperCamelCase							,				data_collator=_UpperCamelCase							,				)

     # 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      (		_UpperCamelCase		:  Tuple       )   ->    List[str]:
     """simple docstring"""




     main()


if __name__ == "__main__":
     main()
