'''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


UpperCAmelCase							   =      logging.getLogger(__name__)






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







def 	UpperCAmelCase       (		)	->	Optional[Any]:
     # 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  :    int				=						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  :    List[Any]				=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
     else:
          a  :    Tuple				=						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'''						,      lowerCAmelCase_       )

     # 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.

     a  :    int				=						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						,      )
     a  :    Union[str, Any]				=						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=lowerCAmelCase_						,      )
     a  :    List[str]				=						AutoModelForQuestionAnswering.from_pretrained(
         model_args.model_name_or_path						,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path       )						,      config=lowerCAmelCase_						,      cache_dir=model_args.cache_dir						,      )

     # Get datasets
     a  :    str				=						hasattr(model.config						,      '''lang2id'''       )
     a  :    Union[str, Any]				=						(
         SquadDataset(
             lowerCAmelCase_						,      tokenizer=lowerCAmelCase_						,      is_language_sensitive=lowerCAmelCase_						,      cache_dir=model_args.cache_dir       )
         if training_args.do_train
         else None
     )
     a  :    List[Any]				=						(
         SquadDataset(
             lowerCAmelCase_						,      tokenizer=lowerCAmelCase_						,      mode='''dev'''						,      is_language_sensitive=lowerCAmelCase_						,      cache_dir=model_args.cache_dir						,      )
         if training_args.do_eval
         else None
     )

     # Data collator
     a  :    List[str]				=						DataCollatorWithPadding(lowerCAmelCase_						,      pad_to_multiple_of=8       ) if training_args.fpaa else None

     # Initialize our Trainer
     a  :    Dict				=						Trainer(
         model=lowerCAmelCase_						,      args=lowerCAmelCase_						,      train_dataset=lowerCAmelCase_						,      eval_dataset=lowerCAmelCase_						,      data_collator=lowerCAmelCase_						,      )

     # 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 	UpperCAmelCase       (		lowerCAmelCase_       )	->	List[Any]:
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
  main()
from __future__ import annotations

UpperCAmelCase							   =      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class 							_SCREAMING_SNAKE_CASE:
   def __init__(    self							,     lowerCAmelCase_    )	->None:
        a  :    int				=						-1
        a  :    int				=						-1
        a  :    list[int]				=						[-1] * length
        a  :    Node | None				=						None
        a  :    Node | None				=						None


   def __repr__(    self    )	->str:
        return F"""Node(min_value={self.minn} max_value={self.maxx})"""







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Node | None:
     a  :    Dict				=						Node(len(lowerCAmelCase_       )       )
     a			,							a  :    Tuple				=						min(lowerCAmelCase_       ), max(lowerCAmelCase_       )
     # Leaf node case where the node contains only one unique value
     if root.minn == root.maxx:
          return root
     a  :    Optional[Any]				=						(root.minn + root.maxx) // 2

     a  :    list[int]				=						[]
     a  :    list[int]				=						[]

     for index, num in enumerate(lowerCAmelCase_       ):
          if num <= pivot:
               left_arr.append(lowerCAmelCase_       )
          else:
               right_arr.append(lowerCAmelCase_       )
          a  :    int				=						len(lowerCAmelCase_       )
     a  :    List[str]				=						build_tree(lowerCAmelCase_       )
     a  :    List[str]				=						build_tree(lowerCAmelCase_       )
     return root







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if index < 0 or node is None:
          return 0
     # Leaf node cases
     if node.minn == node.maxx:
          return index + 1 if node.minn == num else 0
     a  :    List[Any]				=						(node.minn + node.maxx) // 2
     if num <= pivot:
          # go the left subtree and map index to the left subtree
          return rank_till_index(node.left						,      lowerCAmelCase_						,      node.map_left[index] - 1       )
     else:
          # go to the right subtree and map index to the right subtree
          return rank_till_index(node.right						,      lowerCAmelCase_						,      index - node.map_left[index]       )







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if start > end:
          return 0
     a  :    Optional[Any]				=						rank_till_index(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )
     a  :    Any				=						rank_till_index(lowerCAmelCase_						,      lowerCAmelCase_						,      start - 1       )
     return rank_till_end - rank_before_start







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if index > (end - start) or start > end or node is None:
          return -1
     # Leaf node case
     if node.minn == node.maxx:
          return node.minn
     # Number of elements in the left subtree in interval [start, end]
     a  :    Optional[int]				=						node.map_left[end] - (
         node.map_left[start - 1] if start else 0
     )
     if num_elements_in_left_tree > index:
          return quantile(
              node.left						,      lowerCAmelCase_						,      (node.map_left[start - 1] if start else 0)						,      node.map_left[end] - 1						,      )
     else:
          return quantile(
              node.right						,      index - num_elements_in_left_tree						,      start - (node.map_left[start - 1] if start else 0)						,      end - node.map_left[end]						,      )







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if (
         start > end
         or node is None
         or start_num > end_num
         or node.minn > end_num
         or node.maxx < start_num
     ):
          return 0
     if start_num <= node.minn and node.maxx <= end_num:
          return end - start + 1
     a  :    Tuple				=						range_counting(
         node.left						,      (node.map_left[start - 1] if start else 0)						,      node.map_left[end] - 1						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )
     a  :    int				=						range_counting(
         node.right						,      start - (node.map_left[start - 1] if start else 0)						,      end - node.map_left[end]						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )
     return left + right


if __name__ == "__main__":
  import doctest

  doctest.testmod()