


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


_A		:					Optional[int]          =							logging.getLogger(__name__)



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



def      __lowerCAmelCase    (				)							->				str:
   # 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.

   __lowerCamelCase:					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.
      __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase:					Optional[Any]	 =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
   else:
      __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase:					int	 =						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"""    ,   snake_case			)

   # 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.

   __lowerCamelCase:					Dict	 =						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    ,   )
   __lowerCamelCase:					List[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=snake_case    ,   )
   __lowerCamelCase:					List[str]	 =						AutoModelForQuestionAnswering.from_pretrained(
       model_args.model_name_or_path    ,   from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)    ,   config=snake_case    ,   cache_dir=model_args.cache_dir    ,   )

   # Get datasets
   __lowerCamelCase:					Union[str, Any]	 =						hasattr(model.config    ,   """lang2id"""			)
   __lowerCamelCase:					Dict	 =						(
       SquadDataset(
           snake_case    ,   tokenizer=snake_case    ,   is_language_sensitive=snake_case    ,   cache_dir=model_args.cache_dir			)
       if training_args.do_train
       else None
   )
   __lowerCamelCase:					Union[str, Any]	 =						(
       SquadDataset(
           snake_case    ,   tokenizer=snake_case    ,   mode="""dev"""    ,   is_language_sensitive=snake_case    ,   cache_dir=model_args.cache_dir    ,   )
       if training_args.do_eval
       else None
   )

   # Data collator
   __lowerCamelCase:					str	 =						DataCollatorWithPadding(snake_case    ,   pad_to_multiple_of=8			) if training_args.fpaa else None

   # Initialize our Trainer
   __lowerCamelCase:					List[Any]	 =						Trainer(
       model=snake_case    ,   args=snake_case    ,   train_dataset=snake_case    ,   eval_dataset=snake_case    ,   data_collator=snake_case    ,   )

   # 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    (				snake_case						:     List[Any]			)							->				List[str]:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()






import sys



def      __lowerCAmelCase    (				snake_case						:     int    ,   snake_case						:     list[int]			)							->				int:
   if not numbers:
      return 0
   if target == 0 and target in numbers:
      return 0
   if not isinstance(snake_case    ,   (list, tuple)			) or not all(
       isinstance(snake_case    ,   snake_case			) for number in numbers			):
      raise ValueError("""numbers must be an iterable of integers"""			)

   __lowerCamelCase:					Union[str, Any]	 =						0
   __lowerCamelCase:					str	 =						sys.maxsize

   while right < len(snake_case			):
      curr_sum += numbers[right]
      while curr_sum >= target and left <= right:
         __lowerCamelCase:					str	 =						min(snake_case    ,   right - left + 1			)
         curr_sum -= numbers[left]
         left += 1
      right += 1

   return 0 if min_len == sys.maxsize else min_len


