
"""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


_A      :	Union[str, Any]       		=	logging.getLogger(__name__)

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


def   __magic_name__     (				)			->    List[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.

       lowercase   :    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.
              lowercase		,   lowercase		,   lowercase   :    List[str]			=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
       else:
              lowercase		,   lowercase		,   lowercase   :    Dict			=							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.

       lowercase   :    Tuple			=							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					,			)
       lowercase   :    List[str]			=							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					,			)
       lowercase   :    Any			=							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
       lowercase   :    int			=							hasattr(model.config					,			"lang2id"  )
       lowercase   :    str			=							(
           SquadDataset(
               __snake_case					,			tokenizer=__snake_case					,			is_language_sensitive=__snake_case					,			cache_dir=model_args.cache_dir  )
           if training_args.do_train
           else None
       )
       lowercase   :    Optional[int]			=							(
           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
       lowercase   :    List[str]			=							DataCollatorWithPadding(__snake_case					,			pad_to_multiple_of=8  ) if training_args.fpaa else None

       # Initialize our Trainer
       lowercase   :    Dict			=							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   __magic_name__     (				__snake_case    :	List[Any]  )			->    Optional[Any]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
   main()




"""simple docstring"""




from __future__ import annotations

_A      :	List[str]       		=	"""Alexander Joslin"""


def   __magic_name__     (				__snake_case    :	int  )			->    int:

       if number <= 0:
              lowercase   :    Optional[Any]			=							f"""n must be greater than 0. Got n = {number}"""
              raise ValueError(__snake_case  )

       lowercase   :    List[str]			=							[number + 1] * (number + 1)

       # starting position
       lowercase   :    Optional[Any]			=							0
       for i in range(1					,			__snake_case  ):
              lowercase   :    str			=							min(table[i + 1]					,			table[i] + 1  )
              # check if out of bounds
              if i * 2 <= number:
                     lowercase   :    int			=							min(table[i * 2]					,			table[i] + 1  )
              # check if out of bounds
              if i * 3 <= number:
                     lowercase   :    Optional[int]			=							min(table[i * 3]					,			table[i] + 1  )
       return table[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


