




'''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


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

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






def 		a_(			)					->Dict:






       '''simple docstring'''




       lowercase	:		Any    =   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	:		Tuple    =   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''' ,					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.

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

       # Initialize our Trainer
       lowercase	:		Union[str, Any]    =   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 		a_(			lowerCamelCase   )					->Optional[Any]:






       '''simple docstring'''




       main()


if __name__ == "__main__":
      main()










'''simple docstring'''






def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




       if len(lowerCamelCase   ) < len(lowerCamelCase   ):
              return levenshtein_distance(lowerCamelCase ,					lowerCamelCase   )

       if len(lowerCamelCase   ) == 0:
              return len(lowerCamelCase   )

       lowercase	:		List[Any]    =   list(range(len(lowerCamelCase   ) + 1   )   )

       for i, ca in enumerate(lowerCamelCase   ):
              lowercase	:		Any    =   [i + 1]

              for j, ca in enumerate(lowerCamelCase   ):
                     # Calculate insertions, deletions and substitutions
                     lowercase	:		List[Any]    =   previous_row[j + 1] + 1
                     lowercase	:		str    =   current_row[j] + 1
                     lowercase	:		List[Any]    =   previous_row[j] + (ca != ca)

                     # Get the minimum to append to the current row
                     current_row.append(min(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )   )

              # Store the previous row
              lowercase	:		List[str]    =   current_row

       # Returns the last element (distance)
       return previous_row[-1]


if __name__ == "__main__":
      lowercase						:   Dict      						=							input('''Enter the first word:\n''').strip()
      lowercase						:   Tuple      						=							input('''Enter the second word:\n''').strip()

      lowercase						:   Tuple      						=							levenshtein_distance(first_word, second_word)
      print(f'Levenshtein distance between {first_word} and {second_word} is {result}')




