



'''simple docstring'''





import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_UpperCamelCase							    =						logging.getLogger(__name__)

def 	a_					(							lowercase__	)				->				Tuple:
  np.random.seed(args.seed	)
  torch.manual_seed(args.seed	)
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed	)

def 	a_					(							)				->				Any:
  __lowerCamelCase	:	Optional[int]   	=			argparse.ArgumentParser()
  parser.add_argument(
      '--model_name_or_path'	,default=_lowerCAmelCase	,type=_lowerCAmelCase	,required=_lowerCAmelCase	,)
  parser.add_argument('--prompt'	,type=_lowerCAmelCase	,default=''	)
  parser.add_argument('--length'	,type=_lowerCAmelCase	,default=20	)
  parser.add_argument('--stop_token'	,type=_lowerCAmelCase	,default=_lowerCAmelCase	,help='Token at which text generation is stopped'	)
  parser.add_argument(
      '--temperature'	,type=_lowerCAmelCase	,default=1.0	,help='temperature of 1.0 has no effect, lower tend toward greedy sampling'	,)
  parser.add_argument(
      '--repetition_penalty'	,type=_lowerCAmelCase	,default=1.0	,help='primarily useful for CTRL model; in that case, use 1.2'	)
  parser.add_argument('--k'	,type=_lowerCAmelCase	,default=0	)
  parser.add_argument('--penalty_alpha'	,type=_lowerCAmelCase	,default=0.0	)
  parser.add_argument('--p'	,type=_lowerCAmelCase	,default=0.9	)

  parser.add_argument('--prefix'	,type=_lowerCAmelCase	,default=''	,help='Text added prior to input.'	)
  parser.add_argument('--padding_text'	,type=_lowerCAmelCase	,default=''	,help='Deprecated, the use of `--prefix` is preferred.'	)
  parser.add_argument('--xlm_language'	,type=_lowerCAmelCase	,default=''	,help='Optional language when used with the XLM model.'	)

  parser.add_argument('--seed'	,type=_lowerCAmelCase	,default=42	,help='random seed for initialization'	)
  parser.add_argument('--no_cuda'	,action='store_true'	,help='Avoid using CUDA when available'	)
  parser.add_argument(
      '--fp16'	,action='store_true'	,help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'	,)
  __lowerCamelCase	:	Optional[Any]   	=			parser.parse_args()

  __lowerCamelCase	:	Optional[int]   	=			torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'	)
  __lowerCamelCase	:	Optional[int]   	=			0 if args.no_cuda else torch.cuda.device_count()

  logger.warning(F'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'	)

  set_seed(_lowerCAmelCase	)

  # Initialize the model and tokenizer
  __lowerCamelCase	:	Tuple   	=			AutoTokenizer.from_pretrained(args.model_name_or_path	)
  __lowerCamelCase	:	Any   	=			AutoModelForCausalLM.from_pretrained(args.model_name_or_path	)

  # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  model.to(args.device	)

  if args.fpaa:
    model.half()

  logger.info(_lowerCAmelCase	)
  __lowerCamelCase	:	Optional[int]   	=			args.prompt if args.prompt else input('Model prompt >>> '	)

  __lowerCamelCase	:	Optional[Any]   	=			tokenizer(_lowerCAmelCase	,return_tensors='pt'	,add_special_tokens=_lowerCAmelCase	)
  __lowerCamelCase	:	List[Any]   	=			{key: value.to(args.device	) for key, value in inputs.items()}

  __lowerCamelCase	:	Optional[int]   	=			model.generate(
      **_lowerCAmelCase	,max_length=args.length + len(inputs['input_ids'][0]	)	,penalty_alpha=args.penalty_alpha	,top_k=args.k	,)

  __lowerCamelCase	:	str   	=			[]
  for generated_sequence_idx, generated_sequence in enumerate(_lowerCAmelCase	):
    print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='	)
    __lowerCamelCase	:	Dict   	=			generated_sequence.tolist()

    # Decode text
    __lowerCamelCase	:	List[str]   	=			tokenizer.decode(_lowerCAmelCase	,clean_up_tokenization_spaces=_lowerCAmelCase	,add_special_tokens=_lowerCAmelCase	)

    # Remove all text after the stop token
    __lowerCamelCase	:	Optional[Any]   	=			text[: text.find(args.stop_token	) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    __lowerCamelCase	:	List[str]   	=			(
        prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]	,clean_up_tokenization_spaces=_lowerCAmelCase	)	) :]
    )

    generated_sequences.append(_lowerCAmelCase	)
    print(_lowerCAmelCase	)

  return generated_sequences


if __name__ == "__main__":
   main()











'''simple docstring'''





def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				int:
  # The longer word should come first
  if len(_lowerCAmelCase	) < len(_lowerCAmelCase	):
    return levenshtein_distance(_lowerCAmelCase	,_lowerCAmelCase	)

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

  __lowerCamelCase	:	int   	=			list(range(len(_lowerCAmelCase	) + 1	)	)

  for i, ca in enumerate(_lowerCAmelCase	):
    __lowerCamelCase	:	Union[str, Any]   	=			[i + 1]

    for j, ca in enumerate(_lowerCAmelCase	):
      # Calculate insertions, deletions and substitutions
      __lowerCamelCase	:	Any   	=			previous_row[j + 1] + 1
      __lowerCamelCase	:	int   	=			current_row[j] + 1
      __lowerCamelCase	:	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
    __lowerCamelCase	:	Optional[int]   	=			current_row

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


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

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






