






"""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,
)
lowercase_    					=					logging.getLogger(__name__)



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  np.random.seed(args.seed			)
  torch.manual_seed(args.seed			)
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed			)



def  lowerCAmelCase    (   ):




  """simple docstring"""







  __A						      =			argparse.ArgumentParser()
  parser.add_argument(
      '''--model_name_or_path'''		,	default=__UpperCamelCase		,	type=__UpperCamelCase		,	required=__UpperCamelCase		,	)
  parser.add_argument('''--prompt'''		,	type=__UpperCamelCase		,	default=''''''			)
  parser.add_argument('''--length'''		,	type=__UpperCamelCase		,	default=2_0			)
  parser.add_argument('''--stop_token'''		,	type=__UpperCamelCase		,	default=__UpperCamelCase		,	help='''Token at which text generation is stopped'''			)
  parser.add_argument(
      '''--temperature'''		,	type=__UpperCamelCase		,	default=1.0		,	help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''		,	)
  parser.add_argument(
      '''--repetition_penalty'''		,	type=__UpperCamelCase		,	default=1.0		,	help='''primarily useful for CTRL model; in that case, use 1.2'''			)
  parser.add_argument('''--k'''		,	type=__UpperCamelCase		,	default=0			)
  parser.add_argument('''--penalty_alpha'''		,	type=__UpperCamelCase		,	default=0.0			)
  parser.add_argument('''--p'''		,	type=__UpperCamelCase		,	default=0.9			)

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

  parser.add_argument('''--seed'''		,	type=__UpperCamelCase		,	default=4_2		,	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'''		,	)
  __A						      =			parser.parse_args()

  __A						      =			torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
  __A						      =			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(__UpperCamelCase			)

  # Initialize the model and tokenizer
  __A						      =			AutoTokenizer.from_pretrained(args.model_name_or_path			)
  __A						      =			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(__UpperCamelCase			)
  __A						      =			args.prompt if args.prompt else input('''Model prompt >>> '''			)

  __A						      =			tokenizer(__UpperCamelCase		,	return_tensors='''pt'''		,	add_special_tokens=__UpperCamelCase			)
  __A						      =			{key: value.to(args.device			) for key, value in inputs.items()}

  __A						      =			model.generate(
      **__UpperCamelCase		,	max_length=args.length + len(inputs['''input_ids'''][0]			)		,	penalty_alpha=args.penalty_alpha		,	top_k=args.k		,	)

  __A						      =			[]
  for generated_sequence_idx, generated_sequence in enumerate(__UpperCamelCase			):
    print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='			)
    __A						      =			generated_sequence.tolist()

    # Decode text
    __A						      =			tokenizer.decode(__UpperCamelCase		,	clean_up_tokenization_spaces=__UpperCamelCase		,	add_special_tokens=__UpperCamelCase			)

    # Remove all text after the stop token
    __A						      =			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
    __A						      =			(
        prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0]		,	clean_up_tokenization_spaces=__UpperCamelCase			)			) :]
    )

    generated_sequences.append(__UpperCamelCase			)
    print(__UpperCamelCase			)

  return generated_sequences


if __name__ == "__main__":
       main()












"""simple docstring"""


import itertools
from pathlib import Path



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  __A						      =			[tuple(__UpperCamelCase			) for login in logins]

  __A						      =			{char for login in split_logins for char in login}

  for permutation in itertools.permutations(__UpperCamelCase			):
    __A						      =			True
    for login in logins:
      if not (
          permutation.index(login[0]			)
          < permutation.index(login[1]			)
          < permutation.index(login[2]			)
      ):
        __A						      =			False
        break

    if satisfied:
      return int(''''''.join(__UpperCamelCase			)			)

  raise Exception('''Unable to find the secret passcode'''			)



def  lowerCAmelCase    (   __UpperCamelCase = "keylog.txt"			):




  """simple docstring"""







  __A						      =			Path(__UpperCamelCase			).parent.joinpath(__UpperCamelCase			).read_text().splitlines()

  return find_secret_passcode(__UpperCamelCase			)


if __name__ == "__main__":
       print(F'''{solution() = }''')




