


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,
)
_snake_case					=					logging.getLogger(__name__)






def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''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 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: Dict   =    argparse.ArgumentParser()
  parser.add_argument(
      "--model_name_or_path"				,						default=SCREAMING_SNAKE_CASE_				,						type=SCREAMING_SNAKE_CASE_				,						required=SCREAMING_SNAKE_CASE_				,						)
  parser.add_argument("--prompt"				,						type=SCREAMING_SNAKE_CASE_				,						default=""     )
  parser.add_argument("--length"				,						type=SCREAMING_SNAKE_CASE_				,						default=20     )
  parser.add_argument("--stop_token"				,						type=SCREAMING_SNAKE_CASE_				,						default=SCREAMING_SNAKE_CASE_				,						help="Token at which text generation is stopped"     )
  parser.add_argument(
      "--temperature"				,						type=SCREAMING_SNAKE_CASE_				,						default=1.0				,						help="temperature of 1.0 has no effect, lower tend toward greedy sampling"				,						)
  parser.add_argument(
      "--repetition_penalty"				,						type=SCREAMING_SNAKE_CASE_				,						default=1.0				,						help="primarily useful for CTRL model; in that case, use 1.2"     )
  parser.add_argument("--k"				,						type=SCREAMING_SNAKE_CASE_				,						default=0     )
  parser.add_argument("--penalty_alpha"				,						type=SCREAMING_SNAKE_CASE_				,						default=0.0     )
  parser.add_argument("--p"				,						type=SCREAMING_SNAKE_CASE_				,						default=0.9     )

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

  parser.add_argument("--seed"				,						type=SCREAMING_SNAKE_CASE_				,						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[int]   =    parser.parse_args()

  lowerCamelCase			: str   =    torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"     )
  lowerCamelCase			: Union[str, Any]   =    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(SCREAMING_SNAKE_CASE_     )

  # Initialize the model and tokenizer
  lowerCamelCase			: str   =    AutoTokenizer.from_pretrained(args.model_name_or_path     )
  lowerCamelCase			: Union[str, 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(SCREAMING_SNAKE_CASE_     )
  lowerCamelCase			: List[Any]   =    args.prompt if args.prompt else input("Model prompt >>> "     )

  lowerCamelCase			: Any   =    tokenizer(SCREAMING_SNAKE_CASE_				,						return_tensors="pt"				,						add_special_tokens=SCREAMING_SNAKE_CASE_     )
  lowerCamelCase			: Any   =    {key: value.to(args.device     ) for key, value in inputs.items()}

  lowerCamelCase			: List[Any]   =    model.generate(
      **SCREAMING_SNAKE_CASE_				,						max_length=args.length + len(inputs["input_ids"][0]     )				,						penalty_alpha=args.penalty_alpha				,						top_k=args.k				,						)

  lowerCamelCase			: List[Any]   =    []
  for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE_     ):
    print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""     )
    lowerCamelCase			: Dict   =    generated_sequence.tolist()

    # Decode text
    lowerCamelCase			: Dict   =    tokenizer.decode(SCREAMING_SNAKE_CASE_				,						clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_				,						add_special_tokens=SCREAMING_SNAKE_CASE_     )

    # Remove all text after the stop token
    lowerCamelCase			: Dict   =    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			: Optional[Any]   =    (
        prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]				,						clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_     )     ) :]
    )

    generated_sequences.append(SCREAMING_SNAKE_CASE_     )
    print(SCREAMING_SNAKE_CASE_     )

  return generated_sequences


if __name__ == "__main__":
   main()







def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if number != int(SCREAMING_SNAKE_CASE_     ):
    raise ValueError("factorial() only accepts integral values"     )
  if number < 0:
    raise ValueError("factorial() not defined for negative values"     )
  lowerCamelCase			: Optional[Any]   =    1
  for i in range(1				,						number + 1     ):
    value *= i
  return value






def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if not isinstance(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):
    raise ValueError("factorial() only accepts integral values"     )
  if n < 0:
    raise ValueError("factorial() not defined for negative values"     )
  return 1 if n == 0 or n == 1 else n * factorial(n - 1     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _snake_case					=					int(input('''Enter a positive integer: ''').strip() or 0)
   print(f'''factorial{n} is {factorial(n)}''')



