






"""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(    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 							A(    ):
       """simple docstring"""





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

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

       parser.add_argument("--seed"		,  type=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"		,  )
       lowercase__:							Optional[Any]         =     parser.parse_args()

       lowercase__:							List[Any]         =     torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"						)
       lowercase__:							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(snake_case_						)

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

       lowercase__:							Optional[int]         =     tokenizer(snake_case_		,  return_tensors="pt"		,  add_special_tokens=snake_case_						)
       lowercase__:							Optional[Any]         =     {key: value.to(args.device						) for key, value in inputs.items()}

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

       lowercase__:							Tuple         =     []
       for generated_sequence_idx, generated_sequence in enumerate(snake_case_						):
              print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""						)
              lowercase__:							Any         =     generated_sequence.tolist()

              # Decode text
              lowercase__:							Tuple         =     tokenizer.decode(snake_case_		,  clean_up_tokenization_spaces=snake_case_		,  add_special_tokens=snake_case_						)

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

              generated_sequences.append(snake_case_						)
              print(snake_case_						)

       return generated_sequences


if __name__ == "__main__":
  main()











from __future__ import annotations





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					assert (
					    c % greatest_common_divisor(_UpperCAmelCase       ,			_UpperCAmelCase				) == 0
					)  # greatest_common_divisor(a,b) function implemented below
					((lowercase)   ,      (lowercase)   ,      (lowercase))						   =       extended_gcd(_UpperCAmelCase       ,			_UpperCAmelCase				)  # extended_gcd(a,b) function implemented below
					lowercase						   =       c / d
					return (r * x, r * y)





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase = 2				):
					"""simple docstring"""






					((lowercase)   ,      (lowercase))						   =       diophantine(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)  # Initial value
					lowercase						   =       greatest_common_divisor(_UpperCAmelCase       ,			_UpperCAmelCase				)
					lowercase						   =       a // d
					lowercase						   =       b // d

					for i in range(_UpperCAmelCase				):
										lowercase						   =       xa + i * q
										lowercase						   =       ya - i * p
										print(_UpperCAmelCase       ,			_UpperCAmelCase				)





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					if a < b:
										lowercase   ,      lowercase						   =       b, a

					while a % b != 0:
										lowercase   ,      lowercase						   =       b, a % b

					return b





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					assert a >= 0 and b >= 0

					if b == 0:
										lowercase   ,      lowercase   ,      lowercase						   =       a, 1, 0
					else:
										((lowercase)   ,      (lowercase)   ,      (lowercase))						   =       extended_gcd(_UpperCAmelCase       ,			a % b				)
										lowercase						   =       q
										lowercase						   =       p - q * (a // b)

					assert a % d == 0 and b % d == 0
					assert d == a * x + b * y

					return (d, x, y)


if __name__ == "__main__":
		from doctest import testmod

		testmod(name='''diophantine''', verbose=True)
		testmod(name='''diophantine_all_soln''', verbose=True)
		testmod(name='''extended_gcd''', verbose=True)
		testmod(name='''greatest_common_divisor''', verbose=True)






