



"""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:  Any			     =				logging.getLogger(__name__)


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


def  __SCREAMING_SNAKE_CASE (  ):
		_lowercase				: Any     =   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=20      )
		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=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				: str     =   parser.parse_args()

		_lowercase				: str     =   torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""      )
		_lowercase				: List[str]     =   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
		_lowercase				: List[str]     =   AutoTokenizer.from_pretrained(args.model_name_or_path      )
		_lowercase				: 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(__UpperCAmelCase      )
		_lowercase				: Any     =   args.prompt if args.prompt else input("""Model prompt >>> """      )

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

		_lowercase				: Optional[Any]     =   model.generate(
		    **__UpperCAmelCase   ,				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(__UpperCAmelCase      ):
				print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""      )
				_lowercase				: List[Any]     =   generated_sequence.tolist()

				# Decode text
				_lowercase				: Optional[int]     =   tokenizer.decode(__UpperCAmelCase   ,				clean_up_tokenization_spaces=__UpperCAmelCase   ,				add_special_tokens=__UpperCAmelCase      )

				# Remove all text after the stop token
				_lowercase				: 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
				_lowercase				: Tuple     =   (
				    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 math







def       UpperCamelCase_					(     lowerCamelCase							:					int   )						->     list:



       """simple docstring"""







       if n <= 0:
              raise ValueError('''Only positive integers have prime factors'''   )
       __magic_name__			:		Union[str, Any]        =     []
       while n % 2 == 0:
              pf.append(2   )
              __magic_name__			:		Union[str, Any]        =     int(n / 2   )
       for i in range(3     , int(math.sqrt(lowerCamelCase   )   ) + 1     , 2   ):
              while n % i == 0:
                     pf.append(lowerCamelCase   )
                     __magic_name__			:		Any        =     int(n / i   )
       if n > 2:
              pf.append(lowerCamelCase   )
       return pf







def       UpperCamelCase_					(     lowerCamelCase							:					int   )						->     int:



       """simple docstring"""







       if n <= 0:
              raise ValueError('''Only positive numbers are accepted'''   )
       __magic_name__			:		List[Any]        =     1
       __magic_name__			:		Dict        =     1
       while n % 2 == 0:
              temp += 1
              __magic_name__			:		Dict        =     int(n / 2   )
       div *= temp
       for i in range(3     , int(math.sqrt(lowerCamelCase   )   ) + 1     , 2   ):
              __magic_name__			:		Dict        =     1
              while n % i == 0:
                     temp += 1
                     __magic_name__			:		Union[str, Any]        =     int(n / i   )
              div *= temp
       if n > 1:
              div *= 2
       return div







def       UpperCamelCase_					(     lowerCamelCase							:					int   )						->     int:



       """simple docstring"""







       if n <= 0:
              raise ValueError('''Only positive numbers are accepted'''   )
       __magic_name__			:		int        =     1
       __magic_name__			:		Dict        =     1
       while n % 2 == 0:
              temp += 1
              __magic_name__			:		List[Any]        =     int(n / 2   )
       if temp > 1:
              s *= (2**temp - 1) / (2 - 1)
       for i in range(3     , int(math.sqrt(lowerCamelCase   )   ) + 1     , 2   ):
              __magic_name__			:		Any        =     1
              while n % i == 0:
                     temp += 1
                     __magic_name__			:		Any        =     int(n / i   )
              if temp > 1:
                     s *= (i**temp - 1) / (i - 1)
       return int(lowerCamelCase   )







def       UpperCamelCase_					(     lowerCamelCase							:					int   )						->     int:



       """simple docstring"""







       __magic_name__			:		str        =     n
       for x in set(prime_factors(lowerCamelCase   )   ):
              s *= (x - 1) / x
       return int(lowerCamelCase   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
