




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,
)
__lowerCAmelCase		:    Optional[Any]	      =		logging.getLogger(__name__)




def       __magic_name__    (			A		: Union[str, Any]       ):






			'''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       __magic_name__    (			):






			'''simple docstring'''







			a   =      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",  )
			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(SCREAMING_SNAKE_CASE_       )

			# 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(SCREAMING_SNAKE_CASE_       )
			a   =      args.prompt if args.prompt else input("Model prompt >>> "       )

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

			a   =      model.generate(
			    **SCREAMING_SNAKE_CASE_,  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(SCREAMING_SNAKE_CASE_       ):
						print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""       )
						a   =      generated_sequence.tolist()

						# Decode text
						a   =      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
						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=SCREAMING_SNAKE_CASE_       )       ) :]
						)

						generated_sequences.append(SCREAMING_SNAKE_CASE_       )
						print(SCREAMING_SNAKE_CASE_       )

			return generated_sequences


if __name__ == "__main__":
		main()












from ..utils import DummyObject, requires_backends




class    snake_case__							(metaclass=_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= ["""torch""", """transformers""", """k_diffusion"""]





				def __init__(				self    :					Optional[int] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					List[Any]							) ->					Optional[Any]:
							requires_backends(self ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					str ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Tuple							) ->					List[Any]:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					Optional[Any] ,				*__lowerCamelCase    :					List[str] ,				**__lowerCamelCase    :					int							) ->					Dict:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)






