


'''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	:   int   		=					logging.getLogger(__name__)





def 				a_							(    lowercase     )      ->Dict:






				'''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_							(    )      ->int:






				'''simple docstring'''

				__lowerCamelCase		:   str           =       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		:   Tuple           =       parser.parse_args()

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

				__lowerCamelCase		:   List[str]           =       tokenizer(SCREAMING_SNAKE_CASE__				,    return_tensors="pt"				,    add_special_tokens=SCREAMING_SNAKE_CASE__     )
				__lowerCamelCase		:   List[Any]           =       {key: value.to(args.device     ) for key, value in inputs.items()}

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

				__lowerCamelCase		:   Optional[int]           =       []
				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		:   str           =       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		:   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
								__lowerCamelCase		:   Union[str, 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()









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,
)
_A									=		logging.getLogger(__name__)

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

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				=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'     ,	)
     __UpperCamelCase       				=parser.parse_args()

     __UpperCamelCase       				=torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
     __UpperCamelCase       				=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
     __UpperCamelCase       				=AutoTokenizer.from_pretrained(args.model_name_or_path							)
     __UpperCamelCase       				=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__							)
     __UpperCamelCase       				=args.prompt if args.prompt else input('Model prompt >>> '							)

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

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

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

          # Decode text
          __UpperCamelCase       				=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
          __UpperCamelCase       				=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
          __UpperCamelCase       				=(
              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()





