

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_						:     Any			  =				logging.getLogger(__name__)


def  __a    (				SCREAMING_SNAKE_CASE ) ->       List[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  __a    (				) ->       List[Any]:






				'''simple docstring'''
				__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=2_0 )
				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=4_2    ,				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()







from dataclasses import dataclass
from typing import Callable, Optional

import datasets
@dataclass
class    A_				( datasets.BuilderConfig  ):


    '''simple docstring'''






    a__       							=    None
    a__       							=    None
    a__       							=    None


    def        lowerCAmelCase_  (self					)       -> Dict:
        assert self.generator is not None, "generator must be specified"

        if self.gen_kwargs is None:
            __UpperCAmelCase			   =     {}






class    A_				( datasets.GeneratorBasedBuilder  ):


    '''simple docstring'''






    a__       							=    GeneratorConfig


    def        lowerCAmelCase_  (self					)       -> Dict:
        return datasets.DatasetInfo(features=self.config.features					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN       , gen_kwargs=self.config.gen_kwargs					)]






    def        lowerCAmelCase_  (self       , **lowercase__					)       -> str:
        for idx, ex in enumerate(self.config.generator(**lowercase__					)					):
            yield idx, ex




