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     lowerCamelCase_    (  _lowercase				)							->       Dict:
		np.random.seed(args.seed				)
		torch.manual_seed(args.seed				)
		if args.n_gpu > 0:
				torch.cuda.manual_seed_all(args.seed				)



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

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

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

		__A							:    int         =      torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"				)
		__A							:    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(__lowerCamelCase				)

		# Initialize the model and tokenizer
		__A							:    Optional[Any]         =      AutoTokenizer.from_pretrained(args.model_name_or_path				)
		__A							:    Optional[int]         =      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(__lowerCamelCase				)
		__A							:    Optional[Any]         =      args.prompt if args.prompt else input("Model prompt >>> "				)

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

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

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

				# Decode text
				__A							:    Union[str, Any]         =      tokenizer.decode(__lowerCamelCase					,	clean_up_tokenization_spaces=__lowerCamelCase					,	add_special_tokens=__lowerCamelCase				)

				# Remove all text after the stop token
				__A							:    int         =      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							:    str         =      (
				    prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]					,	clean_up_tokenization_spaces=__lowerCamelCase				)				) :]
				)

				generated_sequences.append(__lowerCamelCase				)
				print(__lowerCamelCase				)

		return generated_sequences


if __name__ == "__main__":
					main()






from dataclasses import dataclass
from typing import Callable, Optional

import datasets


@dataclass
class 						a__							(   datasets.BuilderConfig		):
			A__				:   Optional[Callable]								=			None
			A__				:   Optional[dict]								=			None
			A__				:   Optional[datasets.Features]								=			None



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

								if self.gen_kwargs is None:
													__a							=   {}






class 						a__							(   datasets.GeneratorBasedBuilder		):
			A__				:   List[Any]								=			GeneratorConfig



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								return datasets.DatasetInfo(features=self.config.features    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Optional[int]:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,						gen_kwargs=self.config.gen_kwargs    )]







			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    Tuple:
								for idx, ex in enumerate(self.config.generator(**UpperCAmelCase    )    ):
													yield idx, ex




