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_				:		str       						=       logging.getLogger(__name__)
def 							lowerCAmelCase(	__lowerCamelCase						):
					np.random.seed(args.seed						)
					torch.manual_seed(args.seed						)
					if args.n_gpu > 0:
										torch.cuda.manual_seed_all(args.seed						)
def 							lowerCAmelCase(	):
					__a							=   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							=   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(__lowerCamelCase						)

					# 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(__lowerCamelCase						)
					__a							=   args.prompt if args.prompt else input('Model prompt >>> '						)

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

					__a							=   model.generate(
					    **__lowerCamelCase      ,				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(__lowerCamelCase						):
										print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''						)
										__a							=   generated_sequence.tolist()

										# Decode text
										__a							=   tokenizer.decode(__lowerCamelCase      ,				clean_up_tokenization_spaces=__lowerCamelCase      ,				add_special_tokens=__lowerCamelCase						)

										# 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=__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




