

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,
)
__snake_case							:			List[str]      		= logging.getLogger(__name__)


def 				_UpperCAmelCase							(	a__):

			'''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 				_UpperCAmelCase							(	):

			'''simple docstring'''



			a_	:	List[str]          =   argparse.ArgumentParser()
			parser.add_argument(
			    """--model_name_or_path"""     ,  default=a__     ,  type=a__     ,  required=a__     ,  )
			parser.add_argument("""--prompt"""     ,  type=a__     ,  default="""""")
			parser.add_argument("""--length"""     ,  type=a__     ,  default=2_0)
			parser.add_argument("""--stop_token"""     ,  type=a__     ,  default=a__     ,  help="""Token at which text generation is stopped""")
			parser.add_argument(
			    """--temperature"""     ,  type=a__     ,  default=1.0     ,  help="""temperature of 1.0 has no effect, lower tend toward greedy sampling"""     ,  )
			parser.add_argument(
			    """--repetition_penalty"""     ,  type=a__     ,  default=1.0     ,  help="""primarily useful for CTRL model; in that case, use 1.2""")
			parser.add_argument("""--k"""     ,  type=a__     ,  default=0)
			parser.add_argument("""--penalty_alpha"""     ,  type=a__     ,  default=0.0)
			parser.add_argument("""--p"""     ,  type=a__     ,  default=0.9)

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

			parser.add_argument("""--seed"""     ,  type=a__     ,  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"""     ,  )
			a_	:	Dict          =   parser.parse_args()

			a_	:	Optional[int]          =   torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu""")
			a_	:	List[str]          =   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(a__)

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

			a_	:	Any          =   tokenizer(a__     ,  return_tensors="""pt"""     ,  add_special_tokens=a__)
			a_	:	Tuple          =   {key: value.to(args.device) for key, value in inputs.items()}

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

			a_	:	Dict          =   []
			for generated_sequence_idx, generated_sequence in enumerate(a__):
						print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''')
						a_	:	Any          =   generated_sequence.tolist()

						# Decode text
						a_	:	Any          =   tokenizer.decode(a__     ,  clean_up_tokenization_spaces=a__     ,  add_special_tokens=a__)

						# Remove all text after the stop token
						a_	:	Optional[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
						a_	:	Tuple          =   (
						    prompt_text + text[len(tokenizer.decode(inputs["""input_ids"""][0]     ,  clean_up_tokenization_spaces=a__)) :]
						)

						generated_sequences.append(a__)
						print(a__)

			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						:							Optional[Callable]									= None
						_A						:							Optional[dict]									= None
						_A						:							Optional[datasets.Features]									= None





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

									if self.gen_kwargs is None:
												a_	:	Tuple          =   {}







class 					A__(datasets.GeneratorBasedBuilder       ):
						"""simple docstring"""






						_A						:							Dict									= GeneratorConfig





						def  UpperCamelCase__				(						self						)    ->   List[Any]:
									return datasets.DatasetInfo(features=self.config.features						)





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   List[Any]:
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,			gen_kwargs=self.config.gen_kwargs						)]





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


