



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


def     lowerCAmelCase_       (     _lowerCamelCase:       Optional[int] ):
		np.random.seed(args.seed )
		torch.manual_seed(args.seed )
		if args.n_gpu > 0:
				torch.cuda.manual_seed_all(args.seed )


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		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"""	,				)
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		parser.parse_args()

		__SCREAMING_SNAKE_CASE	:		str   	=		torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu""" )
		__SCREAMING_SNAKE_CASE	:		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(_lowerCamelCase )

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

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

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

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

				# Decode text
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		tokenizer.decode(_lowerCamelCase	,				clean_up_tokenization_spaces=_lowerCamelCase	,				add_special_tokens=_lowerCamelCase )

				# Remove all text after the stop token
				__SCREAMING_SNAKE_CASE	:		Tuple   	=		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
				__SCREAMING_SNAKE_CASE	:		Any   	=		(
				    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()




'''simple docstring'''




from __future__ import annotations


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int | float] ):
		if len(_lowerCamelCase ) == 0:
				raise ValueError("""find_min() arg is an empty sequence""" )
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		nums[0]
		for num in nums:
				__SCREAMING_SNAKE_CASE	:		int   	=		min(_lowerCamelCase	,				_lowerCamelCase )
		return min_num


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)