



'''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'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		['''image_processor''', '''tokenizer''']
			_A				:		Union[str, Any]         =		'''AutoImageProcessor'''
			_A				:		Optional[Any]         =		'''AutoTokenizer'''

			def __init__(							self					:			Tuple							,					lowerCAmelCase__					:			Tuple=None							,					lowerCAmelCase__					:			Union[str, Any]=None							,					**lowerCAmelCase__					:			Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""							,					lowerCAmelCase__							,					)
							__SCREAMING_SNAKE_CASE	:		str   	=		kwargs.pop("""feature_extractor"""						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
							raise ValueError("""You need to specify an `image_processor`."""						)
					if tokenizer is None:
							raise ValueError("""You need to specify a `tokenizer`."""						)

					super().__init__(lowerCAmelCase__							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.image_processor
					__SCREAMING_SNAKE_CASE	:		Dict   	=		False

			def __call__(							self					:			List[str]							,					*lowerCAmelCase__					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					if self._in_target_context_manager:
							return self.current_processor(*lowerCAmelCase__							,					**lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		kwargs.pop("""images"""							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		kwargs.pop("""text"""							,					lowerCAmelCase__						)
					if len(lowerCAmelCase__						) > 0:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		args[0]
							__SCREAMING_SNAKE_CASE	:		int   	=		args[1:]

					if images is None and text is None:
							raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

					if images is not None:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.image_processor(lowerCAmelCase__							,					*lowerCAmelCase__							,					**lowerCAmelCase__						)
					if text is not None:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.tokenizer(lowerCAmelCase__							,					**lowerCAmelCase__						)

					if text is None:
							return inputs
					elif images is None:
							return encodings
					else:
							__SCREAMING_SNAKE_CASE	:		int   	=		encodings["""input_ids"""]
							return inputs

			def UpperCamelCase__ (							self					:			Any							,					*lowerCAmelCase__					:			Any							,					**lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					return self.tokenizer.batch_decode(*lowerCAmelCase__							,					**lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int							,					*lowerCAmelCase__					:			str							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					return self.tokenizer.decode(*lowerCAmelCase__							,					**lowerCAmelCase__						)

			@contextmanager
			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					warnings.warn(
					    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
					    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
					    """your images inputs, or in a separate call."""						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		True
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.tokenizer
					yield
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.image_processor
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		False

			@property
			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							,					lowerCAmelCase__							,					)
					return self.image_processor_class



			@property
			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							,					lowerCAmelCase__							,					)
					return self.image_processor