



'''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,
)
_lowercase       :						Tuple						=logging.getLogger(__name__)


def 							__UpperCAmelCase				(				UpperCamelCase__				:int			)		->						Union[str, Any]:
				np.random.seed(args.seed			)
				torch.manual_seed(args.seed			)
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed			)


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

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

				parser.add_argument('''--seed'''   ,				type=UpperCamelCase__   ,				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'''   ,				)
				snake_case__      :   Any      =    parser.parse_args()

				snake_case__      :   List[str]      =    torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
				snake_case__      :   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(UpperCamelCase__			)

				# Initialize the model and tokenizer
				snake_case__      :   Dict      =    AutoTokenizer.from_pretrained(args.model_name_or_path			)
				snake_case__      :   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(UpperCamelCase__			)
				snake_case__      :   str      =    args.prompt if args.prompt else input('''Model prompt >>> '''			)

				snake_case__      :   Union[str, Any]      =    tokenizer(UpperCamelCase__   ,				return_tensors='''pt'''   ,				add_special_tokens=UpperCamelCase__			)
				snake_case__      :   List[Any]      =    {key: value.to(args.device			) for key, value in inputs.items()}

				snake_case__      :   List[str]      =    model.generate(
				    **UpperCamelCase__   ,				max_length=args.length + len(inputs['''input_ids'''][0]			)   ,				penalty_alpha=args.penalty_alpha   ,				top_k=args.k   ,				)

				snake_case__      :   str      =    []
				for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__			):
								print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''			)
								snake_case__      :   Optional[Any]      =    generated_sequence.tolist()

								# Decode text
								snake_case__      :   Optional[int]      =    tokenizer.decode(UpperCamelCase__   ,				clean_up_tokenization_spaces=UpperCamelCase__   ,				add_special_tokens=UpperCamelCase__			)

								# Remove all text after the stop token
								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
								snake_case__      :   int      =    (
								    prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0]   ,				clean_up_tokenization_spaces=UpperCamelCase__			)			) :]
								)

								generated_sequences.append(UpperCamelCase__			)
								print(UpperCamelCase__			)

				return generated_sequences


if __name__ == "__main__":
				main()








"""simple docstring"""






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Union[str, Any]				  =  ['''image_processor''', '''tokenizer''']
							__magic_name__					:     Optional[int]				  =  '''EfficientNetImageProcessor'''
							__magic_name__					:     Optional[Any]				  =  ('''BertTokenizer''', '''BertTokenizerFast''')




							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								super().__init__(lowerCamelCase__	,					lowerCamelCase__)




							def __call__(						self	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__="max_length"	,					lowerCamelCase__=64	,					lowerCamelCase__=None	,					**lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none.")

								if text is not None:
									snake_case__  :						int		=      self.tokenizer(
									    lowerCamelCase__	,					padding=lowerCamelCase__	,					max_length=lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if images is not None:
									snake_case__  :						int		=      self.image_processor(lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if text is not None and images is not None:
									snake_case__  :						List[str]		=      image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**lowerCamelCase__)	,					tensor_type=lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




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







							@property
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.tokenizer.model_input_names
								snake_case__  :						Optional[int]		=      self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





