"""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,
)
a							=logging.getLogger(__name__)





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					int:




						'''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 			lowerCamelCase_ (					)							->					List[Any]:




						'''simple docstring'''



						lowerCamelCase__					=argparse.ArgumentParser()
						parser.add_argument(
						    "--model_name_or_path"   ,    default=lowercase_   ,    type=lowercase_   ,    required=lowercase_   ,    )
						parser.add_argument("--prompt"   ,    type=lowercase_   ,    default=""				)
						parser.add_argument("--length"   ,    type=lowercase_   ,    default=20				)
						parser.add_argument("--stop_token"   ,    type=lowercase_   ,    default=lowercase_   ,    help="Token at which text generation is stopped"				)
						parser.add_argument(
						    "--temperature"   ,    type=lowercase_   ,    default=1.0   ,    help="temperature of 1.0 has no effect, lower tend toward greedy sampling"   ,    )
						parser.add_argument(
						    "--repetition_penalty"   ,    type=lowercase_   ,    default=1.0   ,    help="primarily useful for CTRL model; in that case, use 1.2"				)
						parser.add_argument("--k"   ,    type=lowercase_   ,    default=0				)
						parser.add_argument("--penalty_alpha"   ,    type=lowercase_   ,    default=0.0				)
						parser.add_argument("--p"   ,    type=lowercase_   ,    default=0.9				)

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

						parser.add_argument("--seed"   ,    type=lowercase_   ,    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"   ,    )
						lowerCamelCase__					=parser.parse_args()

						lowerCamelCase__					=torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"				)
						lowerCamelCase__					=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(lowercase_				)

						# Initialize the model and tokenizer
						lowerCamelCase__					=AutoTokenizer.from_pretrained(args.model_name_or_path				)
						lowerCamelCase__					=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(lowercase_				)
						lowerCamelCase__					=args.prompt if args.prompt else input("Model prompt >>> "				)

						lowerCamelCase__					=tokenizer(lowercase_   ,    return_tensors="pt"   ,    add_special_tokens=lowercase_				)
						lowerCamelCase__					={key: value.to(args.device				) for key, value in inputs.items()}

						lowerCamelCase__					=model.generate(
						    **lowercase_   ,    max_length=args.length + len(inputs["input_ids"][0]				)   ,    penalty_alpha=args.penalty_alpha   ,    top_k=args.k   ,    )

						lowerCamelCase__					=[]
						for generated_sequence_idx, generated_sequence in enumerate(lowercase_				):
												print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''				)
												lowerCamelCase__					=generated_sequence.tolist()

												# Decode text
												lowerCamelCase__					=tokenizer.decode(lowercase_   ,    clean_up_tokenization_spaces=lowercase_   ,    add_special_tokens=lowercase_				)

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

												generated_sequences.append(lowercase_				)
												print(lowercase_				)

						return generated_sequences


if __name__ == "__main__":
	main()







"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					logging.get_logger(__name__)


class 							lowerCamelCase_(      A__			):





					'''simple docstring'''



					def __init__(			self ,				*lowerCamelCase__ ,				**lowerCamelCase__      ):
									warnings.warn(
									    '''The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
									    ''' Please use LayoutLMv3ImageProcessor instead.''' ,				lowerCamelCase__ ,				)
									super().__init__(*lowerCamelCase__ ,				**lowerCamelCase__      )
