





from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
		from PIL import Image


class  __UpperCamelCase       (		_a		):
							lowerCamelCase     :      Tuple         ="""dandelin/vilt-b32-finetuned-vqa"""
							lowerCamelCase     :      str         =(
							    """This is a tool that answers a question about an image. It takes an input named `image` which should be the """
							    """image containing the information, as well as a `question` which should be the question in English. It """
							    """returns a text that is the answer to the question."""
							)
							lowerCamelCase     :      Optional[Any]         ="""image_qa"""
							lowerCamelCase     :      Dict         =AutoProcessor
							lowerCamelCase     :      int         =AutoModelForVisualQuestionAnswering

							lowerCamelCase     :      str         =["""image""", """text"""]
							lowerCamelCase     :      List[Any]         =["""text"""]






							def __init__(     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Optional[Any]:
								requires_backends(self   ,      ["vision"]   )
								super().__init__(*snake_case_   ,      **snake_case_   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Any:
								return self.pre_processor(snake_case_   ,      snake_case_   ,      return_tensors="pt"   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Dict:
								with torch.no_grad():
									return self.model(**snake_case_   ).logits






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				List[Any]:
								a   :	List[Any]			=       outputs.argmax(-1   ).item()
								return self.model.config.idalabel[idx]









"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]      )				->int:






	'''simple docstring'''






	a   :	int			=       {}
	a   :	Union[str, Any]			=       tokenizer(example["content"]					, truncation=_lowercase      )["input_ids"]
	a   :	Any			=       len(example["content"]      ) / len(output["input_ids"]      )
	return output


a		:				int  			=    HfArgumentParser(PretokenizationArguments)
a		:				Optional[int]  			=    parser.parse_args()
if args.num_workers is None:
		a		:				Tuple  			=    multiprocessing.cpu_count()
a		:				Optional[int]  			=    AutoTokenizer.from_pretrained(args.tokenizer_dir)

a		:				Dict  			=    time.time()
a		:				Tuple  			=    load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

a		:				Dict  			=    time.time()
a		:				Tuple  			=    ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

a		:				Tuple  			=    time.time()
ds.push_to_hub(args.tokenized_data_repo)







print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')


