





from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

_lowerCAmelCase 				=     logging.get_logger(__name__)

@add_end_docstrings(_lowerCamelCase     )
class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __init__(		self      ,       *a__      ,       **a__       ):
         super().__init__(*a__      ,       **a__       )
         self.check_model_type(a__       )
      def 		_lowerCamelCase      (		self      ,       a__=None      ,       a__=None      ,       a__=None      ,       **a__       ):
         A_     :				Tuple			      =	{}, {}
         if padding is not None:
            A_     :				Dict			      =	padding
         if truncation is not None:
            A_     :				Any			      =	truncation
         if top_k is not None:
            A_     :				Union[str, Any]			      =	top_k
         return preprocess_params, {}, postprocess_params
      def __call__(		self      ,       a__      ,       a__ = None      ,       **a__       ):
         if isinstance(a__      ,       (Image.Image, str)       ) and isinstance(a__      ,       a__       ):
            A_     :				Dict			      =	{"""image""": image, """question""": question}
         else:
            A_     :				Optional[Any]			      =	image
         A_     :				Optional[int]			      =	super().__call__(a__      ,       **a__       )
         return results
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=False      ,       a__=False       ):
         A_     :				List[str]			      =	load_image(inputs["""image"""]       )
         A_     :				str			      =	self.tokenizer(
             inputs["""question"""]      ,       return_tensors=self.framework      ,       padding=a__      ,       truncation=a__       )
         A_     :				str			      =	self.image_processor(images=a__      ,       return_tensors=self.framework       )
         model_inputs.update(a__       )
         return model_inputs
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Optional[Any]			      =	self.model(**a__       )
         return model_outputs






      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=5       ):
         if top_k > self.model.config.num_labels:
            A_     :				Tuple			      =	self.model.config.num_labels

         if self.framework == "pt":
            A_     :				List[str]			      =	model_outputs.logits.sigmoid()[0]
            A_     :				int			      =	probs.topk(a__       )
         else:
            raise ValueError(F"""Unsupported framework: {self.framework}"""       )

         A_     :				Any			      =	scores.tolist()
         A_     :				Optional[Any]			      =	ids.tolist()
         return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(a__      ,       a__       )]






import re



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Any			      =	re.compile(r"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$"""     )
   if match := re.search(_lowerCAmelCase  ,_lowerCAmelCase     ):
      return match.string == phone
   return False


if __name__ == "__main__":
      print(indian_phone_validator("""+918827897895"""))






