"""simple docstring"""



from typing import Any, Dict, List, Union

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


if is_vision_available():
       from ..image_utils import load_image


if is_torch_available():
       import torch

       from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__lowercase														=							logging.get_logger(__name__)


__lowercase														=							Dict[str, Any]
__lowercase														=							List[Prediction]



@add_end_docstrings(_UpperCAmelCase					)
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    def __init__(		self							:     Tuple	,      *A_							:     Any	,      **A_							:     Dict    )    ->       List[str]:
         super().__init__(*A_	,      **A_    )

         if self.framework == "tf":
              raise ValueError(f"The {self.__class__} is only available in PyTorch."    )

         requires_backends(self	,      '''vision'''    )
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()    )    )
    def  lowercase					(		self							:     Dict	,      **A_							:     List[Any]    )    ->       Tuple:
         __snake_case				=							{}
         if "threshold" in kwargs:
              __snake_case				=							kwargs['''threshold''']
         return {}, {}, postprocess_kwargs
    def __call__(		self							:     int	,      *A_							:     Optional[int]	,      **A_							:     List[Any]    )    ->       Union[Predictions, List[Prediction]]:
         return super().__call__(*A_	,      **A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     List[str]    )    ->       List[str]:
         __snake_case				=							load_image(A_    )
         __snake_case				=							torch.IntTensor([[image.height, image.width]]    )
         __snake_case				=							self.image_processor(images=[image]	,      return_tensors='''pt'''    )
         if self.tokenizer is not None:
              __snake_case				=							self.tokenizer(text=inputs['''words''']	,      boxes=inputs['''boxes''']	,      return_tensors='''pt'''    )
         __snake_case				=							target_size
         return inputs
    def  lowercase					(		self							:     List[str]	,      A_							:     List[Any]    )    ->       Tuple:
         __snake_case				=							model_inputs.pop('''target_size'''    )
         __snake_case				=							self.model(**A_    )
         __snake_case				=							outputs.__class__({'''target_size''': target_size, **outputs}    )
         if self.tokenizer is not None:
              __snake_case				=							model_inputs['''bbox''']
         return model_outputs
    def  lowercase					(		self							:     str	,      A_							:     Tuple	,      A_							:     List[str]=0.9    )    ->       List[str]:
         __snake_case				=							model_outputs['''target_size''']
         if self.tokenizer is not None:
              # This is a LayoutLMForTokenClassification variant.
              # The OCR got the boxes and the model classified the words.
              __snake_case		,	__snake_case				=							target_size[0].tolist()

              def unnormalize(A_							:     Dict    ):
                   return self._get_bounding_box(
                       torch.Tensor(
                           [
                               (width * bbox[0] / 1_000),
                               (height * bbox[1] / 1_000),
                               (width * bbox[2] / 1_000),
                               (height * bbox[3] / 1_000),
                           ]    )    )

              __snake_case		,	__snake_case				=							model_outputs['''logits'''].squeeze(0    ).softmax(dim=-1    ).max(dim=-1    )
              __snake_case				=							[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
              __snake_case				=							[unnormalize(A_    ) for bbox in model_outputs['''bbox'''].squeeze(0    )]
              __snake_case				=							['''score''', '''label''', '''box''']
              __snake_case				=							[dict(zip(A_	,      A_    )    ) for vals in zip(scores.tolist()	,      A_	,      A_    ) if vals[0] > threshold]
         else:
              # This is a regular ForObjectDetectionModel
              __snake_case				=							self.image_processor.post_process_object_detection(A_	,      A_	,      A_    )
              __snake_case				=							raw_annotations[0]
              __snake_case				=							raw_annotation['''scores''']
              __snake_case				=							raw_annotation['''labels''']
              __snake_case				=							raw_annotation['''boxes''']

              __snake_case				=							scores.tolist()
              __snake_case				=							[self.model.config.idalabel[label.item()] for label in labels]
              __snake_case				=							[self._get_bounding_box(A_    ) for box in boxes]

              # {"scores": [...], ...} --> [{"score":x, ...}, ...]
              __snake_case				=							['''score''', '''label''', '''box''']
              __snake_case				=							[
                  dict(zip(A_	,      A_    )    )
                  for vals in zip(raw_annotation['''scores''']	,      raw_annotation['''labels''']	,      raw_annotation['''boxes''']    )
              ]

         return annotation







    def  lowercase					(		self							:     Any	,      A_							:     "torch.Tensor"    )    ->       Dict[str, int]:
         if self.framework != "pt":
              raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.'''    )
         __snake_case		,	__snake_case		,	__snake_case		,	__snake_case				=							box.int().tolist()
         __snake_case				=							{
             '''xmin''': xmin,
             '''ymin''': ymin,
             '''xmax''': xmax,
             '''ymax''': ymax,
         }
         return bbox
"""simple docstring"""



import re





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''')
     if match := re.search(snake_case,      snake_case):
          return match.string == phone
     return False


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