


"""simple docstring"""





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def    _snake_case  (  _snake_case :	Dict       ,      _snake_case :	int	):
      lowerCAmelCase    :		Dict				=      k_size // 2
      lowerCAmelCase,					lowerCAmelCase    :		int				=      mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      lowerCAmelCase    :		Dict				=      1 / (2 * pi * sigma) * exp(-(square(_snake_case	) + square(_snake_case	)) / (2 * square(_snake_case	))	)
      return g


def    _snake_case  (  _snake_case :	List[Any]       ,      _snake_case :	Dict       ,      _snake_case :	List[str]	):
      lowerCAmelCase,					lowerCAmelCase    :		Optional[Any]				=      image.shape[0], image.shape[1]
      # dst image height and width
      lowerCAmelCase    :		str				=      height - k_size + 1
      lowerCAmelCase    :		Any				=      width - k_size + 1

      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      lowerCAmelCase    :		int				=      zeros((dst_height * dst_width, k_size * k_size)	)
      lowerCAmelCase    :		Optional[int]				=      0
      for i, j in product(range(_snake_case	)       ,      range(_snake_case	)	):
            lowerCAmelCase    :		Union[str, Any]				=      ravel(image[i : i + k_size, j : j + k_size]	)
            lowerCAmelCase    :		List[str]				=      window
            row += 1

      #  turn the kernel into shape(k*k, 1)
      lowerCAmelCase    :		Dict				=      gen_gaussian_kernel(_snake_case       ,      _snake_case	)
      lowerCAmelCase    :		Union[str, Any]				=      ravel(_snake_case	)

      # reshape and get the dst image
      lowerCAmelCase    :		Any				=      dot(_snake_case       ,      _snake_case	).reshape(_snake_case       ,      _snake_case	).astype(_snake_case	)

      return dst


if __name__ == "__main__":
 # read original image
 snake_case__						:   Dict			 = imread(R'''../image_data/lena.jpg''')
 # turn image in gray scale value
 snake_case__						:   int			 = cvtColor(img, COLOR_BGR2GRAY)

 # get values with two different mask size
 snake_case__						:   Optional[int]			 = gaussian_filter(gray, 3, sigma=1)
 snake_case__						:   Union[str, Any]			 = gaussian_filter(gray, 5, sigma=0.8)

 # show result images
 imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
 imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
 waitKey()






from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
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_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

__lowerCAmelCase		       =							logging.get_logger(__name__)





@add_end_docstrings(__UpperCamelCase      )
class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, **lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        super().__init__(**lowerCAmelCase__ )

        requires_backends(self				, 'vision' )
        self.check_model_type(
            TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == 'tf'
            else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
 def __call__(   self				, lowerCAmelCase__				, **lowerCAmelCase__ )					->				Optional[Any]:




        '''simple docstring'''
        return super().__call__(lowerCAmelCase__				, **lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, **lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        lowercase__:   int  			=   {}
        if "candidate_labels" in kwargs:
               lowercase__:   Dict  			=   kwargs['candidate_labels']
        if "hypothesis_template" in kwargs:
               lowercase__:   List[Any]  			=   kwargs['hypothesis_template']

        return preprocess_params, {}, {}
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__=None				, lowerCAmelCase__="This is a photo of {}." )					->				int:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   load_image(lowerCAmelCase__ )
        lowercase__:   Dict  			=   self.image_processor(images=[image]				, return_tensors=self.framework )
        lowercase__:   Tuple  			=   candidate_labels
        lowercase__:   List[str]  			=   [hypothesis_template.format(lowerCAmelCase__ ) for x in candidate_labels]
        lowercase__:   Optional[Any]  			=   self.tokenizer(lowerCAmelCase__				, return_tensors=self.framework				, padding=lowerCAmelCase__ )
        lowercase__:   str  			=   [text_inputs]
        return inputs
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        lowercase__:   str  			=   model_inputs.pop('candidate_labels' )
        lowercase__:   List[str]  			=   model_inputs.pop('text_inputs' )
        if isinstance(text_inputs[0]				, lowerCAmelCase__ ):
               lowercase__:   Any  			=   text_inputs[0]
        else:
               # Batching case.
               lowercase__:   Optional[int]  			=   text_inputs[0][0]

        lowercase__:   Tuple  			=   self.model(**lowerCAmelCase__				, **lowerCAmelCase__ )

        lowercase__:   Any  			=   {
            'candidate_labels': candidate_labels,
            'logits': outputs.logits_per_image,
        }
        return model_outputs
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Tuple  			=   model_outputs.pop('candidate_labels' )
        lowercase__:   Dict  			=   model_outputs['logits'][0]
        if self.framework == "pt":
               lowercase__:   Optional[int]  			=   logits.softmax(dim=-1 ).squeeze(-1 )
               lowercase__:   Dict  			=   probs.tolist()
               if not isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
                      lowercase__:   Dict  			=   [scores]
        elif self.framework == "tf":
               lowercase__:   Optional[int]  			=   stable_softmax(lowerCAmelCase__				, axis=-1 )
               lowercase__:   Union[str, Any]  			=   probs.numpy().tolist()
        else:
               raise ValueError(F'Unsupported framework: {self.framework}' )

        lowercase__:   List[Any]  			=   [
            {'score': score, 'label': candidate_label}
            for score, candidate_label in sorted(zip(lowerCAmelCase__				, lowerCAmelCase__ )				, key=lambda lowerCAmelCase__ : -x[0] )
        ]
        return result


