

from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore



class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      __UpperCAmelCase      		=				c_float
      @property
      def 	_lowerCAmelCase					(				self					:							Any    ):
           return sizeof(self.value    )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_ = 0      ):





     """simple docstring"""







     if dimension <= 0:
          SCREAMING_SNAKE_CASE										=fixed_dimension

     dimension -= num_token_to_add
     return dimension







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     return num_parameters * dtype.size







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

     try:
          return AutoProcessor.from_pretrained(lowerCAmelCase_      )
     except (ValueError, OSError, KeyError):
          SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=None, None
          try:
               SCREAMING_SNAKE_CASE										=AutoTokenizer.from_pretrained(lowerCAmelCase_      )
          except (OSError, KeyError):
               pass
          try:
               SCREAMING_SNAKE_CASE										=AutoFeatureExtractor.from_pretrained(lowerCAmelCase_      )
          except (OSError, KeyError):
               pass

          if tokenizer is not None and feature_extractor is not None:
               raise ValueError(
                   F'Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'      )
          elif tokenizer is None and feature_extractor is None:
               return None
          elif tokenizer is not None:
               return tokenizer
          else:
               return feature_extractor




import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]      )
     SCREAMING_SNAKE_CASE										=np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]      )

     SCREAMING_SNAKE_CASE										=np.abs(img_convolve(lowerCAmelCase_,   lowerCAmelCase_      )      )
     SCREAMING_SNAKE_CASE										=np.abs(img_convolve(lowerCAmelCase_,   lowerCAmelCase_      )      )
     # modify the pix within [0, 255]
     SCREAMING_SNAKE_CASE										=dst_x * 255 / np.max(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=dst_y * 255 / np.max(lowerCAmelCase_      )

     SCREAMING_SNAKE_CASE										=np.sqrt((np.square(lowerCAmelCase_      )) + (np.square(lowerCAmelCase_      ))      )
     SCREAMING_SNAKE_CASE										=dst_xy * 255 / np.max(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=dst_xy.astype(np.uinta      )

     SCREAMING_SNAKE_CASE										=np.arctana(lowerCAmelCase_,   lowerCAmelCase_      )
     return dst, theta


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

 _lowerCamelCase	,			_lowerCamelCase								=sobel_filter(gray)

 # show result images
 imshow("sobel filter", sobel_grad)
 imshow("sobel theta", sobel_theta)
 waitKey(0)

