






import math
import sys

import cva
import numpy as np






def 				lowerCamelCase__      (    __lowerCamelCase			:     np.ndarray  ,		__lowerCamelCase			:     float		):
  # For applying gaussian function for each element in matrix.
  __UpperCAmelCase							:	Dict           =  math.sqrt(__lowerCamelCase		)
  __UpperCAmelCase							:	str           =  1 / (sigma * math.sqrt(2 * math.pi		))
  return cons * np.exp(-((img / sigma) ** 2) * 0.5		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     np.ndarray  ,		__lowerCamelCase			:     int  ,		__lowerCamelCase			:     int  ,		__lowerCamelCase			:     int		):
  __UpperCAmelCase							:	Optional[Any]           =  kernel_size // 2
  return img[x - half : x + half + 1, y - half : y + half + 1]






def 				lowerCamelCase__      (    __lowerCamelCase			:     int  ,		__lowerCamelCase			:     float		):
  # Creates a gaussian kernel of given dimension.
  __UpperCAmelCase							:	str           =  np.zeros((kernel_size, kernel_size)		)
  for i in range(0  ,		__lowerCamelCase		):
    for j in range(0  ,		__lowerCamelCase		):
      __UpperCAmelCase							:	int           =  math.sqrt(
          abs(i - kernel_size // 2		) ** 2 + abs(j - kernel_size // 2		) ** 2		)
  return vec_gaussian(__lowerCamelCase  ,		__lowerCamelCase		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     np.ndarray  ,		__lowerCamelCase			:     float  ,		__lowerCamelCase			:     float  ,		__lowerCamelCase			:     int  ,		):
  __UpperCAmelCase							:	int           =  np.zeros(img.shape		)
  __UpperCAmelCase							:	Tuple           =  get_gauss_kernel(__lowerCamelCase  ,		__lowerCamelCase		)
  __UpperCAmelCase      , __UpperCAmelCase							:	Optional[int]           =  img.shape
  for i in range(kernel_size // 2  ,		size_x - kernel_size // 2		):
    for j in range(kernel_size // 2  ,		size_y - kernel_size // 2		):
      __UpperCAmelCase							:	str           =  get_slice(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase		)
      __UpperCAmelCase							:	List[Any]           =  img_s - img_s[kernel_size // 2, kernel_size // 2]
      __UpperCAmelCase							:	str           =  vec_gaussian(__lowerCamelCase  ,		__lowerCamelCase		)
      __UpperCAmelCase							:	int           =  np.multiply(__lowerCamelCase  ,		__lowerCamelCase		)
      __UpperCAmelCase							:	Union[str, Any]           =  np.multiply(__lowerCamelCase  ,		__lowerCamelCase		)
      __UpperCAmelCase							:	Dict           =  np.sum(__lowerCamelCase		) / np.sum(__lowerCamelCase		)
      __UpperCAmelCase							:	Dict           =  val
  return imga






def 				lowerCamelCase__      (    __lowerCamelCase			:     list		):
  __UpperCAmelCase							:	Optional[Any]           =  args[1] if args[1:] else """../image_data/lena.jpg"""
  __UpperCAmelCase							:	Optional[Any]           =  float(args[2]		) if args[2:] else 1.0
  __UpperCAmelCase							:	Tuple           =  float(args[3]		) if args[3:] else 1.0
  if args[4:]:
    __UpperCAmelCase							:	List[Any]           =  int(args[4]		)
    __UpperCAmelCase							:	List[Any]           =  kernel_size + abs(kernel_size % 2 - 1		)
  else:
    __UpperCAmelCase							:	Dict           =  5
  return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
       a     ,a     ,a     ,a		:       str			     = parse_args(sys.argv)
       a		:       Dict			     = cva.imread(filename, 0)
       cva.imshow("input image", img)

       a		:       Dict			     = img / 255
       a		:       Tuple			     = out.astype("float32")
       a		:       str			     = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
       a		:       Union[str, Any]			     = out * 255
       a		:       Union[str, Any]			     = np.uinta(out)
       cva.imshow("output image", out)
       cva.waitKey(0)
       cva.destroyAllWindows()










import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase__   )
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : str							       =	field(default='automatic-speech-recognition'							,       metadata={'include_in_asdict_even_if_is_default': True}   )
      a  : ClassVar[Features]							       =	Features({'audio': Audio()}   )
      a  : ClassVar[Features]							       =	Features({'transcription': Value('string'   )}   )
      a  : str							       =	"audio"
      a  : str							       =	"transcription"
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			Optional[int]					)							->      str:
        if self.audio_column not in features:
          raise ValueError(f"""Column {self.audio_column} is not present in features."""					)
        if not isinstance(features[self.audio_column]		,      __lowercase					):
          raise ValueError(f"""Column {self.audio_column} is not an Audio type."""					)
        __UpperCAmelCase							:	int           =  copy.deepcopy(self					)
        __UpperCAmelCase							:	str           =  self.input_schema.copy()
        __UpperCAmelCase							:	List[str]           =  features[self.audio_column]
        __UpperCAmelCase							:	Optional[Any]           =  input_schema
        return task_template




      @property
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      Dict[str, str]:
        return {self.audio_column: "audio", self.transcription_column: "transcription"}


