'''simple docstring'''

import math
import sys

import cva
import numpy as np







def __UpperCamelCase( _A       :					np.ndarray	,      _A       :					float     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Tuple 					=	math.sqrt(__lowercase     )
   UpperCAmelCase__				:					List[str] 					=	1 / (sigma * math.sqrt(2 * math.pi     ))
   return cons * np.exp(-((img / sigma) ** 2) * 0.5     )







def __UpperCamelCase( _A       :					np.ndarray	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[Any] 					=	kernel_size // 2
   return img[x - half : x + half + 1, y - half : y + half + 1]







def __UpperCamelCase( _A       :					int	,      _A       :					float     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Any 					=	np.zeros((kernel_size, kernel_size)     )
   for i in range(0	,      __lowercase     ):
      for j in range(0	,      __lowercase     ):
         UpperCAmelCase__				:					Optional[int] 					=	math.sqrt(
             abs(i - kernel_size // 2     ) ** 2 + abs(j - kernel_size // 2     ) ** 2     )
   return vec_gaussian(__lowercase	,      __lowercase     )







def __UpperCamelCase( _A       :					np.ndarray	,      _A       :					float	,      _A       :					float	,      _A       :					int	,      ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	np.zeros(img.shape     )
   UpperCAmelCase__				:					Optional[Any] 					=	get_gauss_kernel(__lowercase	,      __lowercase     )
   UpperCAmelCase__					,   UpperCAmelCase__				:					Dict 					=	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__				:					Tuple 					=	get_slice(__lowercase	,      __lowercase	,      __lowercase	,      __lowercase     )
         UpperCAmelCase__				:					List[Any] 					=	img_s - img_s[kernel_size // 2, kernel_size // 2]
         UpperCAmelCase__				:					int 					=	vec_gaussian(__lowercase	,      __lowercase     )
         UpperCAmelCase__				:					List[Any] 					=	np.multiply(__lowercase	,      __lowercase     )
         UpperCAmelCase__				:					Any 					=	np.multiply(__lowercase	,      __lowercase     )
         UpperCAmelCase__				:					Tuple 					=	np.sum(__lowercase     ) / np.sum(__lowercase     )
         UpperCAmelCase__				:					List[str] 					=	val
   return imga







def __UpperCamelCase( _A       :					list     ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[str] 					=	args[1] if args[1:] else '''../image_data/lena.jpg'''
   UpperCAmelCase__				:					Optional[Any] 					=	float(args[2]     ) if args[2:] else 1.0
   UpperCAmelCase__				:					Optional[Any] 					=	float(args[3]     ) if args[3:] else 1.0
   if args[4:]:
      UpperCAmelCase__				:					Optional[Any] 					=	int(args[4]     )
      UpperCAmelCase__				:					Optional[int] 					=	kernel_size + abs(kernel_size % 2 - 1     )
   else:
      UpperCAmelCase__				:					Any 					=	5
   return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
  UpperCamelCase__  :						Any					=      parse_args(sys.argv)
  UpperCamelCase__  :						Optional[Any]					=      cva.imread(filename, 0)
  cva.imshow('input image', img)

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












# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
__a   :							Tuple						= "\nHuman: <<task>>\n\nAssistant: "


__a   :							Union[str, Any]						= "huggingface-tools/default-prompts"
__a   :							Optional[int]						= {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      Optional[Any]      ,					__lowercase :      List[Any]="run"     )    ->   Optional[int]:





    """simple docstring"""

    if prompt_or_repo_id is None:
        __A       =						DEFAULT_PROMPTS_REPO

    # prompt is considered a repo ID when it does not contain any kind of space
    if re.search("""\\s"""      ,					__lowercase     ) is not None:
        return prompt_or_repo_id

    __A       =						cached_file(
        __lowercase      ,					PROMPT_FILES[mode]      ,					repo_type="""dataset"""      ,					user_agent={"""agent""": agent_name}     )
    with open(__lowercase      ,					"""r"""      ,					encoding="""utf-8"""     ) as f:
        return f.read()




