"""simple docstring"""




import math
import sys

import cva
import numpy as np





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       np.ndarray			,_lowerCamelCase						:       float	)       ->      np.ndarray:
  # For applying gaussian function for each element in matrix.
  _lowerCAmelCase						:       Tuple  			=     math.sqrt(_lowerCamelCase	)
  _lowerCAmelCase						:       Dict  			=     1 / (sigma * math.sqrt(2 * math.pi	))
  return cons * np.exp(-((img / sigma) ** 2) * 0.5	)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       np.ndarray			,_lowerCamelCase						:       int			,_lowerCamelCase						:       int			,_lowerCamelCase						:       int	)       ->      np.ndarray:
  _lowerCAmelCase						:       List[Any]  			=     kernel_size // 2
  return img[x - half : x + half + 1, y - half : y + half + 1]





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int			,_lowerCamelCase						:       float	)       ->      np.ndarray:
  # Creates a gaussian kernel of given dimension.
  _lowerCAmelCase						:       List[str]  			=     np.zeros((kernel_size, kernel_size)	)
  for i in range(0			,_lowerCamelCase	):
    for j in range(0			,_lowerCamelCase	):
      _lowerCAmelCase						:       Union[str, Any]  			=     math.sqrt(
          abs(i - kernel_size // 2	) ** 2 + abs(j - kernel_size // 2	) ** 2	)
  return vec_gaussian(_lowerCamelCase			,_lowerCamelCase	)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       np.ndarray			,_lowerCamelCase						:       float			,_lowerCamelCase						:       float			,_lowerCamelCase						:       int			,)       ->      np.ndarray:
  _lowerCAmelCase						:       Union[str, Any]  			=     np.zeros(img.shape	)
  _lowerCAmelCase						:       Dict  			=     get_gauss_kernel(_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase			,	_lowerCAmelCase						:       Tuple  			=     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	):
      _lowerCAmelCase						:       Union[str, Any]  			=     get_slice(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
      _lowerCAmelCase						:       List[Any]  			=     img_s - img_s[kernel_size // 2, kernel_size // 2]
      _lowerCAmelCase						:       List[Any]  			=     vec_gaussian(_lowerCamelCase			,_lowerCamelCase	)
      _lowerCAmelCase						:       Union[str, Any]  			=     np.multiply(_lowerCamelCase			,_lowerCamelCase	)
      _lowerCAmelCase						:       int  			=     np.multiply(_lowerCamelCase			,_lowerCamelCase	)
      _lowerCAmelCase						:       str  			=     np.sum(_lowerCamelCase	) / np.sum(_lowerCamelCase	)
      _lowerCAmelCase						:       Optional[int]  			=     val
  return imga





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


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

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











"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_     :						List[str]						=					{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						str						=					['XLNetTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						List[str]						=					['XLNetTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						int						=					[
       'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'XLNetForMultipleChoice',
       'XLNetForQuestionAnswering',
       'XLNetForQuestionAnsweringSimple',
       'XLNetForSequenceClassification',
       'XLNetForTokenClassification',
       'XLNetLMHeadModel',
       'XLNetModel',
       'XLNetPreTrainedModel',
       'load_tf_weights_in_xlnet',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						Union[str, Any]						=					[
       'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFXLNetForMultipleChoice',
       'TFXLNetForQuestionAnsweringSimple',
       'TFXLNetForSequenceClassification',
       'TFXLNetForTokenClassification',
       'TFXLNetLMHeadModel',
       'TFXLNetMainLayer',
       'TFXLNetModel',
       'TFXLNetPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet import XLNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet_fast import XLNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlnet import (
          XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLNetForMultipleChoice,
          XLNetForQuestionAnswering,
          XLNetForQuestionAnsweringSimple,
          XLNetForSequenceClassification,
          XLNetForTokenClassification,
          XLNetLMHeadModel,
          XLNetModel,
          XLNetPreTrainedModel,
          load_tf_weights_in_xlnet,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlnet import (
          TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLNetForMultipleChoice,
          TFXLNetForQuestionAnsweringSimple,
          TFXLNetForSequenceClassification,
          TFXLNetForTokenClassification,
          TFXLNetLMHeadModel,
          TFXLNetMainLayer,
          TFXLNetModel,
          TFXLNetPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_     :						List[Any]						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)