

'''simple docstring'''




import math
import sys

import cva
import numpy as np


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


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


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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : np.ndarray							, SCREAMING_SNAKE_CASE   : float							, SCREAMING_SNAKE_CASE   : float							, SCREAMING_SNAKE_CASE   : int							, ):
  UpperCAmelCase    				=						np.zeros(img.shape    )
  UpperCAmelCase    				=						get_gauss_kernel(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  UpperCAmelCase				,   UpperCAmelCase    				=						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    				=						get_slice(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						img_s - img_s[kernel_size // 2, kernel_size // 2]
      UpperCAmelCase    				=						vec_gaussian(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						np.multiply(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						np.multiply(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						np.sum(SCREAMING_SNAKE_CASE    ) / np.sum(SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						val
  return imga


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list    ):
  UpperCAmelCase    				=						args[1] if args[1:] else '../image_data/lena.jpg'
  UpperCAmelCase    				=						float(args[2]    ) if args[2:] else 1.0
  UpperCAmelCase    				=						float(args[3]    ) if args[3:] else 1.0
  if args[4:]:
    UpperCAmelCase    				=						int(args[4]    )
    UpperCAmelCase    				=						kernel_size + abs(kernel_size % 2 - 1    )
  else:
    UpperCAmelCase    				=						5
  return filename, spatial_variance, intensity_variance, kernel_size


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

  _a					:			Any							=    img / 255
  _a					:			Dict							=    out.astype('float32')
  _a					:			str							=    bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
  _a					:			List[Any]							=    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_tf_available, is_torch_available


_a					:			Dict							=    {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Optional[Any]							=    [
      'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLMForMultipleChoice',
      'XLMForQuestionAnswering',
      'XLMForQuestionAnsweringSimple',
      'XLMForSequenceClassification',
      'XLMForTokenClassification',
      'XLMModel',
      'XLMPreTrainedModel',
      'XLMWithLMHeadModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			int							=    [
      'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLMForMultipleChoice',
      'TFXLMForQuestionAnsweringSimple',
      'TFXLMForSequenceClassification',
      'TFXLMForTokenClassification',
      'TFXLMMainLayer',
      'TFXLMModel',
      'TFXLMPreTrainedModel',
      'TFXLMWithLMHeadModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

  _a					:			Union[str, Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


