



'''simple docstring'''






from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Dict ,       snake_case_    :     List[str]=3  )		->Dict:






							'''simple docstring'''
							_lowercase							:		Tuple				=   int(mask / 2  )
							# copy image size
							_lowercase							:		str				=   zeros_like(snake_case_  )
							for i in range(snake_case_ ,       gray_img.shape[0] - bd  ):
														for j in range(snake_case_ ,       gray_img.shape[1] - bd  ):
																					# get mask according with mask
																					_lowercase							:		Optional[int]				=   ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]  )
																					# calculate mask median
																					_lowercase							:		Union[str, Any]				=   sort(snake_case_  )[inta(divide((multiply(snake_case_ ,       snake_case_  )) ,       2  ) + 1  )]
																					_lowercase							:		Optional[Any]				=   median
							return median_img


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

						# get values with two different mask size
						lowerCamelCase__	  =		median_filter(gray, 3)
						lowerCamelCase__	  =		median_filter(gray, 5)

						# show result images
						imshow('median filter with 3x3 mask', medianaxa)
						imshow('median filter with 5x5 mask', medianaxa)
						waitKey(0)











"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase					=     {
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""LEDTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     [
      """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """LEDForConditionalGeneration""",
      """LEDForQuestionAnswering""",
      """LEDForSequenceClassification""",
      """LEDModel""",
      """LEDPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
  from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
  from .tokenization_led import LEDTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_led_fast import LEDTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_led import (
        LED_PRETRAINED_MODEL_ARCHIVE_LIST,
        LEDForConditionalGeneration,
        LEDForQuestionAnswering,
        LEDForSequenceClassification,
        LEDModel,
        LEDPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
  import sys

  UpperCamelCase					=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






