"""simple docstring"""
import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowercase__   			=      180







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Union[str, Any]:
  a__:	List[str]     				=      k_size // 2
  a__				,       a__:	Union[str, Any]     				=      np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  a__:	Tuple     				=      (
      1
      / (2 * np.pi * sigma)
      * np.exp(-(np.square(_SCREAMING_SNAKE_CASE  ) + np.square(_SCREAMING_SNAKE_CASE  )) / (2 * np.square(_SCREAMING_SNAKE_CASE  ))  )
  )
  return g







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Optional[int]:
  a__:	Union[str, Any]     				=      np.zeros(_SCREAMING_SNAKE_CASE  )

  for row in range(1						,	image_shape[0] - 1  ):
    for col in range(1						,	image_shape[1] - 1  ):
      a__:	int     				=      gradient_direction[row, col]

      if (
          0 <= direction < PI / 8
          or 15 * PI / 8 <= direction <= 2 * PI
          or 7 * PI / 8 <= direction <= 9 * PI / 8
      ):
        a__:	List[Any]     				=      sobel_grad[row, col - 1]
        a__:	Any     				=      sobel_grad[row, col + 1]
        if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
          a__:	Optional[Any]     				=      sobel_grad[row, col]

      elif (
          PI / 8 <= direction < 3 * PI / 8
          or 9 * PI / 8 <= direction < 11 * PI / 8
      ):
        a__:	Optional[int]     				=      sobel_grad[row + 1, col - 1]
        a__:	int     				=      sobel_grad[row - 1, col + 1]
        if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
          a__:	int     				=      sobel_grad[row, col]

      elif (
          3 * PI / 8 <= direction < 5 * PI / 8
          or 11 * PI / 8 <= direction < 13 * PI / 8
      ):
        a__:	List[Any]     				=      sobel_grad[row - 1, col]
        a__:	List[str]     				=      sobel_grad[row + 1, col]
        if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
          a__:	List[str]     				=      sobel_grad[row, col]

      elif (
          5 * PI / 8 <= direction < 7 * PI / 8
          or 13 * PI / 8 <= direction < 15 * PI / 8
      ):
        a__:	Tuple     				=      sobel_grad[row - 1, col - 1]
        a__:	str     				=      sobel_grad[row + 1, col + 1]
        if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
          a__:	Optional[Any]     				=      sobel_grad[row, col]

  return destination







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Any:
  for row in range(1						,	image_shape[0] - 1  ):
    for col in range(1						,	image_shape[1] - 1  ):
      if destination[row, col] >= threshold_high:
        a__:	Union[str, Any]     				=      strong
      elif destination[row, col] <= threshold_low:
        a__:	Dict     				=      0
      else:
        a__:	int     				=      weak







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->int:
  for row in range(1						,	image_shape[0]  ):
    for col in range(1						,	image_shape[1]  ):
      if destination[row, col] == weak:
        if 255 in (
            destination[row, col + 1],
            destination[row, col - 1],
            destination[row - 1, col],
            destination[row + 1, col],
            destination[row - 1, col - 1],
            destination[row + 1, col - 1],
            destination[row - 1, col + 1],
            destination[row + 1, col + 1],
        ):
          a__:	Optional[int]     				=      strong
        else:
          a__:	List[str]     				=      0







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=15						,	_SCREAMING_SNAKE_CASE=30						,	_SCREAMING_SNAKE_CASE=128						,	_SCREAMING_SNAKE_CASE=255  )      ->Tuple:
  # gaussian_filter
  a__:	Union[str, Any]     				=      img_convolve(_SCREAMING_SNAKE_CASE						,	gen_gaussian_kernel(9						,	sigma=1.4  )  )
  # get the gradient and degree by sobel_filter
  a__				,       a__:	Optional[int]     				=      sobel_filter(_SCREAMING_SNAKE_CASE  )
  a__:	Optional[Any]     				=      PI + np.radadeg(_SCREAMING_SNAKE_CASE  )

  a__:	Tuple     				=      suppress_non_maximum(image.shape						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  detect_high_low_threshold(
      image.shape						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  track_edge(image.shape						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  return destination


if __name__ == "__main__":
 # read original image in gray mode
 lowercase__   			=      cva.imread(r'../image_data/lena.jpg', 0)
 # canny edge detection
 lowercase__   			=      canny(lena)
 cva.imshow('canny', canny_destination)
 cva.waitKey(0)



"""simple docstring"""
from typing import TYPE_CHECKING

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


lowercase__   			=      {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      ['MPNetTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MPNetForMaskedLM',
     'MPNetForMultipleChoice',
     'MPNetForQuestionAnswering',
     'MPNetForSequenceClassification',
     'MPNetForTokenClassification',
     'MPNetLayer',
     'MPNetModel',
     'MPNetPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMPNetEmbeddings',
     'TFMPNetForMaskedLM',
     'TFMPNetForMultipleChoice',
     'TFMPNetForQuestionAnswering',
     'TFMPNetForSequenceClassification',
     'TFMPNetForTokenClassification',
     'TFMPNetMainLayer',
     'TFMPNetModel',
     'TFMPNetPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

 lowercase__   			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


