



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

A__						:						Optional[int]          =      180


def     UpperCamelCase(						__UpperCamelCase							:	Optional[Any]  ,__UpperCamelCase							:	Union[str, Any]				):
  lowerCAmelCase_    :  Optional[Any]             =				k_size // 2
  lowerCAmelCase_      ,						lowerCAmelCase_    :  Any             =				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  lowerCAmelCase_    :  Optional[int]             =				(
      1
      / (2 * np.pi * sigma)
      * np.exp(-(np.square(__UpperCamelCase				) + np.square(__UpperCamelCase				)) / (2 * np.square(__UpperCamelCase				))				)
  )
  return g


def     UpperCamelCase(						__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	List[Any]  ,__UpperCamelCase							:	Dict				):
  lowerCAmelCase_    :  str             =				np.zeros(__UpperCamelCase				)

  for row in range(1  ,image_shape[0] - 1				):
    for col in range(1  ,image_shape[1] - 1				):
      lowerCAmelCase_    :  List[Any]             =				gradient_direction[row, col]

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

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

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

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

  return destination


def     UpperCamelCase(						__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	str  ,__UpperCamelCase							:	int  ,__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	List[Any]  ,__UpperCamelCase							:	Union[str, 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:
        lowerCAmelCase_    :  Dict             =				strong
      elif destination[row, col] <= threshold_low:
        lowerCAmelCase_    :  Optional[Any]             =				0
      else:
        lowerCAmelCase_    :  Tuple             =				weak


def     UpperCamelCase(						__UpperCamelCase							:	Optional[int]  ,__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	Optional[Any]  ,__UpperCamelCase							:	Union[str, Any]				):







  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],
        ):
          lowerCAmelCase_    :  int             =				strong
        else:
          lowerCAmelCase_    :  Union[str, Any]             =				0


def     UpperCamelCase(						__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	int=15  ,__UpperCamelCase							:	List[Any]=30  ,__UpperCamelCase							:	int=128  ,__UpperCamelCase							:	str=255				):
  lowerCAmelCase_    :  Any             =				img_convolve(__UpperCamelCase  ,gen_gaussian_kernel(9  ,sigma=1.4				)				)
  # get the gradient and degree by sobel_filter
  lowerCAmelCase_      ,						lowerCAmelCase_    :  List[Any]             =				sobel_filter(__UpperCamelCase				)
  lowerCAmelCase_    :  Optional[int]             =				PI + np.radadeg(__UpperCamelCase				)

  lowerCAmelCase_    :  Any             =				suppress_non_maximum(image.shape  ,__UpperCamelCase  ,__UpperCamelCase				)

  detect_high_low_threshold(
      image.shape  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase				)

  track_edge(image.shape  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase				)

  return destination


if __name__ == "__main__":
      # read original image in gray mode
      A__						:						List[str]          =      cva.imread(R'''../image_data/lena.jpg''', 0)
      # canny edge detection
      A__						:						Tuple          =      canny(lena)
      cva.imshow('''canny''', canny_destination)
      cva.waitKey(0)










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__						:						Dict          =      logging.get_logger(__name__)

A__						:						List[str]          =      {
    '''google/electra-small-generator''': '''https://huggingface.co/google/electra-small-generator/resolve/main/config.json''',
    '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/config.json''',
    '''google/electra-large-generator''': '''https://huggingface.co/google/electra-large-generator/resolve/main/config.json''',
    '''google/electra-small-discriminator''': (
        '''https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'''
    ),
    '''google/electra-base-discriminator''': (
        '''https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'''
    ),
    '''google/electra-large-discriminator''': (
        '''https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'''
    ),
}



class        __snake_case		(  UpperCamelCase_		):
							_a       =				'''electra'''




							def __init__( self  : Dict       ,       A_  : Dict=3_0_5_2_2       ,       A_  : List[Any]=1_2_8       ,       A_  : Dict=2_5_6       ,       A_  : Any=1_2       ,       A_  : Optional[Any]=4       ,       A_  : Optional[int]=1_0_2_4       ,       A_  : Optional[int]="gelu"       ,       A_  : str=0.1       ,       A_  : Optional[int]=0.1       ,       A_  : str=5_1_2       ,       A_  : Optional[int]=2       ,       A_  : Dict=0.02       ,       A_  : Union[str, Any]=1e-12       ,       A_  : List[Any]="first"       ,       A_  : List[str]=True       ,       A_  : List[str]="gelu"       ,       A_  : Any=0.1       ,       A_  : int=0       ,       A_  : Optional[Any]="absolute"       ,       A_  : Optional[int]=True       ,       A_  : str=None       ,       **A_  : List[Any]       ,       ):
									super().__init__(pad_token_id=A_       ,       **A_)

									lowerCAmelCase_    :  str             =				vocab_size
									lowerCAmelCase_    :  Union[str, Any]             =				embedding_size
									lowerCAmelCase_    :  Optional[int]             =				hidden_size
									lowerCAmelCase_    :  List[Any]             =				num_hidden_layers
									lowerCAmelCase_    :  List[str]             =				num_attention_heads
									lowerCAmelCase_    :  str             =				intermediate_size
									lowerCAmelCase_    :  Union[str, Any]             =				hidden_act
									lowerCAmelCase_    :  Union[str, Any]             =				hidden_dropout_prob
									lowerCAmelCase_    :  Tuple             =				attention_probs_dropout_prob
									lowerCAmelCase_    :  Optional[Any]             =				max_position_embeddings
									lowerCAmelCase_    :  Dict             =				type_vocab_size
									lowerCAmelCase_    :  Any             =				initializer_range
									lowerCAmelCase_    :  int             =				layer_norm_eps

									lowerCAmelCase_    :  Union[str, Any]             =				summary_type
									lowerCAmelCase_    :  str             =				summary_use_proj
									lowerCAmelCase_    :  str             =				summary_activation
									lowerCAmelCase_    :  List[Any]             =				summary_last_dropout
									lowerCAmelCase_    :  Optional[Any]             =				position_embedding_type
									lowerCAmelCase_    :  List[Any]             =				use_cache
									lowerCAmelCase_    :  Any             =				classifier_dropout





class        __snake_case		(  UpperCamelCase_		):




							@property
							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									if self.task == "multiple-choice":
											lowerCAmelCase_    :  Any             =				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
											lowerCAmelCase_    :  Union[str, Any]             =				{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									        ('''token_type_ids''', dynamic_axis),
									    ])





