
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

UpperCAmelCase__						:	Union[str, Any]	     = 180






def     lowerCamelCase__				(   a						,						a ) ->  Any:
   _A:    List[Any]						     =			k_size // 2
   _A     ,    _A:    Union[str, Any]						     =			np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
   _A:    Dict						     =			(
       1
       / (2 * np.pi * sigma)
       * np.exp(-(np.square(a ) + np.square(a )) / (2 * np.square(a )) )
   )
   return g






def     lowerCamelCase__				(   a						,						a						,						a ) ->  int:
   _A:    int						     =			np.zeros(a )

   for row in range(1						,						image_shape[0] - 1 ):
      for col in range(1						,						image_shape[1] - 1 ):
         _A:    str						     =			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:    List[str]						     =			sobel_grad[row, col + 1]
            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
               _A:    str						     =			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:    Tuple						     =			sobel_grad[row - 1, col + 1]
            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
               _A:    Optional[int]						     =			sobel_grad[row, col]

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

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

   return destination






def     lowerCamelCase__				(   a						,						a						,						a						,						a						,						a						,						a ) ->  int:
   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:    Optional[Any]						     =			strong
         elif destination[row, col] <= threshold_low:
            _A:    Dict						     =			0
         else:
            _A:    Any						     =			weak






def     lowerCamelCase__				(   a						,						a						,						a						,						a ) ->  Optional[Any]:
   for row in range(1						,						image_shape[0] ):
      for col in range(1						,						image_shape[1] ):
         if destination[row, col] == weak:
            if 2_55 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:    List[str]						     =			strong
            else:
               _A:    Union[str, Any]						     =			0






def     lowerCamelCase__				(   a						,						a=15						,						a=30						,						a=1_28						,						a=2_55 ) ->  List[Any]:
   # gaussian_filter
   _A:    List[Any]						     =			img_convolve(a						,						gen_gaussian_kernel(9						,						sigma=1.4 ) )
   # get the gradient and degree by sobel_filter
   _A     ,    _A:    List[str]						     =			sobel_filter(a )
   _A:    Dict						     =			PI + np.radadeg(a )

   _A:    str						     =			suppress_non_maximum(image.shape						,						a						,						a )

   detect_high_low_threshold(
       image.shape						,						a						,						a						,						a						,						a						,						a )

   track_edge(image.shape						,						a						,						a						,						a )

   return destination


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


UpperCAmelCase__						:	List[str]	     = logging.get_logger(__name__)

UpperCAmelCase__						:	str	     = {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Tuple												=       '''flaubert'''
   __UpperCamelCase						:       str												=       {
       '''hidden_size''': '''emb_dim''',
       '''num_attention_heads''': '''n_heads''',
       '''num_hidden_layers''': '''n_layers''',
       '''n_words''': '''vocab_size''',  # For backward compatibility
   }


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Union[str, Any]=False     ,     lowerCAmelCase_	:				List[str]=0.0     ,     lowerCAmelCase_	:				Dict=3_0_1_4_5     ,     lowerCAmelCase_	:				Tuple=2_0_4_8     ,     lowerCAmelCase_	:				List[Any]=1_2     ,     lowerCAmelCase_	:				str=1_6     ,     lowerCAmelCase_	:				Union[str, Any]=0.1     ,     lowerCAmelCase_	:				List[str]=0.1     ,     lowerCAmelCase_	:				str=True     ,     lowerCAmelCase_	:				Optional[int]=False     ,     lowerCAmelCase_	:				Any=False     ,     lowerCAmelCase_	:				int=False     ,     lowerCAmelCase_	:				int=1     ,     lowerCAmelCase_	:				List[str]=True     ,     lowerCAmelCase_	:				Dict=5_1_2     ,     lowerCAmelCase_	:				Any=2_0_4_8**-0.5     ,     lowerCAmelCase_	:				List[str]=1e-12     ,     lowerCAmelCase_	:				Any=0.02     ,     lowerCAmelCase_	:				Optional[Any]=0     ,     lowerCAmelCase_	:				Union[str, Any]=1     ,     lowerCAmelCase_	:				List[Any]=2     ,     lowerCAmelCase_	:				Dict=3     ,     lowerCAmelCase_	:				Optional[Any]=5     ,     lowerCAmelCase_	:				Any=True     ,     lowerCAmelCase_	:				Union[str, Any]="first"     ,     lowerCAmelCase_	:				List[Any]=True     ,     lowerCAmelCase_	:				Tuple=None     ,     lowerCAmelCase_	:				Optional[int]=True     ,     lowerCAmelCase_	:				int=0.1     ,     lowerCAmelCase_	:				Optional[int]=5     ,     lowerCAmelCase_	:				Union[str, Any]=5     ,     lowerCAmelCase_	:				List[Any]=0     ,     lowerCAmelCase_	:				Dict=0     ,     lowerCAmelCase_	:				Union[str, Any]=2     ,     lowerCAmelCase_	:				Optional[int]=0     ,     **lowerCAmelCase_	:				Any     ,     ):

      """simple docstring"""
      _A:    List[str]						     =			pre_norm
      _A:    List[Any]						     =			layerdrop
      _A:    Dict						     =			vocab_size
      _A:    List[Any]						     =			emb_dim
      _A:    Optional[Any]						     =			n_layers
      _A:    Union[str, Any]						     =			n_heads
      _A:    Optional[Any]						     =			dropout
      _A:    Optional[Any]						     =			attention_dropout
      _A:    Dict						     =			gelu_activation
      _A:    Tuple						     =			sinusoidal_embeddings
      _A:    Tuple						     =			causal
      _A:    List[Any]						     =			asm
      _A:    Dict						     =			n_langs
      _A:    Optional[int]						     =			use_lang_emb
      _A:    Any						     =			layer_norm_eps
      _A:    str						     =			bos_index
      _A:    Optional[int]						     =			eos_index
      _A:    int						     =			pad_index
      _A:    Tuple						     =			unk_index
      _A:    int						     =			mask_index
      _A:    int						     =			is_encoder
      _A:    Union[str, Any]						     =			max_position_embeddings
      _A:    List[str]						     =			embed_init_std
      _A:    List[str]						     =			init_std
      _A:    Tuple						     =			summary_type
      _A:    List[str]						     =			summary_use_proj
      _A:    Union[str, Any]						     =			summary_activation
      _A:    Union[str, Any]						     =			summary_proj_to_labels
      _A:    int						     =			summary_first_dropout
      _A:    Optional[Any]						     =			start_n_top
      _A:    Optional[Any]						     =			end_n_top
      _A:    Dict						     =			mask_token_id
      _A:    str						     =			lang_id

      if "n_words" in kwargs:
         _A:    int						     =			kwargs['''n_words''']

      super().__init__(pad_token_id=lowerCAmelCase_     ,     bos_token_id=lowerCAmelCase_     ,     **lowerCAmelCase_	)






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   @property
   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      if self.task == "multiple-choice":
         _A:    int						     =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
         _A:    Any						     =			{0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]	)



