





"""simple docstring"""


from typing import Mapping

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


A_     :      Any								=logging.get_logger(__name__)







class 	__a   (  lowerCAmelCase__		):
     SCREAMING_SNAKE_CASE__ :      List[str]       		=			"mt5"
     SCREAMING_SNAKE_CASE__ :      int       		=			["past_key_values"]



     def __init__(       self					,     a__=25_01_12					,     a__=5_12					,     a__=64					,     a__=10_24					,     a__=8					,     a__=None					,     a__=6					,     a__=32					,     a__=1_28					,     a__=0.1					,     a__=1e-6					,     a__=1.0					,     a__="gated-gelu"					,     a__=True					,     a__=True					,     a__="T5Tokenizer"					,     a__=False					,     a__=0					,     a__=1					,     a__=0					,     **a__					,     ):
            super().__init__(
                is_encoder_decoder=a__					,     tokenizer_class=a__					,     tie_word_embeddings=a__					,     pad_token_id=a__					,     eos_token_id=a__					,     decoder_start_token_id=a__					,     **a__					,     )
            _lowerCamelCase    	=					vocab_size
            _lowerCamelCase    	=					d_model
            _lowerCamelCase    	=					d_kv
            _lowerCamelCase    	=					d_ff
            _lowerCamelCase    	=					num_layers
            _lowerCamelCase    	=					(
                num_decoder_layers if num_decoder_layers is not None else self.num_layers
            )  # default = symmetry
            _lowerCamelCase    	=					num_heads
            _lowerCamelCase    	=					relative_attention_num_buckets
            _lowerCamelCase    	=					relative_attention_max_distance
            _lowerCamelCase    	=					dropout_rate
            _lowerCamelCase    	=					layer_norm_epsilon
            _lowerCamelCase    	=					initializer_factor
            _lowerCamelCase    	=					feed_forward_proj
            _lowerCamelCase    	=					use_cache

            _lowerCamelCase    	=					self.feed_forward_proj.split('-'   )
            _lowerCamelCase    	=					act_info[-1]
            _lowerCamelCase    	=					act_info[0] == 'gated'

            if len(a__   ) > 1 and act_info[0] != "gated" or len(a__   ) > 2:
                   raise ValueError(
                       F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                       'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                       '\'gated-gelu\' or \'relu\''   )

            # for backwards compatibility
            if feed_forward_proj == "gated-gelu":
                   _lowerCamelCase    	=					'gelu_new'



     @property
     def 						snake_case_			(       self   ):
            return self.d_model



     @property
     def 						snake_case_			(       self   ):
            return self.num_heads



     @property
     def 						snake_case_			(       self   ):
            return self.num_layers







class 	__a   (  lowerCAmelCase__		):



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					{
                'input_ids': {0: 'batch', 1: 'encoder_sequence'},
                'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
            }
            if self.use_past:
                   _lowerCamelCase    	=					'past_encoder_sequence + sequence'
                   _lowerCamelCase    	=					{0: 'batch'}
                   _lowerCamelCase    	=					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                   _lowerCamelCase    	=					{0: 'batch', 1: 'decoder_sequence'}
                   _lowerCamelCase    	=					{0: 'batch', 1: 'decoder_sequence'}

            if self.use_past:
                   self.fill_with_past_key_values_(a__					,     direction='inputs'   )

            return common_inputs



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def 						snake_case_			(       self   ):
            return 13



     @property
     def 						snake_case_			(       self   ):
            return 5e-4













"""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

A_     :      Any								=1_8_0





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Any		)->		Tuple:
       _lowerCamelCase    	=					k_size // 2
       _lowerCamelCase	,	_lowerCamelCase    	=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       _lowerCamelCase    	=					(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(snake_case		) + np.square(snake_case		)) / (2 * np.square(snake_case		))		)
       )
       return g





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[int]						,  snake_case   :					int						,  snake_case   :					Dict		)->		Optional[Any]:
       _lowerCamelCase    	=					np.zeros(snake_case		)

       for row in range(1						,  image_shape[0] - 1		):
              for col in range(1						,  image_shape[1] - 1		):
                     _lowerCamelCase    	=					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    	=					sobel_grad[row, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

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

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

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

       return destination





def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Any]						,  snake_case   :					List[Any]						,  snake_case   :					int						,  snake_case   :					int						,  snake_case   :					List[str]						,  snake_case   :					str		)->		Tuple:
       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    	=					strong
                     elif destination[row, col] <= threshold_low:
                            _lowerCamelCase    	=					0
                     else:
                            _lowerCamelCase    	=					weak





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Union[str, Any]						,  snake_case   :					Optional[int]						,  snake_case   :					Dict						,  snake_case   :					Tuple		)->		Optional[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],
                            ):
                                   _lowerCamelCase    	=					strong
                            else:
                                   _lowerCamelCase    	=					0





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

       _lowerCamelCase    	=					suppress_non_maximum(image.shape						,  snake_case						,  snake_case		)

       detect_high_low_threshold(
           image.shape						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case		)

       track_edge(image.shape						,  snake_case						,  snake_case						,  snake_case		)

       return destination


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






