
"""simple docstring"""





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
				from ... import PreTrainedTokenizerBase, TensorType

_a														=		logging.get_logger(__name__)






class     _UpperCAmelCase(    lowerCamelCase	):
		lowercase__						=      'vision-encoder-decoder'
		lowercase__						=      True



		def __init__(					self				,		**__a)       ->       Optional[int]:






							'''simple docstring'''
							super().__init__(**__a)
							if "encoder" not in kwargs or "decoder" not in kwargs:
												raise ValueError(
												    F'''A configuraton of type {self.model_type} cannot be instantiated because '''
												    F'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}''')

							_UpperCamelCase							 =  kwargs.pop('''encoder''')
							_UpperCamelCase							 =  encoder_config.pop('''model_type''')
							_UpperCamelCase							 =  kwargs.pop('''decoder''')
							_UpperCamelCase							 =  decoder_config.pop('''model_type''')

							_UpperCamelCase							 =  AutoConfig.for_model(__a				,		**__a)
							_UpperCamelCase							 =  AutoConfig.for_model(__a				,		**__a)
							_UpperCamelCase							 =  True



		@classmethod
		def 						UpperCAmelCase							(					cls				,		__a				,		__a				,		**__a)       ->       PretrainedConfig:






							'''simple docstring'''
							logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''')
							_UpperCamelCase							 =  True
							_UpperCamelCase							 =  True

							return cls(encoder=encoder_config.to_dict()				,		decoder=decoder_config.to_dict()				,		**__a)




		def 						UpperCAmelCase							(					self)       ->       str:






							'''simple docstring'''
							_UpperCamelCase							 =  copy.deepcopy(self.__dict__)
							_UpperCamelCase							 =  self.encoder.to_dict()
							_UpperCamelCase							 =  self.decoder.to_dict()
							_UpperCamelCase							 =  self.__class__.model_type
							return output






class     _UpperCAmelCase(    lowerCamelCase	):
		lowercase__						=      version.parse('1.11'	)



		@property
		def 						UpperCAmelCase							(					self)       ->       Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ])



		@property
		def 						UpperCAmelCase							(					self)       ->       float:






							'''simple docstring'''
							return 1e-4




		@property
		def 						UpperCAmelCase							(					self)       ->       Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}})






class     _UpperCAmelCase(    lowerCamelCase	):



		@property
		def 						UpperCAmelCase							(					self)       ->       Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							_UpperCamelCase							 =  OrderedDict()
							_UpperCamelCase							 =  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
							_UpperCamelCase							 =  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
							_UpperCamelCase							 =  {0: '''batch''', 1: '''encoder_sequence'''}

							return common_inputs




		def 						UpperCAmelCase							(					self				,		__a				,		__a = -1				,		__a = -1				,		__a = False				,		__a = None				,		)       ->       Mapping[str, Any]:






							'''simple docstring'''
							import torch

							_UpperCamelCase							 =  OrderedDict()

							_UpperCamelCase							 =  super().generate_dummy_inputs(
							    __a				,		batch_size=__a				,		seq_length=__a				,		is_pair=__a				,		framework=__a)

							_UpperCamelCase ,  _UpperCamelCase							 =  dummy_input['''input_ids'''].shape
							_UpperCamelCase							 =  (batch, encoder_sequence, self._config.encoder_hidden_size)
							_UpperCamelCase							 =  dummy_input.pop('''input_ids''')
							_UpperCamelCase							 =  dummy_input.pop('''attention_mask''')
							_UpperCamelCase							 =  torch.zeros(__a)

							return common_inputs






class     _UpperCAmelCase(    lowerCamelCase	):



		@property
		def 						UpperCAmelCase							(					self)       ->       None:






							'''simple docstring'''
							pass



		def 						UpperCAmelCase							(					self				,		__a)       ->       OnnxConfig:






							'''simple docstring'''
							return VisionEncoderDecoderEncoderOnnxConfig(__a)




		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a = "default")       ->       OnnxConfig:






							'''simple docstring'''
							_UpperCamelCase							 =  encoder_config.hidden_size
							return VisionEncoderDecoderDecoderOnnxConfig(__a				,		__a)








"""simple docstring"""





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		np.ndarray:


					"""simple docstring"""





					if (ksize % 2) == 0:
										_UpperCamelCase							 =  ksize + 1
					_UpperCamelCase							 =  np.zeros((ksize, ksize),     dtype=np.floataa	)

					# each value
					for y in range(__snake_case	):
										for x in range(__snake_case	):
															# distance from center
															_UpperCamelCase							 =  x - ksize // 2
															_UpperCamelCase							 =  y - ksize // 2

															# degree to radiant
															_UpperCamelCase							 =  theta / 1_80 * np.pi
															_UpperCamelCase							 =  np.cos(_theta	)
															_UpperCamelCase							 =  np.sin(_theta	)

															# get kernel x
															_UpperCamelCase							 =  cos_theta * px + sin_theta * py

															# get kernel y
															_UpperCamelCase							 =  -sin_theta * px + cos_theta * py

															# fill kernel
															_UpperCamelCase							 =  np.exp(
															    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)	) * np.cos(2 * np.pi * _x / lambd + psi	)

					return gabor


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# read original image
				_a														=		imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a														=		cvtColor(img, COLOR_BGR2GRAY)

				# Apply multiple Kernel to detect edges
				_a														=		np.zeros(gray.shape[:2])
				for theta in [0, 30, 60, 90, 120, 150]:
								_a														=		gabor_filter_kernel(10, 8, theta, 10, 0, 0)
								out += filteraD(gray, CV_8UC3, kernel_aa)
				_a														=		out / out.max() * 255
				_a														=		out.astype(np.uinta)

				imshow("""Original""", gray)
				imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

				waitKey(0)






