

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

_snake_case	=						logging.get_logger(__name__)



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'vision-encoder-decoder'
							lowerCamelCase__         =	True







							def __init__(     self,    **__a):

														'''simple docstring'''

														super().__init__(**__UpperCamelCase)
														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}")

														_lowerCAmelCase  :							Optional[Any]				     =					kwargs.pop("encoder")
														_lowerCAmelCase  :							List[Any]				     =					encoder_config.pop("model_type")
														_lowerCAmelCase  :							Dict				     =					kwargs.pop("decoder")
														_lowerCAmelCase  :							str				     =					decoder_config.pop("model_type")

														_lowerCAmelCase  :							str				     =					AutoConfig.for_model(__UpperCamelCase,    **__UpperCamelCase)
														_lowerCAmelCase  :							Dict				     =					AutoConfig.for_model(__UpperCamelCase,    **__UpperCamelCase)
														_lowerCAmelCase  :							Dict				     =					True







							@classmethod
							def      snake_case__   (     cls,    __a,    __a,    **__a):

														'''simple docstring'''

														logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
														_lowerCAmelCase  :							Optional[Any]				     =					True
														_lowerCAmelCase  :							Optional[Any]				     =					True

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					copy.deepcopy(self.__dict__)
														_lowerCAmelCase  :							Dict				     =					self.encoder.to_dict()
														_lowerCAmelCase  :							Dict				     =					self.decoder.to_dict()
														_lowerCAmelCase  :							Union[str, Any]				     =					self.__class__.model_type
														return output



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	version.parse('1.11')







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

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







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return 1E-4







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})



class 						UpperCAmelCase_					( a):







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					OrderedDict()
														_lowerCAmelCase  :							str				     =					{0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase  :							Optional[int]				     =					{0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase  :							Optional[Any]				     =					{0: "batch", 1: "encoder_sequence"}

														return common_inputs







							def      snake_case__   (     self,    __a,    __a = -1,    __a = -1,    __a = False,    __a = None,    ):

														'''simple docstring'''

														import torch

														_lowerCAmelCase  :							int				     =					OrderedDict()

														_lowerCAmelCase  :							Tuple				     =					super().generate_dummy_inputs(
														    __UpperCamelCase,    batch_size=__UpperCamelCase,    seq_length=__UpperCamelCase,    is_pair=__UpperCamelCase,    framework=__UpperCamelCase)

														_lowerCAmelCase     ,					_lowerCAmelCase  :							Dict				     =					dummy_input["input_ids"].shape
														_lowerCAmelCase  :							List[Any]				     =					(batch, encoder_sequence, self._config.encoder_hidden_size)
														_lowerCAmelCase  :							Optional[int]				     =					dummy_input.pop("input_ids")
														_lowerCAmelCase  :							Optional[Any]				     =					dummy_input.pop("attention_mask")
														_lowerCAmelCase  :							Any				     =					torch.zeros(__UpperCamelCase)

														return common_inputs



class 						UpperCAmelCase_					( a):







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														pass







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														return VisionEncoderDecoderEncoderOnnxConfig(__UpperCamelCase)







							def      snake_case__   (     self,    __a,    __a,    __a = "default"):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					encoder_config.hidden_size
														return VisionEncoderDecoderDecoderOnnxConfig(__UpperCamelCase,    __UpperCamelCase)





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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if (ksize % 2) == 0:
														_lowerCAmelCase  :							str				     =					ksize + 1
							_lowerCAmelCase  :							List[str]				     =					np.zeros((ksize, ksize)       ,				dtype=np.floataa						)

							# each value
							for y in range(_lowerCamelCase						):
														for x in range(_lowerCamelCase						):
																					# distance from center
																					_lowerCAmelCase  :							int				     =					x - ksize // 2
																					_lowerCAmelCase  :							Dict				     =					y - ksize // 2

																					# degree to radiant
																					_lowerCAmelCase  :							List[Any]				     =					theta / 180 * np.pi
																					_lowerCAmelCase  :							int				     =					np.cos(_theta						)
																					_lowerCAmelCase  :							Optional[int]				     =					np.sin(_theta						)

																					# get kernel x
																					_lowerCAmelCase  :							int				     =					cos_theta * px + sin_theta * py

																					# get kernel y
																					_lowerCAmelCase  :							str				     =					-sin_theta * px + cos_theta * py

																					# fill kernel
																					_lowerCAmelCase  :							Union[str, Any]				     =					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
						_snake_case	=						imread("../image_data/lena.jpg")
						# turn image in gray scale value
						_snake_case	=						cvtColor(img, COLOR_BGR2GRAY)

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

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

						waitKey(0)
