


'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase      :      Union[str, Any]            =					logging.get_logger(__name__)

_lowercase      :      Any            =					{
    """facebook/s2t-small-librispeech-asr""": (
        """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Union[str, Any]  		=	"speech_to_text"
				__magic_name__    :				Any  		=	["past_key_values"]
				__magic_name__    :				List[str]  		=	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




				def __init__(		self      :			Optional[Any]				,   lowerCAmelCase      :			Dict=10000				,   lowerCAmelCase      :			List[Any]=12				,   lowerCAmelCase      :			Optional[int]=2048				,   lowerCAmelCase      :			Dict=4				,   lowerCAmelCase      :			List[Any]=6				,   lowerCAmelCase      :			Dict=2048				,   lowerCAmelCase      :			Optional[int]=4				,   lowerCAmelCase      :			List[Any]=0.0				,   lowerCAmelCase      :			List[str]=0.0				,   lowerCAmelCase      :			int=True				,   lowerCAmelCase      :			Optional[Any]=True				,   lowerCAmelCase      :			List[Any]="relu"				,   lowerCAmelCase      :			Any=256				,   lowerCAmelCase      :			Tuple=0.1				,   lowerCAmelCase      :			Dict=0.0				,   lowerCAmelCase      :			str=0.0				,   lowerCAmelCase      :			str=0.02				,   lowerCAmelCase      :			Tuple=2				,   lowerCAmelCase      :			Optional[Any]=True				,   lowerCAmelCase      :			str=1				,   lowerCAmelCase      :			Tuple=0				,   lowerCAmelCase      :			int=2				,   lowerCAmelCase      :			int=6000				,   lowerCAmelCase      :			Optional[Any]=1024				,   lowerCAmelCase      :			List[str]=2				,   lowerCAmelCase      :			List[str]=(5, 5)				,   lowerCAmelCase      :			Union[str, Any]=1024				,   lowerCAmelCase      :			str=80				,   lowerCAmelCase      :			Optional[int]=1				,   **lowerCAmelCase      :			int				,   )->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     vocab_size
										UpperCAmelCase					    =     d_model
										UpperCAmelCase					    =     encoder_ffn_dim
										UpperCAmelCase					    =     encoder_layers
										UpperCAmelCase					    =     encoder_attention_heads
										UpperCAmelCase					    =     decoder_ffn_dim
										UpperCAmelCase					    =     decoder_layers
										UpperCAmelCase					    =     decoder_attention_heads
										UpperCAmelCase					    =     dropout
										UpperCAmelCase					    =     attention_dropout
										UpperCAmelCase					    =     activation_dropout
										UpperCAmelCase					    =     activation_function
										UpperCAmelCase					    =     init_std
										UpperCAmelCase					    =     encoder_layerdrop
										UpperCAmelCase					    =     decoder_layerdrop
										UpperCAmelCase					    =     use_cache
										UpperCAmelCase					    =     encoder_layers
										UpperCAmelCase					    =     scale_embedding  # scale factor will be sqrt(d_model) if True
										UpperCAmelCase					    =     max_source_positions
										UpperCAmelCase					    =     max_target_positions
										UpperCAmelCase					    =     num_conv_layers
										UpperCAmelCase					    =     list(lowerCAmelCase				)
										UpperCAmelCase					    =     conv_channels
										UpperCAmelCase					    =     input_feat_per_channel
										UpperCAmelCase					    =     input_channels

										if len(self.conv_kernel_sizes				) != self.num_conv_layers:
																raise ValueError(
																    '''Configuration for convolutional module is incorrect. '''
																    '''It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '''
																    F"""but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes				)}`, """
																    F"""`config.num_conv_layers = {self.num_conv_layers}`."""				)

										super().__init__(
										    pad_token_id=lowerCAmelCase				,   bos_token_id=lowerCAmelCase				,   eos_token_id=lowerCAmelCase				,   is_encoder_decoder=lowerCAmelCase				,   decoder_start_token_id=lowerCAmelCase				,   **lowerCAmelCase				,   )









'''simple docstring'''



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


def 				lowerCamelCase__				(	A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int					):




						'''simple docstring'''






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

						# each value
						for y in range(A					):
												for x in range(A					):
																		# 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
	_lowercase      :      Tuple            =					imread("""../image_data/lena.jpg""")
	# turn image in gray scale value
	_lowercase      :      int            =					cvtColor(img, COLOR_BGR2GRAY)

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

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

	waitKey(0)





