


'''simple docstring'''



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


__UpperCAmelCase    :List[Any]				       =     logging.get_logger(__name__)

__UpperCAmelCase    :List[str]				       =     {
    "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 			a       (	lowercase_ ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				List[str]							     =     "speech_to_text"
     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     ["past_key_values"]
     SCREAMING_SNAKE_CASE :				int							     =     {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

     def __init__(       self  :	int							,				snake_case  :	Optional[int]=1_0000							,				snake_case  :	str=12							,				snake_case  :	List[Any]=2048							,				snake_case  :	int=4							,				snake_case  :	Optional[Any]=6							,				snake_case  :	int=2048							,				snake_case  :	List[str]=4							,				snake_case  :	Union[str, Any]=0.0							,				snake_case  :	int=0.0							,				snake_case  :	int=True							,				snake_case  :	Union[str, Any]=True							,				snake_case  :	List[str]="relu"							,				snake_case  :	Optional[Any]=256							,				snake_case  :	Optional[int]=0.1							,				snake_case  :	Tuple=0.0							,				snake_case  :	Optional[int]=0.0							,				snake_case  :	Tuple=0.02							,				snake_case  :	Dict=2							,				snake_case  :	str=True							,				snake_case  :	List[Any]=1							,				snake_case  :	List[Any]=0							,				snake_case  :	List[Any]=2							,				snake_case  :	List[str]=6000							,				snake_case  :	Optional[int]=1024							,				snake_case  :	Any=2							,				snake_case  :	Optional[int]=(5, 5)							,				snake_case  :	int=1024							,				snake_case  :	Optional[Any]=80							,				snake_case  :	Any=1							,				**snake_case  :	int							,				)		->							Optional[Any]:
          __UpperCAmelCase			:				List[Any]													=    vocab_size
          __UpperCAmelCase			:				List[str]													=    d_model
          __UpperCAmelCase			:				Dict													=    encoder_ffn_dim
          __UpperCAmelCase			:				Any													=    encoder_layers
          __UpperCAmelCase			:				List[Any]													=    encoder_attention_heads
          __UpperCAmelCase			:				int													=    decoder_ffn_dim
          __UpperCAmelCase			:				Any													=    decoder_layers
          __UpperCAmelCase			:				Tuple													=    decoder_attention_heads
          __UpperCAmelCase			:				List[Any]													=    dropout
          __UpperCAmelCase			:				Dict													=    attention_dropout
          __UpperCAmelCase			:				Any													=    activation_dropout
          __UpperCAmelCase			:				Tuple													=    activation_function
          __UpperCAmelCase			:				Optional[Any]													=    init_std
          __UpperCAmelCase			:				str													=    encoder_layerdrop
          __UpperCAmelCase			:				Any													=    decoder_layerdrop
          __UpperCAmelCase			:				str													=    use_cache
          __UpperCAmelCase			:				int													=    encoder_layers
          __UpperCAmelCase			:				Optional[Any]													=    scale_embedding  # scale factor will be sqrt(d_model) if True
          __UpperCAmelCase			:				Any													=    max_source_positions
          __UpperCAmelCase			:				Optional[int]													=    max_target_positions
          __UpperCAmelCase			:				Tuple													=    num_conv_layers
          __UpperCAmelCase			:				Union[str, Any]													=    list(lowerCamelCase_    )
          __UpperCAmelCase			:				Dict													=    conv_channels
          __UpperCAmelCase			:				Any													=    input_feat_per_channel
          __UpperCAmelCase			:				Dict													=    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'''



from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros




def 			_a			(		_lowercase      :			Optional[Any]    ,      _lowercase      :			List[Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    k_size // 2
     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __UpperCAmelCase			:				Optional[int]													=    1 / (2 * pi * sigma) * exp(-(square(_lowercase						) + square(_lowercase						)) / (2 * square(_lowercase						))						)
     return g




def 			_a			(		_lowercase      :			str    ,      _lowercase      :			List[str]    ,      _lowercase      :			Optional[Any]						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    image.shape[0], image.shape[1]
     # dst image height and width
     __UpperCAmelCase			:				Any													=    height - k_size + 1
     __UpperCAmelCase			:				str													=    width - k_size + 1

     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     __UpperCAmelCase			:				Optional[int]													=    zeros((dst_height * dst_width, k_size * k_size)						)
     __UpperCAmelCase			:				Optional[Any]													=    0
     for i, j in product(range(_lowercase						)    ,      range(_lowercase						)						):
          __UpperCAmelCase			:				List[Any]													=    ravel(image[i : i + k_size, j : j + k_size]						)
          __UpperCAmelCase			:				Optional[int]													=    window
          row += 1

     #  turn the kernel into shape(k*k, 1)
     __UpperCAmelCase			:				str													=    gen_gaussian_kernel(_lowercase    ,      _lowercase						)
     __UpperCAmelCase			:				Union[str, Any]													=    ravel(_lowercase						)

     # reshape and get the dst image
     __UpperCAmelCase			:				Optional[int]													=    dot(_lowercase    ,      _lowercase						).reshape(_lowercase    ,      _lowercase						).astype(_lowercase						)

     return dst


if __name__ == "__main__":
      # read original image
      __UpperCAmelCase    :Dict				       =     imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      __UpperCAmelCase    :Tuple				       =     cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      __UpperCAmelCase    :Tuple				       =     gaussian_filter(gray, 3, sigma=1)
      __UpperCAmelCase    :Optional[Any]				       =     gaussian_filter(gray, 5, sigma=0.8)

      # show result images
      imshow("gaussian filter with 3x3 mask", gaussianaxa)
      imshow("gaussian filter with 5x5 mask", gaussianaxa)
      waitKey()