
'''simple docstring'''


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

snake_case_ :  Tuple     =					imread(r'digital_image_processing/image_data/lena_small.jpg')
snake_case_ :  str     =					cvtColor(img, COLOR_BGR2GRAY)



def  __snake_case     (    ):
   UpperCamelCase       =  cn.convert_to_negative(_UpperCAmelCase)
   # assert negative_img array for at least one True
   assert negative_img.any()



def  __snake_case     (    ):
   with Image.open('''digital_image_processing/image_data/lena_small.jpg''') as img:
      # Work around assertion for response
      assert str(cc.change_contrast(_UpperCAmelCase,      110)).startswith(
          '''<PIL.Image.Image image mode=RGB size=100x100 at''')



def  __snake_case     (    ):
   UpperCamelCase       =  canny.gen_gaussian_kernel(9,      sigma=1.4)
   # Assert ambiguous array
   assert resp.all()



def  __snake_case     (    ):
   UpperCamelCase       =  imread('''digital_image_processing/image_data/lena_small.jpg''',      0)
   # assert ambiguous array for all == True
   assert canny_img.all()
   UpperCamelCase       =  canny.canny(_UpperCAmelCase)
   # assert canny array for at least one True
   assert canny_array.any()



def  __snake_case     (    ):
   assert gg.gaussian_filter(_UpperCAmelCase,      5,      sigma=0.9).all()



def  __snake_case     (    ):
   # laplace diagonals
   UpperCamelCase       =  array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]])
   UpperCamelCase       =  conv.img_convolve(_UpperCAmelCase,      _UpperCAmelCase).astype(_UpperCAmelCase)
   assert res.any()



def  __snake_case     (    ):
   assert med.median_filter(_UpperCAmelCase,      3).any()



def  __snake_case     (    ):
   UpperCamelCase     , UpperCamelCase       =  sob.sobel_filter(_UpperCAmelCase)
   assert grad.any() and theta.any()



def  __snake_case     (    ):
   UpperCamelCase       =  sp.make_sepia(_UpperCAmelCase,      20)
   assert sepia.all()



def  __snake_case     (    _UpperCAmelCase					:       str = "digital_image_processing/image_data/lena_small.jpg"):
   UpperCamelCase       =  bs.Burkes(imread(_UpperCAmelCase,      1),      120)
   burkes.process()
   assert burkes.output_img.any()



def  __snake_case     (    _UpperCAmelCase					:       str = "digital_image_processing/image_data/lena_small.jpg",      ):
   UpperCamelCase       =  rs.NearestNeighbour(imread(_UpperCAmelCase,      1),      400,      200)
   nn.process()
   assert nn.output.any()



def  __snake_case     (    ):
   UpperCamelCase       =  '''digital_image_processing/image_data/lena.jpg'''

   # Reading the image and converting it to grayscale.
   UpperCamelCase       =  imread(_UpperCAmelCase,      0)

   # Test for get_neighbors_pixel function() return not None
   UpperCamelCase       =  0
   UpperCamelCase       =  0
   UpperCamelCase       =  image[x_coordinate][y_coordinate]

   UpperCamelCase       =  lbp.get_neighbors_pixel(
       _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)

   assert neighbors_pixels is not None

   # Test for local_binary_pattern function()
   # Create a numpy array as the same height and width of read image
   UpperCamelCase       =  np.zeros((image.shape[0], image.shape[1]))

   # Iterating through the image and calculating the local binary pattern value
   # for each pixel.
   for i in range(0,      image.shape[0]):
      for j in range(0,      image.shape[1]):
         UpperCamelCase       =  lbp.local_binary_value(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)

   assert lbp_image.any()







'''simple docstring'''


import math
from typing import Optional

import numpy as np

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


snake_case_ :  Dict     =					logging.get_logger(__name__)

snake_case_ :  Union[str, Any]     =					{
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							'''encodec'''







   def __init__(			self ,   lowerCamelCase__=[1.5, 3.0, 6.0, 12.0, 24.0] ,   lowerCamelCase__=2_4_0_0_0 ,   lowerCamelCase__=1 ,   lowerCamelCase__=False ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__=1_2_8 ,   lowerCamelCase__=3_2 ,   lowerCamelCase__=1 ,   lowerCamelCase__=[8, 5, 4, 2] ,   lowerCamelCase__="weight_norm" ,   lowerCamelCase__=7 ,   lowerCamelCase__=7 ,   lowerCamelCase__=3 ,   lowerCamelCase__=2 ,   lowerCamelCase__=True ,   lowerCamelCase__="reflect" ,   lowerCamelCase__=2 ,   lowerCamelCase__=2 ,   lowerCamelCase__=1.0 ,   lowerCamelCase__=1_0_2_4 ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  target_bandwidths
      UpperCamelCase       =  sampling_rate
      UpperCamelCase       =  audio_channels
      UpperCamelCase       =  normalize
      UpperCamelCase       =  chunk_length_s
      UpperCamelCase       =  overlap
      UpperCamelCase       =  hidden_size
      UpperCamelCase       =  num_filters
      UpperCamelCase       =  num_residual_layers
      UpperCamelCase       =  upsampling_ratios
      UpperCamelCase       =  norm_type
      UpperCamelCase       =  kernel_size
      UpperCamelCase       =  last_kernel_size
      UpperCamelCase       =  residual_kernel_size
      UpperCamelCase       =  dilation_growth_rate
      UpperCamelCase       =  use_causal_conv
      UpperCamelCase       =  pad_mode
      UpperCamelCase       =  compress
      UpperCamelCase       =  num_lstm_layers
      UpperCamelCase       =  trim_right_ratio
      UpperCamelCase       =  codebook_size
      UpperCamelCase       =  codebook_dim if codebook_dim is not None else hidden_size
      UpperCamelCase       =  use_conv_shortcut

      if self.norm_type not in ["weight_norm", "time_group_norm"]:
         raise ValueError(
             f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'   )

      super().__init__(**lowerCamelCase__   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      if self.chunk_length_s is None:
         return None
      else:
         return int(self.chunk_length_s * self.sampling_rate   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      if self.chunk_length_s is None or self.overlap is None:
         return None
      else:
         return max(1 ,   int((1.0 - self.overlap) * self.chunk_length   )   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  np.prod(self.upsampling_ratios   )
      return math.ceil(self.sampling_rate / hop_length   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)   )





