

'''simple docstring'''







import math

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


__lowerCamelCase		       =    logging.get_logger(__name__)

__lowerCamelCase		       =    {
    '''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}


class 					A__      (      _snake_case			):
    lowercase						=		"data2vec-audio"



    def __init__(					self		,				UpperCamelCase__=32		,				UpperCamelCase__=768		,				UpperCamelCase__=12		,				UpperCamelCase__=12		,				UpperCamelCase__=3072		,				UpperCamelCase__="gelu"		,				UpperCamelCase__=0.1		,				UpperCamelCase__=0.1		,				UpperCamelCase__=0.1		,				UpperCamelCase__=0.0		,				UpperCamelCase__=0.1		,				UpperCamelCase__=0.1		,				UpperCamelCase__=0.02		,				UpperCamelCase__=1e-5		,				UpperCamelCase__="gelu"		,				UpperCamelCase__=(512, 512, 512, 512, 512, 512, 512)		,				UpperCamelCase__=(5, 2, 2, 2, 2, 2, 2)		,				UpperCamelCase__=(10, 3, 3, 3, 3, 2, 2)		,				UpperCamelCase__=False		,				UpperCamelCase__=16		,				UpperCamelCase__=19		,				UpperCamelCase__=5		,				UpperCamelCase__=0.05		,				UpperCamelCase__=10		,				UpperCamelCase__=2		,				UpperCamelCase__=0.0		,				UpperCamelCase__=10		,				UpperCamelCase__=0		,				UpperCamelCase__="sum"		,				UpperCamelCase__=False		,				UpperCamelCase__=False		,				UpperCamelCase__=256		,				UpperCamelCase__=(512, 512, 512, 512, 1500)		,				UpperCamelCase__=(5, 3, 3, 1, 1)		,				UpperCamelCase__=(1, 2, 3, 1, 1)		,				UpperCamelCase__=512		,				UpperCamelCase__=0		,				UpperCamelCase__=1		,				UpperCamelCase__=2		,				UpperCamelCase__=False		,				UpperCamelCase__=3		,				UpperCamelCase__=2		,				UpperCamelCase__=3		,				UpperCamelCase__=None		,				**UpperCamelCase__		,				)						->      Tuple:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__		,				pad_token_id=UpperCamelCase__		,				bos_token_id=UpperCamelCase__		,				eos_token_id=UpperCamelCase__       )
           A_						=      hidden_size
           A_						=      feat_extract_activation
           A_						=      list(UpperCamelCase__       )
           A_						=      list(UpperCamelCase__       )
           A_						=      list(UpperCamelCase__       )
           A_						=      conv_bias
           A_						=      num_conv_pos_embeddings
           A_						=      num_conv_pos_embedding_groups
           A_						=      conv_pos_kernel_size
           A_						=      len(self.conv_dim       )
           A_						=      num_hidden_layers
           A_						=      intermediate_size
           A_						=      hidden_act
           A_						=      num_attention_heads
           A_						=      hidden_dropout
           A_						=      attention_dropout
           A_						=      activation_dropout
           A_						=      feat_proj_dropout
           A_						=      final_dropout
           A_						=      layerdrop
           A_						=      layer_norm_eps
           A_						=      initializer_range
           A_						=      vocab_size
           A_						=      use_weighted_layer_sum

           if (
               (len(self.conv_stride       ) != self.num_feat_extract_layers)
               or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
               or (len(self.conv_dim       ) != self.num_feat_extract_layers)
           ):
                  raise ValueError(
                      """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                      """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                      f''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
                      f''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

           # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
           A_						=      mask_time_prob
           A_						=      mask_time_length
           A_						=      mask_time_min_masks
           A_						=      mask_feature_prob
           A_						=      mask_feature_length
           A_						=      mask_feature_min_masks

           # ctc loss
           A_						=      ctc_loss_reduction
           A_						=      ctc_zero_infinity

           # adapter
           A_						=      add_adapter
           A_						=      adapter_kernel_size
           A_						=      adapter_stride
           A_						=      num_adapter_layers
           A_						=      output_hidden_size or hidden_size

           # SequenceClassification-specific parameter. Feel free to ignore for other classes.
           A_						=      classifier_proj_size

           # XVector-specific parameters. Feel free to ignore for other classes.
           A_						=      list(UpperCamelCase__       )
           A_						=      list(UpperCamelCase__       )
           A_						=      list(UpperCamelCase__       )
           A_						=      xvector_output_dim







    @property
    def 					snake_case_			(					self       )						->      Optional[int]:





           '''simple docstring'''
           return math.prod(self.conv_stride       )



'''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

__lowerCamelCase		       =    imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__lowerCamelCase		       =    cvtColor(img, COLOR_BGR2GRAY)
def       UpperCAmelCase__    (      )			->			Dict:
       A_						=      cn.convert_to_negative(UpperCAmelCase__				)
       # assert negative_img array for at least one True
       assert negative_img.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
              # Work around assertion for response
              assert str(cc.change_contrast(UpperCAmelCase__,       1_10				)				).startswith(
                  """<PIL.Image.Image image mode=RGB size=100x100 at"""				)
def       UpperCAmelCase__    (      )			->			str:
       A_						=      canny.gen_gaussian_kernel(9,       sigma=1.4				)
       # Assert ambiguous array
       assert resp.all()
def       UpperCAmelCase__    (      )			->			Union[str, Any]:
       A_						=      imread("""digital_image_processing/image_data/lena_small.jpg""",       0				)
       # assert ambiguous array for all == True
       assert canny_img.all()
       A_						=      canny.canny(UpperCAmelCase__				)
       # assert canny array for at least one True
       assert canny_array.any()
def       UpperCAmelCase__    (      )			->			Dict:
       assert gg.gaussian_filter(UpperCAmelCase__,       5,       sigma=0.9				).all()
def       UpperCAmelCase__    (      )			->			int:
       # laplace diagonals
       A_						=      array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
       A_						=      conv.img_convolve(UpperCAmelCase__,       UpperCAmelCase__				).astype(UpperCAmelCase__				)
       assert res.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       assert med.median_filter(UpperCAmelCase__,       3				).any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       A_					,			A_						=      sob.sobel_filter(UpperCAmelCase__				)
       assert grad.any() and theta.any()
def       UpperCAmelCase__    (      )			->			List[str]:
       A_						=      sp.make_sepia(UpperCAmelCase__,       20				)
       assert sepia.all()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg"				)			->			List[Any]:
       A_						=      bs.Burkes(imread(UpperCAmelCase__,       1				),       1_20				)
       burkes.process()
       assert burkes.output_img.any()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg",       )			->			Optional[int]:
       A_						=      rs.NearestNeighbour(imread(UpperCAmelCase__,       1				),       4_00,       2_00				)
       nn.process()
       assert nn.output.any()







def       UpperCAmelCase__    (      )			->			Optional[int]:
       A_						=      """digital_image_processing/image_data/lena.jpg"""

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

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

       A_						=      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
       A_						=      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]				):
                     A_						=      lbp.local_binary_value(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert lbp_image.any()
