import functools
import operator

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


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Dict      			=   """unispeech"""







    def __init__(			self				,				__lowercase=32				,				__lowercase=768				,				__lowercase=12				,				__lowercase=12				,				__lowercase=3_072				,				__lowercase="gelu"				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.0				,				__lowercase=0.0				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.02				,				__lowercase=1E-5				,				__lowercase="group"				,				__lowercase="gelu"				,				__lowercase=(512, 512, 512, 512, 512, 512, 512)				,				__lowercase=(5, 2, 2, 2, 2, 2, 2)				,				__lowercase=(10, 3, 3, 3, 3, 2, 2)				,				__lowercase=False				,				__lowercase=128				,				__lowercase=16				,				__lowercase=False				,				__lowercase=True				,				__lowercase=0.05				,				__lowercase=10				,				__lowercase=2				,				__lowercase=0.0				,				__lowercase=10				,				__lowercase=0				,				__lowercase=320				,				__lowercase=2				,				__lowercase=0.1				,				__lowercase=100				,				__lowercase=256				,				__lowercase=256				,				__lowercase=0.1				,				__lowercase="mean"				,				__lowercase=False				,				__lowercase=False				,				__lowercase=256				,				__lowercase=80				,				__lowercase=0				,				__lowercase=1				,				__lowercase=2				,				__lowercase=0.5				,				**__lowercase				,				) ->			Optional[int]:
        super().__init__(**__lowercase				,				pad_token_id=__lowercase				,				bos_token_id=__lowercase				,				eos_token_id=__lowercase)
        __UpperCamelCase  :str    						=	hidden_size
        __UpperCamelCase  :List[str]    						=	feat_extract_norm
        __UpperCamelCase  :str    						=	feat_extract_activation
        __UpperCamelCase  :str    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Any    						=	conv_bias
        __UpperCamelCase  :List[Any]    						=	num_conv_pos_embeddings
        __UpperCamelCase  :Tuple    						=	num_conv_pos_embedding_groups
        __UpperCamelCase  :Optional[int]    						=	len(self.conv_dim)
        __UpperCamelCase  :Optional[int]    						=	num_hidden_layers
        __UpperCamelCase  :Union[str, Any]    						=	intermediate_size
        __UpperCamelCase  :Tuple    						=	hidden_act
        __UpperCamelCase  :Optional[int]    						=	num_attention_heads
        __UpperCamelCase  :Any    						=	hidden_dropout
        __UpperCamelCase  :List[str]    						=	attention_dropout
        __UpperCamelCase  :int    						=	activation_dropout
        __UpperCamelCase  :int    						=	feat_proj_dropout
        __UpperCamelCase  :Any    						=	final_dropout
        __UpperCamelCase  :Optional[Any]    						=	layerdrop
        __UpperCamelCase  :Any    						=	layer_norm_eps
        __UpperCamelCase  :List[str]    						=	initializer_range
        __UpperCamelCase  :Tuple    						=	num_ctc_classes
        __UpperCamelCase  :Union[str, Any]    						=	vocab_size
        __UpperCamelCase  :List[Any]    						=	do_stable_layer_norm
        __UpperCamelCase  :Dict    						=	use_weighted_layer_sum
        __UpperCamelCase  :str    						=	classifier_proj_size

        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
        __UpperCamelCase  :List[Any]    						=	apply_spec_augment
        __UpperCamelCase  :Optional[int]    						=	mask_time_prob
        __UpperCamelCase  :int    						=	mask_time_length
        __UpperCamelCase  :Any    						=	mask_time_min_masks
        __UpperCamelCase  :Any    						=	mask_feature_prob
        __UpperCamelCase  :str    						=	mask_feature_length
        __UpperCamelCase  :Tuple    						=	mask_feature_min_masks

        # parameters for pretraining with codevector quantized representations
        __UpperCamelCase  :Optional[Any]    						=	num_codevectors_per_group
        __UpperCamelCase  :Dict    						=	num_codevector_groups
        __UpperCamelCase  :Optional[int]    						=	contrastive_logits_temperature
        __UpperCamelCase  :Union[str, Any]    						=	feat_quantizer_dropout
        __UpperCamelCase  :List[str]    						=	num_negatives
        __UpperCamelCase  :Union[str, Any]    						=	codevector_dim
        __UpperCamelCase  :int    						=	proj_codevector_dim
        __UpperCamelCase  :Tuple    						=	diversity_loss_weight

        # ctc loss
        __UpperCamelCase  :List[Any]    						=	ctc_loss_reduction
        __UpperCamelCase  :int    						=	ctc_zero_infinity

        # pretraining loss
        __UpperCamelCase  :Optional[Any]    						=	replace_prob







    @property
    def     UpperCamelCase__     (			self) ->			Dict:
        return functools.reduce(operator.mul				,				self.conv_stride				,				1)






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

__lowercase											=      imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__lowercase											=      cvtColor(img, COLOR_BGR2GRAY)



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	cn.convert_to_negative(SCREAMING_SNAKE_CASE			)
    # assert negative_img array for at least one True
    assert negative_img.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







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



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	canny.gen_gaussian_kernel(9						,    sigma=1.4			)
    # Assert ambiguous array
    assert resp.all()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	imread('''digital_image_processing/image_data/lena_small.jpg'''						,    0			)
    # assert ambiguous array for all == True
    assert canny_img.all()
    __UpperCamelCase  :str    						=	canny.canny(SCREAMING_SNAKE_CASE			)
    # assert canny array for at least one True
    assert canny_array.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    assert gg.gaussian_filter(SCREAMING_SNAKE_CASE						,    5						,    sigma=0.9			).all()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]			)
    __UpperCamelCase  :Optional[Any]    						=	conv.img_convolve(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			).astype(SCREAMING_SNAKE_CASE			)
    assert res.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    assert med.median_filter(SCREAMING_SNAKE_CASE						,    3			).any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase						, __UpperCamelCase  :Dict    						=	sob.sobel_filter(SCREAMING_SNAKE_CASE			)
    assert grad.any() and theta.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	sp.make_sepia(SCREAMING_SNAKE_CASE						,    20			)
    assert sepia.all()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	bs.Burkes(imread(SCREAMING_SNAKE_CASE						,    1			)						,    120			)
    burkes.process()
    assert burkes.output_img.any()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"						,    ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	rs.NearestNeighbour(imread(SCREAMING_SNAKE_CASE						,    1			)						,    400						,    200			)
    nn.process()
    assert nn.output.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	'''digital_image_processing/image_data/lena.jpg'''

    # Reading the image and converting it to grayscale.
    __UpperCamelCase  :Optional[int]    						=	imread(SCREAMING_SNAKE_CASE						,    0			)

    # Test for get_neighbors_pixel function() return not None
    __UpperCamelCase  :Optional[int]    						=	0
    __UpperCamelCase  :List[Any]    						=	0
    __UpperCamelCase  :Dict    						=	image[x_coordinate][y_coordinate]

    __UpperCamelCase  :int    						=	lbp.get_neighbors_pixel(
        SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    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  :Tuple    						=	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  :Optional[int]    						=	lbp.local_binary_value(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    assert lbp_image.any()





