






'''simple docstring'''







from typing import Callable, Optional, Union

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


_A     :      Optional[int]   					=logging.get_logger(__name__)

_A     :      Any   					={
    '''microsoft/xprophetnet-large-wiki100-cased''': (
        '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
    ),
}

class _lowercase     (    _lowercase		):
							a    				=	"""xlm-prophetnet"""
							a    				=	["""past_key_values"""]
							a    				=	{
							    """num_attention_heads""": """num_encoder_attention_heads""",
							}




							def __init__(							self:    Tuple      ,							UpperCamelCase__:    Optional[float] = 0.1      ,							UpperCamelCase__:    Optional[Union[str, Callable]] = "gelu"      ,							UpperCamelCase__:    Optional[int] = 30_522      ,							UpperCamelCase__:    Optional[int] = 1_024      ,							UpperCamelCase__:    Optional[int] = 4_096      ,							UpperCamelCase__:    Optional[int] = 12      ,							UpperCamelCase__:    Optional[int] = 16      ,							UpperCamelCase__:    Optional[int] = 4_096      ,							UpperCamelCase__:    Optional[int] = 12      ,							UpperCamelCase__:    Optional[int] = 16      ,							UpperCamelCase__:    Optional[float] = 0.1      ,							UpperCamelCase__:    Optional[float] = 0.1      ,							UpperCamelCase__:    Optional[int] = 512      ,							UpperCamelCase__:    Optional[float] = 0.02      ,							UpperCamelCase__:    Optional[bool] = True      ,							UpperCamelCase__:    Optional[bool] = True      ,							UpperCamelCase__:    Optional[int] = 0      ,							UpperCamelCase__:    Optional[int] = 2      ,							UpperCamelCase__:    Optional[int] = 32      ,							UpperCamelCase__:    Optional[int] = 128      ,							UpperCamelCase__:    Optional[bool] = False      ,							UpperCamelCase__:    Optional[float] = 0.0      ,							UpperCamelCase__:    Optional[bool] = True      ,							UpperCamelCase__:    Optional[int] = 0      ,							UpperCamelCase__:    Optional[int] = 1      ,							UpperCamelCase__:    Optional[int] = 2      ,							**UpperCamelCase__:    List[Any]      ,							):
														lowerCamelCase__ :     int	  =	vocab_size
														lowerCamelCase__ :     int	  =	hidden_size
														lowerCamelCase__ :     Dict	  =	encoder_ffn_dim
														lowerCamelCase__ :     List[str]	  =	num_encoder_layers
														lowerCamelCase__ :     Optional[Any]	  =	num_encoder_attention_heads
														lowerCamelCase__ :     int	  =	decoder_ffn_dim
														lowerCamelCase__ :     Tuple	  =	num_decoder_layers
														lowerCamelCase__ :     str	  =	num_decoder_attention_heads
														lowerCamelCase__ :     List[Any]	  =	max_position_embeddings
														lowerCamelCase__ :     Dict	  =	init_std  # Normal(0, this parameter)
														lowerCamelCase__ :     Tuple	  =	activation_function

														# parameters for xlmprophetnet
														lowerCamelCase__ :     Optional[int]	  =	ngram
														lowerCamelCase__ :     Optional[int]	  =	num_buckets
														lowerCamelCase__ :     Union[str, Any]	  =	relative_max_distance
														lowerCamelCase__ :     List[Any]	  =	disable_ngram_loss
														lowerCamelCase__ :     Any	  =	eps

														# 3 Types of Dropout
														lowerCamelCase__ :     Union[str, Any]	  =	attention_dropout
														lowerCamelCase__ :     Optional[int]	  =	activation_dropout
														lowerCamelCase__ :     Optional[Any]	  =	dropout

														lowerCamelCase__ :     int	  =	use_cache

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




							@property
							def 		lowerCamelCase_    (							self:    int					):
														return self.num_encoder_layers + self.num_decoder_layers




							@num_hidden_layers.setter
							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    Optional[Any]					):
														raise NotImplementedError(
														    """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
														    """ `num_decoder_layers`."""					)









'''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

_A     :      int   					=imread(r'''digital_image_processing/image_data/lena_small.jpg''')
_A     :      Optional[Any]   					=cvtColor(img, COLOR_BGR2GRAY)





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__ :     int	  =	cn.convert_to_negative(UpperCamelCase				)
							# assert negative_img array for at least one True
							assert negative_img.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							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 			SCREAMING_SNAKE_CASE_	()				->	Union[str, Any]:
							lowerCamelCase__ :     Optional[int]	  =	canny.gen_gaussian_kernel(9    ,      sigma=1.4				)
							# Assert ambiguous array
							assert resp.all()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     int	  =	imread("""digital_image_processing/image_data/lena_small.jpg"""    ,      0				)
							# assert ambiguous array for all == True
							assert canny_img.all()
							lowerCamelCase__ :     Union[str, Any]	  =	canny.canny(UpperCamelCase				)
							# assert canny array for at least one True
							assert canny_array.any()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							assert gg.gaussian_filter(UpperCamelCase    ,      5    ,      sigma=0.9				).all()





def 			SCREAMING_SNAKE_CASE_	()				->	int:
							# laplace diagonals
							lowerCamelCase__ :     int	  =	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
							lowerCamelCase__ :     int	  =	conv.img_convolve(UpperCamelCase    ,      UpperCamelCase				).astype(UpperCamelCase				)
							assert res.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							assert med.median_filter(UpperCamelCase    ,      3				).any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	sob.sobel_filter(UpperCamelCase				)
							assert grad.any() and theta.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Tuple:
							lowerCamelCase__ :     Union[str, Any]	  =	sp.make_sepia(UpperCamelCase    ,      20				)
							assert sepia.all()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"				)				->	Optional[Any]:
							lowerCamelCase__ :     Union[str, Any]	  =	bs.Burkes(imread(UpperCamelCase    ,      1				)    ,      120				)
							burkes.process()
							assert burkes.output_img.any()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    ,      )				->	Optional[Any]:
							lowerCamelCase__ :     int	  =	rs.NearestNeighbour(imread(UpperCamelCase    ,      1				)    ,      400    ,      200				)
							nn.process()
							assert nn.output.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							lowerCamelCase__ :     Union[str, Any]	  =	"""digital_image_processing/image_data/lena.jpg"""

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

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

							lowerCamelCase__ :     str	  =	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
							lowerCamelCase__ :     List[str]	  =	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]				):
																					lowerCamelCase__ :     List[str]	  =	lbp.local_binary_value(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							assert lbp_image.any()

