



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


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

lowerCAmelCase					:				Dict   	={
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}






class 	_a			(      snake_case_  ):
			_UpperCamelCase:   int	     =		"plbart"
			_UpperCamelCase:   Union[str, Any]	     =		["past_key_values"]
			_UpperCamelCase:   str	     =		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






			def __init__(    self    ,  lowercase_=50005    ,  lowercase_=1024    ,  lowercase_=6    ,  lowercase_=3072    ,  lowercase_=12    ,  lowercase_=6    ,  lowercase_=3072    ,  lowercase_=12    ,  lowercase_=0.0    ,  lowercase_=0.0    ,  lowercase_=True    ,  lowercase_=True    ,  lowercase_="gelu"    ,  lowercase_=768    ,  lowercase_=0.1    ,  lowercase_=0.1    ,  lowercase_=0.0    ,  lowercase_=0.0_2    ,  lowercase_=0.0    ,  lowercase_=True    ,  lowercase_=1    ,  lowercase_=0    ,  lowercase_=2    ,  lowercase_=2    ,  **lowercase_    ,  )	->					List[str]:
									lowerCAmelCase	:   Any					     =					vocab_size
									lowerCAmelCase	:   Optional[Any]					     =					max_position_embeddings
									lowerCAmelCase	:   Optional[Any]					     =					d_model
									lowerCAmelCase	:   List[str]					     =					encoder_ffn_dim
									lowerCAmelCase	:   int					     =					encoder_layers
									lowerCAmelCase	:   Optional[Any]					     =					encoder_attention_heads
									lowerCAmelCase	:   str					     =					decoder_ffn_dim
									lowerCAmelCase	:   int					     =					decoder_layers
									lowerCAmelCase	:   Optional[Any]					     =					decoder_attention_heads
									lowerCAmelCase	:   List[Any]					     =					dropout
									lowerCAmelCase	:   Dict					     =					attention_dropout
									lowerCAmelCase	:   List[str]					     =					activation_dropout
									lowerCAmelCase	:   Union[str, Any]					     =					activation_function
									lowerCAmelCase	:   Union[str, Any]					     =					init_std
									lowerCAmelCase	:   List[str]					     =					encoder_layerdrop
									lowerCAmelCase	:   Any					     =					decoder_layerdrop
									lowerCAmelCase	:   List[Any]					     =					classifier_dropout
									lowerCAmelCase	:   Tuple					     =					use_cache
									lowerCAmelCase	:   Tuple					     =					encoder_layers
									lowerCAmelCase	:   List[Any]					     =					scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=lowercase_    ,  bos_token_id=lowercase_    ,  eos_token_id=lowercase_    ,  is_encoder_decoder=lowercase_    ,  forced_eos_token_id=lowercase_    ,  **lowercase_    ,  )






class 	_a			(      snake_case_  ):






			@property
			def 							_snake_case   (    self )	->					Mapping[str, Mapping[int, str]]:
									return OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
									    ] )






			@property
			def 							_snake_case   (    self )	->					Mapping[str, Mapping[int, str]]:
									if self.use_past:
															return OrderedDict(
															    [
															        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
															        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
															        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
															    ] )
									else:
															return OrderedDict(
															    [
															        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
															        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
															    ] )






import cva
import numpy as np







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						try:
												return int(image[x_coordinate][y_coordinate] >= center  )
						except (IndexError, TypeError):
												return 0







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   Any					     =					image[x_coordinate][y_coordinate]
						lowerCAmelCase	:   int					     =					[1, 2, 4, 8, 1_6, 3_2, 6_4, 1_2_8]

						# skip get_neighbors_pixel if center is null
						if center is None:
												return 0

						# Starting from the top right, assigning value to pixels clockwise
						lowerCAmelCase	:   Union[str, Any]					     =					[
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate - 1			,y_coordinate + 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,y_coordinate + 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate - 1			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate + 1			,y_coordinate + 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate + 1			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate + 1			,y_coordinate - 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,y_coordinate - 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate - 1			,y_coordinate - 1			,SCREAMING_SNAKE_CASE__  ),
						]

						# Converting the binary value to decimal.
						return sum(
						    binary_value * power for binary_value, power in zip(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )  )


if __name__ == "__main__":
		# Reading the image and converting it to grayscale.
		lowerCAmelCase					:				Dict   	=cva.imread(
		    'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
		)

		# Create a numpy array as the same height and width of read image
		lowerCAmelCase					:				Dict   	=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					:				Optional[int]   	=local_binary_value(image, i, j)

		cva.imshow('local binary pattern', lbp_image)
		cva.waitKey(0)
		cva.destroyAllWindows()

