



'''simple docstring'''
import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp


def   lowercase_	(   lowerCAmelCase__	:  Tuple			,							lowerCAmelCase__	:  Dict			,							lowerCAmelCase__	:  List[str]			,							lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  int = 4096 ):

			"""simple docstring"""





			__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      str										=     key.shape[-3:]
			__UpperCAmelCase   :      Optional[Any]										=     value.shape[-1]
			__UpperCAmelCase   :      Optional[Any]										=     min(lowerCAmelCase__			,							lowerCAmelCase__ )
			__UpperCAmelCase   :      List[str]										=     query / jnp.sqrt(lowerCAmelCase__ )

			@functools.partial(jax.checkpoint			,							prevent_cse=lowerCAmelCase__ )
			def summarize_chunk(lowerCAmelCase__	:  Tuple			,							lowerCAmelCase__	:  Dict			,							lowerCAmelCase__	:  Union[str, Any] ):
						__UpperCAmelCase   :      str										=     jnp.einsum("""...qhd,...khd->...qhk"""			,							lowerCAmelCase__			,							lowerCAmelCase__			,							precision=lowerCAmelCase__ )

						__UpperCAmelCase   :      Optional[Any]										=     jnp.max(lowerCAmelCase__			,							axis=-1			,							keepdims=lowerCAmelCase__ )
						__UpperCAmelCase   :      Optional[Any]										=     jax.lax.stop_gradient(lowerCAmelCase__ )
						__UpperCAmelCase   :      Optional[int]										=     jnp.exp(attn_weights - max_score )

						__UpperCAmelCase   :      Tuple										=     jnp.einsum("""...vhf,...qhv->...qhf"""			,							lowerCAmelCase__			,							lowerCAmelCase__			,							precision=lowerCAmelCase__ )
						__UpperCAmelCase   :      Any										=     jnp.einsum("""...qhk->...qh"""			,							lowerCAmelCase__ )

						return (exp_values, exp_weights.sum(axis=-1 ), max_score)

			def chunk_scanner(lowerCAmelCase__	:  Union[str, Any] ):
						# julienne key array
						__UpperCAmelCase   :      Optional[Any]										=     jax.lax.dynamic_slice(
						    operand=lowerCAmelCase__			,							start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]			,							slice_sizes=list(key.shape[:-3] ) + [key_chunk_size, num_heads, k_features]			,							)

						# julienne value array
						__UpperCAmelCase   :      Dict										=     jax.lax.dynamic_slice(
						    operand=lowerCAmelCase__			,							start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]			,							slice_sizes=list(value.shape[:-3] ) + [key_chunk_size, num_heads, v_features]			,							)

						return summarize_chunk(lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__ )

			__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      Dict										=     jax.lax.map(f=lowerCAmelCase__			,							xs=jnp.arange(0			,							lowerCAmelCase__			,							lowerCAmelCase__ ) )

			__UpperCAmelCase   :      Dict										=     jnp.max(lowerCAmelCase__			,							axis=0			,							keepdims=lowerCAmelCase__ )
			__UpperCAmelCase   :      List[Any]										=     jnp.exp(chunk_max - global_max )

			chunk_values *= jnp.expand_dims(lowerCAmelCase__			,							axis=-1 )
			chunk_weights *= max_diffs

			__UpperCAmelCase   :      Optional[Any]										=     chunk_values.sum(axis=0 )
			__UpperCAmelCase   :      Union[str, Any]										=     jnp.expand_dims(lowerCAmelCase__			,							-1 ).sum(axis=0 )

			return all_values / all_weights


def   lowercase_	(   lowerCAmelCase__	:  Optional[int]			,							lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  Union[str, Any]=jax.lax.Precision.HIGHEST			,							lowerCAmelCase__	:  int = 1024			,							lowerCAmelCase__	:  int = 4096 ):

			"""simple docstring"""





			__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      int										=     query.shape[-3:]

			def chunk_scanner(lowerCAmelCase__	:  Optional[Any]			,							lowerCAmelCase__	:  Any ):
						# julienne query array
						__UpperCAmelCase   :      Dict										=     jax.lax.dynamic_slice(
						    operand=lowerCAmelCase__			,							start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]			,							slice_sizes=list(query.shape[:-3] ) + [min(lowerCAmelCase__			,							lowerCAmelCase__ ), num_heads, q_features]			,							)

						return (
						    chunk_idx + query_chunk_size,  # unused ignore it
						    _query_chunk_attention(
						        query=lowerCAmelCase__			,							key=lowerCAmelCase__			,							value=lowerCAmelCase__			,							precision=lowerCAmelCase__			,							key_chunk_size=lowerCAmelCase__ ),
						)

			__UpperCAmelCase				,  __UpperCAmelCase   :      Union[str, Any]										=     jax.lax.scan(
			    f=lowerCAmelCase__			,							init=0			,							xs=lowerCAmelCase__			,							length=math.ceil(num_q / query_chunk_size ) )

			return jnp.concatenate(lowerCAmelCase__			,							axis=-3 )  # fuse the chunked result back



class _A  (    nn.Module    ):
		_SCREAMING_SNAKE_CASE					:    int
		_SCREAMING_SNAKE_CASE					:    int						      =			8
		_SCREAMING_SNAKE_CASE					:    int						      =			64
		_SCREAMING_SNAKE_CASE					:    float						      =			0.0
		_SCREAMING_SNAKE_CASE					:    bool						      =			False
		_SCREAMING_SNAKE_CASE					:    jnp.dtype						      =			jnp.floataa





		def 							__A       (     self				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     self.dim_head * self.heads
					__UpperCAmelCase   :      Optional[int]										=     self.dim_head**-0.5

					# Weights were exported with old names {to_q, to_k, to_v, to_out}
					__UpperCAmelCase   :      List[Any]										=     nn.Dense(__UpperCAmelCase ,       use_bias=__UpperCAmelCase ,       dtype=self.dtype ,       name="""to_q"""				)
					__UpperCAmelCase   :      Any										=     nn.Dense(__UpperCAmelCase ,       use_bias=__UpperCAmelCase ,       dtype=self.dtype ,       name="""to_k"""				)
					__UpperCAmelCase   :      Dict										=     nn.Dense(__UpperCAmelCase ,       use_bias=__UpperCAmelCase ,       dtype=self.dtype ,       name="""to_v"""				)

					__UpperCAmelCase   :      Union[str, Any]										=     nn.Dense(self.query_dim ,       dtype=self.dtype ,       name="""to_out_0"""				)
					__UpperCAmelCase   :      Dict										=     nn.Dropout(rate=self.dropout				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      List[str]										=     tensor.shape
					__UpperCAmelCase   :      Dict										=     self.heads
					__UpperCAmelCase   :      Optional[int]										=     tensor.reshape(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       dim // head_size				)
					__UpperCAmelCase   :      Optional[int]										=     jnp.transpose(__UpperCAmelCase ,       (0, 2, 1, 3)				)
					__UpperCAmelCase   :      List[Any]										=     tensor.reshape(batch_size * head_size ,       __UpperCAmelCase ,       dim // head_size				)
					return tensor





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      Any										=     tensor.shape
					__UpperCAmelCase   :      Tuple										=     self.heads
					__UpperCAmelCase   :      Optional[int]										=     tensor.reshape(batch_size // head_size ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      int										=     jnp.transpose(__UpperCAmelCase ,       (0, 2, 1, 3)				)
					__UpperCAmelCase   :      int										=     tensor.reshape(batch_size // head_size ,       __UpperCAmelCase ,       dim * head_size				)
					return tensor





		def __call__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase=None ,       __UpperCAmelCase=True				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     hidden_states if context is None else context

					__UpperCAmelCase   :      List[Any]										=     self.query(__UpperCAmelCase				)
					__UpperCAmelCase   :      int										=     self.key(__UpperCAmelCase				)
					__UpperCAmelCase   :      str										=     self.value(__UpperCAmelCase				)

					__UpperCAmelCase   :      Union[str, Any]										=     self.reshape_heads_to_batch_dim(__UpperCAmelCase				)
					__UpperCAmelCase   :      Union[str, Any]										=     self.reshape_heads_to_batch_dim(__UpperCAmelCase				)
					__UpperCAmelCase   :      str										=     self.reshape_heads_to_batch_dim(__UpperCAmelCase				)

					if self.use_memory_efficient_attention:
								__UpperCAmelCase   :      Optional[Any]										=     query_states.transpose(1 ,       0 ,       2				)
								__UpperCAmelCase   :      Union[str, Any]										=     key_states.transpose(1 ,       0 ,       2				)
								__UpperCAmelCase   :      List[str]										=     value_states.transpose(1 ,       0 ,       2				)

								# this if statement create a chunk size for each layer of the unet
								# the chunk size is equal to the query_length dimension of the deepest layer of the unet

								__UpperCAmelCase   :      int										=     query_states.shape[-3]
								if flatten_latent_dim % 64 == 0:
											__UpperCAmelCase   :      str										=     int(flatten_latent_dim / 64				)
								elif flatten_latent_dim % 16 == 0:
											__UpperCAmelCase   :      Any										=     int(flatten_latent_dim / 16				)
								elif flatten_latent_dim % 4 == 0:
											__UpperCAmelCase   :      Union[str, Any]										=     int(flatten_latent_dim / 4				)
								else:
											__UpperCAmelCase   :      Union[str, Any]										=     int(__UpperCAmelCase				)

								__UpperCAmelCase   :      str										=     jax_memory_efficient_attention(
								    __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       query_chunk_size=__UpperCAmelCase ,       key_chunk_size=4_096 * 4				)

								__UpperCAmelCase   :      str										=     hidden_states.transpose(1 ,       0 ,       2				)
					else:
								# compute attentions
								__UpperCAmelCase   :      List[Any]										=     jnp.einsum("""b i d, b j d->b i j""" ,       __UpperCAmelCase ,       __UpperCAmelCase				)
								__UpperCAmelCase   :      Any										=     attention_scores * self.scale
								__UpperCAmelCase   :      Any										=     nn.softmax(__UpperCAmelCase ,       axis=2				)

								# attend to values
								__UpperCAmelCase   :      List[Any]										=     jnp.einsum("""b i j, b j d -> b i d""" ,       __UpperCAmelCase ,       __UpperCAmelCase				)

					__UpperCAmelCase   :      Union[str, Any]										=     self.reshape_batch_dim_to_heads(__UpperCAmelCase				)
					__UpperCAmelCase   :      int										=     self.proj_attn(__UpperCAmelCase				)
					return self.dropout_layer(__UpperCAmelCase ,       deterministic=__UpperCAmelCase				)



class _A  (    nn.Module    ):
		_SCREAMING_SNAKE_CASE					:    int
		_SCREAMING_SNAKE_CASE					:    int
		_SCREAMING_SNAKE_CASE					:    int
		_SCREAMING_SNAKE_CASE					:    float						      =			0.0
		_SCREAMING_SNAKE_CASE					:    bool						      =			False
		_SCREAMING_SNAKE_CASE					:    jnp.dtype						      =			jnp.floataa
		_SCREAMING_SNAKE_CASE					:    bool						      =			False





		def 							__A       (     self				)       ->     List[str]:

					'''simple docstring'''
					# self attention (or cross_attention if only_cross_attention is True)
					__UpperCAmelCase   :      Tuple										=     FlaxAttention(
					    self.dim ,       self.n_heads ,       self.d_head ,       self.dropout ,       self.use_memory_efficient_attention ,       dtype=self.dtype				)
					# cross attention
					__UpperCAmelCase   :      List[Any]										=     FlaxAttention(
					    self.dim ,       self.n_heads ,       self.d_head ,       self.dropout ,       self.use_memory_efficient_attention ,       dtype=self.dtype				)
					__UpperCAmelCase   :      int										=     FlaxFeedForward(dim=self.dim ,       dropout=self.dropout ,       dtype=self.dtype				)
					__UpperCAmelCase   :      Tuple										=     nn.LayerNorm(epsilon=1E-5 ,       dtype=self.dtype				)
					__UpperCAmelCase   :      Union[str, Any]										=     nn.LayerNorm(epsilon=1E-5 ,       dtype=self.dtype				)
					__UpperCAmelCase   :      Union[str, Any]										=     nn.LayerNorm(epsilon=1E-5 ,       dtype=self.dtype				)
					__UpperCAmelCase   :      Union[str, Any]										=     nn.Dropout(rate=self.dropout				)





		def __call__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase=True				)       ->     Optional[int]:

					'''simple docstring'''
					# self attention
					__UpperCAmelCase   :      Optional[Any]										=     hidden_states
					if self.only_cross_attention:
								__UpperCAmelCase   :      str										=     self.attna(self.norma(__UpperCAmelCase				) ,       __UpperCAmelCase ,       deterministic=__UpperCAmelCase				)
					else:
								__UpperCAmelCase   :      int										=     self.attna(self.norma(__UpperCAmelCase				) ,       deterministic=__UpperCAmelCase				)
					__UpperCAmelCase   :      int										=     hidden_states + residual

					# cross attention
					__UpperCAmelCase   :      int										=     hidden_states
					__UpperCAmelCase   :      int										=     self.attna(self.norma(__UpperCAmelCase				) ,       __UpperCAmelCase ,       deterministic=__UpperCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     hidden_states + residual

					# feed forward
					__UpperCAmelCase   :      List[Any]										=     hidden_states
					__UpperCAmelCase   :      Optional[Any]										=     self.ff(self.norma(__UpperCAmelCase				) ,       deterministic=__UpperCAmelCase				)
					__UpperCAmelCase   :      Dict										=     hidden_states + residual

					return self.dropout_layer(__UpperCAmelCase ,       deterministic=__UpperCAmelCase				)



class _A  (    nn.Module    ):
		_SCREAMING_SNAKE_CASE					:    int
		_SCREAMING_SNAKE_CASE					:    int
		_SCREAMING_SNAKE_CASE					:    int
		_SCREAMING_SNAKE_CASE					:    int						      =			1
		_SCREAMING_SNAKE_CASE					:    float						      =			0.0
		_SCREAMING_SNAKE_CASE					:    bool						      =			False
		_SCREAMING_SNAKE_CASE					:    bool						      =			False
		_SCREAMING_SNAKE_CASE					:    jnp.dtype						      =			jnp.floataa
		_SCREAMING_SNAKE_CASE					:    bool						      =			False





		def 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     nn.GroupNorm(num_groups=32 ,       epsilon=1E-5				)

					__UpperCAmelCase   :      List[Any]										=     self.n_heads * self.d_head
					if self.use_linear_projection:
								__UpperCAmelCase   :      Tuple										=     nn.Dense(__UpperCAmelCase ,       dtype=self.dtype				)
					else:
								__UpperCAmelCase   :      Dict										=     nn.Conv(
								    __UpperCAmelCase ,       kernel_size=(1, 1) ,       strides=(1, 1) ,       padding="""VALID""" ,       dtype=self.dtype ,       )

					__UpperCAmelCase   :      Optional[int]										=     [
					    FlaxBasicTransformerBlock(
					        __UpperCAmelCase ,       self.n_heads ,       self.d_head ,       dropout=self.dropout ,       only_cross_attention=self.only_cross_attention ,       dtype=self.dtype ,       use_memory_efficient_attention=self.use_memory_efficient_attention ,       )
					    for _ in range(self.depth				)
					]

					if self.use_linear_projection:
								__UpperCAmelCase   :      Optional[Any]										=     nn.Dense(__UpperCAmelCase ,       dtype=self.dtype				)
					else:
								__UpperCAmelCase   :      Any										=     nn.Conv(
								    __UpperCAmelCase ,       kernel_size=(1, 1) ,       strides=(1, 1) ,       padding="""VALID""" ,       dtype=self.dtype ,       )

					__UpperCAmelCase   :      str										=     nn.Dropout(rate=self.dropout				)





		def __call__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase=True				)       ->     List[Any]:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      int										=     hidden_states.shape
					__UpperCAmelCase   :      Optional[int]										=     hidden_states
					__UpperCAmelCase   :      Optional[int]										=     self.norm(__UpperCAmelCase				)
					if self.use_linear_projection:
								__UpperCAmelCase   :      List[str]										=     hidden_states.reshape(__UpperCAmelCase ,       height * width ,       __UpperCAmelCase				)
								__UpperCAmelCase   :      Dict										=     self.proj_in(__UpperCAmelCase				)
					else:
								__UpperCAmelCase   :      Dict										=     self.proj_in(__UpperCAmelCase				)
								__UpperCAmelCase   :      Optional[int]										=     hidden_states.reshape(__UpperCAmelCase ,       height * width ,       __UpperCAmelCase				)

					for transformer_block in self.transformer_blocks:
								__UpperCAmelCase   :      str										=     transformer_block(__UpperCAmelCase ,       __UpperCAmelCase ,       deterministic=__UpperCAmelCase				)

					if self.use_linear_projection:
								__UpperCAmelCase   :      Any										=     self.proj_out(__UpperCAmelCase				)
								__UpperCAmelCase   :      List[Any]										=     hidden_states.reshape(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)
					else:
								__UpperCAmelCase   :      List[str]										=     hidden_states.reshape(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)
								__UpperCAmelCase   :      Any										=     self.proj_out(__UpperCAmelCase				)

					__UpperCAmelCase   :      Union[str, Any]										=     hidden_states + residual
					return self.dropout_layer(__UpperCAmelCase ,       deterministic=__UpperCAmelCase				)



class _A  (    nn.Module    ):
		_SCREAMING_SNAKE_CASE					:    int
		_SCREAMING_SNAKE_CASE					:    float						      =			0.0
		_SCREAMING_SNAKE_CASE					:    jnp.dtype						      =			jnp.floataa





		def 							__A       (     self				)       ->     Optional[Any]:

					'''simple docstring'''
					# The second linear layer needs to be called
					# net_2 for now to match the index of the Sequential layer
					__UpperCAmelCase   :      Dict										=     FlaxGEGLU(self.dim ,       self.dropout ,       self.dtype				)
					__UpperCAmelCase   :      List[str]										=     nn.Dense(self.dim ,       dtype=self.dtype				)





		def __call__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase=True				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     self.net_a(__UpperCAmelCase ,       deterministic=__UpperCAmelCase				)
					__UpperCAmelCase   :      str										=     self.net_a(__UpperCAmelCase				)
					return hidden_states



class _A  (    nn.Module    ):
		_SCREAMING_SNAKE_CASE					:    int
		_SCREAMING_SNAKE_CASE					:    float						      =			0.0
		_SCREAMING_SNAKE_CASE					:    jnp.dtype						      =			jnp.floataa





		def 							__A       (     self				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     self.dim * 4
					__UpperCAmelCase   :      Tuple										=     nn.Dense(inner_dim * 2 ,       dtype=self.dtype				)
					__UpperCAmelCase   :      Optional[Any]										=     nn.Dropout(rate=self.dropout				)





		def __call__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase=True				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      str										=     self.proj(__UpperCAmelCase				)
					__UpperCAmelCase				,  __UpperCAmelCase   :      int										=     jnp.split(__UpperCAmelCase ,       2 ,       axis=2				)
					return self.dropout_layer(hidden_linear * nn.gelu(__UpperCAmelCase				) ,       deterministic=__UpperCAmelCase				)









'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class _A  :





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     0
					# max greyscale value for #FFFFFF
					__UpperCAmelCase   :      List[Any]										=     int(self.get_greyscale(255 ,       255 ,       255				)				)

					if not self.min_threshold < threshold < self.max_threshold:
								__UpperCAmelCase   :      str										=     f'Factor value should be from 0 to {self.max_threshold}'
								raise ValueError(__UpperCAmelCase				)

					__UpperCAmelCase   :      Tuple										=     input_img
					__UpperCAmelCase   :      Optional[Any]										=     threshold
					__UpperCAmelCase				,  __UpperCAmelCase   :      List[Any]										=     self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					__UpperCAmelCase   :      Optional[Any]										=     [
					    [0 for _ in range(self.height + 4				)] for __ in range(self.width + 1				)
					]
					__UpperCAmelCase   :      Any										=     np.ones((self.width, self.height, 3) ,       np.uinta				) * 255





		@classmethod
		def 							__A       (     cls ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     float:

					'''simple docstring'''
					return 0.114 * blue + 0.587 * green + 0.2126 * red





		def 							__A       (     self				)       ->     None:

					'''simple docstring'''
					for y in range(self.height				):
								for x in range(self.width				):
											__UpperCAmelCase   :      Optional[Any]										=     int(self.get_greyscale(*self.input_img[y][x]				)				)
											if self.threshold > greyscale + self.error_table[y][x]:
														__UpperCAmelCase   :      List[Any]										=     (0, 0, 0)
														__UpperCAmelCase   :      Union[str, Any]										=     greyscale + self.error_table[x][y]
											else:
														__UpperCAmelCase   :      Optional[Any]										=     (255, 255, 255)
														__UpperCAmelCase   :      Dict										=     greyscale + self.error_table[x][y] - 255
											self.error_table[y][x + 1] += int(8 / 32 * current_error				)
											self.error_table[y][x + 2] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x] += int(8 / 32 * current_error				)
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error				)
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error				)


if __name__ == "__main__":
					# create Burke's instances with original images in greyscale
					_UpperCamelCase											=			[
					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
					    for threshold in (1, 126, 130, 140)
					]

					for burkes in burkes_instances:
										burkes.process()

					for burkes in burkes_instances:
										imshow(
										    F'Original image with dithering threshold: {burkes.threshold}',
										    burkes.output_img,
										)

					waitKey(0)
					destroyAllWindows()




