

import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


lowerCAmelCase_	     =     [nn.LayerNorm]

lowerCAmelCase_	     =     logging.get_logger(__name__)

lowerCAmelCase_	     =     version.parse(version.parse(torch.__version__).base_version)

lowerCAmelCase_	     =     parsed_torch_version_base >= version.parse('''2.0''')
lowerCAmelCase_	     =     parsed_torch_version_base >= version.parse('''1.12''')
lowerCAmelCase_	     =     parsed_torch_version_base >= version.parse('''1.11''')
lowerCAmelCase_	     =     parsed_torch_version_base < version.parse('''1.11''')


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		self	):
						from torch import _softmax_backward_data

						if is_torch_less_than_1_11:
												return _softmax_backward_data(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		parent.dim  ,		self	)
						else:
												return _softmax_backward_data(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		parent.dim  ,		self.dtype	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = 0	):
						snake_case_       =     index.to(layer.weight.device	)
						snake_case_       =     layer.weight.index_select(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	).clone().detach()
						if layer.bias is not None:
												if dim == 1:
																		snake_case_       =     layer.bias.clone().detach()
												else:
																		snake_case_       =     layer.bias[index].clone().detach()
						snake_case_       =     list(layer.weight.size()	)
						snake_case_       =     len(SCREAMING_SNAKE_CASE__	)
						snake_case_       =     nn.Linear(new_size[1]  ,		new_size[0]  ,		bias=layer.bias is not None	).to(layer.weight.device	)
						snake_case_       =     False
						new_layer.weight.copy_(W.contiguous()	)
						snake_case_       =     True
						if layer.bias is not None:
												snake_case_       =     False
												new_layer.bias.copy_(b.contiguous()	)
												snake_case_       =     True
						return new_layer





class      snake_case_       (		nn.Module ):







			'''simple docstring'''

			def __init__(					self			:    Optional[Any]							,       _UpperCamelCase			:    Optional[Any]							,       _UpperCamelCase			:    int							)				->Optional[Any]:
									super().__init__()
									snake_case_       =     nf
									snake_case_       =     nn.Parameter(torch.empty(_UpperCamelCase							,       _UpperCamelCase							)							)
									snake_case_       =     nn.Parameter(torch.zeros(_UpperCamelCase							)							)
									nn.init.normal_(self.weight							,       std=0.02							)







			def     snake_case__(					self			:    List[Any]							,       _UpperCamelCase			:    Optional[Any]							)				->Tuple:
									snake_case_       =     x.size()[:-1] + (self.nf,)
									snake_case_       =     torch.addmm(self.bias							,       x.view(-1							,       x.size(-1							)							)							,       self.weight							)
									snake_case_       =     x.view(_UpperCamelCase							)
									return x






def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = 1	):
						snake_case_       =     index.to(layer.weight.device	)
						snake_case_       =     layer.weight.index_select(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	).clone().detach()
						if dim == 0:
												snake_case_       =     layer.bias.clone().detach()
						else:
												snake_case_       =     layer.bias[index].clone().detach()
						snake_case_       =     list(layer.weight.size()	)
						snake_case_       =     len(SCREAMING_SNAKE_CASE__	)
						snake_case_       =     ConvaD(new_size[1]  ,		new_size[0]	).to(layer.weight.device	)
						snake_case_       =     False
						new_layer.weight.copy_(W.contiguous()	)
						snake_case_       =     True
						snake_case_       =     False
						new_layer.bias.copy_(b.contiguous()	)
						snake_case_       =     True
						return new_layer


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = None	):
						if isinstance(SCREAMING_SNAKE_CASE__  ,		nn.Linear	):
												return prune_linear_layer(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		dim=0 if dim is None else dim	)
						elif isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
												return prune_convad_layer(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		dim=1 if dim is None else dim	)
						else:
												raise ValueError(F'''Can\'t prune layer of class {layer.__class__}'''	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		*SCREAMING_SNAKE_CASE__	):
						assert len(SCREAMING_SNAKE_CASE__	) > 0, F'''{input_tensors} has to be a tuple/list of tensors'''

						# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
						snake_case_       =     len(inspect.signature(SCREAMING_SNAKE_CASE__	).parameters	)
						if num_args_in_forward_chunk_fn != len(SCREAMING_SNAKE_CASE__	):
												raise ValueError(
												    F'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(SCREAMING_SNAKE_CASE__	)} input '''
												    '''tensors are given'''	)

						if chunk_size > 0:
												snake_case_       =     input_tensors[0].shape[chunk_dim]
												for input_tensor in input_tensors:
																		if input_tensor.shape[chunk_dim] != tensor_shape:
																								raise ValueError(
																								    F'''All input tenors have to be of the same shape: {tensor_shape}, '''
																								    F'''found shape {input_tensor.shape[chunk_dim]}'''	)

												if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
																		raise ValueError(
																		    F'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
																		    F'''size {chunk_size}'''	)

												snake_case_       =     input_tensors[0].shape[chunk_dim] // chunk_size

												# chunk input tensor into tuples
												snake_case_       =     tuple(input_tensor.chunk(SCREAMING_SNAKE_CASE__  ,		dim=SCREAMING_SNAKE_CASE__	) for input_tensor in input_tensors	)
												# apply forward fn to every tuple
												snake_case_       =     tuple(forward_fn(*SCREAMING_SNAKE_CASE__	) for input_tensors_chunk in zip(*SCREAMING_SNAKE_CASE__	)	)
												# concatenate output at same dimension
												return torch.cat(SCREAMING_SNAKE_CASE__  ,		dim=SCREAMING_SNAKE_CASE__	)

						return forward_fn(*SCREAMING_SNAKE_CASE__	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     torch.ones(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	)
						snake_case_       =     set(SCREAMING_SNAKE_CASE__	) - already_pruned_heads  # Convert to set and remove already pruned heads
						for head in heads:
												# Compute how many pruned heads are before the head and move the index accordingly
												snake_case_       =     head - sum(1 if h < head else 0 for h in already_pruned_heads	)
												snake_case_       =     0
						snake_case_       =     mask.view(-1	).contiguous().eq(1	)
						snake_case_       =     torch.arange(len(SCREAMING_SNAKE_CASE__	)	)[mask].long()
						return heads, index


def   __SCREAMING_SNAKE_CASE (*SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = None	):
						return torch.meshgrid(*SCREAMING_SNAKE_CASE__  ,		indexing=SCREAMING_SNAKE_CASE__	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						return tensor.device, storage_ptr(SCREAMING_SNAKE_CASE__	), storage_size(SCREAMING_SNAKE_CASE__	)


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class      snake_case_       :







			'''simple docstring'''

			def __init__(					self			:    Tuple							,       _UpperCamelCase			:    Optional[Any]							,       _UpperCamelCase			:    int							)				->List[Any]:
									snake_case_       =     0
									# max greyscale value for #FFFFFF
									snake_case_       =     int(self.get_greyscale(2_5_5							,       2_5_5							,       2_5_5							)							)

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

									snake_case_       =     input_img
									snake_case_       =     threshold
									snake_case_,   snake_case_       =     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
									snake_case_       =     [
									    [0 for _ in range(self.height + 4							)] for __ in range(self.width + 1							)
									]
									snake_case_       =     np.ones((self.width, self.height, 3)							,       np.uinta							) * 2_5_5







			@classmethod
			def     snake_case__(					cls			:    Optional[Any]							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    int							)				->float:
									return 0.114 * blue + 0.587 * green + 0.2126 * red







			def     snake_case__(					self			:    Any							)				->None:
									for y in range(self.height							):
															for x in range(self.width							):
																					snake_case_       =     int(self.get_greyscale(*self.input_img[y][x]							)							)
																					if self.threshold > greyscale + self.error_table[y][x]:
																											snake_case_       =     (0, 0, 0)
																											snake_case_       =     greyscale + self.error_table[x][y]
																					else:
																											snake_case_       =     (2_5_5, 2_5_5, 2_5_5)
																											snake_case_       =     greyscale + self.error_table[x][y] - 2_5_5
																					self.error_table[y][x + 1] += int(8 / 3_2 * current_error							)
																					self.error_table[y][x + 2] += int(4 / 3_2 * current_error							)
																					self.error_table[y + 1][x] += int(8 / 3_2 * current_error							)
																					self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error							)
																					self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error							)
																					self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error							)
																					self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error							)


if __name__ == "__main__":
					# create Burke's instances with original images in greyscale
					lowerCAmelCase_	     =     [
					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
					    for threshold in (1, 1_26, 1_30, 1_40)
					]

					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()