from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__=3							)					->     int:
							lowerCAmelCase     		=		int(mask / 2							)
							# copy image size
							lowerCAmelCase     		=		zeros_like(snake_case__							)
							for i in range(snake_case__       ,  gray_img.shape[0] - bd							):
														for j in range(snake_case__       ,  gray_img.shape[1] - bd							):
																					# get mask according with mask
																					lowerCAmelCase     		=		ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]							)
																					# calculate mask median
																					lowerCAmelCase     		=		sort(snake_case__							)[inta(divide((multiply(snake_case__       ,  snake_case__							))       ,  2							) + 1							)]
																					lowerCAmelCase     		=		median
							return median_img


if __name__ == "__main__":
	# read original image
	lowercase__      :    Tuple          =     imread('''../image_data/lena.jpg''')
	# turn image in gray scale value
	lowercase__      :    Optional[Any]          =     cvtColor(img, COLOR_BGR2GRAY)

	# get values with two different mask size
	lowercase__      :    List[Any]          =     median_filter(gray, 3)
	lowercase__      :    Any          =     median_filter(gray, 5)

	# show result images
	imshow('''median filter with 3x3 mask''', medianaxa)
	imshow('''median filter with 5x5 mask''', medianaxa)
	waitKey(0)




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


lowercase__      :    str          =     [nn.LayerNorm]

lowercase__      :    str          =     logging.get_logger(__name__)

lowercase__      :    List[Any]          =     version.parse(version.parse(torch.__version__).base_version)

lowercase__      :    Dict          =     parsed_torch_version_base >= version.parse('''2.0''')
lowercase__      :    int          =     parsed_torch_version_base >= version.parse('''1.12''')
lowercase__      :    Optional[int]          =     parsed_torch_version_base >= version.parse('''1.11''')
lowercase__      :    int          =     parsed_torch_version_base < version.parse('''1.11''')

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  self							)					->     Union[str, Any]:
							from torch import _softmax_backward_data

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

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




class        lowercase_  (  nn.Module						):



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Optional[Any]:
								super().__init__()
								lowerCAmelCase     		=		nf
								lowerCAmelCase     		=		nn.Parameter(torch.empty(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )    )
								lowerCAmelCase     		=		nn.Parameter(torch.zeros(__SCREAMING_SNAKE_CASE    )    )
								nn.init.normal_(self.weight		,      std=0.0_2    )




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->List[str]:
								lowerCAmelCase     		=		x.size()[:-1] + (self.nf,)
								lowerCAmelCase     		=		torch.addmm(self.bias		,      x.view(-1		,      x.size(-1    )    )		,      self.weight    )
								lowerCAmelCase     		=		x.view(__SCREAMING_SNAKE_CASE    )
								return x

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

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__ = None							)					->     Union[nn.Linear, ConvaD]:
							if isinstance(snake_case__       ,  nn.Linear							):
														return prune_linear_layer(snake_case__       ,  snake_case__       ,  dim=0 if dim is None else dim							)
							elif isinstance(snake_case__       ,  snake_case__							):
														return prune_convad_layer(snake_case__       ,  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_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  *snake_case__							)					->     torch.Tensor:
							assert len(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
							lowerCAmelCase     		=		len(inspect.signature(snake_case__							).parameters							)
							if num_args_in_forward_chunk_fn != len(snake_case__							):
														raise ValueError(
														    f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(snake_case__							)} input "
														    '''tensors are given'''							)

							if chunk_size > 0:
														lowerCAmelCase     		=		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}"							)

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

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

							return forward_fn(*snake_case__							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__							)					->     Tuple[Set[int], torch.LongTensor]:
							lowerCAmelCase     		=		torch.ones(snake_case__       ,  snake_case__							)
							lowerCAmelCase     		=		set(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
														lowerCAmelCase     		=		head - sum(1 if h < head else 0 for h in already_pruned_heads							)
														lowerCAmelCase     		=		0
							lowerCAmelCase     		=		mask.view(-1							).contiguous().eq(1							)
							lowerCAmelCase     		=		torch.arange(len(snake_case__							)							)[mask].long()
							return heads, index

def        SCREAMING_SNAKE_CASE_					(						*snake_case__       ,  snake_case__ = None							)					->     Tuple[torch.Tensor, ...]:
							return torch.meshgrid(*snake_case__       ,  indexing=snake_case__							)






def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Tuple[torch.device, int, int]:
							return tensor.device, storage_ptr(snake_case__							), storage_size(snake_case__							)



