
'''simple docstring'''
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


A__ :		Optional[Any]					=[nn.LayerNorm]

A__ :		Optional[Any]					=logging.get_logger(__name__)

A__ :		Optional[Any]					=version.parse(version.parse(torch.__version__).base_version)

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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Tuple      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]      ,  __SCREAMING_SNAKE_CASE    :	Tuple      ,  __SCREAMING_SNAKE_CASE    :	Any      ,  self    :	Optional[int]			)					->							List[str]:





  """simple docstring"""

  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 							A_  (    __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	List[str] = 0			)					->							int:





  """simple docstring"""

  __A		:  Dict       				=		index.to(layer.weight.device			)
  __A		:  Tuple       				=		layer.weight.index_select(SCREAMING_SNAKE_CASE__      ,  SCREAMING_SNAKE_CASE__			).clone().detach()
  if layer.bias is not None:
    if dim == 1:
      __A		:  int       				=		layer.bias.clone().detach()
    else:
      __A		:  Any       				=		layer.bias[index].clone().detach()
  __A		:  List[str]       				=		list(layer.weight.size()			)
  __A		:  Optional[Any]       				=		len(SCREAMING_SNAKE_CASE__			)
  __A		:  Optional[Any]       				=		nn.Linear(new_size[1]      ,  new_size[0]      ,  bias=layer.bias is not None			).to(layer.weight.device			)
  __A		:  int       				=		False
  new_layer.weight.copy_(W.contiguous()			)
  __A		:  Optional[int]       				=		True
  if layer.bias is not None:
    __A		:  Any       				=		False
    new_layer.bias.copy_(b.contiguous()			)
    __A		:  Any       				=		True
  return new_layer







class 			__A					(			nn.Module      ):
 def __init__( self      :     Dict					, lowerCamelCase      :     Optional[Any]					, lowerCamelCase      :     Tuple							):



   """simple docstring"""





   super().__init__()
   __A		:  int       				=		nf
   __A		:  Any       				=		nn.Parameter(torch.empty(lowercase_					, lowercase_							)							)
   __A		:  List[Any]       				=		nn.Parameter(torch.zeros(lowercase_							)							)
   nn.init.normal_(self.weight					, std=0.02							)






 def 		lowercase_( self      :     Optional[Any]					, lowerCamelCase      :     Any							):



   """simple docstring"""





   __A		:  List[Any]       				=		x.size()[:-1] + (self.nf,)
   __A		:  Optional[int]       				=		torch.addmm(self.bias					, x.view(-1					, x.size(-1							)							)					, self.weight							)
   __A		:  Optional[int]       				=		x.view(lowercase_							)
   return x







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Dict      ,  __SCREAMING_SNAKE_CASE    :	Any      ,  __SCREAMING_SNAKE_CASE    :	str = 1			)					->							Optional[Any]:





  """simple docstring"""

  __A		:  Optional[int]       				=		index.to(layer.weight.device			)
  __A		:  Optional[int]       				=		layer.weight.index_select(SCREAMING_SNAKE_CASE__      ,  SCREAMING_SNAKE_CASE__			).clone().detach()
  if dim == 0:
    __A		:  str       				=		layer.bias.clone().detach()
  else:
    __A		:  List[Any]       				=		layer.bias[index].clone().detach()
  __A		:  Any       				=		list(layer.weight.size()			)
  __A		:  Dict       				=		len(SCREAMING_SNAKE_CASE__			)
  __A		:  List[str]       				=		ConvaD(new_size[1]      ,  new_size[0]			).to(layer.weight.device			)
  __A		:  Union[str, Any]       				=		False
  new_layer.weight.copy_(W.contiguous()			)
  __A		:  List[str]       				=		True
  __A		:  Optional[Any]       				=		False
  new_layer.bias.copy_(b.contiguous()			)
  __A		:  Dict       				=		True
  return new_layer







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[Any]      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]      ,  __SCREAMING_SNAKE_CASE    :	str = None			)					->							Optional[Any]:





  """simple docstring"""

  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 							A_  (    __SCREAMING_SNAKE_CASE    :	Any      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	Tuple      ,  *__SCREAMING_SNAKE_CASE    :	List[Any]			)					->							Optional[int]:





  """simple docstring"""

  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
  __A		:  Any       				=		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:
    __A		:  Union[str, Any]       				=		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}"			)

    __A		:  List[str]       				=		input_tensors[0].shape[chunk_dim] // chunk_size

    # chunk input tensor into tuples
    __A		:  int       				=		tuple(input_tensor.chunk(SCREAMING_SNAKE_CASE__      ,  dim=SCREAMING_SNAKE_CASE__			) for input_tensor in input_tensors			)
    # apply forward fn to every tuple
    __A		:  List[str]       				=		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 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[Any]      ,  __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	Any      ,  __SCREAMING_SNAKE_CASE    :	Optional[Any]			)					->							List[str]:





  """simple docstring"""

  __A		:  Optional[int]       				=		torch.ones(SCREAMING_SNAKE_CASE__      ,  SCREAMING_SNAKE_CASE__			)
  __A		:  Tuple       				=		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
    __A		:  Union[str, Any]       				=		head - sum(1 if h < head else 0 for h in already_pruned_heads			)
    __A		:  int       				=		0
  __A		:  Optional[Any]       				=		mask.view(-1			).contiguous().eq(1			)
  __A		:  torch.LongTensor       				=		torch.arange(len(SCREAMING_SNAKE_CASE__			)			)[mask].long()
  return heads, index







def 							A_  (    *__SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	str = None			)					->							List[Any]:





  """simple docstring"""

  return torch.meshgrid(*SCREAMING_SNAKE_CASE__      ,  indexing=SCREAMING_SNAKE_CASE__			)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]			)					->							List[str]:





  """simple docstring"""

  return tensor.device, storage_ptr(SCREAMING_SNAKE_CASE__			), storage_size(SCREAMING_SNAKE_CASE__			)








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()

