





"""simple docstring"""






import tensorflow as tf

from ...tf_utils import shape_list




class 					__lowercase      (      tf.keras.layers.Layer				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase=1						,      _UpperCAmelCase=False						,      **_UpperCAmelCase  ):
									super().__init__(**_UpperCAmelCase  )

									__a			:    Dict      							=	vocab_size
									__a			:    Optional[int]      							=	d_embed
									__a			:    Union[str, Any]      							=	d_proj

									__a			:    Optional[Any]      							=	cutoffs + [vocab_size]
									__a			:    Any      							=	[0] + self.cutoffs
									__a			:    Union[str, Any]      							=	div_val

									__a			:    Union[str, Any]      							=	self.cutoffs[0]
									__a			:    List[Any]      							=	len(self.cutoffs  ) - 1
									__a			:    Dict      							=	self.shortlist_size + self.n_clusters
									__a			:    int      							=	keep_order

									__a			:    str      							=	[]
									__a			:    Optional[int]      							=	[]







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									if self.n_clusters > 0:
													__a			:    Union[str, Any]      							=	self.add_weight(
													    shape=(self.n_clusters, self.d_embed)						,      initializer='''zeros'''						,      trainable=_UpperCAmelCase						,      name='''cluster_weight'''  )
													__a			:    List[str]      							=	self.add_weight(
													    shape=(self.n_clusters,)						,      initializer='''zeros'''						,      trainable=_UpperCAmelCase						,      name='''cluster_bias'''  )

									if self.div_val == 1:
													for i in range(len(self.cutoffs  )  ):
																	if self.d_proj != self.d_embed:
																					__a			:    Dict      							=	self.add_weight(
																					    shape=(self.d_embed, self.d_proj)						,      initializer='''zeros'''						,      trainable=_UpperCAmelCase						,      name=f"""out_projs_._{i}"""						,      )
																					self.out_projs.append(_UpperCAmelCase  )
																	else:
																					self.out_projs.append(_UpperCAmelCase  )
																	__a			:    List[str]      							=	self.add_weight(
																	    shape=(self.vocab_size, self.d_embed)						,      initializer='''zeros'''						,      trainable=_UpperCAmelCase						,      name=f"""out_layers_._{i}_._weight"""						,      )
																	__a			:    Any      							=	self.add_weight(
																	    shape=(self.vocab_size,)						,      initializer='''zeros'''						,      trainable=_UpperCAmelCase						,      name=f"""out_layers_._{i}_._bias"""						,      )
																	self.out_layers.append((weight, bias)  )
									else:
													for i in range(len(self.cutoffs  )  ):
																	__a							,    __a			:    Dict      							=	self.cutoff_ends[i], self.cutoff_ends[i + 1]
																	__a			:    Union[str, Any]      							=	self.d_embed // (self.div_val**i)

																	__a			:    List[Any]      							=	self.add_weight(
																	    shape=(d_emb_i, self.d_proj)						,      initializer='''zeros'''						,      trainable=_UpperCAmelCase						,      name=f"""out_projs_._{i}"""  )
																	self.out_projs.append(_UpperCAmelCase  )
																	__a			:    List[Any]      							=	self.add_weight(
																	    shape=(r_idx - l_idx, d_emb_i)						,      initializer='''zeros'''						,      trainable=_UpperCAmelCase						,      name=f"""out_layers_._{i}_._weight"""						,      )
																	__a			:    List[Any]      							=	self.add_weight(
																	    shape=(r_idx - l_idx,)						,      initializer='''zeros'''						,      trainable=_UpperCAmelCase						,      name=f"""out_layers_._{i}_._bias"""						,      )
																	self.out_layers.append((weight, bias)  )
									super().build(_UpperCAmelCase  )







					@staticmethod
					def 			_lowerCamelCase		(    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase=None  ):
									__a			:    Any      							=	x
									if proj is not None:
													__a			:    Optional[int]      							=	tf.einsum('''ibd,ed->ibe'''						,      _UpperCAmelCase						,      _UpperCAmelCase  )
									return tf.einsum('''ibd,nd->ibn'''						,      _UpperCAmelCase						,      _UpperCAmelCase  ) + b







					@staticmethod
					def 			_lowerCamelCase		(    _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    Optional[Any]      							=	shape_list(_UpperCAmelCase  )
									__a			:    List[str]      							=	tf.range(lp_size[0]						,      dtype=target.dtype  )
									__a			:    Optional[int]      							=	tf.stack([r, target]						,      1  )
									return tf.gather_nd(_UpperCAmelCase						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase=True						,      _UpperCAmelCase=False  ):
									__a			:    List[str]      							=	0
									if self.n_clusters == 0:
													__a			:    Union[str, Any]      							=	self._logit(_UpperCAmelCase						,      self.out_layers[0][0]						,      self.out_layers[0][1]						,      self.out_projs[0]  )
													if target is not None:
																	__a			:    str      							=	tf.nn.sparse_softmax_cross_entropy_with_logits(labels=_UpperCAmelCase						,      logits=_UpperCAmelCase  )
													__a			:    Dict      							=	tf.nn.log_softmax(_UpperCAmelCase						,      axis=-1  )
									else:
													__a			:    Any      							=	shape_list(_UpperCAmelCase  )
													__a			:    Optional[Any]      							=	[]
													__a			:    Any      							=	tf.zeros(hidden_sizes[:2]  )
													for i in range(len(self.cutoffs  )  ):
																	__a							,    __a			:    Optional[Any]      							=	self.cutoff_ends[i], self.cutoff_ends[i + 1]
																	if target is not None:
																					__a			:    Dict      							=	(target >= l_idx) & (target < r_idx)
																					__a			:    Any      							=	tf.where(_UpperCAmelCase  )
																					__a			:    Optional[int]      							=	tf.boolean_mask(_UpperCAmelCase						,      _UpperCAmelCase  ) - l_idx

																	if self.div_val == 1:
																					__a			:    Tuple      							=	self.out_layers[0][0][l_idx:r_idx]
																					__a			:    Optional[Any]      							=	self.out_layers[0][1][l_idx:r_idx]
																	else:
																					__a			:    Union[str, Any]      							=	self.out_layers[i][0]
																					__a			:    Optional[int]      							=	self.out_layers[i][1]

																	if i == 0:
																					__a			:    Optional[int]      							=	tf.concat([cur_W, self.cluster_weight]						,      0  )
																					__a			:    List[Any]      							=	tf.concat([cur_b, self.cluster_bias]						,      0  )

																					__a			:    Union[str, Any]      							=	self._logit(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      self.out_projs[0]  )
																					__a			:    List[str]      							=	tf.nn.log_softmax(_UpperCAmelCase  )
																					out.append(head_logprob[..., : self.cutoffs[0]]  )
																					if target is not None:
																									__a			:    List[Any]      							=	tf.boolean_mask(_UpperCAmelCase						,      _UpperCAmelCase  )
																									__a			:    Dict      							=	self._gather_logprob(_UpperCAmelCase						,      _UpperCAmelCase  )
																	else:
																					__a			:    Any      							=	self._logit(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      self.out_projs[i]  )
																					__a			:    Optional[Any]      							=	tf.nn.log_softmax(_UpperCAmelCase  )
																					__a			:    List[str]      							=	self.cutoffs[0] + i - 1  # No probability for the head cluster
																					__a			:    Union[str, Any]      							=	head_logprob[..., cluster_prob_idx, None] + tail_logprob
																					out.append(_UpperCAmelCase  )
																					if target is not None:
																									__a			:    Union[str, Any]      							=	tf.boolean_mask(_UpperCAmelCase						,      _UpperCAmelCase  )
																									__a			:    Optional[Any]      							=	tf.boolean_mask(_UpperCAmelCase						,      _UpperCAmelCase  )
																									__a			:    str      							=	self._gather_logprob(_UpperCAmelCase						,      _UpperCAmelCase  )
																									cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
																	if target is not None:
																					loss += tf.scatter_nd(_UpperCAmelCase						,      -cur_logprob						,      shape_list(_UpperCAmelCase  )  )
													__a			:    str      							=	tf.concat(_UpperCAmelCase						,      axis=-1  )

									if target is not None:
													if return_mean:
																	__a			:    Tuple      							=	tf.reduce_mean(_UpperCAmelCase  )
													# Add the training-time loss value to the layer using `self.add_loss()`.
													self.add_loss(_UpperCAmelCase  )

													# Log the loss as a metric (we could log arbitrary metrics,
													# including different metrics for training and inference.
													self.add_metric(_UpperCAmelCase						,      name=self.name						,      aggregation='''mean''' if return_mean else ''''''  )

									return out


'''simple docstring'''



import math
import sys

import cva
import numpy as np







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = math.sqrt(a__		)
     __SCREAMING_SNAKE_CASE      = 1 / (sigma * math.sqrt(2 * math.pi		))
     return cons * np.exp(-((img / sigma) ** 2) * 0.5		)







def   a__			(    a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = kernel_size // 2
     return img[x - half : x + half + 1, y - half : y + half + 1]







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = np.zeros((kernel_size, kernel_size)		)
     for i in range(0 ,       a__		):
          for j in range(0 ,       a__		):
               __SCREAMING_SNAKE_CASE      = math.sqrt(
                   abs(i - kernel_size // 2		) ** 2 + abs(j - kernel_size // 2		) ** 2		)
     return vec_gaussian(a__ ,       a__		)







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = np.zeros(img.shape		)
     __SCREAMING_SNAKE_CASE      = get_gauss_kernel(a__ ,       a__		)
     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = img.shape
     for i in range(kernel_size // 2 ,       size_x - kernel_size // 2		):
          for j in range(kernel_size // 2 ,       size_y - kernel_size // 2		):
               __SCREAMING_SNAKE_CASE      = get_slice(a__ ,       a__ ,       a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = img_s - img_s[kernel_size // 2, kernel_size // 2]
               __SCREAMING_SNAKE_CASE      = vec_gaussian(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.multiply(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.multiply(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.sum(a__		) / np.sum(a__		)
               __SCREAMING_SNAKE_CASE      = val
     return imga







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = args[1] if args[1:] else """../image_data/lena.jpg"""
     __SCREAMING_SNAKE_CASE      = float(args[2]		) if args[2:] else 1.0
     __SCREAMING_SNAKE_CASE      = float(args[3]		) if args[3:] else 1.0
     if args[4:]:
          __SCREAMING_SNAKE_CASE      = int(args[4]		)
          __SCREAMING_SNAKE_CASE      = kernel_size + abs(kernel_size % 2 - 1		)
     else:
          __SCREAMING_SNAKE_CASE      = 5
     return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
  UpperCAmelCase  ,	UpperCAmelCase  ,	UpperCAmelCase  ,	UpperCAmelCase					:			Any       =	parse_args(sys.argv)
  UpperCAmelCase					:			Union[str, Any]       =	cva.imread(filename, 0)
  cva.imshow('input image', img)

  UpperCAmelCase					:			Optional[int]       =	img / 2_5_5
  UpperCAmelCase					:			Optional[Any]       =	out.astype('float32')
  UpperCAmelCase					:			Tuple       =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
  UpperCAmelCase					:			List[Any]       =	out * 2_5_5
  UpperCAmelCase					:			Any       =	np.uinta(out)
  cva.imshow('output image', out)
  cva.waitKey(0)
  cva.destroyAllWindows()






