
"""simple docstring"""




from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
					import torch

if is_tf_available():
					import tensorflow as tf


class     lowercase (		snake_case_       ):
		__lowercase	:	Any     					=    ["image_processor"]
		__lowercase	:	Union[str, Any]     					=    "SamImageProcessor"



		def __init__(			self    ,				A_	)  -> int:
				"""simple docstring"""
				super().__init__(A_	)
				UpperCamelCase          =					self.image_processor
				UpperCamelCase          =					-10
				UpperCamelCase          =					self.image_processor.size['longest_edge']



		def __call__(			self    ,				A_=None    ,				A_=None    ,				A_=None    ,				A_=None    ,				A_ = None    ,				**A_    ,				)  -> BatchEncoding:
				"""simple docstring"""
				UpperCamelCase          =					self.image_processor(
				    A_    ,				return_tensors=A_    ,				**A_    ,				)

				# pop arguments that are not used in the foward but used nevertheless
				UpperCamelCase          =					encoding_image_processor['original_sizes']

				if hasattr(A_    ,				'numpy'	):  # Checks if Torch or TF tensor
						UpperCamelCase          =					original_sizes.numpy()

				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					self._check_and_preprocess_points(
				    input_points=A_    ,				input_labels=A_    ,				input_boxes=A_    ,				)

				UpperCamelCase          =					self._normalize_and_convert(
				    A_    ,				A_    ,				input_points=A_    ,				input_labels=A_    ,				input_boxes=A_    ,				return_tensors=A_    ,				)

				return encoding_image_processor



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_=None    ,				A_=None    ,				A_=None    ,				A_="pt"    ,				)  -> int:
				"""simple docstring"""
				if input_points is not None:
						if len(A_	) != len(A_	):
								UpperCamelCase          =					[
								    self._normalize_coordinates(self.target_size    ,				A_    ,				original_sizes[0]	) for point in input_points
								]
						else:
								UpperCamelCase          =					[
								    self._normalize_coordinates(self.target_size    ,				A_    ,				A_	)
								    for point, original_size in zip(A_    ,				A_	)
								]
						# check that all arrays have the same shape
						if not all(point.shape == input_points[0].shape for point in input_points	):
								if input_labels is not None:
										UpperCamelCase     ,  UpperCamelCase          =					self._pad_points_and_labels(A_    ,				A_	)

						UpperCamelCase          =					np.array(A_	)

				if input_labels is not None:
						UpperCamelCase          =					np.array(A_	)

				if input_boxes is not None:
						if len(A_	) != len(A_	):
								UpperCamelCase          =					[
								    self._normalize_coordinates(self.target_size    ,				A_    ,				original_sizes[0]    ,				is_bounding_box=A_	)
								    for box in input_boxes
								]
						else:
								UpperCamelCase          =					[
								    self._normalize_coordinates(self.target_size    ,				A_    ,				A_    ,				is_bounding_box=A_	)
								    for box, original_size in zip(A_    ,				A_	)
								]
						UpperCamelCase          =					np.array(A_	)

				if input_boxes is not None:
						if return_tensors == "pt":
								UpperCamelCase          =					torch.from_numpy(A_	)
								# boxes batch size of 1 by default
								UpperCamelCase          =					input_boxes.unsqueeze(1	) if len(input_boxes.shape	) != 3 else input_boxes
						elif return_tensors == "tf":
								UpperCamelCase          =					tf.convert_to_tensor(A_	)
								# boxes batch size of 1 by default
								UpperCamelCase          =					tf.expand_dims(A_    ,				1	) if len(input_boxes.shape	) != 3 else input_boxes
						encoding_image_processor.update({'input_boxes': input_boxes}	)
				if input_points is not None:
						if return_tensors == "pt":
								UpperCamelCase          =					torch.from_numpy(A_	)
								# point batch size of 1 by default
								UpperCamelCase          =					input_points.unsqueeze(1	) if len(input_points.shape	) != 4 else input_points
						elif return_tensors == "tf":
								UpperCamelCase          =					tf.convert_to_tensor(A_	)
								# point batch size of 1 by default
								UpperCamelCase          =					tf.expand_dims(A_    ,				1	) if len(input_points.shape	) != 4 else input_points
						encoding_image_processor.update({'input_points': input_points}	)
				if input_labels is not None:
						if return_tensors == "pt":
								UpperCamelCase          =					torch.from_numpy(A_	)
								# point batch size of 1 by default
								UpperCamelCase          =					input_labels.unsqueeze(1	) if len(input_labels.shape	) != 3 else input_labels
						elif return_tensors == "tf":
								UpperCamelCase          =					tf.convert_to_tensor(A_	)
								# point batch size of 1 by default
								UpperCamelCase          =					tf.expand_dims(A_    ,				1	) if len(input_labels.shape	) != 3 else input_labels
						encoding_image_processor.update({'input_labels': input_labels}	)

				return encoding_image_processor



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					max([point.shape[0] for point in input_points]	)
				UpperCamelCase          =					[]
				for i, point in enumerate(A_	):
						if point.shape[0] != expected_nb_points:
								UpperCamelCase          =					np.concatenate(
								    [point, np.zeros((expected_nb_points - point.shape[0], 2)	) + self.point_pad_value]    ,				axis=0	)
								UpperCamelCase          =					np.append(input_labels[i]    ,				[self.point_pad_value]	)
						processed_input_points.append(A_	)
				UpperCamelCase          =					processed_input_points
				return input_points, input_labels



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_=False	)  -> np.ndarray:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					original_size
				UpperCamelCase     ,  UpperCamelCase          =					self.image_processor._get_preprocess_shape(A_    ,				longest_edge=A_	)
				UpperCamelCase          =					deepcopy(A_	).astype(A_	)

				if is_bounding_box:
						UpperCamelCase          =					coords.reshape(-1    ,				2    ,				2	)

				UpperCamelCase          =					coords[..., 0] * (new_w / old_w)
				UpperCamelCase          =					coords[..., 1] * (new_h / old_h)

				if is_bounding_box:
						UpperCamelCase          =					coords.reshape(-1    ,				4	)

				return coords



		def __UpperCamelCase			(			self    ,				A_=None    ,				A_=None    ,				A_=None    ,				)  -> Optional[int]:
				"""simple docstring"""
				if input_points is not None:
						if hasattr(A_    ,				'numpy'	):  # Checks for TF or Torch tensor
								UpperCamelCase          =					input_points.numpy().tolist()

						if not isinstance(A_    ,				A_	) or not isinstance(input_points[0]    ,				A_	):
								raise ValueError('Input points must be a list of list of floating points.'	)
						UpperCamelCase          =					[np.array(A_	) for input_point in input_points]
				else:
						UpperCamelCase          =					None

				if input_labels is not None:
						if hasattr(A_    ,				'numpy'	):
								UpperCamelCase          =					input_labels.numpy().tolist()

						if not isinstance(A_    ,				A_	) or not isinstance(input_labels[0]    ,				A_	):
								raise ValueError('Input labels must be a list of list integers.'	)
						UpperCamelCase          =					[np.array(A_	) for label in input_labels]
				else:
						UpperCamelCase          =					None

				if input_boxes is not None:
						if hasattr(A_    ,				'numpy'	):
								UpperCamelCase          =					input_boxes.numpy().tolist()

						if (
						    not isinstance(A_    ,				A_	)
						    or not isinstance(input_boxes[0]    ,				A_	)
						    or not isinstance(input_boxes[0][0]    ,				A_	)
						):
								raise ValueError('Input boxes must be a list of list of list of floating points.'	)
						UpperCamelCase          =					[np.array(A_	).astype(np.floataa	) for box in input_boxes]
				else:
						UpperCamelCase          =					None

				return input_points, input_labels, input_boxes



		@property
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.image_processor.model_input_names
				return list(dict.fromkeys(A_	)	)



		def __UpperCamelCase			(			self    ,				*A_    ,				**A_	)  -> Tuple:
				"""simple docstring"""
				return self.image_processor.post_process_masks(*A_    ,				**A_	)


from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block


@dataclass
class     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	torch.FloatTensor






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_=3    ,				A_=3    ,				A_=("DownEncoderBlock2D",)    ,				A_=(64,)    ,				A_=2    ,				A_=32    ,				A_="silu"    ,				A_=True    ,				)  -> List[Any]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					layers_per_block

				UpperCamelCase          =					torch.nn.Convad(
				    A_    ,				block_out_channels[0]    ,				kernel_size=3    ,				stride=1    ,				padding=1    ,				)

				UpperCamelCase          =					None
				UpperCamelCase          =					nn.ModuleList([]	)

				# down
				UpperCamelCase          =					block_out_channels[0]
				for i, down_block_type in enumerate(A_	):
						UpperCamelCase          =					output_channel
						UpperCamelCase          =					block_out_channels[i]
						UpperCamelCase          =					i == len(A_	) - 1

						UpperCamelCase          =					get_down_block(
						    A_    ,				num_layers=self.layers_per_block    ,				in_channels=A_    ,				out_channels=A_    ,				add_downsample=not is_final_block    ,				resnet_eps=1e-6    ,				downsample_padding=0    ,				resnet_act_fn=A_    ,				resnet_groups=A_    ,				attention_head_dim=A_    ,				temb_channels=A_    ,				)
						self.down_blocks.append(A_	)

				# mid
				UpperCamelCase          =					UNetMidBlockaD(
				    in_channels=block_out_channels[-1]    ,				resnet_eps=1e-6    ,				resnet_act_fn=A_    ,				output_scale_factor=1    ,				resnet_time_scale_shift='default'    ,				attention_head_dim=block_out_channels[-1]    ,				resnet_groups=A_    ,				temb_channels=A_    ,				)

				# out
				UpperCamelCase          =					nn.GroupNorm(num_channels=block_out_channels[-1]    ,				num_groups=A_    ,				eps=1e-6	)
				UpperCamelCase          =					nn.SiLU()

				UpperCamelCase          =					2 * out_channels if double_z else out_channels
				UpperCamelCase          =					nn.Convad(block_out_channels[-1]    ,				A_    ,				3    ,				padding=1	)

				UpperCamelCase          =					False



		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					x
				UpperCamelCase          =					self.conv_in(A_	)

				if self.training and self.gradient_checkpointing:

						def create_custom_forward(A_	):
								def custom_forward(*A_	):
										return module(*A_	)

								return custom_forward

						# down
						if is_torch_version('>='    ,				'1.11.0'	):
								for down_block in self.down_blocks:
										UpperCamelCase          =					torch.utils.checkpoint.checkpoint(
										    create_custom_forward(A_	)    ,				A_    ,				use_reentrant=A_	)
								# middle
								UpperCamelCase          =					torch.utils.checkpoint.checkpoint(
								    create_custom_forward(self.mid_block	)    ,				A_    ,				use_reentrant=A_	)
						else:
								for down_block in self.down_blocks:
										UpperCamelCase          =					torch.utils.checkpoint.checkpoint(create_custom_forward(A_	)    ,				A_	)
								# middle
								UpperCamelCase          =					torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block	)    ,				A_	)

				else:
						# down
						for down_block in self.down_blocks:
								UpperCamelCase          =					down_block(A_	)

						# middle
						UpperCamelCase          =					self.mid_block(A_	)

				# post-process
				UpperCamelCase          =					self.conv_norm_out(A_	)
				UpperCamelCase          =					self.conv_act(A_	)
				UpperCamelCase          =					self.conv_out(A_	)

				return sample






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_=3    ,				A_=3    ,				A_=("UpDecoderBlock2D",)    ,				A_=(64,)    ,				A_=2    ,				A_=32    ,				A_="silu"    ,				A_="group"    ,				)  -> Tuple:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					layers_per_block

				UpperCamelCase          =					nn.Convad(
				    A_    ,				block_out_channels[-1]    ,				kernel_size=3    ,				stride=1    ,				padding=1    ,				)

				UpperCamelCase          =					None
				UpperCamelCase          =					nn.ModuleList([]	)

				UpperCamelCase          =					in_channels if norm_type == 'spatial' else None

				# mid
				UpperCamelCase          =					UNetMidBlockaD(
				    in_channels=block_out_channels[-1]    ,				resnet_eps=1e-6    ,				resnet_act_fn=A_    ,				output_scale_factor=1    ,				resnet_time_scale_shift='default' if norm_type == 'group' else norm_type    ,				attention_head_dim=block_out_channels[-1]    ,				resnet_groups=A_    ,				temb_channels=A_    ,				)

				# up
				UpperCamelCase          =					list(reversed(A_	)	)
				UpperCamelCase          =					reversed_block_out_channels[0]
				for i, up_block_type in enumerate(A_	):
						UpperCamelCase          =					output_channel
						UpperCamelCase          =					reversed_block_out_channels[i]

						UpperCamelCase          =					i == len(A_	) - 1

						UpperCamelCase          =					get_up_block(
						    A_    ,				num_layers=self.layers_per_block + 1    ,				in_channels=A_    ,				out_channels=A_    ,				prev_output_channel=A_    ,				add_upsample=not is_final_block    ,				resnet_eps=1e-6    ,				resnet_act_fn=A_    ,				resnet_groups=A_    ,				attention_head_dim=A_    ,				temb_channels=A_    ,				resnet_time_scale_shift=A_    ,				)
						self.up_blocks.append(A_	)
						UpperCamelCase          =					output_channel

				# out
				if norm_type == "spatial":
						UpperCamelCase          =					SpatialNorm(block_out_channels[0]    ,				A_	)
				else:
						UpperCamelCase          =					nn.GroupNorm(num_channels=block_out_channels[0]    ,				num_groups=A_    ,				eps=1e-6	)
				UpperCamelCase          =					nn.SiLU()
				UpperCamelCase          =					nn.Convad(block_out_channels[0]    ,				A_    ,				3    ,				padding=1	)

				UpperCamelCase          =					False



		def __UpperCamelCase			(			self    ,				A_    ,				A_=None	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					z
				UpperCamelCase          =					self.conv_in(A_	)

				UpperCamelCase          =					next(iter(self.up_blocks.parameters()	)	).dtype
				if self.training and self.gradient_checkpointing:

						def create_custom_forward(A_	):
								def custom_forward(*A_	):
										return module(*A_	)

								return custom_forward

						if is_torch_version('>='    ,				'1.11.0'	):
								# middle
								UpperCamelCase          =					torch.utils.checkpoint.checkpoint(
								    create_custom_forward(self.mid_block	)    ,				A_    ,				A_    ,				use_reentrant=A_	)
								UpperCamelCase          =					sample.to(A_	)

								# up
								for up_block in self.up_blocks:
										UpperCamelCase          =					torch.utils.checkpoint.checkpoint(
										    create_custom_forward(A_	)    ,				A_    ,				A_    ,				use_reentrant=A_	)
						else:
								# middle
								UpperCamelCase          =					torch.utils.checkpoint.checkpoint(
								    create_custom_forward(self.mid_block	)    ,				A_    ,				A_	)
								UpperCamelCase          =					sample.to(A_	)

								# up
								for up_block in self.up_blocks:
										UpperCamelCase          =					torch.utils.checkpoint.checkpoint(create_custom_forward(A_	)    ,				A_    ,				A_	)
				else:
						# middle
						UpperCamelCase          =					self.mid_block(A_    ,				A_	)
						UpperCamelCase          =					sample.to(A_	)

						# up
						for up_block in self.up_blocks:
								UpperCamelCase          =					up_block(A_    ,				A_	)

        # post-process
				if latent_embeds is None:
						UpperCamelCase          =					self.conv_norm_out(A_	)
				else:
						UpperCamelCase          =					self.conv_norm_out(A_    ,				A_	)
				UpperCamelCase          =					self.conv_act(A_	)
				UpperCamelCase          =					self.conv_out(A_	)

				return sample






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_    ,				A_    ,				A_    ,				A_=None    ,				A_="random"    ,				A_=False    ,				A_=True	)  -> Tuple:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					n_e
				UpperCamelCase          =					vq_embed_dim
				UpperCamelCase          =					beta
				UpperCamelCase          =					legacy

				UpperCamelCase          =					nn.Embedding(self.n_e    ,				self.vq_embed_dim	)
				self.embedding.weight.data.uniform_(-1.0 / self.n_e    ,				1.0 / self.n_e	)

				UpperCamelCase          =					remap
				if self.remap is not None:
						self.register_buffer('used'    ,				torch.tensor(np.load(self.remap	)	)	)
						UpperCamelCase          =					self.used.shape[0]
						UpperCamelCase          =					unknown_index  # "random" or "extra" or integer
						if self.unknown_index == "extra":
								UpperCamelCase          =					self.re_embed
								UpperCamelCase          =					self.re_embed + 1
						print(
						    F'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
						    F'''Using {self.unknown_index} for unknown indices.'''	)
				else:
						UpperCamelCase          =					n_e

				UpperCamelCase          =					sane_index_shape



		def __UpperCamelCase			(			self    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					inds.shape
				assert len(A_	) > 1
				UpperCamelCase          =					inds.reshape(ishape[0]    ,				-1	)
				UpperCamelCase          =					self.used.to(A_	)
				UpperCamelCase          =					(inds[:, :, None] == used[None, None, ...]).long()
				UpperCamelCase          =					match.argmax(-1	)
				UpperCamelCase          =					match.sum(2	) < 1
				if self.unknown_index == "random":
						UpperCamelCase          =					torch.randint(0    ,				self.re_embed    ,				size=new[unknown].shape	).to(device=new.device	)
				else:
						UpperCamelCase          =					self.unknown_index
				return new.reshape(A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					inds.shape
				assert len(A_	) > 1
				UpperCamelCase          =					inds.reshape(ishape[0]    ,				-1	)
				UpperCamelCase          =					self.used.to(A_	)
				if self.re_embed > self.used.shape[0]:  # extra token
						UpperCamelCase          =					0  # simply set to zero
				UpperCamelCase          =					torch.gather(used[None, :][inds.shape[0] * [0], :]    ,				1    ,				A_	)
				return back.reshape(A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> List[str]:
				"""simple docstring"""
				# reshape z -> (batch, height, width, channel) and flatten
				UpperCamelCase          =					z.permute(0    ,				2    ,				3    ,				1	).contiguous()
				UpperCamelCase          =					z.view(-1    ,				self.vq_embed_dim	)

				# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
				UpperCamelCase          =					torch.argmin(torch.cdist(A_    ,				self.embedding.weight	)    ,				dim=1	)

				UpperCamelCase          =					self.embedding(A_	).view(z.shape	)
				UpperCamelCase          =					None
				UpperCamelCase          =					None

				# compute loss for embedding
				if not self.legacy:
						UpperCamelCase          =					self.beta * torch.mean((z_q.detach() - z) ** 2	) + torch.mean((z_q - z.detach()) ** 2	)
				else:
						UpperCamelCase          =					torch.mean((z_q.detach() - z) ** 2	) + self.beta * torch.mean((z_q - z.detach()) ** 2	)

				# preserve gradients
				UpperCamelCase          =					z + (z_q - z).detach()

				# reshape back to match original input shape
				UpperCamelCase          =					z_q.permute(0    ,				3    ,				1    ,				2	).contiguous()

				if self.remap is not None:
						UpperCamelCase          =					min_encoding_indices.reshape(z.shape[0]    ,				-1	)  # add batch axis
						UpperCamelCase          =					self.remap_to_used(A_	)
						UpperCamelCase          =					min_encoding_indices.reshape(-1    ,				1	)  # flatten

				if self.sane_index_shape:
						UpperCamelCase          =					min_encoding_indices.reshape(z_q.shape[0]    ,				z_q.shape[2]    ,				z_q.shape[3]	)

				return z_q, loss, (perplexity, min_encodings, min_encoding_indices)



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				# shape specifying (batch, height, width, channel)
				if self.remap is not None:
						UpperCamelCase          =					indices.reshape(shape[0]    ,				-1	)  # add batch axis
						UpperCamelCase          =					self.unmap_to_all(A_	)
						UpperCamelCase          =					indices.reshape(-1	)  # flatten again

				# get quantized latent vectors
				UpperCamelCase          =					self.embedding(A_	)

				if shape is not None:
						UpperCamelCase          =					z_q.view(A_	)
						# reshape back to match original input shape
						UpperCamelCase          =					z_q.permute(0    ,				3    ,				1    ,				2	).contiguous()

				return z_q






class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __init__(			self    ,				A_    ,				A_=False	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					parameters
				UpperCamelCase     ,  UpperCamelCase          =					torch.chunk(A_    ,				2    ,				dim=1	)
				UpperCamelCase          =					torch.clamp(self.logvar    ,				-30.0    ,				20.0	)
				UpperCamelCase          =					deterministic
				UpperCamelCase          =					torch.exp(0.5 * self.logvar	)
				UpperCamelCase          =					torch.exp(self.logvar	)
				if self.deterministic:
						UpperCamelCase          =					UpperCamelCase          =					torch.zeros_like(
						    self.mean    ,				device=self.parameters.device    ,				dtype=self.parameters.dtype	)



		def __UpperCamelCase			(			self    ,				A_ = None	)  -> torch.FloatTensor:
				"""simple docstring"""
				# make sure sample is on the same device as the parameters and has same dtype
				UpperCamelCase          =					randn_tensor(
				    self.mean.shape    ,				generator=A_    ,				device=self.parameters.device    ,				dtype=self.parameters.dtype	)
				UpperCamelCase          =					self.mean + self.std * sample
				return x



		def __UpperCamelCase			(			self    ,				A_=None	)  -> Tuple:
				"""simple docstring"""
				if self.deterministic:
						return torch.Tensor([0.0]	)
				else:
						if other is None:
								return 0.5 * torch.sum(torch.pow(self.mean    ,				2	) + self.var - 1.0 - self.logvar    ,				dim=[1, 2, 3]	)
						else:
								return 0.5 * torch.sum(
								    torch.pow(self.mean - other.mean    ,				2	) / other.var
								    + self.var / other.var
								    - 1.0
								    - self.logvar
								    + other.logvar    ,				dim=[1, 2, 3]    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_=[1, 2, 3]	)  -> Optional[Any]:
				"""simple docstring"""
				if self.deterministic:
						return torch.Tensor([0.0]	)
				UpperCamelCase          =					np.log(2.0 * np.pi	)
				return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean    ,				2	) / self.var    ,				dim=A_	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				return self.mean
