



"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


snake_case_				     =       [
    """openmmlab/upernet-convnext-tiny""",
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
snake_case_				     =       """UperNetConfig"""







class       A_			(     nn.Module				):



						"""simple docstring"""






						def __init__(					self :Tuple   ,  lowercase_ :int   ,  lowercase_ :int   ,  lowercase_ :Union[int, Tuple[int, int]]   ,  lowercase_ :Union[int, Tuple[int, int], str] = 0   ,  lowercase_ :bool = False   ,  lowercase_ :Union[int, Tuple[int, int]] = 1   ,  )				->			None:
												super().__init__()
												UpperCAmelCase             = nn.Convad(
												    in_channels=lowercase_   ,  out_channels=lowercase_   ,  kernel_size=lowercase_   ,  padding=lowercase_   ,  bias=lowercase_   ,  dilation=lowercase_   ,  )
												UpperCAmelCase             = nn.BatchNormad(lowercase_					)
												UpperCAmelCase             = nn.ReLU()






						def 	UpperCAmelCase__     (					self :Optional[Any]   ,  lowercase_ :torch.Tensor					)				->			torch.Tensor:
												UpperCAmelCase             = self.conv(lowercase_					)
												UpperCAmelCase             = self.batch_norm(lowercase_					)
												UpperCAmelCase             = self.activation(lowercase_					)

												return output







class       A_			(     nn.Module				):



						"""simple docstring"""






						def __init__(					self :Optional[Any]   ,  lowercase_ :int   ,  lowercase_ :int   ,  lowercase_ :int					)				->			None:
												super().__init__()
												UpperCAmelCase             = [
												    nn.AdaptiveAvgPoolad(lowercase_					),
												    UperNetConvModule(lowercase_   ,  lowercase_   ,  kernel_size=1					),
												]
												for i, layer in enumerate(self.layers					):
																		self.add_module(str(lowercase_					)   ,  lowercase_					)






						def 	UpperCAmelCase__     (					self :List[str]   ,  lowercase_ :torch.Tensor					)				->			torch.Tensor:
												UpperCAmelCase             = input
												for layer in self.layers:
																		UpperCAmelCase             = layer(lowercase_					)
												return hidden_state







class       A_			(     nn.Module				):



						"""simple docstring"""






						def __init__(					self :Optional[Any]   ,  lowercase_ :Tuple[int, ...]   ,  lowercase_ :int   ,  lowercase_ :int   ,  lowercase_ :bool					)				->			None:
												super().__init__()
												UpperCAmelCase             = pool_scales
												UpperCAmelCase             = align_corners
												UpperCAmelCase             = in_channels
												UpperCAmelCase             = channels
												UpperCAmelCase             = []
												for i, pool_scale in enumerate(lowercase_					):
																		UpperCAmelCase             = UperNetPyramidPoolingBlock(pool_scale=lowercase_   ,  in_channels=lowercase_   ,  channels=lowercase_					)
																		self.blocks.append(lowercase_					)
																		self.add_module(str(lowercase_					)   ,  lowercase_					)






						def 	UpperCAmelCase__     (					self :List[Any]   ,  lowercase_ :torch.Tensor					)				->			List[torch.Tensor]:
												UpperCAmelCase             = []
												for ppm in self.blocks:
																		UpperCAmelCase             = ppm(lowercase_					)
																		UpperCAmelCase             = nn.functional.interpolate(
																		    lowercase_   ,  size=x.size()[2:]   ,  mode='bilinear'   ,  align_corners=self.align_corners					)
																		ppm_outs.append(lowercase_					)
												return ppm_outs







class       A_			(     nn.Module				):



						"""simple docstring"""






						def __init__(					self :Dict   ,  lowercase_ :Optional[Any]   ,  lowercase_ :Optional[int]					)				->			Any:
												super().__init__()

												UpperCAmelCase             = config
												UpperCAmelCase             = config.pool_scales  # e.g. (1, 2, 3, 6)
												UpperCAmelCase             = in_channels
												UpperCAmelCase             = config.hidden_size
												UpperCAmelCase             = False
												UpperCAmelCase             = nn.Convad(self.channels   ,  config.num_labels   ,  kernel_size=1					)

												# PSP Module
												UpperCAmelCase             = UperNetPyramidPoolingModule(
												    self.pool_scales   ,  self.in_channels[-1]   ,  self.channels   ,  align_corners=self.align_corners   ,  )
												UpperCAmelCase             = UperNetConvModule(
												    self.in_channels[-1] + len(self.pool_scales					) * self.channels   ,  self.channels   ,  kernel_size=3   ,  padding=1   ,  )
												# FPN Module
												UpperCAmelCase             = nn.ModuleList()
												UpperCAmelCase             = nn.ModuleList()
												for in_channels in self.in_channels[:-1]:  # skip the top layer
																		UpperCAmelCase             = UperNetConvModule(lowercase_   ,  self.channels   ,  kernel_size=1					)
																		UpperCAmelCase             = UperNetConvModule(self.channels   ,  self.channels   ,  kernel_size=3   ,  padding=1					)
																		self.lateral_convs.append(lowercase_					)
																		self.fpn_convs.append(lowercase_					)

												UpperCAmelCase             = UperNetConvModule(
												    len(self.in_channels					) * self.channels   ,  self.channels   ,  kernel_size=3   ,  padding=1   ,  )






						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			List[Any]:
												self.apply(self._init_weights					)






						def 	UpperCAmelCase__     (					self :str   ,  lowercase_ :Union[str, Any]					)				->			str:
												if isinstance(lowercase_   ,  nn.Convad					):
																		module.weight.data.normal_(mean=0.0   ,  std=self.config.initializer_range					)
																		if module.bias is not None:
																								module.bias.data.zero_()






						def 	UpperCAmelCase__     (					self :Dict   ,  lowercase_ :int					)				->			int:
												UpperCAmelCase             = inputs[-1]
												UpperCAmelCase             = [x]
												psp_outs.extend(self.psp_modules(lowercase_					)					)
												UpperCAmelCase             = torch.cat(lowercase_   ,  dim=1					)
												UpperCAmelCase             = self.bottleneck(lowercase_					)

												return output






						def 	UpperCAmelCase__     (					self :str   ,  lowercase_ :torch.Tensor					)				->			torch.Tensor:
												# build laterals
												UpperCAmelCase             = [lateral_conv(encoder_hidden_states[i]					) for i, lateral_conv in enumerate(self.lateral_convs					)]

												laterals.append(self.psp_forward(lowercase_					)					)

												# build top-down path
												UpperCAmelCase             = len(lowercase_					)
												for i in range(used_backbone_levels - 1   ,  0   ,  -1					):
																		UpperCAmelCase             = laterals[i - 1].shape[2:]
																		UpperCAmelCase             = laterals[i - 1] + nn.functional.interpolate(
																		    laterals[i]   ,  size=lowercase_   ,  mode='bilinear'   ,  align_corners=self.align_corners					)

												# build outputs
												UpperCAmelCase             = [self.fpn_convs[i](laterals[i]					) for i in range(used_backbone_levels - 1					)]
												# append psp feature
												fpn_outs.append(laterals[-1]					)

												for i in range(used_backbone_levels - 1   ,  0   ,  -1					):
																		UpperCAmelCase             = nn.functional.interpolate(
																		    fpn_outs[i]   ,  size=fpn_outs[0].shape[2:]   ,  mode='bilinear'   ,  align_corners=self.align_corners					)
												UpperCAmelCase             = torch.cat(lowercase_   ,  dim=1					)
												UpperCAmelCase             = self.fpn_bottleneck(lowercase_					)
												UpperCAmelCase             = self.classifier(lowercase_					)

												return output







class       A_			(     nn.Module				):



						"""simple docstring"""






						def __init__(					self :Optional[Any]   ,  lowercase_ :Optional[Any]   ,  lowercase_ :int = 2   ,  lowercase_ :int = 3   ,  lowercase_ :Union[int, Tuple[int, int]] = 1					)				->			None:
												super().__init__()

												UpperCAmelCase             = config
												UpperCAmelCase             = config.auxiliary_in_channels
												UpperCAmelCase             = config.auxiliary_channels
												UpperCAmelCase             = config.auxiliary_num_convs
												UpperCAmelCase             = config.auxiliary_concat_input
												UpperCAmelCase             = in_index

												UpperCAmelCase             = (kernel_size // 2) * dilation
												UpperCAmelCase             = []
												convs.append(
												    UperNetConvModule(
												        self.in_channels   ,  self.channels   ,  kernel_size=lowercase_   ,  padding=lowercase_   ,  dilation=lowercase_					)					)
												for i in range(self.num_convs - 1					):
																		convs.append(
																		    UperNetConvModule(
																		        self.channels   ,  self.channels   ,  kernel_size=lowercase_   ,  padding=lowercase_   ,  dilation=lowercase_					)					)
												if self.num_convs == 0:
																		UpperCAmelCase             = nn.Identity()
												else:
																		UpperCAmelCase             = nn.Sequential(*lowercase_					)
												if self.concat_input:
																		UpperCAmelCase             = UperNetConvModule(
																		    self.in_channels + self.channels   ,  self.channels   ,  kernel_size=lowercase_   ,  padding=kernel_size // 2					)

												UpperCAmelCase             = nn.Convad(self.channels   ,  config.num_labels   ,  kernel_size=1					)






						def 	UpperCAmelCase__     (					self :List[str]					)				->			Dict:
												self.apply(self._init_weights					)






						def 	UpperCAmelCase__     (					self :int   ,  lowercase_ :Any					)				->			List[Any]:
												if isinstance(lowercase_   ,  nn.Convad					):
																		module.weight.data.normal_(mean=0.0   ,  std=self.config.initializer_range					)
																		if module.bias is not None:
																								module.bias.data.zero_()






						def 	UpperCAmelCase__     (					self :Dict   ,  lowercase_ :torch.Tensor					)				->			torch.Tensor:
												# just take the relevant feature maps
												UpperCAmelCase             = encoder_hidden_states[self.in_index]
												UpperCAmelCase             = self.convs(lowercase_					)
												if self.concat_input:
																		UpperCAmelCase             = self.conv_cat(torch.cat([hidden_states, output]   ,  dim=1					)					)
												UpperCAmelCase             = self.classifier(lowercase_					)
												return output







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""
						__UpperCamelCase						=							UperNetConfig
						__UpperCamelCase						=							"""pixel_values"""
						__UpperCamelCase						=							True






						def 	UpperCAmelCase__     (					self :Tuple   ,  lowercase_ :List[str]					)				->			Union[str, Any]:
												if isinstance(lowercase_   ,  lowercase_					):
																		module.backbone.init_weights()
																		module.decode_head.init_weights()
																		module.auxiliary_head.init_weights()






						def 	UpperCAmelCase__     (					self :int					)				->			Tuple:
												self.backbone.init_weights()
												self.decode_head.init_weights()
												self.auxiliary_head.init_weights()






						def 	UpperCAmelCase__     (					self :Any   ,  lowercase_ :Dict   ,  lowercase_ :Union[str, Any]=False					)				->			Optional[int]:
												if isinstance(lowercase_   ,  lowercase_					):
																		UpperCAmelCase             = value


snake_case_				     =       R"""
    Parameters:
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

snake_case_				     =       R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
            `attentions` under returned tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
            returned tensors for more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""







@add_start_docstrings(
    """UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes."""	,   SCREAMING_SNAKE_CASE_	,   )
class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""






						def __init__(					self :int   ,  lowercase_ :Tuple					)				->			int:
												super().__init__(lowercase_					)

												UpperCAmelCase             = AutoBackbone.from_config(config.backbone_config					)

												# Semantic segmentation head(s)
												UpperCAmelCase             = UperNetHead(lowercase_   ,  in_channels=self.backbone.channels					)
												UpperCAmelCase             = UperNetFCNHead(lowercase_					) if config.use_auxiliary_head else None

												# Initialize weights and apply final processing
												self.post_init()






						@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('batch_size, sequence_length'					)					)
						@replace_return_docstrings(output_type=lowercase_   ,  config_class=_CONFIG_FOR_DOC					)
						def 	UpperCAmelCase__     (					self :Optional[int]   ,  lowercase_ :Optional[torch.Tensor] = None   ,  lowercase_ :Optional[bool] = None   ,  lowercase_ :Optional[bool] = None   ,  lowercase_ :Optional[torch.Tensor] = None   ,  lowercase_ :Optional[bool] = None   ,  )				->			Union[tuple, SemanticSegmenterOutput]:
												UpperCAmelCase             = return_dict if return_dict is not None else self.config.use_return_dict
												UpperCAmelCase             = (
												    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
												)
												UpperCAmelCase             = output_attentions if output_attentions is not None else self.config.output_attentions

												UpperCAmelCase             = self.backbone.forward_with_filtered_kwargs(
												    lowercase_   ,  output_hidden_states=lowercase_   ,  output_attentions=lowercase_					)
												UpperCAmelCase             = outputs.feature_maps

												UpperCAmelCase             = self.decode_head(lowercase_					)
												UpperCAmelCase             = nn.functional.interpolate(lowercase_   ,  size=pixel_values.shape[2:]   ,  mode='bilinear'   ,  align_corners=lowercase_					)

												UpperCAmelCase             = None
												if self.auxiliary_head is not None:
																		UpperCAmelCase             = self.auxiliary_head(lowercase_					)
																		UpperCAmelCase             = nn.functional.interpolate(
																		    lowercase_   ,  size=pixel_values.shape[2:]   ,  mode='bilinear'   ,  align_corners=lowercase_					)

												UpperCAmelCase             = None
												if labels is not None:
																		if self.config.num_labels == 1:
																								raise ValueError('The number of labels should be greater than one'					)
																		else:
																								# compute weighted loss
																								UpperCAmelCase             = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index					)
																								UpperCAmelCase             = loss_fct(lowercase_   ,  lowercase_					)
																								UpperCAmelCase             = loss_fct(lowercase_   ,  lowercase_					)
																								UpperCAmelCase             = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

												if not return_dict:
																		if output_hidden_states:
																								UpperCAmelCase             = (logits,) + outputs[1:]
																		else:
																								UpperCAmelCase             = (logits,) + outputs[2:]
																		return ((loss,) + output) if loss is not None else output

												return SemanticSegmenterOutput(
												    loss=lowercase_   ,  logits=lowercase_   ,  hidden_states=outputs.hidden_states   ,  attentions=outputs.attentions   ,  )







from __future__ import annotations





class       _snake_case					:







			def __init__(				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     data
				__magic_name__		:		Node | None	   =     None
				__magic_name__		:		Node | None	   =     None







def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		None:  # In Order traversal of the tree







	'''simple docstring'''


	if tree:
		display(tree.left   )
		print(tree.data   )
		display(tree.right   )






def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		int:







	'''simple docstring'''


	return 1 + max(depth_of_tree(tree.left   )      ,   depth_of_tree(tree.right   )   ) if tree else 0






def   lowerCAmelCase_						(					_snake_case      :  Node   )				->		bool:







	'''simple docstring'''


	if not tree:
		return True
	if tree.left and tree.right:
		return is_full_binary_tree(tree.left   ) and is_full_binary_tree(tree.right   )
	else:
		return not tree.left and not tree.right






def   lowerCAmelCase_						(					)				->		None:  # Main function for testing.







	'''simple docstring'''


	__magic_name__		:		int	   =     Node(1   )
	__magic_name__		:		Union[str, Any]	   =     Node(2   )
	__magic_name__		:		Tuple	   =     Node(3   )
	__magic_name__		:		Optional[Any]	   =     Node(4   )
	__magic_name__		:		Union[str, Any]	   =     Node(5   )
	__magic_name__		:		Any	   =     Node(6   )
	__magic_name__		:		int	   =     Node(7   )
	__magic_name__		:		List[str]	   =     Node(8   )
	__magic_name__		:		Union[str, Any]	   =     Node(9   )

	print(is_full_binary_tree(_snake_case   )   )
	print(depth_of_tree(_snake_case   )   )
	print("Tree is: "   )
	display(_snake_case   )


if __name__ == "__main__":
				main()
