






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


lowerCamelCase       :       Optional[Any]        =				[
    '''openmmlab/upernet-convnext-tiny''',
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
lowerCamelCase       :       Optional[int]        =				'''UperNetConfig'''

class 						lowerCAmelCase   (       nn.Module	):

					'''simple docstring'''

					def __init__(     self :		Optional[int]						,   __a :		int						,   __a :		int						,   __a :		Union[int, Tuple[int, int]]						,   __a :		Union[int, Tuple[int, int], str] = 0						,   __a :		bool = False						,   __a :		Union[int, Tuple[int, int]] = 1						,   )	->     None:


												"""simple docstring"""

												super().__init__()
												__lowercase					:							Any			=   nn.Convad(
												    in_channels=__a						,   out_channels=__a						,   kernel_size=__a						,   padding=__a						,   bias=__a						,   dilation=__a						,   )
												__lowercase					:							str			=   nn.BatchNormad(__a							)
												__lowercase					:							int			=   nn.ReLU()



					def 					lowerCAmelCase (     self :		List[str]						,   __a :		torch.Tensor							)	->     torch.Tensor:


												"""simple docstring"""

												__lowercase					:							Optional[int]			=   self.conv(__a							)
												__lowercase					:							Optional[int]			=   self.batch_norm(__a							)
												__lowercase					:							int			=   self.activation(__a							)

												return output







class 						lowerCAmelCase   (       nn.Module	):

					'''simple docstring'''

					def __init__(     self :		Dict						,   __a :		int						,   __a :		int						,   __a :		int							)	->     None:


												"""simple docstring"""

												super().__init__()
												__lowercase					:							Any			=   [
												    nn.AdaptiveAvgPoolad(__a							),
												    UperNetConvModule(__a						,   __a						,   kernel_size=1							),
												]
												for i, layer in enumerate(self.layers							):
																			self.add_module(str(__a							)						,   __a							)



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		torch.Tensor							)	->     torch.Tensor:


												"""simple docstring"""

												__lowercase					:							List[str]			=   input
												for layer in self.layers:
																			__lowercase					:							Optional[int]			=   layer(__a							)
												return hidden_state







class 						lowerCAmelCase   (       nn.Module	):

					'''simple docstring'''

					def __init__(     self :		Tuple						,   __a :		Tuple[int, ...]						,   __a :		int						,   __a :		int						,   __a :		bool							)	->     None:


												"""simple docstring"""

												super().__init__()
												__lowercase					:							Union[str, Any]			=   pool_scales
												__lowercase					:							int			=   align_corners
												__lowercase					:							int			=   in_channels
												__lowercase					:							Optional[int]			=   channels
												__lowercase					:							str			=   []
												for i, pool_scale in enumerate(__a							):
																			__lowercase					:							Optional[Any]			=   UperNetPyramidPoolingBlock(pool_scale=__a						,   in_channels=__a						,   channels=__a							)
																			self.blocks.append(__a							)
																			self.add_module(str(__a							)						,   __a							)



					def 					lowerCAmelCase (     self :		Any						,   __a :		torch.Tensor							)	->     List[torch.Tensor]:


												"""simple docstring"""

												__lowercase					:							List[str]			=   []
												for ppm in self.blocks:
																			__lowercase					:							List[str]			=   ppm(__a							)
																			__lowercase					:							List[Any]			=   nn.functional.interpolate(
																			    __a						,   size=x.size()[2:]						,   mode="""bilinear"""						,   align_corners=self.align_corners							)
																			ppm_outs.append(__a							)
												return ppm_outs







class 						lowerCAmelCase   (       nn.Module	):

					'''simple docstring'''

					def __init__(     self :		Any						,   __a :		Optional[int]						,   __a :		Tuple							)	->     List[Any]:


												"""simple docstring"""

												super().__init__()

												__lowercase					:							Any			=   config
												__lowercase					:							int			=   config.pool_scales  # e.g. (1, 2, 3, 6)
												__lowercase					:							Dict			=   in_channels
												__lowercase					:							Tuple			=   config.hidden_size
												__lowercase					:							str			=   False
												__lowercase					:							str			=   nn.Convad(self.channels						,   config.num_labels						,   kernel_size=1							)

												# PSP Module
												__lowercase					:							Tuple			=   UperNetPyramidPoolingModule(
												    self.pool_scales						,   self.in_channels[-1]						,   self.channels						,   align_corners=self.align_corners						,   )
												__lowercase					:							Union[str, Any]			=   UperNetConvModule(
												    self.in_channels[-1] + len(self.pool_scales							) * self.channels						,   self.channels						,   kernel_size=3						,   padding=1						,   )
												# FPN Module
												__lowercase					:							Optional[int]			=   nn.ModuleList()
												__lowercase					:							List[Any]			=   nn.ModuleList()
												for in_channels in self.in_channels[:-1]:  # skip the top layer
																			__lowercase					:							Optional[int]			=   UperNetConvModule(__a						,   self.channels						,   kernel_size=1							)
																			__lowercase					:							Optional[Any]			=   UperNetConvModule(self.channels						,   self.channels						,   kernel_size=3						,   padding=1							)
																			self.lateral_convs.append(__a							)
																			self.fpn_convs.append(__a							)

												__lowercase					:							str			=   UperNetConvModule(
												    len(self.in_channels							) * self.channels						,   self.channels						,   kernel_size=3						,   padding=1						,   )



					def 					lowerCAmelCase (     self :		Union[str, Any]							)	->     List[Any]:


												"""simple docstring"""

												self.apply(self._init_weights							)



					def 					lowerCAmelCase (     self :		Optional[Any]						,   __a :		List[str]							)	->     int:


												"""simple docstring"""

												if isinstance(__a						,   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 					lowerCAmelCase (     self :		Dict						,   __a :		Union[str, Any]							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Optional[int]			=   inputs[-1]
												__lowercase					:							Optional[int]			=   [x]
												psp_outs.extend(self.psp_modules(__a							)							)
												__lowercase					:							Optional[int]			=   torch.cat(__a						,   dim=1							)
												__lowercase					:							Union[str, Any]			=   self.bottleneck(__a							)

												return output



					def 					lowerCAmelCase (     self :		str						,   __a :		torch.Tensor							)	->     torch.Tensor:


												"""simple docstring"""

												__lowercase					:							str			=   [lateral_conv(encoder_hidden_states[i]							) for i, lateral_conv in enumerate(self.lateral_convs							)]

												laterals.append(self.psp_forward(__a							)							)

												# build top-down path
												__lowercase					:							Optional[int]			=   len(__a							)
												for i in range(used_backbone_levels - 1						,   0						,   -1							):
																			__lowercase					:							Optional[Any]			=   laterals[i - 1].shape[2:]
																			__lowercase					:							Any			=   laterals[i - 1] + nn.functional.interpolate(
																			    laterals[i]						,   size=__a						,   mode="""bilinear"""						,   align_corners=self.align_corners							)

												# build outputs
												__lowercase					:							List[Any]			=   [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							):
																			__lowercase					:							Tuple			=   nn.functional.interpolate(
																			    fpn_outs[i]						,   size=fpn_outs[0].shape[2:]						,   mode="""bilinear"""						,   align_corners=self.align_corners							)
												__lowercase					:							str			=   torch.cat(__a						,   dim=1							)
												__lowercase					:							Union[str, Any]			=   self.fpn_bottleneck(__a							)
												__lowercase					:							Dict			=   self.classifier(__a							)

												return output







class 						lowerCAmelCase   (       nn.Module	):

					'''simple docstring'''

					def __init__(     self :		List[str]						,   __a :		Optional[Any]						,   __a :		int = 2						,   __a :		int = 3						,   __a :		Union[int, Tuple[int, int]] = 1							)	->     None:


												"""simple docstring"""

												super().__init__()

												__lowercase					:							List[Any]			=   config
												__lowercase					:							Dict			=   config.auxiliary_in_channels
												__lowercase					:							List[str]			=   config.auxiliary_channels
												__lowercase					:							int			=   config.auxiliary_num_convs
												__lowercase					:							Union[str, Any]			=   config.auxiliary_concat_input
												__lowercase					:							List[str]			=   in_index

												__lowercase					:							Optional[Any]			=   (kernel_size // 2) * dilation
												__lowercase					:							Union[str, Any]			=   []
												convs.append(
												    UperNetConvModule(
												        self.in_channels						,   self.channels						,   kernel_size=__a						,   padding=__a						,   dilation=__a							)							)
												for i in range(self.num_convs - 1							):
																			convs.append(
																			    UperNetConvModule(
																			        self.channels						,   self.channels						,   kernel_size=__a						,   padding=__a						,   dilation=__a							)							)
												if self.num_convs == 0:
																			__lowercase					:							int			=   nn.Identity()
												else:
																			__lowercase					:							List[str]			=   nn.Sequential(*__a							)
												if self.concat_input:
																			__lowercase					:							Any			=   UperNetConvModule(
																			    self.in_channels + self.channels						,   self.channels						,   kernel_size=__a						,   padding=kernel_size // 2							)

												__lowercase					:							List[Any]			=   nn.Convad(self.channels						,   config.num_labels						,   kernel_size=1							)



					def 					lowerCAmelCase (     self :		str							)	->     str:


												"""simple docstring"""

												self.apply(self._init_weights							)



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		Optional[int]							)	->     List[str]:


												"""simple docstring"""

												if isinstance(__a						,   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 					lowerCAmelCase (     self :		str						,   __a :		torch.Tensor							)	->     torch.Tensor:


												"""simple docstring"""

												__lowercase					:							Optional[Any]			=   encoder_hidden_states[self.in_index]
												__lowercase					:							List[str]			=   self.convs(__a							)
												if self.concat_input:
																			__lowercase					:							str			=   self.conv_cat(torch.cat([hidden_states, output]						,   dim=1							)							)
												__lowercase					:							int			=   self.classifier(__a							)
												return output







class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	Any     =					UperNetConfig
					_A  :	Tuple     =					'''pixel_values'''
					_A  :	str     =					True



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		Tuple							)	->     int:


												"""simple docstring"""

												if isinstance(__a						,   __a							):
																			module.backbone.init_weights()
																			module.decode_head.init_weights()
																			module.auxiliary_head.init_weights()



					def 					lowerCAmelCase (     self :		Any							)	->     str:


												"""simple docstring"""

												self.backbone.init_weights()
												self.decode_head.init_weights()
												self.auxiliary_head.init_weights()



					def 					lowerCAmelCase (     self :		str						,   __a :		List[str]						,   __a :		List[Any]=False							)	->     Optional[Any]:


												"""simple docstring"""

												if isinstance(__a						,   __a							):
																			__lowercase					:							Optional[int]			=   value







lowerCamelCase       :       Any        =				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.
'''

lowerCamelCase       :       List[Any]        =				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.'''					,				__a					,				)
class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					def __init__(     self :		Optional[int]						,   __a :		int							)	->     Union[str, Any]:


												"""simple docstring"""

												super().__init__(__a							)

												__lowercase					:							str			=   AutoBackbone.from_config(config.backbone_config							)

												# Semantic segmentation head(s)
												__lowercase					:							str			=   UperNetHead(__a						,   in_channels=self.backbone.channels							)
												__lowercase					:							List[str]			=   UperNetFCNHead(__a							) 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=__a						,   config_class=_CONFIG_FOR_DOC							)
					def 					lowerCAmelCase (     self :		str						,   __a :		Optional[torch.Tensor] = None						,   __a :		Optional[bool] = None						,   __a :		Optional[bool] = None						,   __a :		Optional[torch.Tensor] = None						,   __a :		Optional[bool] = None						,   )	->     Union[tuple, SemanticSegmenterOutput]:


												"""simple docstring"""

												__lowercase					:							int			=   return_dict if return_dict is not None else self.config.use_return_dict
												__lowercase					:							List[Any]			=   (
												    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
												)
												__lowercase					:							List[str]			=   output_attentions if output_attentions is not None else self.config.output_attentions

												__lowercase					:							Tuple			=   self.backbone.forward_with_filtered_kwargs(
												    __a						,   output_hidden_states=__a						,   output_attentions=__a							)
												__lowercase					:							str			=   outputs.feature_maps

												__lowercase					:							List[Any]			=   self.decode_head(__a							)
												__lowercase					:							Optional[Any]			=   nn.functional.interpolate(__a						,   size=pixel_values.shape[2:]						,   mode="""bilinear"""						,   align_corners=__a							)

												__lowercase					:							str			=   None
												if self.auxiliary_head is not None:
																			__lowercase					:							Any			=   self.auxiliary_head(__a							)
																			__lowercase					:							str			=   nn.functional.interpolate(
																			    __a						,   size=pixel_values.shape[2:]						,   mode="""bilinear"""						,   align_corners=__a							)

												__lowercase					:							Dict			=   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
																										__lowercase					:							Tuple			=   CrossEntropyLoss(ignore_index=self.config.loss_ignore_index							)
																										__lowercase					:							Union[str, Any]			=   loss_fct(__a						,   __a							)
																										__lowercase					:							List[Any]			=   loss_fct(__a						,   __a							)
																										__lowercase					:							List[Any]			=   main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

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

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







lowerCamelCase       :       Tuple        =				{'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
lowerCamelCase       :       int        =				['''a''', '''b''', '''c''', '''d''', '''e''']
def 					snake_case_						(   lowerCAmelCase_				:  Optional[int]				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  List[Any]					):
							__lowercase					:							Dict			=   start
							# add current to visited
							visited.append(lowerCAmelCase_					)
							__lowercase					:							Dict			=   edges[current]
							for neighbor in neighbors:
														# if neighbor not in visited, visit
														if neighbor not in visited:
																					__lowercase					:							List[Any]			=   topological_sort(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)
    # if all neighbors visited add current to sort
							sort.append(lowerCAmelCase_					)
							# if all vertices haven't been visited select a new one to visit
							if len(lowerCAmelCase_					) != len(lowerCAmelCase_					):
														for vertice in vertices:
																					if vertice not in visited:
																												__lowercase					:							Tuple			=   topological_sort(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)
    # return sort
							return sort


if __name__ == "__main__":
	lowerCamelCase       :       Any        =				topological_sort('''a''', [], [])
	print(sort)