


"""simple docstring"""




__UpperCamelCase						=      [
    '''Audio''',
    '''Array2D''',
    '''Array3D''',
    '''Array4D''',
    '''Array5D''',
    '''ClassLabel''',
    '''Features''',
    '''Sequence''',
    '''Value''',
    '''Image''',
    '''Translation''',
    '''TranslationVariableLanguages''',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages






"""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


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

# General docstring
__UpperCamelCase						=      '''UperNetConfig'''
class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__ = 0						,       lowerCAmelCase__ = False						,       lowerCAmelCase__ = 1						,       )     ->							None:
								super().__init__()
								SCREAMING_SNAKE_CASE													=					nn.Convad(
								    in_channels=lowerCAmelCase__						,       out_channels=lowerCAmelCase__						,       kernel_size=lowerCAmelCase__						,       padding=lowerCAmelCase__						,       bias=lowerCAmelCase__						,       dilation=lowerCAmelCase__						,       )
								SCREAMING_SNAKE_CASE													=					nn.BatchNormad(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					nn.ReLU()







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							torch.Tensor:
								SCREAMING_SNAKE_CASE													=					self.conv(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.batch_norm(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.activation(lowerCAmelCase__				)

								return output





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







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







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							torch.Tensor:
								SCREAMING_SNAKE_CASE													=					input
								for layer in self.layers:
														SCREAMING_SNAKE_CASE													=					layer(lowerCAmelCase__				)
								return hidden_state





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							None:
								super().__init__()
								SCREAMING_SNAKE_CASE													=					pool_scales
								SCREAMING_SNAKE_CASE													=					align_corners
								SCREAMING_SNAKE_CASE													=					in_channels
								SCREAMING_SNAKE_CASE													=					channels
								SCREAMING_SNAKE_CASE													=					[]
								for i, pool_scale in enumerate(lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					UperNetPyramidPoolingBlock(pool_scale=lowerCAmelCase__						,       in_channels=lowerCAmelCase__						,       channels=lowerCAmelCase__				)
														self.blocks.append(lowerCAmelCase__				)
														self.add_module(str(lowerCAmelCase__				)						,       lowerCAmelCase__				)







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





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							List[str]:
								super().__init__()

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

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

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







		def 							__A						(	self				)     ->							int:
								self.apply(self._init_weights				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Tuple:
								if isinstance(lowerCAmelCase__						,       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 							__A						(	self						,       lowerCAmelCase__				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					inputs[-1]
								SCREAMING_SNAKE_CASE													=					[x]
								psp_outs.extend(self.psp_modules(lowerCAmelCase__				)				)
								SCREAMING_SNAKE_CASE													=					torch.cat(lowerCAmelCase__						,       dim=1				)
								SCREAMING_SNAKE_CASE													=					self.bottleneck(lowerCAmelCase__				)

								return output







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							torch.Tensor:
								# build laterals
								SCREAMING_SNAKE_CASE													=					[lateral_conv(encoder_hidden_states[i]				) for i, lateral_conv in enumerate(self.lateral_convs				)]

								laterals.append(self.psp_forward(lowerCAmelCase__				)				)

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

								# build outputs
								SCREAMING_SNAKE_CASE													=					[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				):
														SCREAMING_SNAKE_CASE													=					nn.functional.interpolate(
														    fpn_outs[i]						,       size=fpn_outs[0].shape[2:]						,       mode='bilinear'						,       align_corners=self.align_corners				)
								SCREAMING_SNAKE_CASE													=					torch.cat(lowerCAmelCase__						,       dim=1				)
								SCREAMING_SNAKE_CASE													=					self.fpn_bottleneck(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.classifier(lowerCAmelCase__				)

								return output





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = 2						,       lowerCAmelCase__ = 3						,       lowerCAmelCase__ = 1				)     ->							None:
								super().__init__()

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

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

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







		def 							__A						(	self				)     ->							Dict:
								self.apply(self._init_weights				)







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





class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     int								=     UperNetConfig
		SCREAMING_SNAKE_CASE_						:     Optional[Any]								=     """pixel_values"""
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     True







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							List[str]:
								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):
														module.backbone.init_weights()
														module.decode_head.init_weights()
														module.auxiliary_head.init_weights()







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







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=False				)     ->							str:
								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					value





__UpperCamelCase						=      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.
'''

__UpperCamelCase						=      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."""	,    lowerCamelCase_	,    )
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__				)     ->							Optional[int]:
								super().__init__(lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					AutoBackbone.from_config(config.backbone_config				)

								# Semantic segmentation head(s)
								SCREAMING_SNAKE_CASE													=					UperNetHead(lowerCAmelCase__						,       in_channels=self.backbone.channels				)
								SCREAMING_SNAKE_CASE													=					UperNetFCNHead(lowerCAmelCase__				) 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=lowerCAmelCase__						,       config_class=_CONFIG_FOR_DOC				)
		def 							__A						(	self						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       )     ->							Union[tuple, SemanticSegmenterOutput]:
								SCREAMING_SNAKE_CASE													=					return_dict if return_dict is not None else self.config.use_return_dict
								SCREAMING_SNAKE_CASE													=					(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								SCREAMING_SNAKE_CASE													=					output_attentions if output_attentions is not None else self.config.output_attentions

								SCREAMING_SNAKE_CASE													=					self.backbone.forward_with_filtered_kwargs(
								    lowerCAmelCase__						,       output_hidden_states=lowerCAmelCase__						,       output_attentions=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					outputs.feature_maps

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

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

								SCREAMING_SNAKE_CASE													=					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
																				SCREAMING_SNAKE_CASE													=					CrossEntropyLoss(ignore_index=self.config.loss_ignore_index				)
																				SCREAMING_SNAKE_CASE													=					loss_fct(lowerCAmelCase__						,       lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					loss_fct(lowerCAmelCase__						,       lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

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

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


