





"""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 			_a						(				nn.Module    ):






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




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : Optional[Any]         =   nn.Convad(
										    in_channels=lowerCAmelCase__,			out_channels=lowerCAmelCase__,			kernel_size=lowerCAmelCase__,			padding=lowerCAmelCase__,			bias=lowerCAmelCase__,			dilation=lowerCAmelCase__,			)
										_UpperCamelCase      : List[str]         =   nn.BatchNormad(lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   nn.ReLU()






				def     snake_case     (			self		:   Tuple,			lowerCAmelCase__		:   torch.Tensor							)     ->				torch.Tensor:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   self.conv(lowerCAmelCase__							)
										_UpperCamelCase      : int         =   self.batch_norm(lowerCAmelCase__							)
										_UpperCamelCase      : Optional[Any]         =   self.activation(lowerCAmelCase__							)

										return output




class 			_a						(				nn.Module    ):






				def __init__(			self		:   Union[str, Any],			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   int							)     ->				None:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Any,			lowerCAmelCase__		:   torch.Tensor							)     ->				torch.Tensor:




										'''simple docstring'''



										_UpperCamelCase      : str         =   input
										for layer in self.layers:
																_UpperCamelCase      : Optional[Any]         =   layer(lowerCAmelCase__							)
										return hidden_state




class 			_a						(				nn.Module    ):






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




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : Any         =   pool_scales
										_UpperCamelCase      : Any         =   align_corners
										_UpperCamelCase      : Union[str, Any]         =   in_channels
										_UpperCamelCase      : Any         =   channels
										_UpperCamelCase      : Any         =   []
										for i, pool_scale in enumerate(lowerCAmelCase__							):
																_UpperCamelCase      : Tuple         =   UperNetPyramidPoolingBlock(pool_scale=lowerCAmelCase__,			in_channels=lowerCAmelCase__,			channels=lowerCAmelCase__							)
																self.blocks.append(lowerCAmelCase__							)
																self.add_module(str(lowerCAmelCase__							),			lowerCAmelCase__							)






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   torch.Tensor							)     ->				List[torch.Tensor]:




										'''simple docstring'''



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




class 			_a						(				nn.Module    ):






				def __init__(			self		:   Any,			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Optional[int]							)     ->				Dict:




										'''simple docstring'''



										super().__init__()

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

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

										_UpperCamelCase      : List[Any]         =   UperNetConvModule(
										    len(self.in_channels							) * self.channels,			self.channels,			kernel_size=3,			padding=1,			)






				def     snake_case     (			self		:   Optional[int]							)     ->				Any:




										'''simple docstring'''



										self.apply(self._init_weights							)






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   Dict							)     ->				Tuple:




										'''simple docstring'''



										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     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   str							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   inputs[-1]
										_UpperCamelCase      : Optional[Any]         =   [x]
										psp_outs.extend(self.psp_modules(lowerCAmelCase__							)							)
										_UpperCamelCase      : int         =   torch.cat(lowerCAmelCase__,			dim=1							)
										_UpperCamelCase      : Optional[int]         =   self.bottleneck(lowerCAmelCase__							)

										return output






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   torch.Tensor							)     ->				torch.Tensor:




										'''simple docstring'''



										_UpperCamelCase      : int         =   [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
										_UpperCamelCase      : Tuple         =   len(lowerCAmelCase__							)
										for i in range(used_backbone_levels - 1,			0,			-1							):
																_UpperCamelCase      : Union[str, Any]         =   laterals[i - 1].shape[2:]
																_UpperCamelCase      : int         =   laterals[i - 1] + nn.functional.interpolate(
																    laterals[i],			size=lowerCAmelCase__,			mode='''bilinear''',			align_corners=self.align_corners							)

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

										return output




class 			_a						(				nn.Module    ):






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




										'''simple docstring'''



										super().__init__()

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

										_UpperCamelCase      : int         =   (kernel_size // 2) * dilation
										_UpperCamelCase      : Tuple         =   []
										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:
																_UpperCamelCase      : int         =   nn.Identity()
										else:
																_UpperCamelCase      : Tuple         =   nn.Sequential(*lowerCAmelCase__							)
										if self.concat_input:
																_UpperCamelCase      : Tuple         =   UperNetConvModule(
																    self.in_channels + self.channels,			self.channels,			kernel_size=lowerCAmelCase__,			padding=kernel_size // 2							)

										_UpperCamelCase      : Tuple         =   nn.Convad(self.channels,			config.num_labels,			kernel_size=1							)






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										self.apply(self._init_weights							)






				def     snake_case     (			self		:   Any,			lowerCAmelCase__		:   int							)     ->				Tuple:




										'''simple docstring'''



										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     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   torch.Tensor							)     ->				torch.Tensor:




										'''simple docstring'''



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




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   UperNetConfig
				UpperCamelCase								=   '''pixel_values'''
				UpperCamelCase								=   True






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   Optional[Any]							)     ->				Dict:




										'''simple docstring'''



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






				def     snake_case     (			self		:   int							)     ->				Union[str, Any]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   Dict,			lowerCAmelCase__		:   Union[str, Any]=False							)     ->				List[str]:




										'''simple docstring'''



										if isinstance(lowerCAmelCase__,			lowerCAmelCase__							):
																_UpperCamelCase      : Optional[Any]         =   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 			_a						(				_lowerCAmelCase    ):






				def __init__(			self		:   Tuple,			lowerCAmelCase__		:   Optional[Any]							)     ->				Union[str, Any]:




										'''simple docstring'''



										super().__init__(lowerCAmelCase__							)

										_UpperCamelCase      : Dict         =   AutoBackbone.from_config(config.backbone_config							)

										# Semantic segmentation head(s)
										_UpperCamelCase      : Tuple         =   UperNetHead(lowerCAmelCase__,			in_channels=self.backbone.channels							)
										_UpperCamelCase      : Union[str, Any]         =   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     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   Optional[torch.Tensor] = None,			lowerCAmelCase__		:   Optional[bool] = None,			lowerCAmelCase__		:   Optional[bool] = None,			lowerCAmelCase__		:   Optional[torch.Tensor] = None,			lowerCAmelCase__		:   Optional[bool] = None,			)     ->				Union[tuple, SemanticSegmenterOutput]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   return_dict if return_dict is not None else self.config.use_return_dict
										_UpperCamelCase      : Tuple         =   (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										_UpperCamelCase      : Union[str, Any]         =   output_attentions if output_attentions is not None else self.config.output_attentions

										_UpperCamelCase      : str         =   self.backbone.forward_with_filtered_kwargs(
										    lowerCAmelCase__,			output_hidden_states=lowerCAmelCase__,			output_attentions=lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   outputs.feature_maps

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

										_UpperCamelCase      : Optional[Any]         =   None
										if self.auxiliary_head is not None:
																_UpperCamelCase      : Optional[int]         =   self.auxiliary_head(lowerCAmelCase__							)
																_UpperCamelCase      : Union[str, Any]         =   nn.functional.interpolate(
																    lowerCAmelCase__,			size=pixel_values.shape[2:],			mode='''bilinear''',			align_corners=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[int]         =   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      : Any         =   CrossEntropyLoss(ignore_index=self.config.loss_ignore_index							)
																						_UpperCamelCase      : Any         =   loss_fct(lowerCAmelCase__,			lowerCAmelCase__							)
																						_UpperCamelCase      : str         =   loss_fct(lowerCAmelCase__,			lowerCAmelCase__							)
																						_UpperCamelCase      : Tuple         =   main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

										if not return_dict:
																if output_hidden_states:
																						_UpperCamelCase      : Tuple         =   (logits,) + outputs[1:]
																else:
																						_UpperCamelCase      : int         =   (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,			)













"""simple docstring"""




UpperCamelCase_									=[
    """DownloadConfig""",
    """DownloadManager""",
    """DownloadMode""",
    """StreamingDownloadManager""",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager



