'''simple docstring'''





from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
    import tensorflow as tf

    from ..tf_utils import stable_softmax


if is_torch_available():
    import torch


SCREAMING_SNAKE_CASE_:     Optional[int]  =logging.get_logger(__name__)




@add_end_docstrings(
    UpperCamelCase__		,	r"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """		,	)
class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    str   ,			__a    :    GenericTensor ):
         if self.framework == "tf":
              UpperCAmelCase_           =   tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
         elif self.framework == "pt":
              UpperCAmelCase_           =   torch.nonzero(input_ids == self.tokenizer.mask_token_id   ,			as_tuple=__a )
         else:
              raise ValueError("Unsupported framework" )
         return masked_index



    def 					_lowercase						(self    :    Tuple   ,			__a    :    GenericTensor ):
         UpperCAmelCase_           =   self.get_masked_index(__a )
         UpperCAmelCase_           =   np.prod(masked_index.shape )
         if numel < 1:
              raise PipelineException(
                  "fill-mask"   ,			self.model.base_model_prefix   ,			f"""No mask_token ({self.tokenizer.mask_token}) found on the input"""   ,			)



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    GenericTensor ):
         if isinstance(__a   ,			__a ):
              for model_input in model_inputs:
                   self._ensure_exactly_one_mask_token(model_input["input_ids"][0] )
         else:
              for input_ids in model_inputs["input_ids"]:
                   self._ensure_exactly_one_mask_token(__a )



    def 					_lowercase						(self    :    Tuple   ,			__a    :    Dict   ,			__a    :    List[str]=None   ,			**__a    :    Any ):
         if return_tensors is None:
              UpperCAmelCase_           =   self.framework
         UpperCAmelCase_           =   self.tokenizer(__a   ,			return_tensors=__a )
         self.ensure_exactly_one_mask_token(__a )
         return model_inputs



    def 					_lowercase						(self    :    str   ,			__a    :    Optional[int] ):
         UpperCAmelCase_           =   self.model(**__a )
         UpperCAmelCase_           =   model_inputs["input_ids"]
         return model_outputs



    def 					_lowercase						(self    :    List[str]   ,			__a    :    Tuple   ,			__a    :    int=5   ,			__a    :    Dict=None ):
         # Cap top_k if there are targets
         if target_ids is not None and target_ids.shape[0] < top_k:
              UpperCAmelCase_           =   target_ids.shape[0]
         UpperCAmelCase_           =   model_outputs["input_ids"][0]
         UpperCAmelCase_           =   model_outputs["logits"]

         if self.framework == "tf":
              UpperCAmelCase_           =   tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]

              UpperCAmelCase_           =   outputs.numpy()

              UpperCAmelCase_           =   outputs[0, masked_index, :]
              UpperCAmelCase_           =   stable_softmax(__a   ,			axis=-1 )
              if target_ids is not None:
                   UpperCAmelCase_           =   tf.gather_nd(tf.squeeze(__a   ,			0 )   ,			target_ids.reshape(-1   ,			1 ) )
                   UpperCAmelCase_           =   tf.expand_dims(__a   ,			0 )

              UpperCAmelCase_           =   tf.math.top_k(__a   ,			k=__a )
              UpperCAmelCase_    , UpperCAmelCase_           =   topk.values.numpy(), topk.indices.numpy()
         else:
              UpperCAmelCase_           =   torch.nonzero(input_ids == self.tokenizer.mask_token_id   ,			as_tuple=__a ).squeeze(-1 )
              # Fill mask pipeline supports only one ${mask_token} per sample

              UpperCAmelCase_           =   outputs[0, masked_index, :]
              UpperCAmelCase_           =   logits.softmax(dim=-1 )
              if target_ids is not None:
                   UpperCAmelCase_           =   probs[..., target_ids]

              UpperCAmelCase_    , UpperCAmelCase_           =   probs.topk(__a )

         UpperCAmelCase_           =   []
         UpperCAmelCase_           =   values.shape[0] == 1
         for i, (_values, _predictions) in enumerate(zip(values.tolist()   ,			predictions.tolist() ) ):
              UpperCAmelCase_           =   []
              for v, p in zip(_values   ,			_predictions ):
                   # Copy is important since we're going to modify this array in place
                   UpperCAmelCase_           =   input_ids.numpy().copy()
                   if target_ids is not None:
                        UpperCAmelCase_           =   target_ids[p].tolist()

                   UpperCAmelCase_           =   p
                   # Filter padding out:
                   UpperCAmelCase_           =   tokens[np.where(tokens != self.tokenizer.pad_token_id )]
                   # Originally we skip special tokens to give readable output.
                   # For multi masks though, the other [MASK] would be removed otherwise
                   # making the output look odd, so we add them back
                   UpperCAmelCase_           =   self.tokenizer.decode(__a   ,			skip_special_tokens=__a )
                   UpperCAmelCase_           =   {"score": v, "token": p, "token_str": self.tokenizer.decode([p] ), "sequence": sequence}
                   row.append(__a )
              result.append(__a )
         if single_mask:
              return result[0]
         return result



    def 					_lowercase						(self    :    Dict   ,			__a    :    List[Any]   ,			__a    :    List[str]=None ):
         if isinstance(__a   ,			__a ):
              UpperCAmelCase_           =   [targets]
         try:
              UpperCAmelCase_           =   self.tokenizer.get_vocab()
         except Exception:
              UpperCAmelCase_           =   {}
         UpperCAmelCase_           =   []
         for target in targets:
              UpperCAmelCase_           =   vocab.get(__a   ,			__a )
              if id_ is None:
                   UpperCAmelCase_           =   self.tokenizer(
                       __a   ,			add_special_tokens=__a   ,			return_attention_mask=__a   ,			return_token_type_ids=__a   ,			max_length=1   ,			truncation=__a   ,			)["input_ids"]
                   if len(__a ) == 0:
                        logger.warning(
                            f"""The specified target token `{target}` does not exist in the model vocabulary. """
                            "We cannot replace it with anything meaningful, ignoring it" )
                        continue
                   UpperCAmelCase_           =   input_ids[0]
                   # XXX: If users encounter this pass
                   # it becomes pretty slow, so let's make sure
                   # The warning enables them to fix the input to
                   # get faster performance.
                   logger.warning(
                       f"""The specified target token `{target}` does not exist in the model vocabulary. """
                       f"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`.""" )
              target_ids.append(id_ )
         UpperCAmelCase_           =   list(set(__a ) )
         if len(__a ) == 0:
              raise ValueError("At least one target must be provided when passed." )
         UpperCAmelCase_           =   np.array(__a )
         return target_ids



    def 					_lowercase						(self    :    Tuple   ,			__a    :    Dict=None   ,			__a    :    List[str]=None ):
         UpperCAmelCase_           =   {}

         if targets is not None:
              UpperCAmelCase_           =   self.get_target_ids(__a   ,			__a )
              UpperCAmelCase_           =   target_ids

         if top_k is not None:
              UpperCAmelCase_           =   top_k

         if self.tokenizer.mask_token_id is None:
              raise PipelineException(
                  "fill-mask"   ,			self.model.base_model_prefix   ,			"The tokenizer does not define a `mask_token`." )
         return {}, {}, postprocess_params






    def __call__(self    :    Union[str, Any]   ,			__a    :    str   ,			*__a    :    Any   ,			**__a    :    Tuple ):
         UpperCAmelCase_           =   super().__call__(__a   ,			**__a )
         if isinstance(__a   ,			__a ) and len(__a ) == 1:
              return outputs[0]
         return outputs








'''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


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

# General docstring
__A					:  int											=      "UperNetConfig"






class    __snake_case       (   nn.Module):







    """simple docstring"""



    def __init__(   self    :  Tuple    ,						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:
      super().__init__()
      lowerCAmelCase_			:      int			=   nn.Convad(
          in_channels=lowerCamelCase    ,						out_channels=lowerCamelCase    ,						kernel_size=lowerCamelCase    ,						padding=lowerCamelCase    ,						bias=lowerCamelCase    ,						dilation=lowerCamelCase    ,						)
      lowerCAmelCase_			:      Dict			=   nn.BatchNormad(lowerCamelCase		)
      lowerCAmelCase_			:      Dict			=   nn.ReLU()





    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  torch.Tensor		)   ->  torch.Tensor:
      lowerCAmelCase_			:      Optional[Any]			=   self.conv(lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   self.batch_norm(lowerCamelCase		)
      lowerCAmelCase_			:      Union[str, Any]			=   self.activation(lowerCamelCase		)

      return output







class    __snake_case       (   nn.Module):







    """simple docstring"""



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





    def __lowercase   (   self    :  List[str]    ,						lowerCamelCase    :  torch.Tensor		)   ->  torch.Tensor:
      lowerCAmelCase_			:      List[Any]			=   input
      for layer in self.layers:
        lowerCAmelCase_			:      Tuple			=   layer(lowerCamelCase		)
      return hidden_state







class    __snake_case       (   nn.Module):







    """simple docstring"""



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





    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  torch.Tensor		)   ->  List[torch.Tensor]:
      lowerCAmelCase_			:      Any			=   []
      for ppm in self.blocks:
        lowerCAmelCase_			:      Any			=   ppm(lowerCamelCase		)
        lowerCAmelCase_			:      Union[str, Any]			=   nn.functional.interpolate(
            lowerCamelCase    ,						size=x.size()[2:]    ,						mode="""bilinear"""    ,						align_corners=self.align_corners		)
        ppm_outs.append(lowerCamelCase		)
      return ppm_outs







class    __snake_case       (   nn.Module):







    """simple docstring"""



    def __init__(   self    :  int    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  List[Any]		)   ->  Dict:
      super().__init__()

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

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

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

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

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

      return output





    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  torch.Tensor		)   ->  torch.Tensor:
      # build laterals
      lowerCAmelCase_			:      Optional[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
      lowerCAmelCase_			:      Tuple			=   len(lowerCamelCase		)
      for i in range(used_backbone_levels - 1    ,						0    ,						-1		):
        lowerCAmelCase_			:      Union[str, Any]			=   laterals[i - 1].shape[2:]
        lowerCAmelCase_			:      Optional[Any]			=   laterals[i - 1] + nn.functional.interpolate(
            laterals[i]    ,						size=lowerCamelCase    ,						mode="""bilinear"""    ,						align_corners=self.align_corners		)

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

      return output







class    __snake_case       (   nn.Module):







    """simple docstring"""



    def __init__(   self    :  List[str]    ,						lowerCamelCase    :  Dict    ,						lowerCamelCase    :  int = 2    ,						lowerCamelCase    :  int = 3    ,						lowerCamelCase    :  Union[int, Tuple[int, int]] = 1		)   ->  None:
      super().__init__()

      lowerCAmelCase_			:      List[Any]			=   config
      lowerCAmelCase_			:      Dict			=   config.auxiliary_in_channels
      lowerCAmelCase_			:      Optional[Any]			=   config.auxiliary_channels
      lowerCAmelCase_			:      Dict			=   config.auxiliary_num_convs
      lowerCAmelCase_			:      int			=   config.auxiliary_concat_input
      lowerCAmelCase_			:      List[Any]			=   in_index

      lowerCAmelCase_			:      List[Any]			=   (kernel_size // 2) * dilation
      lowerCAmelCase_			:      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:
        lowerCAmelCase_			:      Optional[Any]			=   nn.Identity()
      else:
        lowerCAmelCase_			:      List[str]			=   nn.Sequential(*lowerCamelCase		)
      if self.concat_input:
        lowerCAmelCase_			:      Union[str, Any]			=   UperNetConvModule(
            self.in_channels + self.channels    ,						self.channels    ,						kernel_size=lowerCamelCase    ,						padding=kernel_size // 2		)

      lowerCAmelCase_			:      Any			=   nn.Convad(self.channels    ,						config.num_labels    ,						kernel_size=1		)

    def __lowercase   (   self    :  int		)   ->  List[Any]:
      self.apply(self._init_weights		)

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  Optional[Any]		)   ->  Optional[Any]:
      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 __lowercase   (   self    :  Any    ,						lowerCamelCase    :  torch.Tensor		)   ->  torch.Tensor:
      # just take the relevant feature maps
      lowerCAmelCase_			:      Dict			=   encoder_hidden_states[self.in_index]
      lowerCAmelCase_			:      List[str]			=   self.convs(lowerCamelCase		)
      if self.concat_input:
        lowerCAmelCase_			:      int			=   self.conv_cat(torch.cat([hidden_states, output]    ,						dim=1		)		)
      lowerCAmelCase_			:      Union[str, Any]			=   self.classifier(lowerCamelCase		)
      return output







class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					UperNetConfig
    lowercase    =					'pixel_values'
    lowercase    =					True

    def __lowercase   (   self    :  List[str]    ,						lowerCamelCase    :  Dict		)   ->  Optional[Any]:
      if isinstance(lowerCamelCase    ,						lowerCamelCase		):
        module.backbone.init_weights()
        module.decode_head.init_weights()
        module.auxiliary_head.init_weights()

    def __lowercase   (   self    :  Optional[int]		)   ->  int:
      self.backbone.init_weights()
      self.decode_head.init_weights()
      self.auxiliary_head.init_weights()





    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  Any=False		)   ->  Optional[int]:
      if isinstance(lowerCamelCase    ,						lowerCamelCase		):
        lowerCAmelCase_			:      str			=   value







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

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






@add_start_docstrings(
    'UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.'	,_SCREAMING_SNAKE_CASE	,)
class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(   self    :  Any    ,						lowerCamelCase    :  List[Any]		)   ->  Union[str, Any]:
      super().__init__(lowerCamelCase		)

      lowerCAmelCase_			:      Union[str, Any]			=   AutoBackbone.from_config(config.backbone_config		)

      # Semantic segmentation head(s)
      lowerCAmelCase_			:      Optional[Any]			=   UperNetHead(lowerCamelCase    ,						in_channels=self.backbone.channels		)
      lowerCAmelCase_			:      List[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 __lowercase   (   self    :  Any    ,						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]:
      lowerCAmelCase_			:      int			=   return_dict if return_dict is not None else self.config.use_return_dict
      lowerCAmelCase_			:      Any			=   (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      lowerCAmelCase_			:      Tuple			=   output_attentions if output_attentions is not None else self.config.output_attentions

      lowerCAmelCase_			:      Dict			=   self.backbone.forward_with_filtered_kwargs(
          lowerCamelCase    ,						output_hidden_states=lowerCamelCase    ,						output_attentions=lowerCamelCase		)
      lowerCAmelCase_			:      Union[str, Any]			=   outputs.feature_maps

      lowerCAmelCase_			:      Dict			=   self.decode_head(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[Any]			=   nn.functional.interpolate(lowerCamelCase    ,						size=pixel_values.shape[2:]    ,						mode="""bilinear"""    ,						align_corners=lowerCamelCase		)

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

      lowerCAmelCase_			:      str			=   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
          lowerCAmelCase_			:      str			=   CrossEntropyLoss(ignore_index=self.config.loss_ignore_index		)
          lowerCAmelCase_			:      int			=   loss_fct(lowerCamelCase    ,						lowerCamelCase		)
          lowerCAmelCase_			:      List[str]			=   loss_fct(lowerCamelCase    ,						lowerCamelCase		)
          lowerCAmelCase_			:      str			=   main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

      if not return_dict:
        if output_hidden_states:
          lowerCAmelCase_			:      int			=   (logits,) + outputs[1:]
        else:
          lowerCAmelCase_			:      List[str]			=   (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    ,						)



