






"""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


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

# General docstring
_SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						'''UperNetConfig'''






class      a						(					nn.Module		):






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






    def    UpperCamelCase       (       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							torch.Tensor					)     ->		torch.Tensor:
      lowerCamelCase_         =						self.conv(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.batch_norm(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.activation(__SCREAMING_SNAKE_CASE					)

      return output






class      a						(					nn.Module		):






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






    def    UpperCamelCase       (       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							torch.Tensor					)     ->		torch.Tensor:
      lowerCamelCase_         =						input
      for layer in self.layers:
        lowerCamelCase_         =						layer(__SCREAMING_SNAKE_CASE					)
      return hidden_state






class      a						(					nn.Module		):






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






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






class      a						(					nn.Module		):






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

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

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

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






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






    def    UpperCamelCase       (       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		Tuple:
      if isinstance(__SCREAMING_SNAKE_CASE       ,						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     :							str       ,						__SCREAMING_SNAKE_CASE     :							Dict					)     ->		List[Any]:
      lowerCamelCase_         =						inputs[-1]
      lowerCamelCase_         =						[x]
      psp_outs.extend(self.psp_modules(__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						torch.cat(__SCREAMING_SNAKE_CASE       ,						dim=1					)
      lowerCamelCase_         =						self.bottleneck(__SCREAMING_SNAKE_CASE					)

      return output






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							torch.Tensor					)     ->		torch.Tensor:
      # build laterals
      lowerCamelCase_         =						[lateral_conv(encoder_hidden_states[i]					) for i, lateral_conv in enumerate(self.lateral_convs					)]

      laterals.append(self.psp_forward(__SCREAMING_SNAKE_CASE					)					)

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

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

      return output






class      a						(					nn.Module		):






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

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

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

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






    def    UpperCamelCase       (       self     :							Any					)     ->		Optional[int]:
      self.apply(self._init_weights					)






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






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     Any						= UperNetConfig
    SCREAMING_SNAKE_CASE						:     str						= """pixel_values"""
    SCREAMING_SNAKE_CASE						:     Tuple						= True






    def    UpperCamelCase       (       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		List[Any]:
      if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
        module.backbone.init_weights()
        module.decode_head.init_weights()
        module.auxiliary_head.init_weights()






    def    UpperCamelCase       (       self     :							Dict					)     ->		str:
      self.backbone.init_weights()
      self.decode_head.init_weights()
      self.auxiliary_head.init_weights()






    def    UpperCamelCase       (       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any=False					)     ->		Optional[Any]:
      if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						value
_SCREAMING_SNAKE_CASE					:				List[str]					    =						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.
'''

_SCREAMING_SNAKE_CASE					:				List[str]					    =						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."""  ,      __snake_case  ,      )
class      a						(					__snake_case		):






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

      lowerCamelCase_         =						AutoBackbone.from_config(config.backbone_config					)

      # Semantic segmentation head(s)
      lowerCamelCase_         =						UperNetHead(__SCREAMING_SNAKE_CASE       ,						in_channels=self.backbone.channels					)
      lowerCamelCase_         =						UperNetFCNHead(__SCREAMING_SNAKE_CASE					) 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=__SCREAMING_SNAKE_CASE       ,						config_class=_CONFIG_FOR_DOC					)
    def    UpperCamelCase       (       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.Tensor] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[bool] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[bool] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.Tensor] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[bool] = None       ,						)     ->		Union[tuple, SemanticSegmenterOutput]:
      lowerCamelCase_         =						return_dict if return_dict is not None else self.config.use_return_dict
      lowerCamelCase_         =						(
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      lowerCamelCase_         =						output_attentions if output_attentions is not None else self.config.output_attentions

      lowerCamelCase_         =						self.backbone.forward_with_filtered_kwargs(
          __SCREAMING_SNAKE_CASE       ,						output_hidden_states=__SCREAMING_SNAKE_CASE       ,						output_attentions=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						outputs.feature_maps

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

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

      lowerCamelCase_         =						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_         =						CrossEntropyLoss(ignore_index=self.config.loss_ignore_index					)
          lowerCamelCase_         =						loss_fct(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						loss_fct(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

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

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












"""simple docstring"""





from cva import destroyAllWindows, imread, imshow, waitKey







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple )      ->				Dict:
  # getting number of pixels in the image
  lowerCamelCase_     ,	lowerCamelCase_         =						img.shape[0], img.shape[1]

  # converting each pixel's color to its negative
  for i in range(_lowerCamelCase ):
    for j in range(_lowerCamelCase ):
      lowerCamelCase_         =						[255, 255, 255] - img[i][j]

  return img


if __name__ == "__main__":
  # read original image
  _SCREAMING_SNAKE_CASE					:				List[Any]					    =						imread('''image_data/lena.jpg''', 1)

  # convert to its negative
  _SCREAMING_SNAKE_CASE					:				List[Any]					    =						convert_to_negative(img)

  # show result image
  imshow('''negative of original image''', img)
  waitKey(0)
  destroyAllWindows()




