"""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


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

# General docstring
__lowercase		:		Dict														=							"UperNetConfig"



class      _A	(						nn.Module					):




    """simple docstring"""




    def __init__(		self							:     Optional[Any]	,      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:
         super().__init__()
         __snake_case				=							nn.Convad(
             in_channels=A_	,      out_channels=A_	,      kernel_size=A_	,      padding=A_	,      bias=A_	,      dilation=A_	,      )
         __snake_case				=							nn.BatchNormad(A_    )
         __snake_case				=							nn.ReLU()







    def  lowercase					(		self							:     Tuple	,      A_							:     torch.Tensor    )    ->       torch.Tensor:
         __snake_case				=							self.conv(A_    )
         __snake_case				=							self.batch_norm(A_    )
         __snake_case				=							self.activation(A_    )

         return output






class      _A	(						nn.Module					):




    """simple docstring"""




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







    def  lowercase					(		self							:     Dict	,      A_							:     torch.Tensor    )    ->       torch.Tensor:
         __snake_case				=							input
         for layer in self.layers:
              __snake_case				=							layer(A_    )
         return hidden_state






class      _A	(						nn.Module					):




    """simple docstring"""




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







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






class      _A	(						nn.Module					):




    """simple docstring"""




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

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

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

         __snake_case				=							UperNetConvModule(
             len(self.in_channels    ) * self.channels	,      self.channels	,      kernel_size=3	,      padding=1	,      )
    def  lowercase					(		self							:     List[Any]    )    ->       Optional[Any]:
         self.apply(self._init_weights    )
    def  lowercase					(		self							:     int	,      A_							:     List[str]    )    ->       List[str]:
         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  lowercase					(		self							:     str	,      A_							:     Any    )    ->       str:
         __snake_case				=							inputs[-1]
         __snake_case				=							[x]
         psp_outs.extend(self.psp_modules(A_    )    )
         __snake_case				=							torch.cat(A_	,      dim=1    )
         __snake_case				=							self.bottleneck(A_    )

         return output







    def  lowercase					(		self							:     Tuple	,      A_							:     torch.Tensor    )    ->       torch.Tensor:
         # build laterals
         __snake_case				=							[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
         __snake_case				=							len(A_    )
         for i in range(used_backbone_levels - 1	,      0	,      -1    ):
              __snake_case				=							laterals[i - 1].shape[2:]
              __snake_case				=							laterals[i - 1] + nn.functional.interpolate(
                  laterals[i]	,      size=A_	,      mode='''bilinear'''	,      align_corners=self.align_corners    )

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

         return output






class      _A	(						nn.Module					):




    """simple docstring"""




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

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

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

         __snake_case				=							nn.Convad(self.channels	,      config.num_labels	,      kernel_size=1    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Optional[Any]:
         self.apply(self._init_weights    )
    def  lowercase					(		self							:     int	,      A_							:     Dict    )    ->       str:
         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  lowercase					(		self							:     Optional[int]	,      A_							:     torch.Tensor    )    ->       torch.Tensor:
         # just take the relevant feature maps
         __snake_case				=							encoder_hidden_states[self.in_index]
         __snake_case				=							self.convs(A_    )
         if self.concat_input:
              __snake_case				=							self.conv_cat(torch.cat([hidden_states, output]	,      dim=1    )    )
         __snake_case				=							self.classifier(A_    )
         return output






class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					UperNetConfig
    UpperCamelCase_	:					Optional[Any]							=					'''pixel_values'''
    UpperCamelCase_	:					int							=					True
    def  lowercase					(		self							:     int	,      A_							:     Union[str, Any]    )    ->       List[str]:
         if isinstance(A_	,      A_    ):
              module.backbone.init_weights()
              module.decode_head.init_weights()
              module.auxiliary_head.init_weights()
    def  lowercase					(		self							:     List[str]    )    ->       Union[str, Any]:
         self.backbone.init_weights()
         self.decode_head.init_weights()
         self.auxiliary_head.init_weights()







    def  lowercase					(		self							:     int	,      A_							:     Dict	,      A_							:     List[str]=False    )    ->       Dict:
         if isinstance(A_	,      A_    ):
              __snake_case				=							value






__lowercase		:		Tuple														=							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"

__lowercase		:		Optional[Any]														=							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.'''							, _UpperCAmelCase							, )
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




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

         __snake_case				=							AutoBackbone.from_config(config.backbone_config    )

         # Semantic segmentation head(s)
         __snake_case				=							UperNetHead(A_	,      in_channels=self.backbone.channels    )
         __snake_case				=							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  lowercase					(		self							:     List[Any]	,      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]:
         __snake_case				=							return_dict if return_dict is not None else self.config.use_return_dict
         __snake_case				=							(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         __snake_case				=							output_attentions if output_attentions is not None else self.config.output_attentions

         __snake_case				=							self.backbone.forward_with_filtered_kwargs(
             A_	,      output_hidden_states=A_	,      output_attentions=A_    )
         __snake_case				=							outputs.feature_maps

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

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

         __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
                   __snake_case				=							CrossEntropyLoss(ignore_index=self.config.loss_ignore_index    )
                   __snake_case				=							loss_fct(A_	,      A_    )
                   __snake_case				=							loss_fct(A_	,      A_    )
                   __snake_case				=							main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

         if not return_dict:
              if output_hidden_states:
                   __snake_case				=							(logits,) + outputs[1:]
              else:
                   __snake_case				=							(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	,      )
"""simple docstring"""



def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							len(snake_case)
     for i in range(length - 1):
          __snake_case				=							i
          for k in range(i + 1,      snake_case):
               if collection[k] < collection[least]:
                    __snake_case				=							k
          if least != i:
               __snake_case		,	__snake_case				=							(collection[i], collection[least])
     return collection


if __name__ == "__main__":
       __lowercase		:		Optional[Any]														=							input("Enter numbers separated by a comma:\n").strip()
       __lowercase		:		Tuple														=							[int(item) for item in user_input.split(",")]
       print(selection_sort(unsorted))