'''simple docstring'''




import math
import sys






def __lowerCAmelCase (							UpperCamelCase__				)	->    str:
    __lowerCamelCase		=  ''''''
    try:
        with open(UpperCamelCase__				,  '''rb'''				) as binary_file:
            __lowerCamelCase		=  binary_file.read()
        for dat in data:
            __lowerCamelCase		=  f"""{dat:08b}"""
            result += curr_byte
        return result
    except OSError:
        print('''File not accessible'''				)
        sys.exit()






def __lowerCAmelCase (							UpperCamelCase__				)	->    str:
    __lowerCamelCase		=  {'''0''': '''0''', '''1''': '''1'''}
    __lowerCamelCase	,  __lowerCamelCase		=  '''''', ''''''
    __lowerCamelCase		=  len(UpperCamelCase__				)

    for i in range(len(UpperCamelCase__				)				):
        curr_string += data_bits[i]
        if curr_string not in lexicon:
            continue

        __lowerCamelCase		=  lexicon[curr_string]
        result += last_match_id
        __lowerCamelCase		=  last_match_id + '''0'''

        if math.loga(UpperCamelCase__				).is_integer():
            __lowerCamelCase		=  {}
            for curr_key in list(UpperCamelCase__				):
                __lowerCamelCase		=  lexicon.pop(UpperCamelCase__				)
            __lowerCamelCase		=  new_lex

        __lowerCamelCase		=  last_match_id + '''1'''
        index += 1
        __lowerCamelCase		=  ''''''
    return result






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    None:
    __lowerCamelCase		=  8
    try:
        with open(UpperCamelCase__				,  '''wb'''				) as opened_file:
            __lowerCamelCase		=  [
                to_write[i : i + byte_length]
                for i in range(0				,  len(UpperCamelCase__				)				,  UpperCamelCase__				)
            ]

            if len(result_byte_array[-1]				) % byte_length == 0:
                result_byte_array.append('''10000000'''				)
            else:
                result_byte_array[-1] += "1" + "0" * (
                    byte_length - len(result_byte_array[-1]				) - 1
                )

            for elem in result_byte_array[:-1]:
                opened_file.write(int(UpperCamelCase__				,  2				).to_bytes(1				,  byteorder='''big'''				)				)
    except OSError:
        print('''File not accessible'''				)
        sys.exit()






def __lowerCAmelCase (							UpperCamelCase__				)	->    str:
    __lowerCamelCase		=  0
    for letter in data_bits:
        if letter == "1":
            break
        counter += 1

    __lowerCamelCase		=  data_bits[counter:]
    __lowerCamelCase		=  data_bits[counter + 1 :]
    return data_bits






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    None:
    __lowerCamelCase		=  read_file_binary(UpperCamelCase__				)
    __lowerCamelCase		=  remove_prefix(UpperCamelCase__				)
    __lowerCamelCase		=  decompress_data(UpperCamelCase__				)
    write_file_binary(UpperCamelCase__				,  UpperCamelCase__				)


if __name__ == "__main__":
  compress(sys.argv[1], sys.argv[2])




'''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    ,						)



