


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

# General docstring
a__    :							List[str]            =					'''UperNetConfig'''




class 			UpperCamelCase_     (			nn.Module):

	"""simple docstring"""





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




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

						return output




class 			UpperCamelCase_     (			nn.Module):

	"""simple docstring"""





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




	def   UpperCAmelCase_				(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    torch.Tensor       )	->     torch.Tensor:
						__SCREAMING_SNAKE_CASE						       =    input
						for layer in self.layers:
											__SCREAMING_SNAKE_CASE						       =    layer(UpperCAmelCase__       )
						return hidden_state




class 			UpperCamelCase_     (			nn.Module):

	"""simple docstring"""





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




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




class 			UpperCamelCase_     (			nn.Module):

	"""simple docstring"""





	def __init__(			self      :    Any      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    str       )	->     int:
						super().__init__()

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

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

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




	def   UpperCAmelCase_				(			self      :    Tuple       )	->     Tuple:
						self.apply(self._init_weights       )




	def   UpperCAmelCase_				(			self      :    Tuple      ,      UpperCAmelCase__      :    List[Any]       )	->     List[Any]:
						if isinstance(UpperCAmelCase__      ,      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      :    Tuple      ,      UpperCAmelCase__      :    Optional[Any]       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    inputs[-1]
						__SCREAMING_SNAKE_CASE						       =    [x]
						psp_outs.extend(self.psp_modules(UpperCAmelCase__       )       )
						__SCREAMING_SNAKE_CASE						       =    torch.cat(UpperCAmelCase__      ,      dim=1       )
						__SCREAMING_SNAKE_CASE						       =    self.bottleneck(UpperCAmelCase__       )

						return output




	def   UpperCAmelCase_				(			self      :    Optional[int]      ,      UpperCAmelCase__      :    torch.Tensor       )	->     torch.Tensor:
						# build laterals
						__SCREAMING_SNAKE_CASE						       =    [lateral_conv(encoder_hidden_states[i]       ) for i, lateral_conv in enumerate(self.lateral_convs       )]

						laterals.append(self.psp_forward(UpperCAmelCase__       )       )

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

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

						return output




class 			UpperCamelCase_     (			nn.Module):

	"""simple docstring"""





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

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

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

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




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




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




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Optional[int]    =      UperNetConfig
	snake_case__    :  Optional[Any]    =      "pixel_values"
	snake_case__    :  Union[str, Any]    =      True




	def   UpperCAmelCase_				(			self      :    Optional[int]      ,      UpperCAmelCase__      :    Optional[int]       )	->     Any:
						if isinstance(UpperCAmelCase__      ,      UpperCAmelCase__       ):
											module.backbone.init_weights()
											module.decode_head.init_weights()
											module.auxiliary_head.init_weights()




	def   UpperCAmelCase_				(			self      :    Tuple       )	->     int:
						self.backbone.init_weights()
						self.decode_head.init_weights()
						self.auxiliary_head.init_weights()




	def   UpperCAmelCase_				(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    Union[str, Any]=False       )	->     Optional[Any]:
						if isinstance(UpperCAmelCase__      ,      UpperCAmelCase__       ):
											__SCREAMING_SNAKE_CASE						       =    value


a__    :							Optional[Any]            =					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.
'''

a__    :							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."      ,  UpperCamelCase      ,  )
class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	def __init__(			self      :    Dict      ,      UpperCAmelCase__      :    Union[str, Any]       )	->     Optional[Any]:
						super().__init__(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    AutoBackbone.from_config(config.backbone_config       )

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

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

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

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

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

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

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




"""simple docstring"""







a__    :							Optional[Any]            =					'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''




def        UpperCAmelCase__	():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    input("Enter message: "       )
					__SCREAMING_SNAKE_CASE						       =    input("Enter key [alphanumeric]: "       )
					__SCREAMING_SNAKE_CASE						       =    input("Encrypt/Decrypt [e/d]: "       )

					if mode.lower().startswith("e"       ):
										__SCREAMING_SNAKE_CASE						       =    "encrypt"
										__SCREAMING_SNAKE_CASE						       =    encrypt_message(lowerCAmelCase_  ,						lowerCAmelCase_       )
					elif mode.lower().startswith("d"       ):
										__SCREAMING_SNAKE_CASE						       =    "decrypt"
										__SCREAMING_SNAKE_CASE						       =    decrypt_message(lowerCAmelCase_  ,						lowerCAmelCase_       )

					print(f"""\n{mode.title()}ed message:"""       )
					print(lowerCAmelCase_       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return translate_message(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"encrypt"       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return translate_message(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"decrypt"       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    []
					__SCREAMING_SNAKE_CASE						       =    0
					__SCREAMING_SNAKE_CASE						       =    key.upper()

					for symbol in message:
										__SCREAMING_SNAKE_CASE						       =    LETTERS.find(symbol.upper()       )
										if num != -1:
															if mode == "encrypt":
																				num += LETTERS.find(key[key_index]       )
															elif mode == "decrypt":
																				num -= LETTERS.find(key[key_index]       )

															num %= len(lowerCAmelCase_       )

															if symbol.isupper():
																				translated.append(LETTERS[num]       )
															elif symbol.islower():
																				translated.append(LETTERS[num].lower()       )

															key_index += 1
															if key_index == len(lowerCAmelCase_       ):
																				__SCREAMING_SNAKE_CASE						       =    0
										else:
															translated.append(lowerCAmelCase_       )
					return "".join(lowerCAmelCase_       )


if __name__ == "__main__":
		main()
