





def       _UpperCamelCase       (a__				:list    ):






       """simple docstring"""





       if not isinstance(__lowerCAmelCase				,     __lowerCAmelCase    ):
              raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""    )
       if len(__lowerCAmelCase    ) == 0:
              raise ValueError("""Input list must be a non empty list"""    )
       if len(__lowerCAmelCase    ) == 1:
              return True
       UpperCamelCase__			       =     series[1] - series[0]
       for index in range(len(__lowerCAmelCase    ) - 1    ):
              if series[index + 1] - series[index] != common_diff:
                     return False
       return True





def       _UpperCamelCase       (a__				:list    ):






       """simple docstring"""





       if not isinstance(__lowerCAmelCase				,     __lowerCAmelCase    ):
              raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""    )
       if len(__lowerCAmelCase    ) == 0:
              raise ValueError("""Input list must be a non empty list"""    )
       UpperCamelCase__			       =     0
       for val in series:
              answer += val
       return answer / len(__lowerCAmelCase    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()











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


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

# General docstring
UpperCamelCase__     =	"UperNetConfig"

class     __SCREAMING_SNAKE_CASE   (       nn.Module    ):






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase = 0					,	__lowerCAmelCase = False					,	__lowerCAmelCase = 1					,	):
														super().__init__()
														UpperCamelCase__			       =     nn.Convad(
														    in_channels=__lowerCAmelCase					,	out_channels=__lowerCAmelCase					,	kernel_size=__lowerCAmelCase					,	padding=__lowerCAmelCase					,	bias=__lowerCAmelCase					,	dilation=__lowerCAmelCase					,	)
														UpperCamelCase__			       =     nn.BatchNormad(__lowerCAmelCase					)
														UpperCamelCase__			       =     nn.ReLU()







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     self.conv(__lowerCAmelCase					)
														UpperCamelCase__			       =     self.batch_norm(__lowerCAmelCase					)
														UpperCamelCase__			       =     self.activation(__lowerCAmelCase					)

														return output






class     __SCREAMING_SNAKE_CASE   (       nn.Module    ):






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														super().__init__()
														UpperCamelCase__			       =     [
														    nn.AdaptiveAvgPoolad(__lowerCAmelCase					),
														    UperNetConvModule(__lowerCAmelCase					,	__lowerCAmelCase					,	kernel_size=1					),
														]
														for i, layer in enumerate(self.layers					):
																					self.add_module(str(__lowerCAmelCase					)					,	__lowerCAmelCase					)







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     input
														for layer in self.layers:
																					UpperCamelCase__			       =     layer(__lowerCAmelCase					)
														return hidden_state






class     __SCREAMING_SNAKE_CASE   (       nn.Module    ):






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														super().__init__()
														UpperCamelCase__			       =     pool_scales
														UpperCamelCase__			       =     align_corners
														UpperCamelCase__			       =     in_channels
														UpperCamelCase__			       =     channels
														UpperCamelCase__			       =     []
														for i, pool_scale in enumerate(__lowerCAmelCase					):
																					UpperCamelCase__			       =     UperNetPyramidPoolingBlock(pool_scale=__lowerCAmelCase					,	in_channels=__lowerCAmelCase					,	channels=__lowerCAmelCase					)
																					self.blocks.append(__lowerCAmelCase					)
																					self.add_module(str(__lowerCAmelCase					)					,	__lowerCAmelCase					)







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     []
														for ppm in self.blocks:
																					UpperCamelCase__			       =     ppm(__lowerCAmelCase					)
																					UpperCamelCase__			       =     nn.functional.interpolate(
																					    __lowerCAmelCase					,	size=x.size()[2:]					,	mode="""bilinear"""					,	align_corners=self.align_corners					)
																					ppm_outs.append(__lowerCAmelCase					)
														return ppm_outs






class     __SCREAMING_SNAKE_CASE   (       nn.Module    ):






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														super().__init__()

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

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

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






							def 			_lowerCamelCase						(  self					):
														self.apply(self._init_weights					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														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 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     inputs[-1]
														UpperCamelCase__			       =     [x]
														psp_outs.extend(self.psp_modules(__lowerCAmelCase					)					)
														UpperCamelCase__			       =     torch.cat(__lowerCAmelCase					,	dim=1					)
														UpperCamelCase__			       =     self.bottleneck(__lowerCAmelCase					)

														return output







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														# build laterals
														UpperCamelCase__			       =     [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
														UpperCamelCase__			       =     len(__lowerCAmelCase					)
														for i in range(used_backbone_levels - 1					,	0					,	-1					):
																					UpperCamelCase__			       =     laterals[i - 1].shape[2:]
																					UpperCamelCase__			       =     laterals[i - 1] + nn.functional.interpolate(
																					    laterals[i]					,	size=__lowerCAmelCase					,	mode="""bilinear"""					,	align_corners=self.align_corners					)

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

														return output






class     __SCREAMING_SNAKE_CASE   (       nn.Module    ):






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = 2					,	__lowerCAmelCase = 3					,	__lowerCAmelCase = 1					):
														super().__init__()

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

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

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






							def 			_lowerCamelCase						(  self					):
														self.apply(self._init_weights					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														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 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														# just take the relevant feature maps
														UpperCamelCase__			       =     encoder_hidden_states[self.in_index]
														UpperCamelCase__			       =     self.convs(__lowerCAmelCase					)
														if self.concat_input:
																					UpperCamelCase__			       =     self.conv_cat(torch.cat([hidden_states, output]					,	dim=1					)					)
														UpperCamelCase__			       =     self.classifier(__lowerCAmelCase					)
														return output






class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : Any         =    UperNetConfig
							snake_case       : List[Any]         =    """pixel_values"""
							snake_case       : Optional[Any]         =    True






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														if isinstance(__lowerCAmelCase					,	__lowerCAmelCase					):
																					module.backbone.init_weights()
																					module.decode_head.init_weights()
																					module.auxiliary_head.init_weights()






							def 			_lowerCamelCase						(  self					):
														self.backbone.init_weights()
														self.decode_head.init_weights()
														self.auxiliary_head.init_weights()







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase=False					):
														if isinstance(__lowerCAmelCase					,	__lowerCAmelCase					):
																					UpperCamelCase__			       =     value






UpperCamelCase__     =	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"

UpperCamelCase__     =	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."""				,						_a				,						)
class     __SCREAMING_SNAKE_CASE   (       _a    ):






							def __init__(  self					,	__lowerCAmelCase					):
														super().__init__(__lowerCAmelCase					)

														UpperCamelCase__			       =     AutoBackbone.from_config(config.backbone_config					)

														# Semantic segmentation head(s)
														UpperCamelCase__			       =     UperNetHead(__lowerCAmelCase					,	in_channels=self.backbone.channels					)
														UpperCamelCase__			       =     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 			_lowerCamelCase						(  self					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	):
														UpperCamelCase__			       =     return_dict if return_dict is not None else self.config.use_return_dict
														UpperCamelCase__			       =     (
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														UpperCamelCase__			       =     output_attentions if output_attentions is not None else self.config.output_attentions

														UpperCamelCase__			       =     self.backbone.forward_with_filtered_kwargs(
														    __lowerCAmelCase					,	output_hidden_states=__lowerCAmelCase					,	output_attentions=__lowerCAmelCase					)
														UpperCamelCase__			       =     outputs.feature_maps

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

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

														UpperCamelCase__			       =     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
																												UpperCamelCase__			       =     CrossEntropyLoss(ignore_index=self.config.loss_ignore_index					)
																												UpperCamelCase__			       =     loss_fct(__lowerCAmelCase					,	__lowerCAmelCase					)
																												UpperCamelCase__			       =     loss_fct(__lowerCAmelCase					,	__lowerCAmelCase					)
																												UpperCamelCase__			       =     main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

														if not return_dict:
																					if output_hidden_states:
																												UpperCamelCase__			       =     (logits,) + outputs[1:]
																					else:
																												UpperCamelCase__			       =     (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					,	)




