


from .imports import is_tqdm_available


if is_tqdm_available():
  from tqdm.auto import tqdm as _tqdm

from ..state import PartialState


def      a	(							SCREAMING_SNAKE_CASE_      :		bool = True							,			*SCREAMING_SNAKE_CASE_      :		List[str]							,			**SCREAMING_SNAKE_CASE_      :		Tuple       ):






       """simple docstring"""





       if not is_tqdm_available():
              raise ImportError('''Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.'''       )
       UpperCamelCase    :	int   	=		False
       if main_process_only:
              UpperCamelCase    :	int   	=		PartialState().local_process_index == 0
       return _tqdm(*SCREAMING_SNAKE_CASE_							,			**SCREAMING_SNAKE_CASE_							,			disable=SCREAMING_SNAKE_CASE_       )





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

# General docstring
__UpperCAmelCase : List[str] 			=				"UperNetConfig"



class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 0 ,					__SCREAMING_SNAKE_CASE = False ,					__SCREAMING_SNAKE_CASE = 1 ,					):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	str   	=		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 ,					)
              UpperCamelCase    :	int   	=		nn.BatchNormad(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[Any]   	=		nn.ReLU()






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		self.conv(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[Any]   	=		self.batch_norm(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		self.activation(__SCREAMING_SNAKE_CASE						)

              return output




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	List[Any]   	=		[
                  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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		input
              for layer in self.layers:
                     UpperCamelCase    :	int   	=		layer(__SCREAMING_SNAKE_CASE						)
              return hidden_state




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	List[Any]   	=		pool_scales
              UpperCamelCase    :	Dict   	=		align_corners
              UpperCamelCase    :	Optional[int]   	=		in_channels
              UpperCamelCase    :	Union[str, Any]   	=		channels
              UpperCamelCase    :	List[str]   	=		[]
              for i, pool_scale in enumerate(__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Union[str, Any]   	=		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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		[]
              for ppm in self.blocks:
                     UpperCamelCase    :	List[str]   	=		ppm(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Tuple   	=		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    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()

              UpperCamelCase    :	int   	=		config
              UpperCamelCase    :	List[str]   	=		config.pool_scales  # e.g. (1, 2, 3, 6)
              UpperCamelCase    :	Optional[int]   	=		in_channels
              UpperCamelCase    :	str   	=		config.hidden_size
              UpperCamelCase    :	str   	=		False
              UpperCamelCase    :	List[str]   	=		nn.Convad(self.channels ,					config.num_labels ,					kernel_size=1						)

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

              UpperCamelCase    :	Optional[Any]   	=		UperNetConvModule(
                  len(self.in_channels						) * self.channels ,					self.channels ,					kernel_size=3 ,					padding=1 ,					)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.apply(self._init_weights						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		inputs[-1]
              UpperCamelCase    :	int   	=		[x]
              psp_outs.extend(self.psp_modules(__SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase    :	Any   	=		torch.cat(__SCREAMING_SNAKE_CASE ,					dim=1						)
              UpperCamelCase    :	Union[str, Any]   	=		self.bottleneck(__SCREAMING_SNAKE_CASE						)

              return output






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		[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
              UpperCamelCase    :	int   	=		len(__SCREAMING_SNAKE_CASE						)
              for i in range(used_backbone_levels - 1 ,					0 ,					-1						):
                     UpperCamelCase    :	Optional[int]   	=		laterals[i - 1].shape[2:]
                     UpperCamelCase    :	Optional[Any]   	=		laterals[i - 1] + nn.functional.interpolate(
                         laterals[i] ,					size=__SCREAMING_SNAKE_CASE ,					mode='''bilinear''' ,					align_corners=self.align_corners						)

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

              return output




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 2 ,					__SCREAMING_SNAKE_CASE = 3 ,					__SCREAMING_SNAKE_CASE = 1						):




              """simple docstring"""




              super().__init__()

              UpperCamelCase    :	Dict   	=		config
              UpperCamelCase    :	Optional[Any]   	=		config.auxiliary_in_channels
              UpperCamelCase    :	Union[str, Any]   	=		config.auxiliary_channels
              UpperCamelCase    :	Union[str, Any]   	=		config.auxiliary_num_convs
              UpperCamelCase    :	Optional[Any]   	=		config.auxiliary_concat_input
              UpperCamelCase    :	List[str]   	=		in_index

              UpperCamelCase    :	Any   	=		(kernel_size // 2) * dilation
              UpperCamelCase    :	Optional[Any]   	=		[]
              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:
                     UpperCamelCase    :	str   	=		nn.Identity()
              else:
                     UpperCamelCase    :	Dict   	=		nn.Sequential(*__SCREAMING_SNAKE_CASE						)
              if self.concat_input:
                     UpperCamelCase    :	Union[str, Any]   	=		UperNetConvModule(
                         self.in_channels + self.channels ,					self.channels ,					kernel_size=__SCREAMING_SNAKE_CASE ,					padding=kernel_size // 2						)

              UpperCamelCase    :	Optional[Any]   	=		nn.Convad(self.channels ,					config.num_labels ,					kernel_size=1						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.apply(self._init_weights						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		encoder_hidden_states[self.in_index]
              UpperCamelCase    :	str   	=		self.convs(__SCREAMING_SNAKE_CASE						)
              if self.concat_input:
                     UpperCamelCase    :	int   	=		self.conv_cat(torch.cat([hidden_states, output] ,					dim=1						)						)
              UpperCamelCase    :	Union[str, Any]   	=		self.classifier(__SCREAMING_SNAKE_CASE						)
              return output




class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[Any]							=					UperNetConfig
       __UpperCamelCase      :     Optional[int]							=					"pixel_values"
       __UpperCamelCase      :     Dict							=					True






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     module.backbone.init_weights()
                     module.decode_head.init_weights()
                     module.auxiliary_head.init_weights()






       def  _lowercase   (     self						):




              """simple docstring"""




              self.backbone.init_weights()
              self.decode_head.init_weights()
              self.auxiliary_head.init_weights()






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=False						):




              """simple docstring"""




              if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	str   	=		value




__UpperCAmelCase : List[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"

__UpperCAmelCase : Union[str, 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.",     _a,     )
class    UpperCAmelCase_	(							_a):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Dict   	=		AutoBackbone.from_config(config.backbone_config						)

              # Semantic segmentation head(s)
              UpperCamelCase    :	int   	=		UperNetHead(__SCREAMING_SNAKE_CASE ,					in_channels=self.backbone.channels						)
              UpperCamelCase    :	int   	=		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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		return_dict if return_dict is not None else self.config.use_return_dict
              UpperCamelCase    :	str   	=		(
                  output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
              )
              UpperCamelCase    :	Tuple   	=		output_attentions if output_attentions is not None else self.config.output_attentions

              UpperCamelCase    :	Tuple   	=		self.backbone.forward_with_filtered_kwargs(
                  __SCREAMING_SNAKE_CASE ,					output_hidden_states=__SCREAMING_SNAKE_CASE ,					output_attentions=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		outputs.feature_maps

              UpperCamelCase    :	Union[str, Any]   	=		self.decode_head(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		nn.functional.interpolate(__SCREAMING_SNAKE_CASE ,					size=pixel_values.shape[2:] ,					mode='''bilinear''' ,					align_corners=__SCREAMING_SNAKE_CASE						)

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

              UpperCamelCase    :	Union[str, Any]   	=		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    :	Optional[int]   	=		CrossEntropyLoss(ignore_index=self.config.loss_ignore_index						)
                            UpperCamelCase    :	Tuple   	=		loss_fct(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
                            UpperCamelCase    :	Optional[Any]   	=		loss_fct(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
                            UpperCamelCase    :	Union[str, Any]   	=		main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

              if not return_dict:
                     if output_hidden_states:
                            UpperCamelCase    :	Optional[Any]   	=		(logits,) + outputs[1:]
                     else:
                            UpperCamelCase    :	int   	=		(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 ,					)

