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


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

# General docstring
SCREAMING_SNAKE_CASE :	List[Any]							     =		"""UperNetConfig"""
class    A_ (       nn.Module ):

    def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      Union[int, Tuple[int, int]]     ,							__SCREAMING_SNAKE_CASE :      Union[int, Tuple[int, int], str] = 0     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      Union[int, Tuple[int, int]] = 1     ,							):
     super().__init__()
     __a       =  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     ,							)
     __a       =  nn.BatchNormad(__SCREAMING_SNAKE_CASE       )
     __a       =  nn.ReLU()

    def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      torch.Tensor       ):
     __a       =  self.conv(__SCREAMING_SNAKE_CASE       )
     __a       =  self.batch_norm(__SCREAMING_SNAKE_CASE       )
     __a       =  self.activation(__SCREAMING_SNAKE_CASE       )

     return output
class    A_ (       nn.Module ):

    def __init__(		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int       ):
     super().__init__()
     __a       =  [
         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 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      torch.Tensor       ):
     __a       =  input
     for layer in self.layers:
      __a       =  layer(__SCREAMING_SNAKE_CASE       )
     return hidden_state
class    A_ (       nn.Module ):

    def __init__(		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Tuple[int, ...]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      bool       ):
     super().__init__()
     __a       =  pool_scales
     __a       =  align_corners
     __a       =  in_channels
     __a       =  channels
     __a       =  []
     for i, pool_scale in enumerate(__SCREAMING_SNAKE_CASE       ):
      __a       =  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 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      torch.Tensor       ):
     __a       =  []
     for ppm in self.blocks:
      __a       =  ppm(__SCREAMING_SNAKE_CASE       )
      __a       =  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    A_ (       nn.Module ):

    def __init__(		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
     super().__init__()

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

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

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

    def 		_UpperCAmelCase   (		self :      str       ):
     self.apply(self._init_weights       )

    def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      int       ):
     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 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]       ):
     __a       =  inputs[-1]
     __a       =  [x]
     psp_outs.extend(self.psp_modules(__SCREAMING_SNAKE_CASE       )       )
     __a       =  torch.cat(__SCREAMING_SNAKE_CASE     ,							dim=1       )
     __a       =  self.bottleneck(__SCREAMING_SNAKE_CASE       )

     return output

    def 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      torch.Tensor       ):
     # build laterals
     __a       =  [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
     __a       =  len(__SCREAMING_SNAKE_CASE       )
     for i in range(used_backbone_levels - 1     ,							0     ,							-1       ):
      __a       =  laterals[i - 1].shape[2:]
      __a       =  laterals[i - 1] + nn.functional.interpolate(
          laterals[i]     ,							size=__SCREAMING_SNAKE_CASE     ,							mode="bilinear"     ,							align_corners=self.align_corners       )

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

     return output
class    A_ (       nn.Module ):

    def __init__(		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      int = 2     ,							__SCREAMING_SNAKE_CASE :      int = 3     ,							__SCREAMING_SNAKE_CASE :      Union[int, Tuple[int, int]] = 1       ):
     super().__init__()

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

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

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

    def 		_UpperCAmelCase   (		self :      Dict       ):
     self.apply(self._init_weights       )

    def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
     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 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      torch.Tensor       ):
     # just take the relevant feature maps
     __a       =  encoder_hidden_states[self.in_index]
     __a       =  self.convs(__SCREAMING_SNAKE_CASE       )
     if self.concat_input:
      __a       =  self.conv_cat(torch.cat([hidden_states, output]     ,							dim=1       )       )
     __a       =  self.classifier(__SCREAMING_SNAKE_CASE       )
     return output
class    A_ (       a_ ):
    _SCREAMING_SNAKE_CASE    				=			UperNetConfig
    _SCREAMING_SNAKE_CASE    				=			"""pixel_values"""
    _SCREAMING_SNAKE_CASE    				=			True

    def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]       ):
     if isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
      module.backbone.init_weights()
      module.decode_head.init_weights()
      module.auxiliary_head.init_weights()

    def 		_UpperCAmelCase   (		self :      int       ):
     self.backbone.init_weights()
     self.decode_head.init_weights()
     self.auxiliary_head.init_weights()

    def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple=False       ):
     if isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
      __a       =  value
SCREAMING_SNAKE_CASE :	Union[str, 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.
"""

SCREAMING_SNAKE_CASE :	Optional[int]							     =		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."""			,       a_			,       )
class    A_ (       a_ ):

    def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      Any       ):
     super().__init__(__SCREAMING_SNAKE_CASE       )

     __a       =  AutoBackbone.from_config(config.backbone_config       )

     # Semantic segmentation head(s)
     __a       =  UperNetHead(__SCREAMING_SNAKE_CASE     ,							in_channels=self.backbone.channels       )
     __a       =  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 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      Optional[torch.Tensor] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[torch.Tensor] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							):
     __a       =  return_dict if return_dict is not None else self.config.use_return_dict
     __a       =  (
         output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
     )
     __a       =  output_attentions if output_attentions is not None else self.config.output_attentions

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

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

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

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

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



from ... import PretrainedConfig


SCREAMING_SNAKE_CASE :	Any							     =		{
    """sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
  _SCREAMING_SNAKE_CASE    				=			"""nezha"""

  def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]=2_11_28     ,							__SCREAMING_SNAKE_CASE :      Dict=7_68     ,							__SCREAMING_SNAKE_CASE :      List[Any]=12     ,							__SCREAMING_SNAKE_CASE :      str=12     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=30_72     ,							__SCREAMING_SNAKE_CASE :      Any="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      List[str]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_12     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=64     ,							__SCREAMING_SNAKE_CASE :      List[Any]=2     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=1E-12     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Any=0     ,							__SCREAMING_SNAKE_CASE :      Any=2     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							**__SCREAMING_SNAKE_CASE :      List[str]     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  max_relative_position
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  classifier_dropout
   __a       =  use_cache


