'''simple docstring'''

from __future__ import annotations

from dataclasses import dataclass







@dataclass
class 				_lowercase     :


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       float
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None







def      UpperCamelCase_						(					snake_case_       :       TreeNode | None      )					->     bool:






 '''simple docstring'''

 def is_valid_tree(snake_case_       :       TreeNode | None      ) -> bool:
  if node is None:
   return True

  if not isinstance(snake_case_						,		snake_case_      ):
   return False

  try:
   float(node.data      )
  except (TypeError, ValueError):
   return False

  return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

 if not is_valid_tree(snake_case_      ):
  raise ValueError(
      """Each node should be type of TreeNode and data should be float."""      )

 def is_binary_search_tree_recursive_check(
     snake_case_       :       TreeNode | None						,		snake_case_       :       float						,		snake_case_       :       float      ) -> bool:

  if node is None:
   return True

  return (
      left_bound < node.data < right_bound
      and is_binary_search_tree_recursive_check(node.left						,		snake_case_						,		node.data      )
      and is_binary_search_tree_recursive_check(
          node.right						,		node.data						,		snake_case_      )
  )

 return is_binary_search_tree_recursive_check(snake_case_						,		-float("""inf"""      )						,		float("""inf"""      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()







'''simple docstring'''

from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


_A			:      Optional[Any]					      =				logging.get_logger(__name__)

# General docstring
_A			:      Optional[Any]					      =				'''ResNetConfig'''

# Base docstring
_A			:      Tuple					      =				'''microsoft/resnet-50'''
_A			:      List[str]					      =				[1, 2048, 7, 7]

# Image classification docstring
_A			:      str					      =				'''microsoft/resnet-50'''
_A			:      Dict					      =				'''tiger cat'''

_A			:      List[Any]					      =				[
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	int = 3      ,       SCREAMING_SNAKE_CASE__   :	int = 1      ,       SCREAMING_SNAKE_CASE__   :	str = "relu"							)  ->    Any:
  super().__init__()
  __lowerCAmelCase											=						nn.Convad(
      SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       kernel_size=SCREAMING_SNAKE_CASE__      ,       stride=SCREAMING_SNAKE_CASE__      ,       padding=kernel_size // 2      ,       bias=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						nn.BatchNormad(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						ACTaFN[activation] if activation is not None else nn.Identity()





 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tensor							)  ->    Tensor:
  __lowerCAmelCase											=						self.convolution(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.normalization(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.activation(SCREAMING_SNAKE_CASE__							)
  return hidden_state







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	ResNetConfig							)  ->    List[str]:
  super().__init__()
  __lowerCAmelCase											=						ResNetConvLayer(
      config.num_channels      ,       config.embedding_size      ,       kernel_size=7      ,       stride=2      ,       activation=config.hidden_act							)
  __lowerCAmelCase											=						nn.MaxPoolad(kernel_size=3      ,       stride=2      ,       padding=1							)
  __lowerCAmelCase											=						config.num_channels





 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tensor							)  ->    Tensor:
  __lowerCAmelCase											=						pixel_values.shape[1]
  if num_channels != self.num_channels:
   raise ValueError(
       """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""							)
  __lowerCAmelCase											=						self.embedder(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.pooler(SCREAMING_SNAKE_CASE__							)
  return embedding







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	int = 2							)  ->    Dict:
  super().__init__()
  __lowerCAmelCase											=						nn.Convad(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       kernel_size=1      ,       stride=SCREAMING_SNAKE_CASE__      ,       bias=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						nn.BatchNormad(SCREAMING_SNAKE_CASE__							)





 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tensor							)  ->    Tensor:
  __lowerCAmelCase											=						self.convolution(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.normalization(SCREAMING_SNAKE_CASE__							)
  return hidden_state







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	int = 1      ,       SCREAMING_SNAKE_CASE__   :	str = "relu"							)  ->    Dict:
  super().__init__()
  __lowerCAmelCase											=						in_channels != out_channels or stride != 1
  __lowerCAmelCase											=						(
      ResNetShortCut(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       stride=SCREAMING_SNAKE_CASE__							) if should_apply_shortcut else nn.Identity()
  )
  __lowerCAmelCase											=						nn.Sequential(
      ResNetConvLayer(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       stride=SCREAMING_SNAKE_CASE__							)      ,       ResNetConvLayer(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       activation=SCREAMING_SNAKE_CASE__							)      ,       )
  __lowerCAmelCase											=						ACTaFN[activation]





 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    int:
  __lowerCAmelCase											=						hidden_state
  __lowerCAmelCase											=						self.layer(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.shortcut(SCREAMING_SNAKE_CASE__							)
  hidden_state += residual
  __lowerCAmelCase											=						self.activation(SCREAMING_SNAKE_CASE__							)
  return hidden_state







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	int = 1      ,       SCREAMING_SNAKE_CASE__   :	str = "relu"      ,       SCREAMING_SNAKE_CASE__   :	int = 4							)  ->    int:
  super().__init__()
  __lowerCAmelCase											=						in_channels != out_channels or stride != 1
  __lowerCAmelCase											=						out_channels // reduction
  __lowerCAmelCase											=						(
      ResNetShortCut(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       stride=SCREAMING_SNAKE_CASE__							) if should_apply_shortcut else nn.Identity()
  )
  __lowerCAmelCase											=						nn.Sequential(
      ResNetConvLayer(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       kernel_size=1							)      ,       ResNetConvLayer(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       stride=SCREAMING_SNAKE_CASE__							)      ,       ResNetConvLayer(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       kernel_size=1      ,       activation=SCREAMING_SNAKE_CASE__							)      ,       )
  __lowerCAmelCase											=						ACTaFN[activation]





 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    Tuple:
  __lowerCAmelCase											=						hidden_state
  __lowerCAmelCase											=						self.layer(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.shortcut(SCREAMING_SNAKE_CASE__							)
  hidden_state += residual
  __lowerCAmelCase											=						self.activation(SCREAMING_SNAKE_CASE__							)
  return hidden_state







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	ResNetConfig      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	int = 2      ,       SCREAMING_SNAKE_CASE__   :	int = 2      ,       )  ->    int:
  super().__init__()

  __lowerCAmelCase											=						ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

  __lowerCAmelCase											=						nn.Sequential(
      # downsampling is done in the first layer with stride of 2
      layer(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       stride=SCREAMING_SNAKE_CASE__      ,       activation=config.hidden_act							)      ,       *[layer(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       activation=config.hidden_act							) for _ in range(depth - 1							)]      ,       )





 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tensor							)  ->    Tensor:
  __lowerCAmelCase											=						input
  for layer in self.layers:
   __lowerCAmelCase											=						layer(SCREAMING_SNAKE_CASE__							)
  return hidden_state







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	ResNetConfig							)  ->    Optional[int]:
  super().__init__()
  __lowerCAmelCase											=						nn.ModuleList([]							)
  # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
  self.stages.append(
      ResNetStage(
          SCREAMING_SNAKE_CASE__      ,       config.embedding_size      ,       config.hidden_sizes[0]      ,       stride=2 if config.downsample_in_first_stage else 1      ,       depth=config.depths[0]      ,       )							)
  __lowerCAmelCase											=						zip(config.hidden_sizes      ,       config.hidden_sizes[1:]							)
  for (in_channels, out_channels), depth in zip(SCREAMING_SNAKE_CASE__      ,       config.depths[1:]							):
   self.stages.append(ResNetStage(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       depth=SCREAMING_SNAKE_CASE__							)							)





 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Tensor      ,       SCREAMING_SNAKE_CASE__   :	bool = False      ,       SCREAMING_SNAKE_CASE__   :	bool = True							)  ->    BaseModelOutputWithNoAttention:
  __lowerCAmelCase											=						() if output_hidden_states else None

  for stage_module in self.stages:
   if output_hidden_states:
    __lowerCAmelCase											=						hidden_states + (hidden_state,)

   __lowerCAmelCase											=						stage_module(SCREAMING_SNAKE_CASE__							)

  if output_hidden_states:
   __lowerCAmelCase											=						hidden_states + (hidden_state,)

  if not return_dict:
   return tuple(v for v in [hidden_state, hidden_states] if v is not None							)

  return BaseModelOutputWithNoAttention(
      last_hidden_state=SCREAMING_SNAKE_CASE__      ,       hidden_states=SCREAMING_SNAKE_CASE__      ,       )







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       int      	=					ResNetConfig
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					"""resnet"""
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					"""pixel_values"""
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					True
 def a					(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    str:
  if isinstance(SCREAMING_SNAKE_CASE__      ,       nn.Convad							):
   nn.init.kaiming_normal_(module.weight      ,       mode="""fan_out"""      ,       nonlinearity="""relu"""							)
  elif isinstance(SCREAMING_SNAKE_CASE__      ,       (nn.BatchNormad, nn.GroupNorm)							):
   nn.init.constant_(module.weight      ,       1							)
   nn.init.constant_(module.bias      ,       0							)





 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=False							)  ->    int:
  if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						value







_A			:      Dict					      =				r'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): 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			:      Optional[int]					      =				r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. 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(
    """The bare ResNet model outputting raw features without any specific head on top."""  ,							UpperCAmelCase__  ,							)
class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 def __init__(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    List[str]:
  super().__init__(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						config
  __lowerCAmelCase											=						ResNetEmbeddings(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						ResNetEncoder(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						nn.AdaptiveAvgPoolad((1, 1)							)
  # Initialize weights and apply final processing
  self.post_init()





 @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__							)
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC      ,       output_type=SCREAMING_SNAKE_CASE__      ,       config_class=_CONFIG_FOR_DOC      ,       modality="""vision"""      ,       expected_output=_EXPECTED_OUTPUT_SHAPE      ,       )
 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tensor      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None							)  ->    BaseModelOutputWithPoolingAndNoAttention:
  __lowerCAmelCase											=						(
      output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
  )
  __lowerCAmelCase											=						return_dict if return_dict is not None else self.config.use_return_dict

  __lowerCAmelCase											=						self.embedder(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						self.encoder(
      SCREAMING_SNAKE_CASE__      ,       output_hidden_states=SCREAMING_SNAKE_CASE__      ,       return_dict=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						encoder_outputs[0]

  __lowerCAmelCase											=						self.pooler(SCREAMING_SNAKE_CASE__							)

  if not return_dict:
   return (last_hidden_state, pooled_output) + encoder_outputs[1:]

  return BaseModelOutputWithPoolingAndNoAttention(
      last_hidden_state=SCREAMING_SNAKE_CASE__      ,       pooler_output=SCREAMING_SNAKE_CASE__      ,       hidden_states=encoder_outputs.hidden_states      ,       )







@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """  ,							UpperCAmelCase__  ,							)
class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 def __init__(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Tuple							)  ->    Any:
  super().__init__(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						config.num_labels
  __lowerCAmelCase											=						ResNetModel(SCREAMING_SNAKE_CASE__							)
  # classification head
  __lowerCAmelCase											=						nn.Sequential(
      nn.Flatten()      ,       nn.Linear(config.hidden_sizes[-1]      ,       config.num_labels							) if config.num_labels > 0 else nn.Identity()      ,       )
  # initialize weights and apply final processing
  self.post_init()





 @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__							)
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT      ,       output_type=SCREAMING_SNAKE_CASE__      ,       config_class=_CONFIG_FOR_DOC      ,       expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,       )
 def a					(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	Optional[torch.FloatTensor] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[torch.LongTensor] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None      ,       )  ->    ImageClassifierOutputWithNoAttention:
  __lowerCAmelCase											=						return_dict if return_dict is not None else self.config.use_return_dict

  __lowerCAmelCase											=						self.resnet(SCREAMING_SNAKE_CASE__      ,       output_hidden_states=SCREAMING_SNAKE_CASE__      ,       return_dict=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						outputs.pooler_output if return_dict else outputs[1]

  __lowerCAmelCase											=						self.classifier(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						None

  if labels is not None:
   if self.config.problem_type is None:
    if self.num_labels == 1:
     __lowerCAmelCase											=						"""regression"""
    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
     __lowerCAmelCase											=						"""single_label_classification"""
    else:
     __lowerCAmelCase											=						"""multi_label_classification"""
   if self.config.problem_type == "regression":
    __lowerCAmelCase											=						MSELoss()
    if self.num_labels == 1:
     __lowerCAmelCase											=						loss_fct(logits.squeeze()      ,       labels.squeeze()							)
    else:
     __lowerCAmelCase											=						loss_fct(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
   elif self.config.problem_type == "single_label_classification":
    __lowerCAmelCase											=						CrossEntropyLoss()
    __lowerCAmelCase											=						loss_fct(logits.view(-1      ,       self.num_labels							)      ,       labels.view(-1							)							)
   elif self.config.problem_type == "multi_label_classification":
    __lowerCAmelCase											=						BCEWithLogitsLoss()
    __lowerCAmelCase											=						loss_fct(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

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

  return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE__      ,       logits=SCREAMING_SNAKE_CASE__      ,       hidden_states=outputs.hidden_states							)







@add_start_docstrings(
    """
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    """  ,							UpperCAmelCase__  ,							)
class 				_lowercase     (				UpperCAmelCase__  ,							UpperCAmelCase__			):


 '''simple docstring'''






 def __init__(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    Tuple:
  super().__init__(SCREAMING_SNAKE_CASE__							)
  super()._init_backbone(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						[config.embedding_size] + config.hidden_sizes
  __lowerCAmelCase											=						ResNetEmbeddings(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						ResNetEncoder(SCREAMING_SNAKE_CASE__							)

  # initialize weights and apply final processing
  self.post_init()





 @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__							)
 @replace_return_docstrings(output_type=SCREAMING_SNAKE_CASE__      ,       config_class=_CONFIG_FOR_DOC							)
 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tensor      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None							)  ->    BackboneOutput:
  __lowerCAmelCase											=						return_dict if return_dict is not None else self.config.use_return_dict
  __lowerCAmelCase											=						(
      output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
  )

  __lowerCAmelCase											=						self.embedder(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						self.encoder(SCREAMING_SNAKE_CASE__      ,       output_hidden_states=SCREAMING_SNAKE_CASE__      ,       return_dict=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						outputs.hidden_states

  __lowerCAmelCase											=						()
  for idx, stage in enumerate(self.stage_names							):
   if stage in self.out_features:
    feature_maps += (hidden_states[idx],)

  if not return_dict:
   __lowerCAmelCase											=						(feature_maps,)
   if output_hidden_states:
    output += (outputs.hidden_states,)
   return output

  return BackboneOutput(
      feature_maps=SCREAMING_SNAKE_CASE__      ,       hidden_states=outputs.hidden_states if output_hidden_states else None      ,       attentions=SCREAMING_SNAKE_CASE__      ,       )






