


import string
from math import logaa



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str )       ->							int:
   __lowerCamelCase:				Any     =	document.translate(
       str.maketrans(""""""						,      """"""						,      string.punctuation ) ).replace("""\n"""						,      """""" )
   __lowerCamelCase:				List[Any]     =	document_without_punctuation.split(""" """ )  # word tokenization
   return len([word for word in tokenize_document if word.lower() == term.lower()] )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str )       ->							tuple[int, int]:
   __lowerCamelCase:				Any     =	corpus.lower().translate(
       str.maketrans(""""""						,      """"""						,      string.punctuation ) )  # strip all punctuation and replace it with ''
   __lowerCamelCase:				Any     =	corpus_without_punctuation.split("""\n""" )
   __lowerCamelCase:				str     =	term.lower()
   return (len([doc for doc in docs if term in doc] ), len(snake_case ))



def     __lowerCAmelCase						(							snake_case	:    int						,      snake_case	:    int						,      snake_case	:    int=False )       ->							float:
   if smoothing:
      if n == 0:
         raise ValueError("""log10(0) is undefined.""" )
      return round(1 + logaa(n / (1 + df) )						,      3 )

   if df == 0:
      raise ZeroDivisionError("""df must be > 0""" )
   elif n == 0:
      raise ValueError("""log10(0) is undefined.""" )
   return round(logaa(n / df )						,      3 )







def     __lowerCAmelCase						(							snake_case	:    int						,      snake_case	:    int )       ->							float:
   return round(tf * idf						,      3 )






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		:				int									=       '''ResNetConfig'''

# Base docstring
_A		:				Union[str, Any]									=       '''microsoft/resnet-50'''
_A		:				str									=       [1, 2_048, 7, 7]

# Image classification docstring
_A		:				List[str]									=       '''microsoft/resnet-50'''
_A		:				Optional[int]									=       '''tiger cat'''

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



class        a					(			nn.Module   ):





     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"							):
        super().__init__()
        __lowerCamelCase:				Optional[int]     =	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:				List[str]     =	nn.BatchNormad(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	ACTaFN[activation] if activation is not None else nn.Identity()







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Tensor							):
        __lowerCamelCase:				Optional[int]     =	self.convolution(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	self.normalization(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	self.activation(SCREAMING_SNAKE_CASE_							)
        return hidden_state



class        a					(			nn.Module   ):





     def __init__(    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				ResNetConfig							):
        super().__init__()
        __lowerCamelCase:				Tuple     =	ResNetConvLayer(
            config.num_channels    ,	config.embedding_size    ,	kernel_size=7    ,	stride=2    ,	activation=config.hidden_act							)
        __lowerCamelCase:				Tuple     =	nn.MaxPoolad(kernel_size=3    ,	stride=2    ,	padding=1							)
        __lowerCamelCase:				List[Any]     =	config.num_channels







     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Tensor							):
        __lowerCamelCase:				Optional[Any]     =	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:				Optional[Any]     =	self.embedder(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	self.pooler(SCREAMING_SNAKE_CASE_							)
        return embedding



class        a					(			nn.Module   ):





     def __init__(    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int = 2							):
        super().__init__()
        __lowerCamelCase:				Optional[Any]     =	nn.Convad(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	kernel_size=1    ,	stride=SCREAMING_SNAKE_CASE_    ,	bias=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	nn.BatchNormad(SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Tensor							):
        __lowerCamelCase:				Optional[int]     =	self.convolution(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	self.normalization(SCREAMING_SNAKE_CASE_							)
        return hidden_state



class        a					(			nn.Module   ):





     def __init__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int = 1    ,	SCREAMING_SNAKE_CASE_					:				str = "relu"							):
        super().__init__()
        __lowerCamelCase:				Union[str, Any]     =	in_channels != out_channels or stride != 1
        __lowerCamelCase:				int     =	(
            ResNetShortCut(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	stride=SCREAMING_SNAKE_CASE_							) if should_apply_shortcut else nn.Identity()
        )
        __lowerCamelCase:				List[str]     =	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:				Dict     =	ACTaFN[activation]







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Any							):
        __lowerCamelCase:				List[Any]     =	hidden_state
        __lowerCamelCase:				Optional[Any]     =	self.layer(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	self.shortcut(SCREAMING_SNAKE_CASE_							)
        hidden_state += residual
        __lowerCamelCase:				str     =	self.activation(SCREAMING_SNAKE_CASE_							)
        return hidden_state



class        a					(			nn.Module   ):





     def __init__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int = 1    ,	SCREAMING_SNAKE_CASE_					:				str = "relu"    ,	SCREAMING_SNAKE_CASE_					:				int = 4							):
        super().__init__()
        __lowerCamelCase:				str     =	in_channels != out_channels or stride != 1
        __lowerCamelCase:				Any     =	out_channels // reduction
        __lowerCamelCase:				str     =	(
            ResNetShortCut(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	stride=SCREAMING_SNAKE_CASE_							) if should_apply_shortcut else nn.Identity()
        )
        __lowerCamelCase:				Any     =	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:				str     =	ACTaFN[activation]







     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        __lowerCamelCase:				Optional[Any]     =	hidden_state
        __lowerCamelCase:				int     =	self.layer(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	self.shortcut(SCREAMING_SNAKE_CASE_							)
        hidden_state += residual
        __lowerCamelCase:				List[str]     =	self.activation(SCREAMING_SNAKE_CASE_							)
        return hidden_state



class        a					(			nn.Module   ):





     def __init__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				ResNetConfig    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int = 2    ,	SCREAMING_SNAKE_CASE_					:				int = 2    ,	):
        super().__init__()

        __lowerCamelCase:				str     =	ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

        __lowerCamelCase:				str     =	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  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Tensor							):
        __lowerCamelCase:				Tuple     =	input
        for layer in self.layers:
           __lowerCamelCase:				int     =	layer(SCREAMING_SNAKE_CASE_							)
        return hidden_state



class        a					(			nn.Module   ):





     def __init__(    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				ResNetConfig							):
        super().__init__()
        __lowerCamelCase:				Any     =	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:				List[str]     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Tensor    ,	SCREAMING_SNAKE_CASE_					:				bool = False    ,	SCREAMING_SNAKE_CASE_					:				bool = True							):
        __lowerCamelCase:				List[Any]     =	() if output_hidden_states else None

        for stage_module in self.stages:
           if output_hidden_states:
              __lowerCamelCase:				Optional[Any]     =	hidden_states + (hidden_state,)

           __lowerCamelCase:				Union[str, Any]     =	stage_module(SCREAMING_SNAKE_CASE_							)

        if output_hidden_states:
           __lowerCamelCase:				Union[str, Any]     =	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        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  Dict						      =	ResNetConfig
     UpperCAmelCase__ :  str						      =	"resnet"
     UpperCAmelCase__ :  Optional[int]						      =	"pixel_values"
     UpperCAmelCase__ :  List[str]						      =	True





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        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  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				str=False							):
        if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				Union[str, Any]     =	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        a					(			_UpperCAmelCase   ):





     def __init__(    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]							):
        super().__init__(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	config
        __lowerCamelCase:				Tuple     =	ResNetEmbeddings(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	ResNetEncoder(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Tensor    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None							):
        __lowerCamelCase:				Dict     =	(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        __lowerCamelCase:				List[Any]     =	return_dict if return_dict is not None else self.config.use_return_dict

        __lowerCamelCase:				Any     =	self.embedder(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	self.encoder(
            SCREAMING_SNAKE_CASE_    ,	output_hidden_states=SCREAMING_SNAKE_CASE_    ,	return_dict=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	encoder_outputs[0]

        __lowerCamelCase:				List[Any]     =	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(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "     ,_UpperCAmelCase     ,)
class        a					(			_UpperCAmelCase   ):





     def __init__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        super().__init__(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	config.num_labels
        __lowerCamelCase:				int     =	ResNetModel(SCREAMING_SNAKE_CASE_							)
        # classification head
        __lowerCamelCase:				str     =	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  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	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    ,	):
        __lowerCamelCase:				Tuple     =	return_dict if return_dict is not None else self.config.use_return_dict

        __lowerCamelCase:				List[str]     =	self.resnet(SCREAMING_SNAKE_CASE_    ,	output_hidden_states=SCREAMING_SNAKE_CASE_    ,	return_dict=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	outputs.pooler_output if return_dict else outputs[1]

        __lowerCamelCase:				str     =	self.classifier(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Dict     =	None

        if labels is not None:
           if self.config.problem_type is None:
              if self.num_labels == 1:
                 __lowerCamelCase:				Dict     =	"""regression"""
              elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                 __lowerCamelCase:				Dict     =	"""single_label_classification"""
              else:
                 __lowerCamelCase:				Tuple     =	"""multi_label_classification"""
           if self.config.problem_type == "regression":
              __lowerCamelCase:				Any     =	MSELoss()
              if self.num_labels == 1:
                 __lowerCamelCase:				Dict     =	loss_fct(logits.squeeze()    ,	labels.squeeze()							)
              else:
                 __lowerCamelCase:				str     =	loss_fct(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
           elif self.config.problem_type == "single_label_classification":
              __lowerCamelCase:				List[Any]     =	CrossEntropyLoss()
              __lowerCamelCase:				Tuple     =	loss_fct(logits.view(-1    ,	self.num_labels							)    ,	labels.view(-1							)							)
           elif self.config.problem_type == "multi_label_classification":
              __lowerCamelCase:				int     =	BCEWithLogitsLoss()
              __lowerCamelCase:				str     =	loss_fct(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

        if not return_dict:
           __lowerCamelCase:				List[str]     =	(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(
    "\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    "     ,_UpperCAmelCase     ,)
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase   ):





     def __init__(    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        super().__init__(SCREAMING_SNAKE_CASE_							)
        super()._init_backbone(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[int]     =	[config.embedding_size] + config.hidden_sizes
        __lowerCamelCase:				str     =	ResNetEmbeddings(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	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  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Tensor    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None							):
        __lowerCamelCase:				Dict     =	return_dict if return_dict is not None else self.config.use_return_dict
        __lowerCamelCase:				Dict     =	(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        __lowerCamelCase:				str     =	self.embedder(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Dict     =	self.encoder(SCREAMING_SNAKE_CASE_    ,	output_hidden_states=SCREAMING_SNAKE_CASE_    ,	return_dict=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	outputs.hidden_states

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

        if not return_dict:
           __lowerCamelCase:				Union[str, Any]     =	(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_    ,	)


