





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


_lowerCAmelCase 				=     logging.get_logger(__name__)

_lowerCAmelCase 				=     {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase 				=     {
    """vocab_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
        ),
        """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-openqa""": (
            """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-reader""": (
            """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-openqa""": (
            """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-reader""": (
            """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
        ),
    },
}

_lowerCAmelCase 				=     {
    """google/realm-cc-news-pretrained-embedder""": 512,
    """google/realm-cc-news-pretrained-encoder""": 512,
    """google/realm-cc-news-pretrained-scorer""": 512,
    """google/realm-cc-news-pretrained-openqa""": 512,
    """google/realm-orqa-nq-openqa""": 512,
    """google/realm-orqa-nq-reader""": 512,
    """google/realm-orqa-wq-openqa""": 512,
    """google/realm-orqa-wq-reader""": 512,
}

_lowerCAmelCase 				=     {
    """google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							VOCAB_FILES_NAMES
      a						       =							PRETRAINED_VOCAB_FILES_MAP
      a						       =							PRETRAINED_INIT_CONFIGURATION
      a						       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a						       =							RealmTokenizer
      def __init__(		self      ,       a__=None      ,       a__=None      ,       a__=True      ,       a__="[UNK]"      ,       a__="[SEP]"      ,       a__="[PAD]"      ,       a__="[CLS]"      ,       a__="[MASK]"      ,       a__=True      ,       a__=None      ,       **a__      ,       ):
         super().__init__(
             a__      ,       tokenizer_file=a__      ,       do_lower_case=a__      ,       unk_token=a__      ,       sep_token=a__      ,       pad_token=a__      ,       cls_token=a__      ,       mask_token=a__      ,       tokenize_chinese_chars=a__      ,       strip_accents=a__      ,       **a__      ,       )

         A_     :				str			      =	json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
         if (
             normalizer_state.get("""lowercase"""      ,       a__       ) != do_lower_case
             or normalizer_state.get("""strip_accents"""      ,       a__       ) != strip_accents
             or normalizer_state.get("""handle_chinese_chars"""      ,       a__       ) != tokenize_chinese_chars
         ):
            A_     :				str			      =	getattr(a__      ,       normalizer_state.pop("""type"""       )       )
            A_     :				Optional[Any]			      =	do_lower_case
            A_     :				int			      =	strip_accents
            A_     :				Optional[Any]			      =	tokenize_chinese_chars
            A_     :				Optional[Any]			      =	normalizer_class(**a__       )

         A_     :				Optional[int]			      =	do_lower_case
      def 		_lowerCamelCase      (		self      ,       a__      ,       **a__       ):
         A_     :				Optional[int]			      =	PaddingStrategy.MAX_LENGTH

         A_     :				int			      =	text
         A_     :				int			      =	kwargs.pop("""text_pair"""      ,       a__       )
         A_     :				Optional[Any]			      =	kwargs.pop("""return_tensors"""      ,       a__       )

         A_     :				Dict			      =	{
             """input_ids""": [],
             """attention_mask""": [],
             """token_type_ids""": [],
         }

         for idx, candidate_text in enumerate(a__       ):
            if batch_text_pair is not None:
               A_     :				str			      =	batch_text_pair[idx]
            else:
               A_     :				Any			      =	None

            A_     :				Dict			      =	super().__call__(a__      ,       a__      ,       return_tensors=a__      ,       **a__       )

            A_     :				int			      =	encoded_candidates.get("""input_ids"""       )
            A_     :				List[Any]			      =	encoded_candidates.get("""attention_mask"""       )
            A_     :				Optional[Any]			      =	encoded_candidates.get("""token_type_ids"""       )

            if encoded_input_ids is not None:
               output_data["input_ids"].append(a__       )
            if encoded_attention_mask is not None:
               output_data["attention_mask"].append(a__       )
            if encoded_token_type_ids is not None:
               output_data["token_type_ids"].append(a__       )

         A_     :				str			      =	{key: item for key, item in output_data.items() if len(a__       ) != 0}

         return BatchEncoding(a__      ,       tensor_type=a__       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=None       ):
         A_     :				Dict			      =	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
            output += token_ids_a + [self.sep_token_id]

         return output
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         A_     :				Optional[int]			      =	[self.sep_token_id]
         A_     :				Dict			      =	[self.cls_token_id]
         if token_ids_a is None:
            return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]






      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         A_     :				List[str]			      =	self._tokenizer.model.save(a__      ,       name=a__       )
         return tuple(a__       )













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


_lowerCAmelCase 				=     logging.get_logger(__name__)

# General docstring
_lowerCAmelCase 				=     """ResNetConfig"""

# Base docstring
_lowerCAmelCase 				=     """microsoft/resnet-50"""
_lowerCAmelCase 				=     [1, 2_048, 7, 7]

# Image classification docstring
_lowerCAmelCase 				=     """microsoft/resnet-50"""
_lowerCAmelCase 				=     """tiger cat"""

_lowerCAmelCase 				=     [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]

class    _UpperCAmelCase							(		nn.Module     ):
      def __init__(		self      ,       a__      ,       a__      ,       a__ = 3      ,       a__ = 1      ,       a__ = "relu"       ):
         super().__init__()
         A_     :				Tuple			      =	nn.Convad(
             a__      ,       a__      ,       kernel_size=a__      ,       stride=a__      ,       padding=kernel_size // 2      ,       bias=a__       )
         A_     :				Optional[Any]			      =	nn.BatchNormad(a__       )
         A_     :				List[Any]			      =	ACTaFN[activation] if activation is not None else nn.Identity()






      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				List[str]			      =	self.convolution(a__       )
         A_     :				Optional[int]			      =	self.normalization(a__       )
         A_     :				int			      =	self.activation(a__       )
         return hidden_state






class    _UpperCAmelCase							(		nn.Module     ):
      def __init__(		self      ,       a__       ):
         super().__init__()
         A_     :				Union[str, Any]			      =	ResNetConvLayer(
             config.num_channels      ,       config.embedding_size      ,       kernel_size=7      ,       stride=2      ,       activation=config.hidden_act       )
         A_     :				Any			      =	nn.MaxPoolad(kernel_size=3      ,       stride=2      ,       padding=1       )
         A_     :				Optional[Any]			      =	config.num_channels






      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Tuple			      =	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."""       )
         A_     :				str			      =	self.embedder(a__       )
         A_     :				Dict			      =	self.pooler(a__       )
         return embedding






class    _UpperCAmelCase							(		nn.Module     ):
      def __init__(		self      ,       a__      ,       a__      ,       a__ = 2       ):
         super().__init__()
         A_     :				Union[str, Any]			      =	nn.Convad(a__      ,       a__      ,       kernel_size=1      ,       stride=a__      ,       bias=a__       )
         A_     :				int			      =	nn.BatchNormad(a__       )






      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				List[str]			      =	self.convolution(a__       )
         A_     :				List[str]			      =	self.normalization(a__       )
         return hidden_state






class    _UpperCAmelCase							(		nn.Module     ):
      def __init__(		self      ,       a__      ,       a__      ,       a__ = 1      ,       a__ = "relu"       ):
         super().__init__()
         A_     :				Union[str, Any]			      =	in_channels != out_channels or stride != 1
         A_     :				List[Any]			      =	(
             ResNetShortCut(a__      ,       a__      ,       stride=a__       ) if should_apply_shortcut else nn.Identity()
         )
         A_     :				List[str]			      =	nn.Sequential(
             ResNetConvLayer(a__      ,       a__      ,       stride=a__       )      ,       ResNetConvLayer(a__      ,       a__      ,       activation=a__       )      ,       )
         A_     :				Any			      =	ACTaFN[activation]






      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Any			      =	hidden_state
         A_     :				Optional[Any]			      =	self.layer(a__       )
         A_     :				Any			      =	self.shortcut(a__       )
         hidden_state += residual
         A_     :				Optional[Any]			      =	self.activation(a__       )
         return hidden_state






class    _UpperCAmelCase							(		nn.Module     ):
      def __init__(		self      ,       a__      ,       a__      ,       a__ = 1      ,       a__ = "relu"      ,       a__ = 4       ):
         super().__init__()
         A_     :				int			      =	in_channels != out_channels or stride != 1
         A_     :				List[str]			      =	out_channels // reduction
         A_     :				Optional[Any]			      =	(
             ResNetShortCut(a__      ,       a__      ,       stride=a__       ) if should_apply_shortcut else nn.Identity()
         )
         A_     :				Any			      =	nn.Sequential(
             ResNetConvLayer(a__      ,       a__      ,       kernel_size=1       )      ,       ResNetConvLayer(a__      ,       a__      ,       stride=a__       )      ,       ResNetConvLayer(a__      ,       a__      ,       kernel_size=1      ,       activation=a__       )      ,       )
         A_     :				Dict			      =	ACTaFN[activation]






      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				int			      =	hidden_state
         A_     :				Dict			      =	self.layer(a__       )
         A_     :				Optional[Any]			      =	self.shortcut(a__       )
         hidden_state += residual
         A_     :				Dict			      =	self.activation(a__       )
         return hidden_state






class    _UpperCAmelCase							(		nn.Module     ):
      def __init__(		self      ,       a__      ,       a__      ,       a__      ,       a__ = 2      ,       a__ = 2      ,       ):
         super().__init__()

         A_     :				List[Any]			      =	ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

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






      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				List[Any]			      =	input
         for layer in self.layers:
            A_     :				List[str]			      =	layer(a__       )
         return hidden_state






class    _UpperCAmelCase							(		nn.Module     ):
      def __init__(		self      ,       a__       ):
         super().__init__()
         A_     :				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(
                 a__      ,       config.embedding_size      ,       config.hidden_sizes[0]      ,       stride=2 if config.downsample_in_first_stage else 1      ,       depth=config.depths[0]      ,       )       )
         A_     :				Any			      =	zip(config.hidden_sizes      ,       config.hidden_sizes[1:]       )
         for (in_channels, out_channels), depth in zip(a__      ,       config.depths[1:]       ):
            self.stages.append(ResNetStage(a__      ,       a__      ,       a__      ,       depth=a__       )       )






      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = False      ,       a__ = True       ):
         A_     :				Union[str, Any]			      =	() if output_hidden_states else None

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

            A_     :				str			      =	stage_module(a__       )

         if output_hidden_states:
            A_     :				int			      =	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=a__      ,       hidden_states=a__      ,       )






class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							ResNetConfig
      a						       =							'''resnet'''
      a						       =							'''pixel_values'''
      a						       =							True
      def 		_lowerCamelCase      (		self      ,       a__       ):
         if isinstance(a__      ,       nn.Convad       ):
            nn.init.kaiming_normal_(module.weight      ,       mode="""fan_out"""      ,       nonlinearity="""relu"""       )
         elif isinstance(a__      ,       (nn.BatchNormad, nn.GroupNorm)       ):
            nn.init.constant_(module.weight      ,       1       )
            nn.init.constant_(module.bias      ,       0       )






      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=False       ):
         if isinstance(a__      ,       a__       ):
            A_     :				Dict			      =	value






_lowerCAmelCase 				=     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.
"""

_lowerCAmelCase 				=     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.'''			,       _lowerCamelCase			,       )
class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __init__(		self      ,       a__       ):
         super().__init__(a__       )
         A_     :				Tuple			      =	config
         A_     :				List[str]			      =	ResNetEmbeddings(a__       )
         A_     :				Optional[int]			      =	ResNetEncoder(a__       )
         A_     :				int			      =	nn.AdaptiveAvgPoolad((1, 1)       )
         # Initialize weights and apply final processing
         self.post_init()






      @add_start_docstrings_to_model_forward(a__       )
      @add_code_sample_docstrings(
          checkpoint=_CHECKPOINT_FOR_DOC      ,       output_type=a__      ,       config_class=_CONFIG_FOR_DOC      ,       modality="""vision"""      ,       expected_output=_EXPECTED_OUTPUT_SHAPE      ,       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None      ,       a__ = None       ):
         A_     :				str			      =	(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         A_     :				Dict			      =	return_dict if return_dict is not None else self.config.use_return_dict

         A_     :				Any			      =	self.embedder(a__       )

         A_     :				int			      =	self.encoder(
             a__      ,       output_hidden_states=a__      ,       return_dict=a__       )

         A_     :				Optional[Any]			      =	encoder_outputs[0]

         A_     :				List[str]			      =	self.pooler(a__       )

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

         return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=a__      ,       pooler_output=a__      ,       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.
    '''			,       _lowerCamelCase			,       )
class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __init__(		self      ,       a__       ):
         super().__init__(a__       )
         A_     :				Optional[Any]			      =	config.num_labels
         A_     :				Tuple			      =	ResNetModel(a__       )
         # classification head
         A_     :				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(a__       )
      @add_code_sample_docstrings(
          checkpoint=_IMAGE_CLASS_CHECKPOINT      ,       output_type=a__      ,       config_class=_CONFIG_FOR_DOC      ,       expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,       )
      def 		_lowerCamelCase      (		self      ,       a__ = None      ,       a__ = None      ,       a__ = None      ,       a__ = None      ,       ):
         A_     :				Optional[int]			      =	return_dict if return_dict is not None else self.config.use_return_dict

         A_     :				Any			      =	self.resnet(a__      ,       output_hidden_states=a__      ,       return_dict=a__       )

         A_     :				List[Any]			      =	outputs.pooler_output if return_dict else outputs[1]

         A_     :				Dict			      =	self.classifier(a__       )

         A_     :				Optional[int]			      =	None

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

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

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






@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''			,       _lowerCamelCase			,       )
class    _UpperCAmelCase							(		_lowerCamelCase			,       _lowerCamelCase     ):
      def __init__(		self      ,       a__       ):
         super().__init__(a__       )
         super()._init_backbone(a__       )

         A_     :				Optional[Any]			      =	[config.embedding_size] + config.hidden_sizes
         A_     :				Any			      =	ResNetEmbeddings(a__       )
         A_     :				Tuple			      =	ResNetEncoder(a__       )

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






      @add_start_docstrings_to_model_forward(a__       )
      @replace_return_docstrings(output_type=a__      ,       config_class=_CONFIG_FOR_DOC       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None      ,       a__ = None       ):
         A_     :				List[str]			      =	return_dict if return_dict is not None else self.config.use_return_dict
         A_     :				List[str]			      =	(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )

         A_     :				Dict			      =	self.embedder(a__       )

         A_     :				Tuple			      =	self.encoder(a__      ,       output_hidden_states=a__      ,       return_dict=a__       )

         A_     :				Dict			      =	outputs.hidden_states

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

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

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






