



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__  :  str								=					logging.get_logger(__name__)

# General docstring
A__  :  List[str]								=					'''ResNetConfig'''

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

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

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



class 				__snake_case						(     nn.Module						):




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







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Tensor):
									lowerCAmelCase_			:     Tuple									= self.convolution(A_)
									lowerCAmelCase_			:     str									= self.normalization(A_)
									lowerCAmelCase_			:     Tuple									= self.activation(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




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







							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       Tensor):
									lowerCAmelCase_			:     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.''')
									lowerCAmelCase_			:     int									= self.embedder(A_)
									lowerCAmelCase_			:     List[str]									= self.pooler(A_)
									return embedding





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       List[str]				,				A_     :       int				,				A_     :       int				,				A_     :       int = 2):
									super().__init__()
									lowerCAmelCase_			:     Optional[Any]									= nn.Convad(A_				,				A_				,				kernel_size=1				,				stride=A_				,				bias=A_)
									lowerCAmelCase_			:     int									= nn.BatchNormad(A_)







							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       Tensor):
									lowerCAmelCase_			:     str									= self.convolution(A_)
									lowerCAmelCase_			:     Optional[int]									= self.normalization(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




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







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Dict):
									lowerCAmelCase_			:     Optional[int]									= hidden_state
									lowerCAmelCase_			:     Any									= self.layer(A_)
									lowerCAmelCase_			:     Optional[int]									= self.shortcut(A_)
									hidden_state += residual
									lowerCAmelCase_			:     Tuple									= self.activation(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




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







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Dict):
									lowerCAmelCase_			:     List[str]									= hidden_state
									lowerCAmelCase_			:     Optional[Any]									= self.layer(A_)
									lowerCAmelCase_			:     Optional[Any]									= self.shortcut(A_)
									hidden_state += residual
									lowerCAmelCase_			:     Dict									= self.activation(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       int				,				A_     :       ResNetConfig				,				A_     :       int				,				A_     :       int				,				A_     :       int = 2				,				A_     :       int = 2				,				):
									super().__init__()

									lowerCAmelCase_			:     List[str]									= ResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else ResNetBasicLayer

									lowerCAmelCase_			:     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    UpperCAmelCase__			(       self     :       Tuple				,				A_     :       Tensor):
									lowerCAmelCase_			:     Union[str, Any]									= input
									for layer in self.layers:
											lowerCAmelCase_			:     List[str]									= layer(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       List[str]				,				A_     :       ResNetConfig):
									super().__init__()
									lowerCAmelCase_			:     Union[str, 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]				,				))
									lowerCAmelCase_			:     Tuple									= 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    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Tensor				,				A_     :       bool = False				,				A_     :       bool = True):
									lowerCAmelCase_			:     Union[str, Any]									= () if output_hidden_states else None

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

											lowerCAmelCase_			:     int									= stage_module(A_)

									if output_hidden_states:
											lowerCAmelCase_			:     List[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=A_				,				hidden_states=A_				,				)





class 				__snake_case						(     UpperCamelCase_						):
							_a			      = ResNetConfig
							_a			      = '''resnet'''
							_a			      = '''pixel_values'''
							_a			      = True




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Any):
									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    UpperCAmelCase__			(       self     :       str				,				A_     :       int				,				A_     :       Union[str, Any]=False):
									if isinstance(A_				,				A_):
											lowerCAmelCase_			:     Union[str, Any]									= value





A__  :  Any								=					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__  :  Union[str, Any]								=					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 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Any				,				A_     :       Any):
									super().__init__(A_)
									lowerCAmelCase_			:     str									= config
									lowerCAmelCase_			:     Union[str, Any]									= ResNetEmbeddings(A_)
									lowerCAmelCase_			:     Dict									= ResNetEncoder(A_)
									lowerCAmelCase_			:     Any									= 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    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       Tensor				,				A_     :       Optional[bool] = None				,				A_     :       Optional[bool] = None):
									lowerCAmelCase_			:     Optional[int]									= (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									lowerCAmelCase_			:     Optional[int]									= return_dict if return_dict is not None else self.config.use_return_dict

									lowerCAmelCase_			:     Tuple									= self.embedder(A_)

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

									lowerCAmelCase_			:     List[Any]									= encoder_outputs[0]

									lowerCAmelCase_			:     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.
    '''						,UpperCamelCase_						,)
class 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Dict				,				A_     :       List[str]):
									super().__init__(A_)
									lowerCAmelCase_			:     Optional[Any]									= config.num_labels
									lowerCAmelCase_			:     Union[str, Any]									= ResNetModel(A_)
									# classification head
									lowerCAmelCase_			:     List[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    UpperCAmelCase__			(       self     :       int				,				A_     :       Optional[torch.FloatTensor] = None				,				A_     :       Optional[torch.LongTensor] = None				,				A_     :       Optional[bool] = None				,				A_     :       Optional[bool] = None				,				):
									lowerCAmelCase_			:     Dict									= return_dict if return_dict is not None else self.config.use_return_dict

									lowerCAmelCase_			:     Dict									= self.resnet(A_				,				output_hidden_states=A_				,				return_dict=A_)

									lowerCAmelCase_			:     Optional[Any]									= outputs.pooler_output if return_dict else outputs[1]

									lowerCAmelCase_			:     List[str]									= self.classifier(A_)

									lowerCAmelCase_			:     List[str]									= None

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

									if not return_dict:
											lowerCAmelCase_			:     int									= (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.
    '''						,UpperCamelCase_						,)
class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						):




							def __init__(       self     :       Dict				,				A_     :       int):
									super().__init__(A_)
									super()._init_backbone(A_)

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

									lowerCAmelCase_			:     Any									= self.embedder(A_)

									lowerCAmelCase_			:     str									= self.encoder(A_				,				output_hidden_states=A_				,				return_dict=A_)

									lowerCAmelCase_			:     Union[str, Any]									= outputs.hidden_states

									lowerCAmelCase_			:     List[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_			:     Any									= (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_				,				)









'''simple docstring'''






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					['image_processor', 'tokenizer']
    lowercase    =					'AutoImageProcessor'
    lowercase    =					'AutoTokenizer'

    def __init__(   self    :  int    ,						lowerCamelCase    :  List[str]=None    ,						lowerCamelCase    :  Union[str, Any]=None    ,						**lowerCamelCase    :  str		)   ->  Tuple:
      lowerCAmelCase_			:      str			=   None
      if "feature_extractor" in kwargs:
        warnings.warn(
            """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
            """ instead."""    ,						lowerCamelCase    ,						)
        lowerCAmelCase_			:      Tuple			=   kwargs.pop("""feature_extractor"""		)

      lowerCAmelCase_			:      Tuple			=   image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError("""You need to specify an `image_processor`."""		)
      if tokenizer is None:
        raise ValueError("""You need to specify a `tokenizer`."""		)

      super().__init__(lowerCamelCase    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Optional[int]			=   self.image_processor
      lowerCAmelCase_			:      Any			=   False

    def __call__(   self    :  List[Any]    ,						*lowerCamelCase    :  str    ,						**lowerCamelCase    :  Tuple		)   ->  Union[str, Any]:
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*lowerCamelCase    ,						**lowerCamelCase		)

      lowerCAmelCase_			:      Any			=   kwargs.pop("""images"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Dict			=   kwargs.pop("""text"""    ,						lowerCamelCase		)
      if len(lowerCamelCase		) > 0:
        lowerCAmelCase_			:      str			=   args[0]
        lowerCAmelCase_			:      Dict			=   args[1:]

      if images is None and text is None:
        raise ValueError("""You need to specify either an `images` or `text` input to process."""		)

      if images is not None:
        lowerCAmelCase_			:      Any			=   self.image_processor(lowerCamelCase    ,						*lowerCamelCase    ,						**lowerCamelCase		)
      if text is not None:
        lowerCAmelCase_			:      str			=   self.tokenizer(lowerCamelCase    ,						**lowerCamelCase		)

      if text is None:
        return inputs
      elif images is None:
        return encodings
      else:
        lowerCAmelCase_			:      Dict			=   encodings["""input_ids"""]
        return inputs

    def __lowercase   (   self    :  str    ,						*lowerCamelCase    :  Union[str, Any]    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  List[str]:
      return self.tokenizer.batch_decode(*lowerCamelCase    ,						**lowerCamelCase		)

    def __lowercase   (   self    :  List[Any]    ,						*lowerCamelCase    :  Any    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  List[str]:
      return self.tokenizer.decode(*lowerCamelCase    ,						**lowerCamelCase		)

    @contextmanager
    def __lowercase   (   self    :  str		)   ->  Union[str, Any]:
      warnings.warn(
          """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
          """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
          """your images inputs, or in a separate call."""		)
      lowerCAmelCase_			:      List[str]			=   True
      lowerCAmelCase_			:      Optional[Any]			=   self.tokenizer
      yield
      lowerCAmelCase_			:      List[Any]			=   self.image_processor
      lowerCAmelCase_			:      List[str]			=   False

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  str=False    ,						lowerCamelCase    :  List[Any]=None		)   ->  Optional[int]:
      if added_vocab is None:
        lowerCAmelCase_			:      str			=   self.tokenizer.get_added_vocab()

      lowerCAmelCase_			:      Union[str, Any]			=   {}

      while tokens:
        lowerCAmelCase_			:      Dict			=   re.search(R"""<s_(.*?)>"""    ,						lowerCamelCase    ,						re.IGNORECASE		)
        if start_token is None:
          break
        lowerCAmelCase_			:      Tuple			=   start_token.group(1		)
        lowerCAmelCase_			:      Tuple			=   re.search(RF'</s_{key}>'    ,						lowerCamelCase    ,						re.IGNORECASE		)
        lowerCAmelCase_			:      Tuple			=   start_token.group()
        if end_token is None:
          lowerCAmelCase_			:      str			=   tokens.replace(lowerCamelCase    ,						""""""		)
        else:
          lowerCAmelCase_			:      List[str]			=   end_token.group()
          lowerCAmelCase_			:      Dict			=   re.escape(lowerCamelCase		)
          lowerCAmelCase_			:      int			=   re.escape(lowerCamelCase		)
          lowerCAmelCase_			:      Dict			=   re.search(F'{start_token_escaped}(.*?){end_token_escaped}'    ,						lowerCamelCase    ,						re.IGNORECASE		)
          if content is not None:
            lowerCAmelCase_			:      str			=   content.group(1		).strip()
            if r"<s_" in content and r"</s_" in content:  # non-leaf node
              lowerCAmelCase_			:      str			=   self.tokenajson(lowerCamelCase    ,						is_inner_value=lowerCamelCase    ,						added_vocab=lowerCamelCase		)
              if value:
                if len(lowerCamelCase		) == 1:
                  lowerCAmelCase_			:      List[Any]			=   value[0]
                lowerCAmelCase_			:      Optional[Any]			=   value
            else:  # leaf nodes
              lowerCAmelCase_			:      List[str]			=   []
              for leaf in content.split(R"""<sep/>"""		):
                lowerCAmelCase_			:      Union[str, Any]			=   leaf.strip()
                if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                  lowerCAmelCase_			:      Any			=   leaf[1:-2]  # for categorical special tokens
                output[key].append(lowerCamelCase		)
              if len(output[key]		) == 1:
                lowerCAmelCase_			:      Optional[Any]			=   output[key][0]

          lowerCAmelCase_			:      List[Any]			=   tokens[tokens.find(lowerCamelCase		) + len(lowerCamelCase		) :].strip()
          if tokens[:6] == r"<sep/>":  # non-leaf nodes
            return [output] + self.tokenajson(tokens[6:]    ,						is_inner_value=lowerCamelCase    ,						added_vocab=lowerCamelCase		)

      if len(lowerCamelCase		):
        return [output] if is_inner_value else output
      else:
        return [] if is_inner_value else {"text_sequence": tokens}

    @property
    def __lowercase   (   self    :  Dict		)   ->  int:
      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,						lowerCamelCase    ,						)
      return self.image_processor_class





    @property
    def __lowercase   (   self    :  Dict		)   ->  Optional[Any]:
      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,						lowerCamelCase    ,						)
      return self.image_processor



