


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


UpperCAmelCase_      : int  					=       logging.get_logger(__name__)

# General docstring
UpperCAmelCase_      : Tuple  					=       'ResNetConfig'

# Base docstring
UpperCAmelCase_      : List[str]  					=       'microsoft/resnet-50'
UpperCAmelCase_      : Optional[int]  					=       [1, 2048, 7, 7]

# Image classification docstring
UpperCAmelCase_      : str  					=       'microsoft/resnet-50'
UpperCAmelCase_      : Tuple  					=       'tiger cat'

UpperCAmelCase_      : str  					=       [
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int = 3     ,							SCREAMING_SNAKE_CASE__				:       int = 1     ,							SCREAMING_SNAKE_CASE__				:       str = "relu"     )		->			int:
             super().__init__()
             a_					:		Optional[Any]							=  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__     )
             a_					:		int							=  nn.BatchNormad(SCREAMING_SNAKE_CASE__     )
             a_					:		Any							=  ACTaFN[activation] if activation is not None else nn.Identity()

      def        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       Tensor     )		->			Tensor:
             a_					:		Tuple							=  self.convolution(SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  self.normalization(SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  self.activation(SCREAMING_SNAKE_CASE__     )
             return hidden_state
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       ResNetConfig     )		->			int:
             super().__init__()
             a_					:		str							=  ResNetConvLayer(
                 config.num_channels     ,							config.embedding_size     ,							kernel_size=7     ,							stride=2     ,							activation=config.hidden_act     )
             a_					:		Dict							=  nn.MaxPoolad(kernel_size=3     ,							stride=2     ,							padding=1     )
             a_					:		Optional[Any]							=  config.num_channels

      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Tensor     )		->			Tensor:
             a_					:		List[str]							=  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_					:		Optional[Any]							=  self.embedder(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  self.pooler(SCREAMING_SNAKE_CASE__     )
             return embedding
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int = 2     )		->			Dict:
             super().__init__()
             a_					:		Any							=  nn.Convad(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							kernel_size=1     ,							stride=SCREAMING_SNAKE_CASE__     ,							bias=SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  nn.BatchNormad(SCREAMING_SNAKE_CASE__     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Tensor     )		->			Tensor:
             a_					:		Any							=  self.convolution(SCREAMING_SNAKE_CASE__     )
             a_					:		List[str]							=  self.normalization(SCREAMING_SNAKE_CASE__     )
             return hidden_state
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int = 1     ,							SCREAMING_SNAKE_CASE__				:       str = "relu"     )		->			Optional[Any]:
             super().__init__()
             a_					:		Union[str, Any]							=  in_channels != out_channels or stride != 1
             a_					:		Union[str, Any]							=  (
                 ResNetShortCut(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							stride=SCREAMING_SNAKE_CASE__     ) if should_apply_shortcut else nn.Identity()
             )
             a_					:		int							=  nn.Sequential(
                 ResNetConvLayer(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							stride=SCREAMING_SNAKE_CASE__     )     ,							ResNetConvLayer(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							activation=SCREAMING_SNAKE_CASE__     )     ,							)
             a_					:		Any							=  ACTaFN[activation]

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Any     )		->			Union[str, Any]:
             a_					:		List[str]							=  hidden_state
             a_					:		Optional[Any]							=  self.layer(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[Any]							=  self.shortcut(SCREAMING_SNAKE_CASE__     )
             hidden_state += residual
             a_					:		int							=  self.activation(SCREAMING_SNAKE_CASE__     )
             return hidden_state
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int = 1     ,							SCREAMING_SNAKE_CASE__				:       str = "relu"     ,							SCREAMING_SNAKE_CASE__				:       int = 4     )		->			Optional[Any]:
             super().__init__()
             a_					:		Tuple							=  in_channels != out_channels or stride != 1
             a_					:		Any							=  out_channels // reduction
             a_					:		Optional[Any]							=  (
                 ResNetShortCut(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							stride=SCREAMING_SNAKE_CASE__     ) if should_apply_shortcut else nn.Identity()
             )
             a_					:		int							=  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__     )     ,							)
             a_					:		Optional[int]							=  ACTaFN[activation]

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       List[str]     )		->			Tuple:
             a_					:		Any							=  hidden_state
             a_					:		Dict							=  self.layer(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  self.shortcut(SCREAMING_SNAKE_CASE__     )
             hidden_state += residual
             a_					:		Union[str, Any]							=  self.activation(SCREAMING_SNAKE_CASE__     )
             return hidden_state
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       ResNetConfig     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int = 2     ,							SCREAMING_SNAKE_CASE__				:       int = 2     ,							)		->			Optional[int]:
             super().__init__()

             a_					:		List[Any]							=  ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

             a_					:		Union[str, Any]							=  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     )		->			Tensor:
             a_					:		Any							=  input
             for layer in self.layers:
                    a_					:		Optional[Any]							=  layer(SCREAMING_SNAKE_CASE__     )
             return hidden_state
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       ResNetConfig     )		->			str:
             super().__init__()
             a_					:		Tuple							=  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]     ,							)     )
             a_					:		Union[str, Any]							=  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				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Tensor     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       bool = True     )		->			BaseModelOutputWithNoAttention:
             a_					:		Union[str, Any]							=  () if output_hidden_states else None

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

                    a_					:		List[Any]							=  stage_module(SCREAMING_SNAKE_CASE__     )

             if output_hidden_states:
                    a_					:		Dict							=  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  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     List[Any]        =						ResNetConfig
      snake_case__				:     Optional[int]        =						'''resnet'''
      snake_case__				:     int        =						'''pixel_values'''
      snake_case__				:     str        =						True
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Any     )		->			Optional[int]:
             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				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       str=False     )		->			Any:
             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		List[Any]							=  value
UpperCAmelCase_      : List[str]  					=       R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

UpperCAmelCase_      : Union[str, Any]  					=       R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''						,    lowercase__						,    )
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Any:
             super().__init__(SCREAMING_SNAKE_CASE__     )
             a_					:		int							=  config
             a_					:		Optional[int]							=  ResNetEmbeddings(SCREAMING_SNAKE_CASE__     )
             a_					:		Any							=  ResNetEncoder(SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  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				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Tensor     ,							SCREAMING_SNAKE_CASE__				:       Optional[bool] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[bool] = None     )		->			BaseModelOutputWithPoolingAndNoAttention:
             a_					:		Tuple							=  (
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )
             a_					:		List[str]							=  return_dict if return_dict is not None else self.config.use_return_dict

             a_					:		List[str]							=  self.embedder(SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  self.encoder(
                 SCREAMING_SNAKE_CASE__     ,							output_hidden_states=SCREAMING_SNAKE_CASE__     ,							return_dict=SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  encoder_outputs[0]

             a_					:		List[str]							=  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.
    '''						,    lowercase__						,    )
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      def __init__(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			List[str]:
             super().__init__(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  config.num_labels
             a_					:		List[str]							=  ResNetModel(SCREAMING_SNAKE_CASE__     )
             # classification head
             a_					:		Dict							=  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				:       List[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     ,							)		->			ImageClassifierOutputWithNoAttention:
             a_					:		str							=  return_dict if return_dict is not None else self.config.use_return_dict

             a_					:		Dict							=  self.resnet(SCREAMING_SNAKE_CASE__     ,							output_hidden_states=SCREAMING_SNAKE_CASE__     ,							return_dict=SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  outputs.pooler_output if return_dict else outputs[1]

             a_					:		Any							=  self.classifier(SCREAMING_SNAKE_CASE__     )

             a_					:		Dict							=  None

             if labels is not None:
                    if self.config.problem_type is None:
                           if self.num_labels == 1:
                                  a_					:		Optional[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_					:		Optional[int]							=  'multi_label_classification'
                    if self.config.problem_type == "regression":
                           a_					:		str							=  MSELoss()
                           if self.num_labels == 1:
                                  a_					:		Optional[Any]							=  loss_fct(logits.squeeze()     ,							labels.squeeze()     )
                           else:
                                  a_					:		List[Any]							=  loss_fct(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
                    elif self.config.problem_type == "single_label_classification":
                           a_					:		Optional[int]							=  CrossEntropyLoss()
                           a_					:		Any							=  loss_fct(logits.view(-1     ,							self.num_labels     )     ,							labels.view(-1     )     )
                    elif self.config.problem_type == "multi_label_classification":
                           a_					:		List[Any]							=  BCEWithLogitsLoss()
                           a_					:		Optional[Any]							=  loss_fct(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             if not return_dict:
                    a_					:		Union[str, Any]							=  (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.
    '''						,    lowercase__						,    )
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    lowercase__			):
      def __init__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Any     )		->			Optional[int]:
             super().__init__(SCREAMING_SNAKE_CASE__     )
             super()._init_backbone(SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  [config.embedding_size] + config.hidden_sizes
             a_					:		Dict							=  ResNetEmbeddings(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, 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     )		->			BackboneOutput:
             a_					:		Optional[Any]							=  return_dict if return_dict is not None else self.config.use_return_dict
             a_					:		str							=  (
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )

             a_					:		Dict							=  self.embedder(SCREAMING_SNAKE_CASE__     )

             a_					:		List[Any]							=  self.encoder(SCREAMING_SNAKE_CASE__     ,							output_hidden_states=SCREAMING_SNAKE_CASE__     ,							return_dict=SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[Any]							=  outputs.hidden_states

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

             if not return_dict:
                    a_					:		Optional[int]							=  (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__     ,							)






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Tuple        =						['''image_processor''', '''tokenizer''']
      snake_case__				:     Union[str, Any]        =						'''CLIPImageProcessor'''
      snake_case__				:     Dict        =						('''CLIPTokenizer''', '''CLIPTokenizerFast''')
      def __init__(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							SCREAMING_SNAKE_CASE__				:       List[str]=None     ,							**SCREAMING_SNAKE_CASE__				:       int     )		->			Any:
             a_					:		List[Any]							=  None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                        ' instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
                    a_					:		Tuple							=  kwargs.pop('feature_extractor'     )

             a_					:		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__(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def __call__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							**SCREAMING_SNAKE_CASE__				:       str     )		->			Optional[Any]:

             if text is None and images is None:
                    raise ValueError('You have to specify either text or images. Both cannot be none.'     )

             if text is not None:
                    a_					:		List[str]							=  self.tokenizer(SCREAMING_SNAKE_CASE__     ,							return_tensors=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             if images is not None:
                    a_					:		Dict							=  self.image_processor(SCREAMING_SNAKE_CASE__     ,							return_tensors=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             if text is not None and images is not None:
                    a_					:		Dict							=  image_features.pixel_values
                    return encoding
             elif text is not None:
                    return encoding
             else:
                    return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__     )     ,							tensor_type=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							*SCREAMING_SNAKE_CASE__				:       List[Any]     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			List[Any]:
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							*SCREAMING_SNAKE_CASE__				:       List[Any]     ,							**SCREAMING_SNAKE_CASE__				:       Dict     )		->			Optional[Any]:
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Any:
             a_					:		str							=  self.tokenizer.model_input_names
             a_					:		Tuple							=  self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			str:
             warnings.warn(
                 '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
             return self.image_processor_class

      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Tuple:
             warnings.warn(
                 '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
             return self.image_processor


