

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


lowercase_   							=  logging.get_logger(__name__)

# General docstring
lowercase_   							=  'ResNetConfig'

# Base docstring
lowercase_   							=  'microsoft/resnet-50'
lowercase_   							=  [1, 2_0_4_8, 7, 7]

# Image classification docstring
lowercase_   							=  'microsoft/resnet-50'
lowercase_   							=  'tiger cat'

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





class 					__lowerCAmelCase	(   nn.Module				):




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = 3				,  lowerCAmelCase = 1				,  lowerCAmelCase = "relu"   )    ->					str:
        '''simple docstring'''






        super().__init__()
        _lowercase      			=nn.Convad(
            lowerCAmelCase				,  lowerCAmelCase				,  kernel_size=lowerCAmelCase				,  stride=lowerCAmelCase				,  padding=kernel_size // 2				,  bias=lowerCAmelCase   )
        _lowercase      			=nn.BatchNormad(lowerCAmelCase   )
        _lowercase      			=ACTaFN[activation] if activation is not None else nn.Identity()




  def     A__	(							self				,  lowerCAmelCase   )    ->					Tensor:
        '''simple docstring'''






        _lowercase      			=self.convolution(lowerCAmelCase   )
        _lowercase      			=self.normalization(lowerCAmelCase   )
        _lowercase      			=self.activation(lowerCAmelCase   )
        return hidden_state





class 					__lowerCAmelCase	(   nn.Module				):




  def __init__(							self				,  lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        super().__init__()
        _lowercase      			=ResNetConvLayer(
            config.num_channels				,  config.embedding_size				,  kernel_size=7				,  stride=2				,  activation=config.hidden_act   )
        _lowercase      			=nn.MaxPoolad(kernel_size=3				,  stride=2				,  padding=1   )
        _lowercase      			=config.num_channels




  def     A__	(							self				,  lowerCAmelCase   )    ->					Tensor:
        '''simple docstring'''






        _lowercase      			=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.'   )
        _lowercase      			=self.embedder(lowerCAmelCase   )
        _lowercase      			=self.pooler(lowerCAmelCase   )
        return embedding





class 					__lowerCAmelCase	(   nn.Module				):




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = 2   )    ->					List[Any]:
        '''simple docstring'''






        super().__init__()
        _lowercase      			=nn.Convad(lowerCAmelCase				,  lowerCAmelCase				,  kernel_size=1				,  stride=lowerCAmelCase				,  bias=lowerCAmelCase   )
        _lowercase      			=nn.BatchNormad(lowerCAmelCase   )




  def     A__	(							self				,  lowerCAmelCase   )    ->					Tensor:
        '''simple docstring'''






        _lowercase      			=self.convolution(lowerCAmelCase   )
        _lowercase      			=self.normalization(lowerCAmelCase   )
        return hidden_state





class 					__lowerCAmelCase	(   nn.Module				):




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = 1				,  lowerCAmelCase = "relu"   )    ->					List[str]:
        '''simple docstring'''






        super().__init__()
        _lowercase      			=in_channels != out_channels or stride != 1
        _lowercase      			=(
            ResNetShortCut(lowerCAmelCase				,  lowerCAmelCase				,  stride=lowerCAmelCase   ) if should_apply_shortcut else nn.Identity()
        )
        _lowercase      			=nn.Sequential(
            ResNetConvLayer(lowerCAmelCase				,  lowerCAmelCase				,  stride=lowerCAmelCase   )				,  ResNetConvLayer(lowerCAmelCase				,  lowerCAmelCase				,  activation=lowerCAmelCase   )				,  )
        _lowercase      			=ACTaFN[activation]




  def     A__	(							self				,  lowerCAmelCase   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase      			=hidden_state
        _lowercase      			=self.layer(lowerCAmelCase   )
        _lowercase      			=self.shortcut(lowerCAmelCase   )
        hidden_state += residual
        _lowercase      			=self.activation(lowerCAmelCase   )
        return hidden_state





class 					__lowerCAmelCase	(   nn.Module				):




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = 1				,  lowerCAmelCase = "relu"				,  lowerCAmelCase = 4   )    ->					List[Any]:
        '''simple docstring'''






        super().__init__()
        _lowercase      			=in_channels != out_channels or stride != 1
        _lowercase      			=out_channels // reduction
        _lowercase      			=(
            ResNetShortCut(lowerCAmelCase				,  lowerCAmelCase				,  stride=lowerCAmelCase   ) if should_apply_shortcut else nn.Identity()
        )
        _lowercase      			=nn.Sequential(
            ResNetConvLayer(lowerCAmelCase				,  lowerCAmelCase				,  kernel_size=1   )				,  ResNetConvLayer(lowerCAmelCase				,  lowerCAmelCase				,  stride=lowerCAmelCase   )				,  ResNetConvLayer(lowerCAmelCase				,  lowerCAmelCase				,  kernel_size=1				,  activation=lowerCAmelCase   )				,  )
        _lowercase      			=ACTaFN[activation]




  def     A__	(							self				,  lowerCAmelCase   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=hidden_state
        _lowercase      			=self.layer(lowerCAmelCase   )
        _lowercase      			=self.shortcut(lowerCAmelCase   )
        hidden_state += residual
        _lowercase      			=self.activation(lowerCAmelCase   )
        return hidden_state





class 					__lowerCAmelCase	(   nn.Module				):




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = 2				,  lowerCAmelCase = 2				,  )    ->					Optional[Any]:
        '''simple docstring'''






        super().__init__()

        _lowercase      			=ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

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




  def     A__	(							self				,  lowerCAmelCase   )    ->					Tensor:
        '''simple docstring'''






        _lowercase      			=input
        for layer in self.layers:
              _lowercase      			=layer(lowerCAmelCase   )
        return hidden_state





class 					__lowerCAmelCase	(   nn.Module				):




  def __init__(							self				,  lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        super().__init__()
        _lowercase      			=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(
                lowerCAmelCase				,  config.embedding_size				,  config.hidden_sizes[0]				,  stride=2 if config.downsample_in_first_stage else 1				,  depth=config.depths[0]				,  )   )
        _lowercase      			=zip(config.hidden_sizes				,  config.hidden_sizes[1:]   )
        for (in_channels, out_channels), depth in zip(lowerCAmelCase				,  config.depths[1:]   ):
              self.stages.append(ResNetStage(lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  depth=lowerCAmelCase   )   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = False				,  lowerCAmelCase = True   )    ->					BaseModelOutputWithNoAttention:
        '''simple docstring'''






        _lowercase      			=() if output_hidden_states else None

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

              _lowercase      			=stage_module(lowerCAmelCase   )

        if output_hidden_states:
              _lowercase      			=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=lowerCAmelCase				,  hidden_states=lowerCAmelCase				,  )





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    ResNetConfig
  _a		     =    """resnet"""
  _a		     =    """pixel_values"""
  _a		     =    True




  def     A__	(							self				,  lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        if isinstance(lowerCAmelCase				,  nn.Convad   ):
              nn.init.kaiming_normal_(module.weight				,  mode='fan_out'				,  nonlinearity='relu'   )
        elif isinstance(lowerCAmelCase				,  (nn.BatchNormad, nn.GroupNorm)   ):
              nn.init.constant_(module.weight				,  1   )
              nn.init.constant_(module.bias				,  0   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=False   )    ->					str:
        '''simple docstring'''






        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              _lowercase      			=value
lowercase_   							=  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'

lowercase_   							=  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."""     ,     SCREAMING_SNAKE_CASE     ,     )
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  lowerCAmelCase   )    ->					Any:
        '''simple docstring'''






        super().__init__(lowerCAmelCase   )
        _lowercase      			=config
        _lowercase      			=ResNetEmbeddings(lowerCAmelCase   )
        _lowercase      			=ResNetEncoder(lowerCAmelCase   )
        _lowercase      			=nn.AdaptiveAvgPoolad((1, 1)   )
        # Initialize weights and apply final processing
        self.post_init()




  @add_start_docstrings_to_model_forward(lowerCAmelCase   )
  @add_code_sample_docstrings(
      checkpoint=_CHECKPOINT_FOR_DOC				,  output_type=lowerCAmelCase				,  config_class=_CONFIG_FOR_DOC				,  modality='vision'				,  expected_output=_EXPECTED_OUTPUT_SHAPE				,  )
  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = None				,  lowerCAmelCase = None   )    ->					BaseModelOutputWithPoolingAndNoAttention:
        '''simple docstring'''






        _lowercase      			=(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        _lowercase      			=return_dict if return_dict is not None else self.config.use_return_dict

        _lowercase      			=self.embedder(lowerCAmelCase   )

        _lowercase      			=self.encoder(
            lowerCAmelCase				,  output_hidden_states=lowerCAmelCase				,  return_dict=lowerCAmelCase   )

        _lowercase      			=encoder_outputs[0]

        _lowercase      			=self.pooler(lowerCAmelCase   )

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

        return BaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=lowerCAmelCase				,  pooler_output=lowerCAmelCase				,  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.
    """     ,     SCREAMING_SNAKE_CASE     ,     )
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  lowerCAmelCase   )    ->					int:
        '''simple docstring'''






        super().__init__(lowerCAmelCase   )
        _lowercase      			=config.num_labels
        _lowercase      			=ResNetModel(lowerCAmelCase   )
        # classification head
        _lowercase      			=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(lowerCAmelCase   )
  @add_code_sample_docstrings(
      checkpoint=_IMAGE_CLASS_CHECKPOINT				,  output_type=lowerCAmelCase				,  config_class=_CONFIG_FOR_DOC				,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,  )
  def     A__	(							self				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  )    ->					ImageClassifierOutputWithNoAttention:
        '''simple docstring'''






        _lowercase      			=return_dict if return_dict is not None else self.config.use_return_dict

        _lowercase      			=self.resnet(lowerCAmelCase				,  output_hidden_states=lowerCAmelCase				,  return_dict=lowerCAmelCase   )

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

        _lowercase      			=self.classifier(lowerCAmelCase   )

        _lowercase      			=None

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

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

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





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




  def __init__(							self				,  lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        super().__init__(lowerCAmelCase   )
        super()._init_backbone(lowerCAmelCase   )

        _lowercase      			=[config.embedding_size] + config.hidden_sizes
        _lowercase      			=ResNetEmbeddings(lowerCAmelCase   )
        _lowercase      			=ResNetEncoder(lowerCAmelCase   )

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




  @add_start_docstrings_to_model_forward(lowerCAmelCase   )
  @replace_return_docstrings(output_type=lowerCAmelCase				,  config_class=_CONFIG_FOR_DOC   )
  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = None				,  lowerCAmelCase = None   )    ->					BackboneOutput:
        '''simple docstring'''






        _lowercase      			=return_dict if return_dict is not None else self.config.use_return_dict
        _lowercase      			=(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        _lowercase      			=self.embedder(lowerCAmelCase   )

        _lowercase      			=self.encoder(lowerCAmelCase				,  output_hidden_states=lowerCAmelCase				,  return_dict=lowerCAmelCase   )

        _lowercase      			=outputs.hidden_states

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

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

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_   							=  {
    'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_   							=  [
      'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TimesformerModel',
      'TimesformerForVideoClassification',
      'TimesformerPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

  lowercase_   							=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


