


"""simple docstring"""







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, 2048, 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 						_A  (  nn.Module  ):







    """simple docstring"""




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






    def 	__snake_case							(					self				:    Optional[Any]				, __UpperCAmelCase				:    Tensor):
      a						:		Union[str, Any]      							=			self.convolution(__UpperCAmelCase)
      a						:		Optional[Any]      							=			self.normalization(__UpperCAmelCase)
      a						:		Optional[int]      							=			self.activation(__UpperCAmelCase)
      return hidden_state






class 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__(					self				:    Optional[int]				, __UpperCAmelCase				:    ResNetConfig):
      super().__init__()
      a						:		str      							=			ResNetConvLayer(
          config.num_channels				, config.embedding_size				, kernel_size=7				, stride=2				, activation=config.hidden_act)
      a						:		List[str]      							=			nn.MaxPoolad(kernel_size=3				, stride=2				, padding=1)
      a						:		str      							=			config.num_channels






    def 	__snake_case							(					self				:    str				, __UpperCAmelCase				:    Tensor):
      a						:		Optional[int]      							=			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[int]      							=			self.embedder(__UpperCAmelCase)
      a						:		Optional[int]      							=			self.pooler(__UpperCAmelCase)
      return embedding






class 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__(					self				:    Any				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int = 2):
      super().__init__()
      a						:		Union[str, Any]      							=			nn.Convad(__UpperCAmelCase				, __UpperCAmelCase				, kernel_size=1				, stride=__UpperCAmelCase				, bias=__UpperCAmelCase)
      a						:		int      							=			nn.BatchNormad(__UpperCAmelCase)






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    Tensor):
      a						:		Any      							=			self.convolution(__UpperCAmelCase)
      a						:		Any      							=			self.normalization(__UpperCAmelCase)
      return hidden_state






class 						_A  (  nn.Module  ):







    """simple docstring"""




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






    def 	__snake_case							(					self				:    str				, __UpperCAmelCase				:    Any):
      a						:		Optional[int]      							=			hidden_state
      a						:		Optional[int]      							=			self.layer(__UpperCAmelCase)
      a						:		Optional[Any]      							=			self.shortcut(__UpperCAmelCase)
      hidden_state += residual
      a						:		str      							=			self.activation(__UpperCAmelCase)
      return hidden_state






class 						_A  (  nn.Module  ):







    """simple docstring"""




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






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    Dict):
      a						:		List[str]      							=			hidden_state
      a						:		List[Any]      							=			self.layer(__UpperCAmelCase)
      a						:		Optional[int]      							=			self.shortcut(__UpperCAmelCase)
      hidden_state += residual
      a						:		str      							=			self.activation(__UpperCAmelCase)
      return hidden_state






class 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__(					self				:    List[str]				, __UpperCAmelCase				:    ResNetConfig				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int = 2				, __UpperCAmelCase				:    int = 2				, ):
      super().__init__()

      a						:		Tuple      							=			ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer

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






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    Tensor):
      a						:		str      							=			input
      for layer in self.layers:
        a						:		Tuple      							=			layer(__UpperCAmelCase)
      return hidden_state






class 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__(					self				:    Union[str, Any]				, __UpperCAmelCase				:    ResNetConfig):
      super().__init__()
      a						:		List[str]      							=			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(
              __UpperCAmelCase				, 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(__UpperCAmelCase				, config.depths[1:]):
        self.stages.append(ResNetStage(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, depth=__UpperCAmelCase))






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    Tensor				, __UpperCAmelCase				:    bool = False				, __UpperCAmelCase				:    bool = True):
      a						:		Tuple      							=			() if output_hidden_states else None

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

        a						:		int      							=			stage_module(__UpperCAmelCase)

      if output_hidden_states:
        a						:		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=__UpperCAmelCase				, hidden_states=__UpperCAmelCase				, )






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							int	=       ResNetConfig
    UpperCAmelCase       :							List[Any]	=       """resnet"""
    UpperCAmelCase       :							Optional[Any]	=       """pixel_values"""
    UpperCAmelCase       :							str	=       True






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Tuple):
      if isinstance(__UpperCAmelCase				, nn.Convad):
        nn.init.kaiming_normal_(module.weight				, mode="fan_out"				, nonlinearity="relu")
      elif isinstance(__UpperCAmelCase				, (nn.BatchNormad, nn.GroupNorm)):
        nn.init.constant_(module.weight				, 1)
        nn.init.constant_(module.bias				, 0)






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    Any				, __UpperCAmelCase				:    Any=False):
      if isinstance(__UpperCAmelCase				, __UpperCAmelCase):
        a						:		Tuple      							=			value






__lowercase    					=      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.
"""

__lowercase    					=      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."""    ,_a    ,)
class 						_A  (  _a  ):







    """simple docstring"""




    def __init__(					self				:    List[str]				, __UpperCAmelCase				:    Union[str, Any]):
      super().__init__(__UpperCAmelCase)
      a						:		Tuple      							=			config
      a						:		str      							=			ResNetEmbeddings(__UpperCAmelCase)
      a						:		List[str]      							=			ResNetEncoder(__UpperCAmelCase)
      a						:		Dict      							=			nn.AdaptiveAvgPoolad((1, 1))
      # Initialize weights and apply final processing
      self.post_init()






    @add_start_docstrings_to_model_forward(__UpperCAmelCase)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC				, output_type=__UpperCAmelCase				, config_class=_CONFIG_FOR_DOC				, modality="vision"				, expected_output=_EXPECTED_OUTPUT_SHAPE				, )
    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    Tensor				, __UpperCAmelCase				:    Optional[bool] = None				, __UpperCAmelCase				:    Optional[bool] = None):
      a						:		str      							=			(
          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						:		Any      							=			self.embedder(__UpperCAmelCase)

      a						:		Optional[int]      							=			self.encoder(
          __UpperCAmelCase				, output_hidden_states=__UpperCAmelCase				, return_dict=__UpperCAmelCase)

      a						:		Tuple      							=			encoder_outputs[0]

      a						:		Dict      							=			self.pooler(__UpperCAmelCase)

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

      return BaseModelOutputWithPoolingAndNoAttention(
          last_hidden_state=__UpperCAmelCase				, pooler_output=__UpperCAmelCase				, 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.
    """    ,_a    ,)
class 						_A  (  _a  ):







    """simple docstring"""




    def __init__(					self				:    List[Any]				, __UpperCAmelCase				:    List[str]):
      super().__init__(__UpperCAmelCase)
      a						:		List[str]      							=			config.num_labels
      a						:		Union[str, Any]      							=			ResNetModel(__UpperCAmelCase)
      # classification head
      a						:		Tuple      							=			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(__UpperCAmelCase)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT				, output_type=__UpperCAmelCase				, config_class=_CONFIG_FOR_DOC				, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				, )
    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Optional[torch.FloatTensor] = None				, __UpperCAmelCase				:    Optional[torch.LongTensor] = None				, __UpperCAmelCase				:    Optional[bool] = None				, __UpperCAmelCase				:    Optional[bool] = None				, ):
      a						:		Optional[Any]      							=			return_dict if return_dict is not None else self.config.use_return_dict

      a						:		int      							=			self.resnet(__UpperCAmelCase				, output_hidden_states=__UpperCAmelCase				, return_dict=__UpperCAmelCase)

      a						:		Dict      							=			outputs.pooler_output if return_dict else outputs[1]

      a						:		List[str]      							=			self.classifier(__UpperCAmelCase)

      a						:		Tuple      							=			None

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

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

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






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







    """simple docstring"""




    def __init__(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Dict):
      super().__init__(__UpperCAmelCase)
      super()._init_backbone(__UpperCAmelCase)

      a						:		List[str]      							=			[config.embedding_size] + config.hidden_sizes
      a						:		Tuple      							=			ResNetEmbeddings(__UpperCAmelCase)
      a						:		List[str]      							=			ResNetEncoder(__UpperCAmelCase)

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






    @add_start_docstrings_to_model_forward(__UpperCAmelCase)
    @replace_return_docstrings(output_type=__UpperCAmelCase				, config_class=_CONFIG_FOR_DOC)
    def 	__snake_case							(					self				:    Dict				, __UpperCAmelCase				:    Tensor				, __UpperCAmelCase				:    Optional[bool] = None				, __UpperCAmelCase				:    Optional[bool] = None):
      a						:		Tuple      							=			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						:		str      							=			self.embedder(__UpperCAmelCase)

      a						:		Union[str, Any]      							=			self.encoder(__UpperCAmelCase				, output_hidden_states=__UpperCAmelCase				, return_dict=__UpperCAmelCase)

      a						:		Optional[int]      							=			outputs.hidden_states

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

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

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










"""simple docstring"""







def      lowercase			(				A_       )->      str:
  '''simple docstring'''







  if isinstance(A_  ,   A_       ):
    raise TypeError("'float' object cannot be interpreted as an integer"       )
  if isinstance(A_  ,   A_       ):
    raise TypeError("'str' object cannot be interpreted as an integer"       )

  if num == 0:
    return "0b0"

  a						:		Optional[Any]      							=			False

  if num < 0:
    a						:		Tuple      							=			True
    a						:		str      							=			-num

  a						:		list[int]      							=			[]
  while num > 0:
    binary.insert(0  ,   num % 2       )
    num >>= 1

  if negative:
    return "-0b" + "".join(str(A_       ) for e in binary       )

  return "0b" + "".join(str(A_       ) for e in binary       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






