


"""simple docstring"""

from timeit import timeit


def lowerCAmelCase_   (					UpperCamelCase__						:			int			):






 """simple docstring"""







 if number < 0:
  raise ValueError("""the value of input must not be negative"""			)
 __lowercase						=						0
 while number:
  number &= number - 1
  result += 1
 return result


def lowerCAmelCase_   (					UpperCamelCase__						:			int			):






 """simple docstring"""







 if number < 0:
  raise ValueError("""the value of input must not be negative"""			)
 __lowercase						=						0
 while number:
  if number % 2 == 1:
   result += 1
  number >>= 1
 return result


def lowerCAmelCase_   (					):






 """simple docstring"""







 def do_benchmark(UpperCamelCase__						:			int			) -> None:
  __lowercase						=						"""import __main__ as z"""
  print(f'''Benchmark when {number = }:'''			)
  print(f'''{get_set_bits_count_using_modulo_operator(UpperCamelCase__			) = }'''			)
  __lowercase						=						timeit("""z.get_set_bits_count_using_modulo_operator(25)"""					, setup=UpperCamelCase__			)
  print(f'''timeit() runs in {timing} seconds'''			)
  print(f'''{get_set_bits_count_using_brian_kernighans_algorithm(UpperCamelCase__			) = }'''			)
  __lowercase						=						timeit(
      """z.get_set_bits_count_using_brian_kernighans_algorithm(25)"""					, setup=UpperCamelCase__					, )
  print(f'''timeit() runs in {timing} seconds'''			)

 for number in (25, 37, 58, 0):
  do_benchmark(UpperCamelCase__			)
  print()


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()




"""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 ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


UpperCAmelCase__					=logging.get_logger(__name__)

# General docstring
UpperCAmelCase__					="RegNetConfig"

# Base docstring
UpperCAmelCase__					="facebook/regnet-y-040"
UpperCAmelCase__					=[1, 1088, 7, 7]

# Image classification docstring
UpperCAmelCase__					="facebook/regnet-y-040"
UpperCAmelCase__					="tabby, tabby cat"

UpperCAmelCase__					=[
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]





class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						int   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 3   ,							A_						:						int = 1   ,							A_						:						int = 1   ,							A_						:						Optional[str] = "relu"   ,							):






       '''simple docstring'''


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






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						self.convolution(A_				)
       __lowercase						=						self.normalization(A_				)
       __lowercase						=						self.activation(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Dict   ,							A_						:						RegNetConfig				):






       '''simple docstring'''


       super().__init__()
       __lowercase						=						RegNetConvLayer(
           config.num_channels   ,							config.embedding_size   ,							kernel_size=3   ,							stride=2   ,							activation=config.hidden_act				)
       __lowercase						=						config.num_channels






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						Optional[int]				):






       '''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(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Optional[int]   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 2				):






       '''simple docstring'''


       super().__init__()
       __lowercase						=						nn.Convad(A_   ,							A_   ,							kernel_size=1   ,							stride=A_   ,							bias=A_				)
       __lowercase						=						nn.BatchNormad(A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						Tensor				):






       '''simple docstring'''


       __lowercase						=						self.convolution(A_				)
       __lowercase						=						self.normalization(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						int   ,							A_						:						int   ,							A_						:						int				):






       '''simple docstring'''


       super().__init__()

       __lowercase						=						nn.AdaptiveAvgPoolad((1, 1)				)
       __lowercase						=						nn.Sequential(
           nn.Convad(A_   ,							A_   ,							kernel_size=1				)   ,							nn.ReLU()   ,							nn.Convad(A_   ,							A_   ,							kernel_size=1				)   ,							nn.Sigmoid()   ,							)






      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						self.pooler(A_				)
       __lowercase						=						self.attention(A_				)
       __lowercase						=						hidden_state * attention
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Any   ,							A_						:						RegNetConfig   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 1				):






       '''simple docstring'''


       super().__init__()
       __lowercase						=						in_channels != out_channels or stride != 1
       __lowercase						=						max(1   ,							out_channels // config.groups_width				)
       __lowercase						=						(
           RegNetShortCut(A_   ,							A_   ,							stride=A_				) if should_apply_shortcut else nn.Identity()
       )
       __lowercase						=						nn.Sequential(
           RegNetConvLayer(A_   ,							A_   ,							kernel_size=1   ,							activation=config.hidden_act				)   ,							RegNetConvLayer(A_   ,							A_   ,							stride=A_   ,							groups=A_   ,							activation=config.hidden_act				)   ,							RegNetConvLayer(A_   ,							A_   ,							kernel_size=1   ,							activation=A_				)   ,							)
       __lowercase						=						ACTaFN[config.hidden_act]






      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						hidden_state
       __lowercase						=						self.layer(A_				)
       __lowercase						=						self.shortcut(A_				)
       hidden_state += residual
       __lowercase						=						self.activation(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						List[str]   ,							A_						:						RegNetConfig   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 1				):






       '''simple docstring'''


       super().__init__()
       __lowercase						=						in_channels != out_channels or stride != 1
       __lowercase						=						max(1   ,							out_channels // config.groups_width				)
       __lowercase						=						(
           RegNetShortCut(A_   ,							A_   ,							stride=A_				) if should_apply_shortcut else nn.Identity()
       )
       __lowercase						=						nn.Sequential(
           RegNetConvLayer(A_   ,							A_   ,							kernel_size=1   ,							activation=config.hidden_act				)   ,							RegNetConvLayer(A_   ,							A_   ,							stride=A_   ,							groups=A_   ,							activation=config.hidden_act				)   ,							RegNetSELayer(A_   ,							reduced_channels=int(round(in_channels / 4				)				)				)   ,							RegNetConvLayer(A_   ,							A_   ,							kernel_size=1   ,							activation=A_				)   ,							)
       __lowercase						=						ACTaFN[config.hidden_act]






      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						hidden_state
       __lowercase						=						self.layer(A_				)
       __lowercase						=						self.shortcut(A_				)
       hidden_state += residual
       __lowercase						=						self.activation(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Optional[int]   ,							A_						:						RegNetConfig   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 2   ,							A_						:						int = 2   ,							):






       '''simple docstring'''


       super().__init__()

       __lowercase						=						RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

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






      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						self.layers(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Tuple   ,							A_						:						RegNetConfig				):






       '''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(
           RegNetStage(
               A_   ,							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(A_   ,							config.depths[1:]				):
        self.stages.append(RegNetStage(A_   ,							A_   ,							A_   ,							depth=A_				)				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						Tensor   ,							A_						:						bool = False   ,							A_						:						bool = True				):






       '''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(A_				)

       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=A_   ,							hidden_states=A_				)






class  lowerCamelCase__    (		_a     ):
      a			:  List[str]           =		RegNetConfig
      a			:  int           =		"""regnet"""
      a			:  Tuple           =		"""pixel_values"""
      a			:  Dict           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							A_						:						List[Any]				):






       '''simple docstring'''


       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 SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						Dict   ,							A_						:						List[str]=False				):






       '''simple docstring'''


       if isinstance(A_   ,							A_				):
        __lowercase						=						value






UpperCAmelCase__					=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 ([`RegNetConfig`]): 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__					=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 [`~file_utils.ModelOutput`] instead of a plain tuple.\n"





@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""      , _a      , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						Any   ,							A_						:						Any				):






       '''simple docstring'''


       super().__init__(A_				)
       __lowercase						=						config
       __lowercase						=						RegNetEmbeddings(A_				)
       __lowercase						=						RegNetEncoder(A_				)
       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						Tensor   ,							A_						:						Optional[bool] = None   ,							A_						:						Optional[bool] = None				):






       '''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(A_				)

       __lowercase						=						self.encoder(
           A_   ,							output_hidden_states=A_   ,							return_dict=A_				)

       __lowercase						=						encoder_outputs[0]

       __lowercase						=						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(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """      , _a      , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						int   ,							A_						:						int				):






       '''simple docstring'''


       super().__init__(A_				)
       __lowercase						=						config.num_labels
       __lowercase						=						RegNetModel(A_				)
       # 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(A_				)
      @add_code_sample_docstrings(
          checkpoint=_IMAGE_CLASS_CHECKPOINT   ,							output_type=A_   ,							config_class=_CONFIG_FOR_DOC   ,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,							)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Optional[torch.FloatTensor] = None   ,							A_						:						Optional[torch.LongTensor] = None   ,							A_						:						Optional[bool] = None   ,							A_						:						Optional[bool] = None   ,							):






       '''simple docstring'''


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

       __lowercase						=						self.regnet(A_   ,							output_hidden_states=A_   ,							return_dict=A_				)

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

       __lowercase						=						self.classifier(A_				)

       __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(A_   ,							A_				)
        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(A_   ,							A_				)

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

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