



'''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     ):
  """simple docstring"""




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




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	Union[str, Any]   )					->					Union[str, Any]:
    __lowerCamelCase       :       List[str]           = self.convolution(a__   )
    __lowerCamelCase       :       Union[str, Any]           = self.normalization(a__   )
    __lowerCamelCase       :       List[Any]           = self.activation(a__   )
    return hidden_state






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	List[Any]			,	_a      :	Optional[Any]   )					->					Dict:
    super().__init__()
    __lowerCamelCase       :       Optional[int]           = RegNetConvLayer(
        config.num_channels			,	config.embedding_size			,	kernel_size=3			,	stride=2			,	activation=config.hidden_act   )
    __lowerCamelCase       :       List[Any]           = config.num_channels




  def 						_lowercase     ( self      :	int			,	_a      :	Optional[int]   )					->					str:
    __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       :       List[Any]           = self.embedder(a__   )
    return hidden_state






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	str			,	_a      :	Union[str, Any]			,	_a      :	Any			,	_a      :	Union[str, Any] = 2   )					->					int:
    super().__init__()
    __lowerCamelCase       :       Union[str, Any]           = nn.Convad(a__			,	a__			,	kernel_size=1			,	stride=a__			,	bias=a__   )
    __lowerCamelCase       :       Optional[int]           = nn.BatchNormad(a__   )




  def 						_lowercase     ( self      :	int			,	_a      :	Optional[Any]   )					->					int:
    __lowerCamelCase       :       str           = self.convolution(a__   )
    __lowerCamelCase       :       Tuple           = self.normalization(a__   )
    return hidden_state






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	Union[str, Any]			,	_a      :	Optional[Any]			,	_a      :	List[str]   )					->					int:
    super().__init__()

    __lowerCamelCase       :       Dict           = nn.AdaptiveAvgPoolad((1, 1)   )
    __lowerCamelCase       :       Tuple           = nn.Sequential(
        nn.Convad(a__			,	a__			,	kernel_size=1   )			,	nn.ReLU()			,	nn.Convad(a__			,	a__			,	kernel_size=1   )			,	nn.Sigmoid()			,	)




  def 						_lowercase     ( self      :	str			,	_a      :	Optional[int]   )					->					List[Any]:
    # b c h w -> b c 1 1
    __lowerCamelCase       :       Tuple           = self.pooler(a__   )
    __lowerCamelCase       :       int           = self.attention(a__   )
    __lowerCamelCase       :       Optional[Any]           = hidden_state * attention
    return hidden_state






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	Any			,	_a      :	str			,	_a      :	Tuple			,	_a      :	str			,	_a      :	Any = 1   )					->					Optional[Any]:
    super().__init__()
    __lowerCamelCase       :       Optional[Any]           = in_channels != out_channels or stride != 1
    __lowerCamelCase       :       Optional[Any]           = max(1			,	out_channels // config.groups_width   )
    __lowerCamelCase       :       Tuple           = (
        RegNetShortCut(a__			,	a__			,	stride=a__   ) if should_apply_shortcut else nn.Identity()
    )
    __lowerCamelCase       :       str           = 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__   )			,	)
    __lowerCamelCase       :       Union[str, Any]           = ACTaFN[config.hidden_act]




  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	int   )					->					Tuple:
    __lowerCamelCase       :       Any           = hidden_state
    __lowerCamelCase       :       Any           = self.layer(a__   )
    __lowerCamelCase       :       str           = self.shortcut(a__   )
    hidden_state += residual
    __lowerCamelCase       :       int           = self.activation(a__   )
    return hidden_state






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	Union[str, Any]			,	_a      :	Dict			,	_a      :	List[Any]			,	_a      :	Union[str, Any]			,	_a      :	Any = 1   )					->					Optional[Any]:
    super().__init__()
    __lowerCamelCase       :       Dict           = in_channels != out_channels or stride != 1
    __lowerCamelCase       :       List[Any]           = max(1			,	out_channels // config.groups_width   )
    __lowerCamelCase       :       List[Any]           = (
        RegNetShortCut(a__			,	a__			,	stride=a__   ) if should_apply_shortcut else nn.Identity()
    )
    __lowerCamelCase       :       Tuple           = 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__   )			,	)
    __lowerCamelCase       :       Dict           = ACTaFN[config.hidden_act]




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	Optional[Any]   )					->					str:
    __lowerCamelCase       :       List[str]           = hidden_state
    __lowerCamelCase       :       Optional[int]           = self.layer(a__   )
    __lowerCamelCase       :       List[Any]           = self.shortcut(a__   )
    hidden_state += residual
    __lowerCamelCase       :       List[str]           = self.activation(a__   )
    return hidden_state






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	Optional[Any]			,	_a      :	Union[str, Any]			,	_a      :	str			,	_a      :	Union[str, Any]			,	_a      :	Union[str, Any] = 2			,	_a      :	Union[str, Any] = 2			,	)					->					Dict:
    super().__init__()

    __lowerCamelCase       :       List[Any]           = RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

    __lowerCamelCase       :       str           = 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 						_lowercase     ( self      :	Optional[Any]			,	_a      :	Optional[int]   )					->					str:
    __lowerCamelCase       :       List[Any]           = self.layers(a__   )
    return hidden_state






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	Union[str, Any]			,	_a      :	Optional[int]   )					->					Dict:
    super().__init__()
    __lowerCamelCase       :       Optional[int]           = 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]			,	)   )
    __lowerCamelCase       :       List[str]           = 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 						_lowercase     ( self      :	Any			,	_a      :	Optional[int]			,	_a      :	Dict = False			,	_a      :	Dict = True   )					->					Tuple:
    __lowerCamelCase       :       Any           = () if output_hidden_states else None

    for stage_module in self.stages:
      if output_hidden_states:
        __lowerCamelCase       :       Union[str, Any]           = hidden_states + (hidden_state,)

      __lowerCamelCase       :       Union[str, Any]           = stage_module(a__   )

    if output_hidden_states:
      __lowerCamelCase       :       Union[str, 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 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=RegNetConfig
  a_												="regnet"
  a_												="pixel_values"
  a_												=True




  def 						_lowercase     ( self      :	List[str]			,	_a      :	List[str]   )					->					Dict:
    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 						_lowercase     ( self      :	Any			,	_a      :	Optional[int]			,	_a      :	Any=False   )					->					Any:
    if isinstance(a__			,	a__   ):
      __lowerCamelCase       :       Optional[int]           = 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."""   ,   SCREAMING_SNAKE_CASE_   ,   )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def __init__( self      :	List[Any]			,	_a      :	List[Any]   )					->					str:
    super().__init__(a__   )
    __lowerCamelCase       :       List[Any]           = config
    __lowerCamelCase       :       Any           = RegNetEmbeddings(a__   )
    __lowerCamelCase       :       List[str]           = RegNetEncoder(a__   )
    __lowerCamelCase       :       str           = 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 						_lowercase     ( self      :	List[str]			,	_a      :	Optional[Any]			,	_a      :	Optional[Any] = None			,	_a      :	Dict = None   )					->					Dict:
    __lowerCamelCase       :       Any           = (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    __lowerCamelCase       :       Tuple           = return_dict if return_dict is not None else self.config.use_return_dict

    __lowerCamelCase       :       List[Any]           = self.embedder(a__   )

    __lowerCamelCase       :       Optional[Any]           = self.encoder(
        a__			,	output_hidden_states=a__			,	return_dict=a__   )

    __lowerCamelCase       :       str           = encoder_outputs[0]

    __lowerCamelCase       :       Optional[Any]           = 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(
    """\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    """   ,   SCREAMING_SNAKE_CASE_   ,   )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def __init__( self      :	Any			,	_a      :	Dict   )					->					Optional[Any]:
    super().__init__(a__   )
    __lowerCamelCase       :       Any           = config.num_labels
    __lowerCamelCase       :       Optional[int]           = RegNetModel(a__   )
    # classification head
    __lowerCamelCase       :       Optional[Any]           = 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 						_lowercase     ( self      :	Dict			,	_a      :	Optional[Any] = None			,	_a      :	Tuple = None			,	_a      :	List[str] = None			,	_a      :	str = None			,	)					->					Any:
    __lowerCamelCase       :       List[Any]           = return_dict if return_dict is not None else self.config.use_return_dict

    __lowerCamelCase       :       Union[str, Any]           = self.regnet(a__			,	output_hidden_states=a__			,	return_dict=a__   )

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

    __lowerCamelCase       :       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       :       str           = """regression"""
        elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
          __lowerCamelCase       :       Optional[int]           = """single_label_classification"""
        else:
          __lowerCamelCase       :       Optional[int]           = """multi_label_classification"""
      if self.config.problem_type == "regression":
        __lowerCamelCase       :       Optional[Any]           = MSELoss()
        if self.num_labels == 1:
          __lowerCamelCase       :       int           = loss_fct(logits.squeeze()			,	labels.squeeze()   )
        else:
          __lowerCamelCase       :       Dict           = loss_fct(a__			,	a__   )
      elif self.config.problem_type == "single_label_classification":
        __lowerCamelCase       :       Any           = CrossEntropyLoss()
        __lowerCamelCase       :       Dict           = loss_fct(logits.view(-1			,	self.num_labels   )			,	labels.view(-1   )   )
      elif self.config.problem_type == "multi_label_classification":
        __lowerCamelCase       :       Dict           = BCEWithLogitsLoss()
        __lowerCamelCase       :       Tuple           = loss_fct(a__			,	a__   )

    if not return_dict:
      __lowerCamelCase       :       Optional[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   )







"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      List[Any]:  # noqa: E741
  _lowerCAmelCase						:       Optional[int]  			=     len(_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     0
  _lowerCAmelCase						:       Any  			=     [0] * n
  _lowerCAmelCase						:       str  			=     [False] * n
  _lowerCAmelCase						:       str  			=     [False] * n

  def dfs(_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       str	):
    if parent == root:
      out_edge_count += 1
    _lowerCAmelCase						:       Any  			=     True
    _lowerCAmelCase						:       int  			=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _lowerCAmelCase						:       Union[str, Any]  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
        _lowerCAmelCase						:       Optional[int]  			=     min(low[at]			,low[to]	)

        # AP found via bridge
        if at < low[to]:
          _lowerCAmelCase						:       int  			=     True
        # AP found via cycle
        if at == low[to]:
          _lowerCAmelCase						:       Tuple  			=     True
      else:
        _lowerCAmelCase						:       Union[str, Any]  			=     min(low[at]			,_lowerCamelCase	)
    return out_edge_count

  for i in range(_lowerCamelCase	):
    if not visited[i]:
      _lowerCAmelCase						:       int  			=     0
      _lowerCAmelCase						:       Dict  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,-1			,_lowerCamelCase	)
      _lowerCAmelCase						:       List[str]  			=     out_edge_count > 1

  for x in range(len(_lowerCamelCase	)	):
    if is_art[x] is True:
      print(_lowerCamelCase	)


# Adjacency list of graph
_a      :		Optional[Any]           =   {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}






compute_ap(data)




