





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


__lowercase  :   Tuple						      =logging.get_logger(__name__)

# General docstring
__lowercase  :   List[str]						      ="""RegNetConfig"""

# Base docstring
__lowercase  :   str						      ="""facebook/regnet-y-040"""
__lowercase  :   Any						      =[1, 1088, 7, 7]

# Image classification docstring
__lowercase  :   Optional[Any]						      ="""facebook/regnet-y-040"""
__lowercase  :   List[Any]						      ="""tabby, tabby cat"""

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


class      A       (	nn.Module						):



      def __init__(	self:						str		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int = 3		,  _lowerCAmelCase:						int = 1		,  _lowerCAmelCase:						int = 1		,  _lowerCAmelCase:						Optional[str] = "relu"		,  )						->    int:
         '''simple docstring'''

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






      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						List[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.convolution(_lowerCAmelCase       )
         UpperCAmelCase_											=self.normalization(_lowerCAmelCase       )
         UpperCAmelCase_											=self.activation(_lowerCAmelCase       )
         return hidden_state


class      A       (	nn.Module						):



      def __init__(	self:						Union[str, Any]		,  _lowerCAmelCase:						RegNetConfig       )						->    Optional[Any]:
         '''simple docstring'''

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






      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						Union[str, Any]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=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."       )
         UpperCAmelCase_											=self.embedder(_lowerCAmelCase       )
         return hidden_state


class      A       (	nn.Module						):



      def __init__(	self:						List[str]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int = 2       )						->    Dict:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_											=nn.Convad(_lowerCAmelCase		,  _lowerCAmelCase		,  kernel_size=1		,  stride=_lowerCAmelCase		,  bias=_lowerCAmelCase       )
         UpperCAmelCase_											=nn.BatchNormad(_lowerCAmelCase       )






      def 	lowerCAmelCase__    (	self:						List[Any]		,  _lowerCAmelCase:						Tensor       )						->    Tensor:
         '''simple docstring'''

         UpperCAmelCase_											=self.convolution(_lowerCAmelCase       )
         UpperCAmelCase_											=self.normalization(_lowerCAmelCase       )
         return hidden_state


class      A       (	nn.Module						):



      def __init__(	self:						Union[str, Any]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int       )						->    List[str]:
         '''simple docstring'''

         super().__init__()

         UpperCAmelCase_											=nn.AdaptiveAvgPoolad((1, 1)       )
         UpperCAmelCase_											=nn.Sequential(
             nn.Convad(_lowerCAmelCase		,  _lowerCAmelCase		,  kernel_size=1       )		,  nn.ReLU()		,  nn.Convad(_lowerCAmelCase		,  _lowerCAmelCase		,  kernel_size=1       )		,  nn.Sigmoid()		,  )






      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						Tuple       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=self.pooler(_lowerCAmelCase       )
         UpperCAmelCase_											=self.attention(_lowerCAmelCase       )
         UpperCAmelCase_											=hidden_state * attention
         return hidden_state


class      A       (	nn.Module						):



      def __init__(	self:						Tuple		,  _lowerCAmelCase:						RegNetConfig		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int = 1       )						->    Any:
         '''simple docstring'''

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






      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						Any       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=hidden_state
         UpperCAmelCase_											=self.layer(_lowerCAmelCase       )
         UpperCAmelCase_											=self.shortcut(_lowerCAmelCase       )
         hidden_state += residual
         UpperCAmelCase_											=self.activation(_lowerCAmelCase       )
         return hidden_state


class      A       (	nn.Module						):



      def __init__(	self:						Dict		,  _lowerCAmelCase:						RegNetConfig		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int = 1       )						->    Any:
         '''simple docstring'''

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






      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						int       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=hidden_state
         UpperCAmelCase_											=self.layer(_lowerCAmelCase       )
         UpperCAmelCase_											=self.shortcut(_lowerCAmelCase       )
         hidden_state += residual
         UpperCAmelCase_											=self.activation(_lowerCAmelCase       )
         return hidden_state


class      A       (	nn.Module						):



      def __init__(	self:						str		,  _lowerCAmelCase:						RegNetConfig		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int = 2		,  _lowerCAmelCase:						int = 2		,  )						->    str:
         '''simple docstring'''

         super().__init__()

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

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






      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						int       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=self.layers(_lowerCAmelCase       )
         return hidden_state


class      A       (	nn.Module						):



      def __init__(	self:						Union[str, Any]		,  _lowerCAmelCase:						RegNetConfig       )						->    List[Any]:
         '''simple docstring'''

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






      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						Tensor		,  _lowerCAmelCase:						bool = False		,  _lowerCAmelCase:						bool = True       )						->    BaseModelOutputWithNoAttention:
         '''simple docstring'''

         UpperCAmelCase_											=() if output_hidden_states else None

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

            UpperCAmelCase_											=stage_module(_lowerCAmelCase       )

         if output_hidden_states:
            UpperCAmelCase_											=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      A       (	__lowercase						):
      _snake_case							     =RegNetConfig
      _snake_case							     ='''regnet'''
      _snake_case							     ='''pixel_values'''
      _snake_case							     =True



      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						List[str]       )						->    Tuple:
         '''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 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						List[str]=False       )						->    List[Any]:
         '''simple docstring'''

         if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ):
            UpperCAmelCase_											=value

__lowercase  :   Optional[int]						      =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 ([`RegNetConfig`]): 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  :   str						      =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 [`~file_utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    '''The bare RegNet model outputting raw features without any specific head on top.'''		,      __lowercase		,      )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class      A       (	__lowercase						):



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

         super().__init__(_lowerCAmelCase       )
         UpperCAmelCase_											=config
         UpperCAmelCase_											=RegNetEmbeddings(_lowerCAmelCase       )
         UpperCAmelCase_											=RegNetEncoder(_lowerCAmelCase       )
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						Tensor		,  _lowerCAmelCase:						Optional[bool] = None		,  _lowerCAmelCase:						Optional[bool] = None       )						->    BaseModelOutputWithPoolingAndNoAttention:
         '''simple docstring'''

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

         UpperCAmelCase_											=self.embedder(_lowerCAmelCase       )

         UpperCAmelCase_											=self.encoder(
             _lowerCAmelCase		,  output_hidden_states=_lowerCAmelCase		,  return_dict=_lowerCAmelCase       )

         UpperCAmelCase_											=encoder_outputs[0]

         UpperCAmelCase_											=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(
    '''
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''		,      __lowercase		,      )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class      A       (	__lowercase						):



      def __init__(	self:						List[str]		,  _lowerCAmelCase:						Any       )						->    Union[str, Any]:
         '''simple docstring'''

         super().__init__(_lowerCAmelCase       )
         UpperCAmelCase_											=config.num_labels
         UpperCAmelCase_											=RegNetModel(_lowerCAmelCase       )
         # classification head
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						Optional[torch.FloatTensor] = None		,  _lowerCAmelCase:						Optional[torch.LongTensor] = None		,  _lowerCAmelCase:						Optional[bool] = None		,  _lowerCAmelCase:						Optional[bool] = None		,  )						->    ImageClassifierOutputWithNoAttention:
         '''simple docstring'''

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

         UpperCAmelCase_											=self.regnet(_lowerCAmelCase		,  output_hidden_states=_lowerCAmelCase		,  return_dict=_lowerCAmelCase       )

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

         UpperCAmelCase_											=self.classifier(_lowerCAmelCase       )

         UpperCAmelCase_											=None

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

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

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











from __future__ import annotations




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_						,       UpperCAmelCase_											=set(lowercase__  ), [start]

   while stack:
      UpperCAmelCase_											=stack.pop()
      explored.add(lowercase__  )
      # Differences from BFS:
      # 1) pop last element instead of first one
      # 2) add adjacent elements to stack without exploring them
      for adj in reversed(graph[v]  ):
         if adj not in explored:
            stack.append(lowercase__  )
   return explored


__lowercase  :   Dict						      ={
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, """A"""))




