

from collections import deque

def       __snake_case						( __UpperCamelCase	: int    ):



      """simple docstring"""

      A_						  =				len(__UpperCamelCase    )
      A_						  =				deque()
      A_						  =				[False for _ in range(__UpperCamelCase    )]
      A_						  =				[-1 for _ in range(__UpperCamelCase    )]
      A_						  =				index_of[:]

      def strong_connect(__UpperCamelCase	: List[str]				,__UpperCamelCase	: str				,__UpperCamelCase	: Union[str, Any]    ):
            A_						  =				index  # the number when this node is seen
            A_						  =				index  # lowest rank node reachable from here
            index += 1
            stack.append(__UpperCamelCase    )
            A_						  =				True

            for w in g[v]:
                  if index_of[w] == -1:
                        A_						  =				strong_connect(__UpperCamelCase				,__UpperCamelCase				,__UpperCamelCase    )
                        A_						  =				(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )
                  elif on_stack[w]:
                        A_						  =				(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )

            if lowlink_of[v] == index_of[v]:
                  A_						  =				[]
                  A_						  =				stack.pop()
                  A_						  =				False
                  component.append(__UpperCamelCase    )
                  while w != v:
                        A_						  =				stack.pop()
                        A_						  =				False
                        component.append(__UpperCamelCase    )
                  components.append(__UpperCamelCase    )
            return index

      A_						  =				[]
      for v in range(__UpperCamelCase    ):
            if index_of[v] == -1:
                  strong_connect(__UpperCamelCase				,0				,__UpperCamelCase    )

      return components

def       __snake_case						( __UpperCamelCase	: Any				,__UpperCamelCase	: Tuple    ):



      """simple docstring"""

      A_						  =				[[] for _ in range(__UpperCamelCase    )]
      for u, v in edges:
            g[u].append(__UpperCamelCase    )
      return g


if __name__ == "__main__":
      # Test
      __a    :Optional[int]      =			7
      __a    :Optional[int]      =			[0, 0, 1, 2, 3, 3, 4, 4, 6]
      __a    :str      =			[1, 3, 2, 0, 1, 4, 5, 6, 5]
      __a    :str      =			[(u, v) for u, v in zip(source, target)]
      __a    :int      =			create_graph(n_vertices, edges)

      assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)






from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
					import timm


if is_torch_available():
					from torch import Tensor

class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a							):
							snake_case  :   int      =							"""pixel_values"""
							snake_case  :   List[Any]      =							False
							snake_case  :   str      =							TimmBackboneConfig






							def __init__(    self	,			__lowerCAmelCase	,			**__lowerCAmelCase	):
														requires_backends(self	,			"""timm"""	)
														super().__init__(__lowerCAmelCase	)
														UpperCamelCase__					  =      config

														if config.backbone is None:
																					raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""	)

														if config.backbone not in timm.list_models():
																					raise ValueError(f"""backbone {config.backbone} is not supported by timm."""	)

														if hasattr(__lowerCAmelCase	,			"""out_features"""	) and config.out_features is not None:
																					raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""	)

														UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			"""use_pretrained_backbone"""	,			__lowerCAmelCase	)
														if pretrained is None:
																					raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""	)

														# We just take the final layer by default. This matches the default for the transformers models.
														UpperCamelCase__					  =      config.out_indices if getattr(__lowerCAmelCase	,			"""out_indices"""	,			__lowerCAmelCase	) is not None else (-1,)

														UpperCamelCase__					  =      timm.create_model(
														    config.backbone	,			pretrained=__lowerCAmelCase	,			features_only=config.features_only	,			in_chans=config.num_channels	,			out_indices=__lowerCAmelCase	,			**__lowerCAmelCase	,			)
														# These are used to control the output of the model when called. If output_hidden_states is True, then
														# return_layers is modified to include all layers.
														UpperCamelCase__					  =      self._backbone.return_layers
														UpperCamelCase__					  =      {layer["""module"""]: str(__lowerCAmelCase	) for i, layer in enumerate(self._backbone.feature_info.info	)}
														super()._init_backbone(__lowerCAmelCase	)






							@classmethod
							def 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														requires_backends(cls	,			["""vision""", """timm"""]	)
														from ...models.timm_backbone import TimmBackboneConfig

														UpperCamelCase__					  =      kwargs.pop("""config"""	,			TimmBackboneConfig()	)

														UpperCamelCase__					  =      kwargs.pop("""use_timm_backbone"""	,			__lowerCAmelCase	)
														if not use_timm:
																					raise ValueError("""use_timm_backbone must be True for timm backbones"""	)

														UpperCamelCase__					  =      kwargs.pop("""num_channels"""	,			config.num_channels	)
														UpperCamelCase__					  =      kwargs.pop("""features_only"""	,			config.features_only	)
														UpperCamelCase__					  =      kwargs.pop("""use_pretrained_backbone"""	,			config.use_pretrained_backbone	)
														UpperCamelCase__					  =      kwargs.pop("""out_indices"""	,			config.out_indices	)
														UpperCamelCase__					  =      TimmBackboneConfig(
														    backbone=__lowerCAmelCase	,			num_channels=__lowerCAmelCase	,			features_only=__lowerCAmelCase	,			use_pretrained_backbone=__lowerCAmelCase	,			out_indices=__lowerCAmelCase	,			)
														return super()._from_config(__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														pass







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      return_dict if return_dict is not None else self.config.use_return_dict
														UpperCamelCase__					  =      (
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														UpperCamelCase__					  =      output_attentions if output_attentions is not None else self.config.output_attentions

														if output_attentions:
																					raise ValueError("""Cannot output attentions for timm backbones at the moment"""	)

														if output_hidden_states:
																					# We modify the return layers to include all the stages of the backbone
																					UpperCamelCase__					  =      self._all_layers
																					UpperCamelCase__					  =      self._backbone(__lowerCAmelCase	,			**__lowerCAmelCase	)
																					UpperCamelCase__					  =      self._return_layers
																					UpperCamelCase__					  =      tuple(hidden_states[i] for i in self.out_indices	)
														else:
																					UpperCamelCase__					  =      self._backbone(__lowerCAmelCase	,			**__lowerCAmelCase	)
																					UpperCamelCase__					  =      None

														UpperCamelCase__					  =      tuple(__lowerCAmelCase	)
														UpperCamelCase__					  =      tuple(__lowerCAmelCase	) if hidden_states is not None else None

														if not return_dict:
																					UpperCamelCase__					  =      (feature_maps,)
																					if output_hidden_states:
																												UpperCamelCase__					  =      output + (hidden_states,)
																					return output

														return BackboneOutput(feature_maps=__lowerCAmelCase	,			hidden_states=__lowerCAmelCase	,			attentions=__lowerCAmelCase	)




