



"""simple docstring"""

from collections import deque





def       __magic_name__    (   _lowerCamelCase				: int					):
							__a		:		Optional[Any]									=       len(_lowerCamelCase					)
							__a		:		int									=       deque()
							__a		:		List[str]									=       [False for _ in range(_lowerCamelCase					)]
							__a		:		int									=       [-1 for _ in range(_lowerCamelCase					)]
							__a		:		Optional[Any]									=       index_of[:]

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

														for w in g[v]:
																					if index_of[w] == -1:
																												__a		:		Optional[int]									=       strong_connect(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
																												__a		:		Dict									=       (
																												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																												)
																					elif on_stack[w]:
																												__a		:		Optional[int]									=       (
																												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																												)

														if lowlink_of[v] == index_of[v]:
																					__a		:		Union[str, Any]									=       []
																					__a		:		Any									=       stack.pop()
																					__a		:		Optional[int]									=       False
																					component.append(_lowerCamelCase					)
																					while w != v:
																												__a		:		str									=       stack.pop()
																												__a		:		Dict									=       False
																												component.append(_lowerCamelCase					)
																					components.append(_lowerCamelCase					)
														return index

							__a		:		Union[str, Any]									=       []
							for v in range(_lowerCamelCase					):
														if index_of[v] == -1:
																					strong_connect(_lowerCamelCase  ,       0  ,       _lowerCamelCase					)

							return components





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: List[Any]					):
							__a		:		int									=       [[] for _ in range(_lowerCamelCase					)]
							for u, v in edges:
														g[u].append(_lowerCamelCase					)
							return g


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

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






"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"vit_msn"



						def __init__(self						,     _lowercase=768						,     _lowercase=12						,     _lowercase=12						,     _lowercase=3072						,     _lowercase="gelu"						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=0.02						,     _lowercase=1e-06						,     _lowercase=224						,     _lowercase=16						,     _lowercase=3						,     _lowercase=True						,     **_lowercase						,     ):





													'''simple docstring'''

													super().__init__(**_lowercase							)

													__a		:		int									=       hidden_size
													__a		:		str									=       num_hidden_layers
													__a		:		str									=       num_attention_heads
													__a		:		Optional[Any]									=       intermediate_size
													__a		:		Union[str, Any]									=       hidden_act
													__a		:		Tuple									=       hidden_dropout_prob
													__a		:		Any									=       attention_probs_dropout_prob
													__a		:		List[Any]									=       initializer_range
													__a		:		Any									=       layer_norm_eps
													__a		:		Dict									=       image_size
													__a		:		List[Any]									=       patch_size
													__a		:		Dict									=       num_channels
													__a		:		Optional[Any]									=       qkv_bias

