





"""simple docstring"""


from __future__ import annotations

from typing import Generic, TypeVar

__magic_name__       :					Optional[Any]						=			TypeVar("""T""")






class 							lowercase__			(   Generic[T]	):

			"""simple docstring"""







			def __init__(      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			data
									UpperCamelCase				:		List[str]				   =			self
									UpperCamelCase				:		int				   =			0






class 							lowercase__			(   Generic[T]	):

			"""simple docstring"""







			def __init__(      self    ):






									'''simple docstring'''







									UpperCamelCase				:		dict[T, DisjointSetTreeNode[T]]				   =			{}







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			DisjointSetTreeNode(_A    )







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			self.map[data]
									if elem_ref != elem_ref.parent:
															UpperCamelCase				:		Dict				   =			self.find_set(elem_ref.parent.data    )
									return elem_ref.parent







			def   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									if nodea.rank > nodea.rank:
															UpperCamelCase				:		Optional[int]				   =			nodea
									else:
															UpperCamelCase				:		str				   =			nodea
															if nodea.rank == nodea.rank:
																					nodea.rank += 1







			def   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									self.link(self.find_set(_A    )						,   self.find_set(_A    )    )






class 							lowercase__			(   Generic[T]	):

			"""simple docstring"""







			def __init__(      self    ):






									'''simple docstring'''







									UpperCamelCase				:		dict[T, dict[T, int]]				   =			{}







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									if node not in self.connections:
															UpperCamelCase				:		List[str]				   =			{}







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									self.add_node(_A    )
									self.add_node(_A    )
									UpperCamelCase				:		int				   =			weight
									UpperCamelCase				:		Optional[int]				   =			weight







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			[]
									UpperCamelCase				:		List[Any]				   =			set()
									for start in self.connections:
															for end in self.connections[start]:
																					if (start, end) not in seen:
																											seen.add((end, start)    )
																											edges.append((start, end, self.connections[start][end])    )
									edges.sort(key=lambda _A    : x[2]    )

									# creating the disjoint set
									UpperCamelCase				:		Dict				   =			DisjointSetTree[T]()
									for node in self.connections:
															disjoint_set.make_set(_A    )

									# MST generation
									UpperCamelCase				:		List[Any]				   =			0
									UpperCamelCase				:		int				   =			0
									UpperCamelCase				:		int				   =			GraphUndirectedWeighted[T]()
									while num_edges < len(self.connections    ) - 1:
															UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		Any				   =			edges[index]
															index += 1
															UpperCamelCase				:		Optional[int]				   =			disjoint_set.find_set(_A    )
															UpperCamelCase				:		Union[str, Any]				   =			disjoint_set.find_set(_A    )
															if parent_u != parent_v:
																					num_edges += 1
																					graph.add_edge(_A						,   _A						,   _A    )
																					disjoint_set.union(_A						,   _A    )
									return graph










"""simple docstring"""


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


__magic_name__       :					Dict						=			logging.get_logger(__name__)

__magic_name__       :					List[str]						=			{
    """microsoft/swinv2-tiny-patch4-window8-256""": (
        """https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"""
    ),
}






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				List[str]										=  """swinv2"""

			__lowerCAmelCase     :				Optional[Any]										=  {
			    """num_attention_heads""": """num_heads""",
			    """num_hidden_layers""": """num_layers""",
			}







			def __init__(      self						,   _A=2_2_4						,   _A=4						,   _A=3						,   _A=9_6						,   _A=[2, 2, 6, 2]						,   _A=[3, 6, 1_2, 2_4]						,   _A=7						,   _A=4.0						,   _A=True						,   _A=0.0						,   _A=0.0						,   _A=0.1						,   _A="gelu"						,   _A=False						,   _A=0.02						,   _A=1e-5						,   _A=3_2						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(**_A    )

									UpperCamelCase				:		Optional[Any]				   =			image_size
									UpperCamelCase				:		Tuple				   =			patch_size
									UpperCamelCase				:		str				   =			num_channels
									UpperCamelCase				:		Dict				   =			embed_dim
									UpperCamelCase				:		List[str]				   =			depths
									UpperCamelCase				:		Optional[Any]				   =			len(_A    )
									UpperCamelCase				:		Optional[int]				   =			num_heads
									UpperCamelCase				:		Optional[int]				   =			window_size
									UpperCamelCase				:		List[Any]				   =			mlp_ratio
									UpperCamelCase				:		str				   =			qkv_bias
									UpperCamelCase				:		str				   =			hidden_dropout_prob
									UpperCamelCase				:		Tuple				   =			attention_probs_dropout_prob
									UpperCamelCase				:		List[Any]				   =			drop_path_rate
									UpperCamelCase				:		str				   =			hidden_act
									UpperCamelCase				:		Optional[Any]				   =			use_absolute_embeddings
									UpperCamelCase				:		Dict				   =			layer_norm_eps
									UpperCamelCase				:		Dict				   =			initializer_range
									UpperCamelCase				:		str				   =			encoder_stride
									# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									UpperCamelCase				:		Any				   =			int(embed_dim * 2 ** (len(_A    ) - 1)    )
									UpperCamelCase				:		str				   =			(0, 0, 0, 0)



