


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A							     =       logging.get_logger(__name__)

_A							     =       {
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):
							_lowerCamelCase				: Tuple       =					"""oneformer"""
							_lowerCamelCase				: Optional[int]       =					{"""hidden_size""": """hidden_dim"""}





							def __init__(							self			,       _SCREAMING_SNAKE_CASE = None			,       _SCREAMING_SNAKE_CASE = 255			,       _SCREAMING_SNAKE_CASE = 150			,       _SCREAMING_SNAKE_CASE = 0.1			,       _SCREAMING_SNAKE_CASE = 2.0			,       _SCREAMING_SNAKE_CASE = 5.0			,       _SCREAMING_SNAKE_CASE = 5.0			,       _SCREAMING_SNAKE_CASE = 0.5			,       _SCREAMING_SNAKE_CASE = 0.0_7			,       _SCREAMING_SNAKE_CASE = 1_2544			,       _SCREAMING_SNAKE_CASE = 3.0			,       _SCREAMING_SNAKE_CASE = 0.7_5			,       _SCREAMING_SNAKE_CASE = 0.0_2			,       _SCREAMING_SNAKE_CASE = 1.0			,       _SCREAMING_SNAKE_CASE = 1E-05			,       _SCREAMING_SNAKE_CASE = False			,       _SCREAMING_SNAKE_CASE = True			,       _SCREAMING_SNAKE_CASE = True			,       _SCREAMING_SNAKE_CASE = [4, 8, 16, 32]			,       _SCREAMING_SNAKE_CASE = 77			,       _SCREAMING_SNAKE_CASE = 256			,       _SCREAMING_SNAKE_CASE = 77			,       _SCREAMING_SNAKE_CASE = 6			,       _SCREAMING_SNAKE_CASE = 4_9408			,       _SCREAMING_SNAKE_CASE = 2			,       _SCREAMING_SNAKE_CASE = 16			,       _SCREAMING_SNAKE_CASE = 256			,       _SCREAMING_SNAKE_CASE = 256			,       _SCREAMING_SNAKE_CASE = 256			,       _SCREAMING_SNAKE_CASE = 1024			,       _SCREAMING_SNAKE_CASE = "GN"			,       _SCREAMING_SNAKE_CASE = 6			,       _SCREAMING_SNAKE_CASE = 10			,       _SCREAMING_SNAKE_CASE = True			,       _SCREAMING_SNAKE_CASE = 8			,       _SCREAMING_SNAKE_CASE = 0.1			,       _SCREAMING_SNAKE_CASE = 2048			,       _SCREAMING_SNAKE_CASE = False			,       _SCREAMING_SNAKE_CASE = False			,       _SCREAMING_SNAKE_CASE = 2			,       _SCREAMING_SNAKE_CASE = 4			,       **_SCREAMING_SNAKE_CASE			,       ):
								if backbone_config is None:
									logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""    )
									a_     			=   CONFIG_MAPPING["""swin"""](
									    image_size=224			,       in_channels=3			,       patch_size=4			,       embed_dim=96			,       depths=[2, 2, 6, 2]			,       num_heads=[3, 6, 12, 24]			,       window_size=7			,       drop_path_rate=0.3			,       use_absolute_embeddings=_SCREAMING_SNAKE_CASE			,       out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]			,       )
								elif isinstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
									a_     			=   backbone_config.get("""model_type"""    )
									a_     			=   CONFIG_MAPPING[backbone_model_type]
									a_     			=   config_class.from_dict(_SCREAMING_SNAKE_CASE    )

								a_     			=   backbone_config

								a_     			=   ignore_value
								a_     			=   num_queries
								a_     			=   no_object_weight
								a_     			=   class_weight
								a_     			=   mask_weight
								a_     			=   dice_weight
								a_     			=   contrastive_weight
								a_     			=   contrastive_temperature
								a_     			=   train_num_points
								a_     			=   oversample_ratio
								a_     			=   importance_sample_ratio
								a_     			=   init_std
								a_     			=   init_xavier_std
								a_     			=   layer_norm_eps
								a_     			=   is_training
								a_     			=   use_auxiliary_loss
								a_     			=   output_auxiliary_logits
								a_     			=   strides
								a_     			=   task_seq_len
								a_     			=   text_encoder_width
								a_     			=   text_encoder_context_length
								a_     			=   text_encoder_num_layers
								a_     			=   text_encoder_vocab_size
								a_     			=   text_encoder_proj_layers
								a_     			=   text_encoder_n_ctx
								a_     			=   conv_dim
								a_     			=   mask_dim
								a_     			=   hidden_dim
								a_     			=   encoder_feedforward_dim
								a_     			=   norm
								a_     			=   encoder_layers
								a_     			=   decoder_layers
								a_     			=   use_task_norm
								a_     			=   num_attention_heads
								a_     			=   dropout
								a_     			=   dim_feedforward
								a_     			=   pre_norm
								a_     			=   enforce_input_proj
								a_     			=   query_dec_layers
								a_     			=   common_stride
								a_     			=   decoder_layers

								super().__init__(**_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   copy.deepcopy(self.__dict__    )
								a_     			=   self.backbone_config.to_dict()
								a_     			=   self.__class__.model_type
								return output



from collections import deque

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Dict	)     ->	Tuple:



	"""simple docstring"""






	return input(UpperCamelCase	).strip().split(""" """	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      int	)     ->	dict[int, list[int]]:



	"""simple docstring"""






	a_     			=   {}
	for i in range(UpperCamelCase	):
		a_     			=   []

	for e in range(UpperCamelCase	):
		a_			, a_     			=   (int(UpperCamelCase	) for i in _input(F"""Edge {e + 1}: <node1> <node2> """	))
		graph[x].append(UpperCamelCase	)
	return graph

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      int	)     ->	dict[int, list[int]]:



	"""simple docstring"""






	a_     			=   {}
	for i in range(UpperCamelCase	):
		a_     			=   []

	for e in range(UpperCamelCase	):
		a_			, a_     			=   (int(UpperCamelCase	) for i in _input(F"""Edge {e + 1}: <node1> <node2> """	))
		graph[x].append(UpperCamelCase	)
		graph[y].append(UpperCamelCase	)
	return graph

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      int	)     ->	dict[int, list[tuple[int, int]]]:



	"""simple docstring"""






	a_     			=   {}
	for i in range(UpperCamelCase	):
		a_     			=   []

	for e in range(UpperCamelCase	):
		a_			, a_			, a_     			=   (int(UpperCamelCase	) for i in _input(F"""Edge {e + 1}: <node1> <node2> <weight> """	))
		graph[x].append((y, w)	)
		graph[y].append((x, w)	)
	return graph


if __name__ == "__main__":
			_A     ,				_A							     =       (int(i) for i in _input('Number of nodes and edges: '))

			_A							     =       int(
			    _input(
			        'Press 1 or 2 or 3 \n'
			        '1. Unweighted directed \n'
			        '2. Unweighted undirected \n'
			        '3. Weighted undirected \n'
			    )[0]
			)

			_A							     =       {
			    1: initialize_unweighted_directed_graph,
			    2: initialize_unweighted_undirected_graph,
			    3: initialize_weighted_undirected_graph,
			}[graph_choice](n, m)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Tuple     ,					UpperCamelCase		:      Tuple	)     ->	List[Any]:



	"""simple docstring"""






	a_			, a_     			=   {s}, [s]
	print(UpperCamelCase	)
	while _s:
		a_     			=   0
		for i in g[_s[-1]]:
			if i not in vis:
				_s.append(UpperCamelCase	)
				vis.add(UpperCamelCase	)
				a_     			=   1
				print(UpperCamelCase	)
				break
		if not flag:
			_s.pop()

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Tuple     ,					UpperCamelCase		:      int	)     ->	int:



	"""simple docstring"""






	a_			, a_     			=   {s}, deque([s]	)
	print(UpperCamelCase	)
	while q:
		a_     			=   q.popleft()
		for v in g[u]:
			if v not in vis:
				vis.add(UpperCamelCase	)
				q.append(UpperCamelCase	)
				print(UpperCamelCase	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      Any	)     ->	Union[str, Any]:



	"""simple docstring"""






	a_			, a_			, a_     			=   {s: 0}, set(), {s: 0}
	while True:
		if len(UpperCamelCase	) == len(UpperCamelCase	) - 1:
			break
		a_     			=   10_0000
		for i in dist:
			if i not in known and dist[i] < mini:
				a_     			=   dist[i]
				a_     			=   i
		known.add(UpperCamelCase	)
		for v in g[u]:
			if v[0] not in known and dist[u] + v[1] < dist.get(v[0]     ,					10_0000	):
				a_     			=   dist[u] + v[1]
				a_     			=   u
	for i in dist:
		if i != s:
			print(dist[i]	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Dict     ,					UpperCamelCase		:      int=None     ,					UpperCamelCase		:      int=None	)     ->	Union[str, Any]:



	"""simple docstring"""






	if q is None:
		a_     			=   [1]
	if ind is None:
		a_     			=   [0] * (len(UpperCamelCase	) + 1)  # SInce oth Index is ignored
		for u in g:
			for v in g[u]:
				ind[v] += 1
		a_     			=   deque()
		for i in g:
			if ind[i] == 0:
				q.append(UpperCamelCase	)
	if len(UpperCamelCase	) == 0:
		return
	a_     			=   q.popleft()
	print(UpperCamelCase	)
	for w in g[v]:
		ind[w] -= 1
		if ind[w] == 0:
			q.append(UpperCamelCase	)
	topo(UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase	)

def 			__SCREAMING_SNAKE_CASE		(		)     ->	List[Any]:



	"""simple docstring"""






	a_     			=   input().strip()
	a_     			=   []
	for _ in range(UpperCamelCase	):
		a.append(map(UpperCamelCase     ,					input().strip().split()	)	)
	return a, n

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[Any]	)     ->	List[str]:



	"""simple docstring"""






	((a_)			, (a_))     			=   a_and_n
	a_     			=   list(UpperCamelCase	)
	a_     			=   [[0] * n for i in range(UpperCamelCase	)]
	for k in range(UpperCamelCase	):
		for i in range(UpperCamelCase	):
			for j in range(UpperCamelCase	):
				if dist[i][j] > dist[i][k] + dist[k][j]:
					a_     			=   dist[i][k] + dist[k][j]
					a_     			=   k
	print(UpperCamelCase	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Dict     ,					UpperCamelCase		:      List[Any]	)     ->	str:



	"""simple docstring"""






	a_			, a_			, a_     			=   {s: 0}, set(), {s: 0}
	while True:
		if len(UpperCamelCase	) == len(UpperCamelCase	) - 1:
			break
		a_     			=   10_0000
		for i in dist:
			if i not in known and dist[i] < mini:
				a_     			=   dist[i]
				a_     			=   i
		known.add(UpperCamelCase	)
		for v in g[u]:
			if v[0] not in known and v[1] < dist.get(v[0]     ,					10_0000	):
				a_     			=   v[1]
				a_     			=   u
	return dist

def 			__SCREAMING_SNAKE_CASE		(		)     ->	int:



	"""simple docstring"""






	a_			, a_     			=   map(UpperCamelCase     ,					input().split(""" """	)	)
	a_     			=   []
	for _ in range(UpperCamelCase	):
		edges.append(map(UpperCamelCase     ,					input().split(""" """	)	)	)
	return edges, n

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[str]	)     ->	List[str]:



	"""simple docstring"""






	((a_)			, (a_))     			=   e_and_n
	e.sort(reverse=UpperCamelCase     ,					key=lambda UpperCamelCase	: x[2]	)
	a_     			=   [{i} for i in range(1     ,					n + 1	)]
	while True:
		if len(UpperCamelCase	) == 1:
			break
		print(UpperCamelCase	)
		a_     			=   e.pop()
		for i in range(len(UpperCamelCase	)	):
			if x[0] in s[i]:
				break
		for j in range(len(UpperCamelCase	)	):
			if x[1] in s[j]:
				if i == j:
					break
				s[j].update(s[i]	)
				s.pop(UpperCamelCase	)
				break

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	Optional[Any]:



	"""simple docstring"""






	a_     			=   []
	for node in graph:
		if not graph[node]:
			isolated.append(UpperCamelCase	)
	return isolated