


import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor

class 			lowerCamelCase_					(    unittest.TestCase    ):





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE = False    ):
								a_     			=   10

								if only_cross_attention:
									a_     			=   12
								else:
									# when only cross attention is not set, the cross attention dim must be the same as the query dim
									a_     			=   query_dim

								return {
								    "query_dim": query_dim,
								    "cross_attention_dim": cross_attention_dim,
								    "heads": 2,
								    "dim_head": 4,
								    "added_kv_proj_dim": 6,
								    "norm_num_groups": 1,
								    "only_cross_attention": only_cross_attention,
								    "processor": AttnAddedKVProcessor(),
								}





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   2

								a_     			=   torch.rand(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       3			,       2    )
								a_     			=   torch.rand(_SCREAMING_SNAKE_CASE			,       4			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   None

								return {
								    "hidden_states": hidden_states,
								    "encoder_hidden_states": encoder_hidden_states,
								    "attention_mask": attention_mask,
								}





							def   __magic_name__      (							self    ):
								# self and cross attention

								torch.manual_seed(0    )

								a_     			=   self.get_constructor_arguments(only_cross_attention=_SCREAMING_SNAKE_CASE    )
								a_     			=   Attention(**_SCREAMING_SNAKE_CASE    )

								self.assertTrue(attn.to_k is not None    )
								self.assertTrue(attn.to_v is not None    )

								a_     			=   self.get_forward_arguments(
								    query_dim=constructor_args["""query_dim"""]			,       added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]    )

								a_     			=   attn(**_SCREAMING_SNAKE_CASE    )

								# only self attention

								torch.manual_seed(0    )

								a_     			=   self.get_constructor_arguments(only_cross_attention=_SCREAMING_SNAKE_CASE    )
								a_     			=   Attention(**_SCREAMING_SNAKE_CASE    )

								self.assertTrue(attn.to_k is None    )
								self.assertTrue(attn.to_v is None    )

								a_     			=   self.get_forward_arguments(
								    query_dim=constructor_args["""query_dim"""]			,       added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]    )

								a_     			=   attn(**_SCREAMING_SNAKE_CASE    )

								self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()    )







class 			lowerCamelCase_					(    unittest.TestCase    ):





							def   __magic_name__      (							self    ):
								a_     			=   DiffusionPipeline.from_pretrained("""hf-internal-testing/tiny-stable-diffusion-pipe"""			,       safety_checker=_SCREAMING_SNAKE_CASE    )

								a_     			=   pipe(
								    """foo"""			,       num_inference_steps=2			,       generator=torch.Generator("""cpu"""    ).manual_seed(0    )			,       output_type="""np"""			,       ).images

								# the initial conversion succeeds
								a_     			=   DiffusionPipeline.from_pretrained(
								    """hf-internal-testing/tiny-stable-diffusion-pipe"""			,       device_map="""sequential"""			,       safety_checker=_SCREAMING_SNAKE_CASE    )

								a_     			=   pipe(
								    """foo"""			,       num_inference_steps=2			,       generator=torch.Generator("""cpu"""    ).manual_seed(0    )			,       output_type="""np"""			,       ).images

								with tempfile.TemporaryDirectory() as tmpdir:
									# save the converted model
									pipe.save_pretrained(_SCREAMING_SNAKE_CASE    )

									# can also load the converted weights
									a_     			=   DiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE			,       device_map="""sequential"""			,       safety_checker=_SCREAMING_SNAKE_CASE    )

								a_     			=   pipe(
								    """foo"""			,       num_inference_steps=2			,       generator=torch.Generator("""cpu"""    ).manual_seed(0    )			,       output_type="""np"""			,       ).images

								self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       atol=1E-5    )    )
								self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       atol=1E-5    )    )



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