
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE = False							):




			"""simple docstring"""


			if device_specific:
						seed += AcceleratorState().process_index
			random.seed(SCREAMING_SNAKE_CASE							)
			np.random.seed(SCREAMING_SNAKE_CASE							)
			torch.manual_seed(SCREAMING_SNAKE_CASE							)
			if is_xpu_available():
						torch.xpu.manual_seed_all(SCREAMING_SNAKE_CASE							)
			elif is_npu_available():
						torch.npu.manual_seed_all(SCREAMING_SNAKE_CASE							)
			else:
						torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE							)
			# ^^ safe to call this function even if cuda is not available
			if is_tpu_available():
						xm.set_rng_state(SCREAMING_SNAKE_CASE							)




def 		A						(  SCREAMING_SNAKE_CASE = None     ,						SCREAMING_SNAKE_CASE = None							):




			"""simple docstring"""


			if rng_type == RNGType.TORCH:
						UpperCAmelCase__		:List[Any] 		=    torch.get_rng_state()
			elif rng_type == RNGType.CUDA:
						UpperCAmelCase__		:Dict 		=    torch.cuda.get_rng_state()
			elif rng_type == RNGType.XLA:
						assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
						UpperCAmelCase__		:Union[str, Any] 		=    torch.tensor(xm.get_rng_state()							)
			elif rng_type == RNGType.NPU:
						assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
						UpperCAmelCase__		:Optional[int] 		=    torch.npu.get_rng_state()
			elif rng_type == RNGType.XPU:
						assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
						UpperCAmelCase__		:Dict 		=    torch.xpu.get_rng_state()
			elif rng_type == RNGType.GENERATOR:
						assert generator is not None, "Need a generator to synchronize its seed."
						UpperCAmelCase__		:Union[str, Any] 		=    generator.get_state()

			# Broadcast the rng state from device 0 to other devices
			UpperCAmelCase__		:Optional[int] 		=    AcceleratorState()
			if state.distributed_type == DistributedType.TPU:
						UpperCAmelCase__		:Optional[Any] 		=    rng_state.to(xm.xla_device()							)
						xm.collective_broadcast([rng_state]							)
						xm.mark_step()
						UpperCAmelCase__		:List[Any] 		=    rng_state.cpu()
			elif (
			    state.distributed_type in CUDA_DISTRIBUTED_TYPES
			    or state.distributed_type == DistributedType.MULTI_NPU
			    or state.distributed_type == DistributedType.MULTI_XPU
			):
						UpperCAmelCase__		:Optional[int] 		=    rng_state.to(state.device							)
						torch.distributed.broadcast(SCREAMING_SNAKE_CASE     ,						0							)
						UpperCAmelCase__		:Dict 		=    rng_state.cpu()
			elif state.distributed_type == DistributedType.MULTI_CPU:
						torch.distributed.broadcast(SCREAMING_SNAKE_CASE     ,						0							)

			# Set the broadcast rng state
			if rng_type == RNGType.TORCH:
						torch.set_rng_state(SCREAMING_SNAKE_CASE							)
			elif rng_type == RNGType.CUDA:
						torch.cuda.set_rng_state(SCREAMING_SNAKE_CASE							)
			elif rng_type == RNGType.NPU:
						torch.npu.set_rng_state(SCREAMING_SNAKE_CASE							)
			elif rng_type == RNGType.XPU:
						torch.xpu.set_rng_state(SCREAMING_SNAKE_CASE							)
			elif rng_type == RNGType.XLA:
						xm.set_rng_state(rng_state.item()							)
			elif rng_type == RNGType.GENERATOR:
						generator.set_state(SCREAMING_SNAKE_CASE							)




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE = None							):




			"""simple docstring"""


			for rng_type in rng_types:
						synchronize_rng_state(RNGType(SCREAMING_SNAKE_CASE							)     ,						generator=SCREAMING_SNAKE_CASE							)







from __future__ import annotations

from queue import Queue


class 	UpperCamelCase__       :

						'''simple docstring'''



						def __init__(  self	)  ->None:
									UpperCAmelCase__		:dict[int, list[int]] 		=    {}


						def    A__       (  self	)  ->None:
									for i in self.vertices:
												print(A  , ' : '  , ' -> '.join([str(A	) for j in self.vertices[i]]	)	)


						def    A__       (  self  , A  , A	)  ->None:
									if from_vertex in self.vertices:
												self.vertices[from_vertex].append(A	)
									else:
												UpperCAmelCase__		:Union[str, Any] 		=    [to_vertex]


						def    A__       (  self  , A	)  ->set[int]:
									UpperCAmelCase__		:str 		=    set()

									# create a first in first out queue to store all the vertices for BFS
									UpperCAmelCase__		:Queue 		=    Queue()

									# mark the source node as visited and enqueue it
									visited.add(A	)
									queue.put(A	)

									while not queue.empty():
												UpperCAmelCase__		:List[str] 		=    queue.get()

												# loop through all adjacent vertex and enqueue it if not yet visited
												for adjacent_vertex in self.vertices[vertex]:
															if adjacent_vertex not in visited:
																		queue.put(A	)
																		visited.add(A	)
									return visited


if __name__ == "__main__":
						from doctest import testmod

						testmod(verbose=True)

						__snake_case					:							Any           =							Graph()
						g.add_edge(0, 1)
						g.add_edge(0, 2)
						g.add_edge(1, 2)
						g.add_edge(2, 0)
						g.add_edge(2, 3)
						g.add_edge(3, 3)

						g.print_graph()
						# 0  :  1 -> 2
						# 1  :  2
						# 2  :  0 -> 3
						# 3  :  3

						assert sorted(g.bfs(2)) == [0, 1, 2, 3]





