
"""simple docstring"""







import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=()							,    SCREAMING_SNAKE_CASE=None							,    SCREAMING_SNAKE_CASE="no"							,    SCREAMING_SNAKE_CASE="29500" )  ->     Dict:







							"""simple docstring"""


							__snake_case     			=       False
							__snake_case     			=       False
							if any(key.startswith("KAGGLE" ) for key in os.environ.keys() ):
														__snake_case     			=       True
							elif "IPython" in sys.modules:
														__snake_case     			=       "google.colab" in str(sys.modules["IPython"].get_ipython() )

							try:
														__snake_case     			=       PrecisionType(mixed_precision.lower() )
							except ValueError:
														raise ValueError(
														    F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.''' )

							if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"							,    SCREAMING_SNAKE_CASE ) is not None):
														# TPU launch
														import torch_xla.distributed.xla_multiprocessing as xmp

														if len(AcceleratorState._shared_state ) > 0:
																					raise ValueError(
																					    "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
																					    "your training function. Restart your notebook and make sure no cells initializes an "
																					    "`Accelerator`." )
														if num_processes is None:
																					__snake_case     			=       8

														__snake_case     			=       PrepareForLaunch(SCREAMING_SNAKE_CASE							,    distributed_type="TPU" )
														print(F'''Launching a training on {num_processes} TPU cores.''' )
														xmp.spawn(SCREAMING_SNAKE_CASE							,    args=SCREAMING_SNAKE_CASE							,    nprocs=SCREAMING_SNAKE_CASE							,    start_method="fork" )
							elif in_colab:
														# No need for a distributed launch otherwise as it's either CPU or one GPU.
														if torch.cuda.is_available():
																					print("Launching training on one GPU." )
														else:
																					print("Launching training on one CPU." )
														function(*SCREAMING_SNAKE_CASE )
							else:
														if num_processes is None:
																					raise ValueError(
																					    "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call." )

														if num_processes > 1:
																					# Multi-GPU launch
																					from torch.multiprocessing import start_processes
																					from torch.multiprocessing.spawn import ProcessRaisedException

																					if len(AcceleratorState._shared_state ) > 0:
																												raise ValueError(
																												    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
																												    "inside your training function. Restart your notebook and make sure no cells initializes an "
																												    "`Accelerator`." )

																					if torch.cuda.is_initialized():
																												raise ValueError(
																												    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
																												    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
																												    "function." )

																					# torch.distributed will expect a few environment variable to be here. We set the ones common to each
																					# process here (the other ones will be set be the launcher).
																					with patch_environment(
																					    world_size=SCREAMING_SNAKE_CASE							,    master_addr="127.0.01"							,    master_port=SCREAMING_SNAKE_CASE							,    mixed_precision=SCREAMING_SNAKE_CASE ):
																												__snake_case     			=       PrepareForLaunch(SCREAMING_SNAKE_CASE							,    distributed_type="MULTI_GPU" )
																												print(F'''Launching training on {num_processes} GPUs.''' )
																												try:
																																			start_processes(SCREAMING_SNAKE_CASE							,    args=SCREAMING_SNAKE_CASE							,    nprocs=SCREAMING_SNAKE_CASE							,    start_method="fork" )
																												except ProcessRaisedException as e:
																																			if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																																										raise RuntimeError(
																																										    "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
																																										    "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
																																										    "Please review your imports and test them when running the `notebook_launcher()` to identify "
																																										    "which one is problematic." ) from e

														else:
																					# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
																					if is_mps_available():
																												__snake_case     			=       "1"
																												print("Launching training on MPS." )
																					elif torch.cuda.is_available():
																												print("Launching training on one GPU." )
																					else:
																												print("Launching training on CPU." )
																					function(*SCREAMING_SNAKE_CASE )



def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=()							,    SCREAMING_SNAKE_CASE=2 )  ->     List[Any]:







							"""simple docstring"""


							from torch.multiprocessing import start_processes

							with tempfile.NamedTemporaryFile() as tmp_file:
														# torch.distributed will expect a few environment variable to be here. We set the ones common to each
														# process here (the other ones will be set be the launcher).
														with patch_environment(
														    world_size=SCREAMING_SNAKE_CASE							,    master_addr="127.0.01"							,    master_port="29500"							,    accelerate_mixed_precision="no"							,    accelerate_debug_rdv_file=tmp_file.name							,    accelerate_use_cpu="yes"							,    ):
																					__snake_case     			=       PrepareForLaunch(SCREAMING_SNAKE_CASE							,    debug=SCREAMING_SNAKE_CASE )
																					start_processes(SCREAMING_SNAKE_CASE							,    args=SCREAMING_SNAKE_CASE							,    nprocs=SCREAMING_SNAKE_CASE							,    start_method="fork" )


"""simple docstring"""







from __future__ import annotations

_SCREAMING_SNAKE_CASE	       =      {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}







class 							__magic_name__     :





					def __init__(				self   :							Any							,						snake_case_   :							dict[str, list[str]]							,						snake_case_   :							str    ):
												__snake_case     			=       graph
												# mapping node to its parent in resulting breadth first tree
												__snake_case     			=       {}
												__snake_case     			=       source_vertex





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												__snake_case     			=       {self.source_vertex}
												__snake_case     			=       None
												__snake_case     			=       [self.source_vertex]  # first in first out queue

												while queue:
																			__snake_case     			=       queue.pop(0    )
																			for adjacent_vertex in self.graph[vertex]:
																										if adjacent_vertex not in visited:
																																	visited.add(snake_case_    )
																																	__snake_case     			=       vertex
																																	queue.append(snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							str    ):
												if target_vertex == self.source_vertex:
																			return self.source_vertex

												__snake_case     			=       self.parent.get(snake_case_    )
												if target_vertex_parent is None:
																			__snake_case     			=       (
																			    F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
																			)
																			raise ValueError(snake_case_    )

												return self.shortest_path(snake_case_    ) + F'''->{target_vertex}'''


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      Graph(graph, """G""")
		g.breath_first_search()
		print(g.shortest_path("""D"""))
		print(g.shortest_path("""G"""))
		print(g.shortest_path("""Foo"""))
