
"""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 heapq import heappop, heappush

import numpy as np
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    )  ->     tuple[float | int, list[tuple[int, int]]]:







							"""simple docstring"""


							__snake_case			,   __snake_case     			=       grid.shape
							__snake_case     			=       [-1, 1, 0, 0]
							__snake_case     			=       [0, 0, -1, 1]
							if allow_diagonal:
														dx += [-1, -1, 1, 1]
														dy += [-1, 1, -1, 1]

							__snake_case			,   __snake_case     			=       [(0, source)], set()
							__snake_case     			=       np.full((rows, cols)							,    np.inf )
							__snake_case     			=       0
							__snake_case     			=       np.empty((rows, cols)							,    dtype=SCREAMING_SNAKE_CASE )
							__snake_case     			=       None

							while queue:
														((__snake_case)			,   (__snake_case))     			=       heappop(SCREAMING_SNAKE_CASE )
														if (x, y) in visited:
																					continue
														visited.add((x, y) )

														if (x, y) == destination:
																					__snake_case     			=       []
																					while (x, y) != source:
																												path.append((x, y) )
																												__snake_case			,   __snake_case     			=       predecessors[x, y]
																					path.append(SCREAMING_SNAKE_CASE )  # add the source manually
																					path.reverse()
																					return matrix[destination], path

														for i in range(len(SCREAMING_SNAKE_CASE ) ):
																					__snake_case			,   __snake_case     			=       x + dx[i], y + dy[i]
																					if 0 <= nx < rows and 0 <= ny < cols:
																												__snake_case     			=       grid[nx][ny]
																												if next_node == 1 and matrix[nx, ny] > dist + 1:
																																			heappush(SCREAMING_SNAKE_CASE							,    (dist + 1, (nx, ny)) )
																																			__snake_case     			=       dist + 1
																																			__snake_case     			=       (x, y)

							return np.inf, []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
