



'''simple docstring'''

import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment

class       __magic_name__		(       unittest.TestCase  ):







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Dict								=				inspect.getfile(accelerate.test_utils						)
									_a	: Dict								=				os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['scripts', 'test_script.py']						)
									_a	: List[Any]								=				os.path.sep.join(
									    mod_file.split(os.path.sep						)[:-1] + ['scripts', 'test_distributed_data_loop.py']						)
									_a	: List[str]								=				os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['scripts', 'test_ops.py']						)







							@require_multi_gpu
							def 				__lowercase     (				self   :			List[str]						):
									print(F"""Found {torch.cuda.device_count()} devices."""						)
									_a	: Any								=				['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
									with patch_environment(omp_num_threads=1						):
											execute_subprocess_async(_UpperCAmelCase      ,env=os.environ.copy()						)







							@require_multi_gpu
							def 				__lowercase     (				self   :			Optional[int]						):
									print(F"""Found {torch.cuda.device_count()} devices."""						)
									_a	: int								=				['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
									print(F"""Command: {cmd}"""						)
									with patch_environment(omp_num_threads=1						):
											execute_subprocess_async(_UpperCAmelCase      ,env=os.environ.copy()						)







							@require_multi_gpu
							def 				__lowercase     (				self   :			str						):
									_a	: int								=				['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__						)]
									with patch_environment(omp_num_threads=1						):
											execute_subprocess_async(_UpperCAmelCase      ,env=os.environ.copy()						)







							@require_multi_gpu
							def 				__lowercase     (				self   :			List[Any]						):
									print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""						)
									_a	: Any								=				['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
									with patch_environment(omp_num_threads=1      ,cuda_visible_devices='0,1'						):
											execute_subprocess_async(_UpperCAmelCase      ,env=os.environ.copy()						)


if __name__ == "__main__":
							__lowerCAmelCase		     =  Accelerator()
							__lowerCAmelCase		     =  (accelerator.state.process_index + 2, 10)
							__lowerCAmelCase		     =  torch.randint(0, 10, shape).to(accelerator.device)

							__lowerCAmelCase		     =  ''''''

							__lowerCAmelCase		     =  accelerator.pad_across_processes(tensor)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
														error_msg += "Padding was not done with the right value (0)."

							__lowerCAmelCase		     =  accelerator.pad_across_processes(tensor, pad_first=True)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							__lowerCAmelCase		     =  accelerator.state.num_processes - accelerator.state.process_index - 1
							if not torch.equal(tensora[index:], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[:index] == 0):
														error_msg += "Padding was not done with the right value (0)."

							# Raise error at the end to make sure we don't stop at the first failure.
							if len(error_msg) > 0:
														raise ValueError(error_msg)









'''simple docstring'''

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__lowerCAmelCase		     =  TypeVar('''T''')

class       __magic_name__		(       Generic[T]  ):







							def __init__(				self   :			int      ,_UpperCAmelCase   :			bool = True						):
									_a	: dict[T, list[T]]								=				{}  # dictionary of lists
									_a	: Tuple								=				directed







							def 				__lowercase     (				self   :			Union[str, Any]      ,_UpperCAmelCase   :			T      ,_UpperCAmelCase   :			T						):

									if not self.directed:  # For undirected graphs
											# if both source vertex and destination vertex are both present in the
											# adjacency list, add destination vertex to source vertex list of adjacent
											# vertices and add source vertex to destination vertex list of adjacent
											# vertices.
											if source_vertex in self.adj_list and destination_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													self.adj_list[destination_vertex].append(_UpperCAmelCase						)
											# if only source vertex is present in adjacency list, add destination vertex
											# to source vertex list of adjacent vertices, then create a new vertex with
											# destination vertex as key and assign a list containing the source vertex
											# as it's first adjacent vertex.
											elif source_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													_a	: Optional[Any]								=				[source_vertex]
											# if only destination vertex is present in adjacency list, add source vertex
											# to destination vertex list of adjacent vertices, then create a new vertex
											# with source vertex as key and assign a list containing the source vertex
											# as it's first adjacent vertex.
											elif destination_vertex in self.adj_list:
													self.adj_list[destination_vertex].append(_UpperCAmelCase						)
													_a	: Union[str, Any]								=				[destination_vertex]
											# if both source vertex and destination vertex are not present in adjacency
											# list, create a new vertex with source vertex as key and assign a list
											# containing the destination vertex as it's first adjacent vertex also
											# create a new vertex with destination vertex as key and assign a list
											# containing the source vertex as it's first adjacent vertex.
											else:
													_a	: Union[str, Any]								=				[destination_vertex]
													_a	: Optional[int]								=				[source_vertex]
									else:  # For directed graphs
											# if both source vertex and destination vertex are present in adjacency
											# list, add destination vertex to source vertex list of adjacent vertices.
											if source_vertex in self.adj_list and destination_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
											# if only source vertex is present in adjacency list, add destination
											# vertex to source vertex list of adjacent vertices and create a new vertex
											# with destination vertex as key, which has no adjacent vertex
											elif source_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													_a	: int								=				[]
											# if only destination vertex is present in adjacency list, create a new
											# vertex with source vertex as key and assign a list containing destination
											# vertex as first adjacent vertex
											elif destination_vertex in self.adj_list:
													_a	: Tuple								=				[destination_vertex]
											# if both source vertex and destination vertex are not present in adjacency
											# list, create a new vertex with source vertex as key and a list containing
											# destination vertex as it's first adjacent vertex. Then create a new vertex
											# with destination vertex as key, which has no adjacent vertex
											else:
													_a	: Tuple								=				[destination_vertex]
													_a	: str								=				[]

									return self







							def __repr__(				self   :			int						):
									return pformat(self.adj_list						)




