



'''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     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    Union[str, Any]    						=      inspect.getfile(accelerate.test_utils						)
          snake_case__  :    Dict    						=      os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["""scripts""", """test_script.py"""]						)
          snake_case__  :    int    						=      os.path.sep.join(
              mod_file.split(os.path.sep						)[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]						)
          snake_case__  :    Union[str, Any]    						=      os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["""scripts""", """test_ops.py"""]						)




    @require_multi_gpu
    def lowerCamelCase       (    self					:     Any						):
          print(f"Found {torch.cuda.device_count()} devices."						)
          snake_case__  :    Optional[int]    						=      ['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
          with patch_environment(omp_num_threads=1						):
                execute_subprocess_async(SCREAMING_SNAKE_CASE_ ,     env=os.environ.copy()						)




    @require_multi_gpu
    def lowerCamelCase       (    self					:     Optional[Any]						):
          print(f"Found {torch.cuda.device_count()} devices."						)
          snake_case__  :    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(SCREAMING_SNAKE_CASE_ ,     env=os.environ.copy()						)




    @require_multi_gpu
    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    List[Any]    						=      ['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__						)]
          with patch_environment(omp_num_threads=1						):
                execute_subprocess_async(SCREAMING_SNAKE_CASE_ ,     env=os.environ.copy()						)




    @require_multi_gpu
    def lowerCamelCase       (    self					:     Union[str, Any]						):
          print(f"Found {torch.cuda.device_count()} devices, using 2 devices only"						)
          snake_case__  :    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(SCREAMING_SNAKE_CASE_ ,     env=os.environ.copy()						)


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

       __a          =		""""""

       __a          =		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)."

       __a          =		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."
       __a          =		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"""







def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= [0] * len(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[str]								= []
  lowerCAmelCase_   :				Tuple								= [1] * len(lowerCAmelCase__						)

  for values in graph.values():
    for i in values:
      indegree[i] += 1

  for i in range(len(lowerCAmelCase__						)						):
    if indegree[i] == 0:
      queue.append(lowerCAmelCase__						)

  while queue:
    lowerCAmelCase_   :				Union[str, Any]								= queue.pop(0						)
    for x in graph[vertex]:
      indegree[x] -= 1

      if long_dist[vertex] + 1 > long_dist[x]:
        lowerCAmelCase_   :				Any								= long_dist[vertex] + 1

      if indegree[x] == 0:
        queue.append(lowerCAmelCase__						)

  print(max(lowerCAmelCase__						)						)


# Adjacency list of Graph
lowercase__		:				Any          =	{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)





