"""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        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      inspect.getfile(accelerate.test_utils					)
      _UpperCAmelCase						 =      os.path.sep.join(mod_file.split(os.path.sep					)[:-1] + ["""scripts""", """test_script.py"""]					)
      _UpperCAmelCase						 =      os.path.sep.join(
          mod_file.split(os.path.sep					)[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]					)
      _UpperCAmelCase						 =      os.path.sep.join(mod_file.split(os.path.sep					)[:-1] + ["""scripts""", """test_ops.py"""]					)


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


    @require_multi_gpu
    def      lowerCAmelCase_  (					self	:  Any					):
      print(f'''Found {torch.cuda.device_count()} devices.'''					)
      _UpperCAmelCase						 =      ["""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(__lowerCAmelCase   ,				env=os.environ.copy()					)


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


    @require_multi_gpu
    def      lowerCAmelCase_  (					self	:  Dict					):
      print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''					)
      _UpperCAmelCase						 =      ["""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(__lowerCAmelCase   ,				env=os.environ.copy()					)


if __name__ == "__main__":
    UpperCAmelCase__  		=						Accelerator()
    UpperCAmelCase__  		=						(accelerator.state.process_index + 2, 1_0)
    UpperCAmelCase__  		=						torch.randint(0, 1_0, shape).to(accelerator.device)

    UpperCAmelCase__  		=						""""""

    UpperCAmelCase__  		=						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)."

    UpperCAmelCase__  		=						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."
    UpperCAmelCase__  		=						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"""


import random






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase = False		):







  """simple docstring"""
  _UpperCAmelCase						 =      {i: [] for i in range(lowercase		)}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(lowercase		)
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(lowercase		):
    for j in range(i + 1   ,lowercase		):
      if random.random() < probability:
        graph[i].append(lowercase		)
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(lowercase		)
  return graph






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return {
      i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
  }


if __name__ == "__main__":
    import doctest

    doctest.testmod()
