





def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Tuple					,   UpperCamelCase__:							str    ):
  SCREAMING_SNAKE_CASE__			=      len(lowerCAmelCase__    )
  print("""The following activities are selected:"""    )

  # The first activity is always selected
  SCREAMING_SNAKE_CASE__			=      0
  print(lowerCAmelCase__					,   end=""","""    )

  # Consider rest of the activities
  for j in range(lowerCAmelCase__    ):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(lowerCAmelCase__					,   end=""","""    )
      SCREAMING_SNAKE_CASE__			=      j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _lowerCamelCase   				=			[1, 3, 0, 5, 8, 5]
 _lowerCamelCase   				=			[2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)






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    ):


							def      _snake_case  (							self    :Any		)   ->   int:



									"""simple docstring"""



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


							@require_multi_gpu
							def      _snake_case  (							self    :Optional[Any]		)   ->   Tuple:



									"""simple docstring"""



									print(f'''Found {torch.cuda.device_count()} devices.'''		)
									SCREAMING_SNAKE_CASE__			=      ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
									with patch_environment(omp_num_threads=1		):
											execute_subprocess_async(__A							,				env=os.environ.copy()		)


							@require_multi_gpu
							def      _snake_case  (							self    :Tuple		)   ->   Optional[Any]:



									"""simple docstring"""



									print(f'''Found {torch.cuda.device_count()} devices.'''		)
									SCREAMING_SNAKE_CASE__			=      ["""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(__A							,				env=os.environ.copy()		)


							@require_multi_gpu
							def      _snake_case  (							self    :Optional[Any]		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__		)]
									with patch_environment(omp_num_threads=1		):
											execute_subprocess_async(__A							,				env=os.environ.copy()		)


							@require_multi_gpu
							def      _snake_case  (							self    :Optional[int]		)   ->   str:



									"""simple docstring"""



									print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''		)
									SCREAMING_SNAKE_CASE__			=      ["""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(__A							,				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)