

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  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     inspect.getfile(accelerate.test_utils					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     os.path.sep.join(mod_file.split(os.path.sep					)[:-1] + ['scripts', 'test_script.py']					)
        SCREAMING_SNAKE_CASE_				:  int           =     os.path.sep.join(
            mod_file.split(os.path.sep					)[:-1] + ['scripts', 'test_distributed_data_loop.py']					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     os.path.sep.join(mod_file.split(os.path.sep					)[:-1] + ['scripts', 'test_ops.py']					)

  @require_multi_gpu
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        print(f"Found {torch.cuda.device_count()} devices."					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     ['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  UpperCAmelCase    (				self					):





        """simple docstring"""







        print(f"Found {torch.cuda.device_count()} devices."					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ['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  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  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  UpperCAmelCase    (				self					):





        """simple docstring"""







        print(f"Found {torch.cuda.device_count()} devices, using 2 devices only"					)
        SCREAMING_SNAKE_CASE_				:  Union[str, 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__":
    lowerCAmelCase     :   int						     =  Accelerator()
    lowerCAmelCase     :   List[Any]						     =  (accelerator.state.process_index + 2, 10)
    lowerCAmelCase     :   Dict						     =  torch.randint(0, 10, shape).to(accelerator.device)

    lowerCAmelCase     :   Optional[Any]						     =  ''

    lowerCAmelCase     :   Any						     =  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     :   Optional[int]						     =  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     :   List[str]						     =  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)









def    A_  (							a  ):

      """simple docstring"""



      if upper_limit < 0:
            raise ValueError('Limit for the Catalan sequence must be ≥ 0'  )

      SCREAMING_SNAKE_CASE_				:  List[str]           =     [0] * (upper_limit + 1)

      # Base case: C(0) = C(1) = 1
      SCREAMING_SNAKE_CASE_				:  Any           =     1
      if upper_limit > 0:
            SCREAMING_SNAKE_CASE_				:  int           =     1

      # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
      for i in range(2					,   upper_limit + 1  ):
            for j in range(a  ):
                  catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

      return catalan_list


if __name__ == "__main__":
    print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
    print('\n*** Enter -1 at any time to quit ***')
    print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
    try:
        while True:
            lowerCAmelCase     :   Union[str, Any]						     =  int(input().strip())
            if N < 0:
                print('\n********* Goodbye!! ************')
                break
            else:
                print(F'The Catalan numbers from 0 through {N} are:')
                print(catalan_numbers(N))
                print('Try another upper limit for the sequence: ', end='')
    except (NameError, ValueError):
        print('\n********* Invalid input, goodbye! ************\n')

    import doctest

    doctest.testmod()






