
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       _snake_case   (	unittest.TestCase							):


 def      lowercase__     (					self):


   '''simple docstring'''




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


 @require_multi_gpu
 def      lowercase__     (					self):


   '''simple docstring'''




   print(f'Found {torch.cuda.device_count()} devices.')
   lowercase__  :   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(A_					,  env=os.environ.copy())


 @require_multi_gpu
 def      lowercase__     (					self):


   '''simple docstring'''




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


   '''simple docstring'''




   lowercase__  :   List[str]    		=      ["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      lowercase__     (					self):


   '''simple docstring'''




   print(f'Found {torch.cuda.device_count()} devices, using 2 devices only')
   lowercase__  :   Tuple    		=      ["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__    :		Tuple          =   Accelerator()
     lowerCamelCase__    :		List[str]          =   (accelerator.state.process_index + 2, 1_0)
     lowerCamelCase__    :		Tuple          =   torch.randint(0, 1_0, shape).to(accelerator.device)

     lowerCamelCase__    :		str          =   """"""

     lowerCamelCase__    :		Union[str, 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__    :		List[Any]          =   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)








from math import factorial

lowerCamelCase__    :		dict[str, int]          =   {str(digit): factorial(digit) for digit in range(1_0)}

def 		UpperCamelCase	(      lowercase_   )		->	int:


  '''simple docstring'''






  if not isinstance(lowercase_ , lowercase_   ):
    raise TypeError("""Parameter number must be int"""   )

  if number < 0:
    raise ValueError("""Parameter number must be greater than or equal to 0"""   )

  # Converts number in string to iterate on its digits and adds its factorial.
  return sum(DIGIT_FACTORIAL[digit] for digit in str(lowercase_   )   )

def 		UpperCamelCase	(      lowercase_ = 60 , lowercase_ = 1_00_00_00   )		->	int:


  '''simple docstring'''






  if not isinstance(lowercase_ , lowercase_   ) or not isinstance(lowercase_ , lowercase_   ):
    raise TypeError("""Parameters chain_length and number_limit must be int"""   )

  if chain_length <= 0 or number_limit <= 0:
    raise ValueError(
        """Parameters chain_length and number_limit must be greater than 0"""   )

  # the counter for the chains with the exact desired length
  lowercase__  :   List[Any]    		=      0
  # the cached sizes of the previous chains
  lowercase__  :   dict[int, int]    		=      {}

  for start_chain_element in range(1 , lowercase_   ):
    # The temporary set will contain the elements of the chain
    lowercase__  :   Optional[Any]    		=      set()
    lowercase__  :   Union[str, Any]    		=      0

    # Stop computing the chain when you find a cached size, a repeating item or the
    # length is greater then the desired one.
    lowercase__  :   Union[str, Any]    		=      start_chain_element
    while (
        chain_element not in chain_sets_lengths
        and chain_element not in chain_set
        and chain_set_length <= chain_length
    ):
      chain_set.add(lowercase_   )
      chain_set_length += 1
      lowercase__  :   str    		=      digit_factorial_sum(lowercase_   )

    if chain_element in chain_sets_lengths:
      chain_set_length += chain_sets_lengths[chain_element]

    lowercase__  :   str    		=      chain_set_length

    # If chain contains the exact amount of elements increase the counter
    if chain_set_length == chain_length:
      chains_counter += 1

  return chains_counter


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(f'''{solution()}''')






