


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   ):




      '''simple docstring'''


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




      @require_multi_gpu
      def 					_snake_case     (    self:	Tuple    ):
       print(F'Found {torch.cuda.device_count()} devices.'    )
       __lowerCamelCase		:							Tuple		 =						['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:	Dict    ):
       print(F'Found {torch.cuda.device_count()} devices.'    )
       __lowerCamelCase		:							Tuple		 =						['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:	str    ):
       __lowerCamelCase		:							Any		 =						['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:	str    ):
       print(F'Found {torch.cuda.device_count()} devices, using 2 devices only'    )
       __lowerCamelCase		:							str		 =						['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__":
       lowercase_	   =     Accelerator()
       lowercase_	   =     (accelerator.state.process_index + 2, 1_0)
       lowercase_	   =     torch.randint(0, 1_0, shape).to(accelerator.device)

       lowercase_	   =     ''

       lowercase_	   =     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)."

       lowercase_	   =     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."
       lowercase_	   =     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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							str		 =						0
 __lowerCamelCase		:							Tuple		 =						len(SCREAMING_SNAKE_CASE__						)

 for i in range(n - 1						):
  for j in range(i + 1      ,	SCREAMING_SNAKE_CASE__						):
   if arr[i] > arr[j]:
    num_inversions += 1

 return num_inversions
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 if len(SCREAMING_SNAKE_CASE__						) <= 1:
  return arr, 0
 __lowerCamelCase		:							Optional[int]		 =						len(SCREAMING_SNAKE_CASE__						) // 2
 __lowerCamelCase		:							Union[str, Any]		 =						arr[0:mid]
 __lowerCamelCase		:							List[Any]		 =						arr[mid:]

 __lowerCamelCase ,					__lowerCamelCase		:							Any		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							List[str]		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						_count_cross_inversions(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							List[str]		 =						inversion_p + inversions_q + cross_inversions
 return c, num_inversions
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						[]
 __lowerCamelCase		:							List[Any]		 =						0
 while i < len(SCREAMING_SNAKE_CASE__						) and j < len(SCREAMING_SNAKE_CASE__						):
  if p[i] > q[j]:
   # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
   # These are all inversions. The claim emerges from the
   # property that P is sorted.
   num_inversion += len(SCREAMING_SNAKE_CASE__						) - i
   r.append(q[j]						)
   j += 1
  else:
   r.append(p[i]						)
   i += 1

 if i < len(SCREAMING_SNAKE_CASE__						):
  r.extend(p[i:]						)
 else:
  r.extend(q[j:]						)

 return r, num_inversion
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							Optional[int]		 =						[10, 2, 1, 5, 5, 2, 11]

 # this arr has 8 inversions:
 # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

 __lowerCamelCase		:							Optional[Any]		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 8

 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)

 # testing an array with zero inversion (a sorted arr_1)

 arr_a.sort()
 __lowerCamelCase		:							Optional[Any]		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							int		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 0
 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)

 # an empty list should also have zero inversions
 __lowerCamelCase		:							List[str]		 =						[]
 __lowerCamelCase		:							Dict		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 0
 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       main()

