




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 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 def 				lowerCAmelCase_  (							self  :		Dict						):
       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 				lowerCAmelCase_  (							self  :		Tuple						):
       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(_lowerCAmelCase    ,    env=os.environ.copy()						)



 @require_multi_gpu
 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       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(_lowerCAmelCase    ,    env=os.environ.copy()						)



 @require_multi_gpu
 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       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(_lowerCAmelCase    ,    env=os.environ.copy()						)




 @require_multi_gpu
 def 				lowerCAmelCase_  (							self  :		str						):
       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(_lowerCAmelCase    ,    env=os.environ.copy()						)


if __name__ == "__main__":
   lowerCamelCase__			:       int         =							Accelerator()
   lowerCamelCase__			:       Dict         =							(accelerator.state.process_index + 2, 10)
   lowerCamelCase__			:       Any         =							torch.randint(0, 10, shape).to(accelerator.device)

   lowerCamelCase__			:       Union[str, Any]         =							''

   lowerCamelCase__			:       List[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[str]         =							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__			:       Union[str, Any]         =							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 abc import ABC, abstractmethod
from argparse import ArgumentParser





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 @staticmethod
 @abstractmethod
 def 				lowerCAmelCase_  (							_lowerCAmelCase  :		ArgumentParser						):
       raise NotImplementedError()




 @abstractmethod
 def 				lowerCAmelCase_  (							self  :		Dict						):
       raise NotImplementedError()