




'''simple docstring'''







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 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	inspect.getfile(accelerate.test_utils       )
           UpperCAmelCase     =	os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['''scripts''', '''test_script.py''']       )
           UpperCAmelCase     =	os.path.sep.join(
               mod_file.split(os.path.sep       )[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']       )
           UpperCAmelCase     =	os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['''scripts''', '''test_ops.py''']       )
       @require_multi_gpu
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           print(f"Found {torch.cuda.device_count()} devices."       )
           UpperCAmelCase     =	['''torchrun''', f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
           with patch_environment(omp_num_threads=1       ):
               execute_subprocess_async(lowercase     ,       env=os.environ.copy()       )
       @require_multi_gpu
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           print(f"Found {torch.cuda.device_count()} devices."       )
           UpperCAmelCase     =	['''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(lowercase     ,       env=os.environ.copy()       )
       @require_multi_gpu
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

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







       @require_multi_gpu
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           print(f"Found {torch.cuda.device_count()} devices, using 2 devices only"       )
           UpperCAmelCase     =	['''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(lowercase     ,       env=os.environ.copy()       )


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

   A									=''

   A									=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)."

   A									=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."
   A									=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)










'''simple docstring'''







from datetime import datetime

import matplotlib.pyplot as plt
import torch





def 		snake_case_							(_a			:							List[str]  ):
    for param in module.parameters():
        UpperCAmelCase     =	False





def 		snake_case_							():
    UpperCAmelCase     =	'''cuda''' if torch.cuda.is_available() else '''cpu'''
    if torch.backends.mps.is_available() and torch.backends.mps.is_built():
        UpperCAmelCase     =	'''mps'''
    if device == "mps":
        print(
            '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
            ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
            ''' with generations.'''  )
    return device





def 		snake_case_							(_a			:							Optional[Any]  ):
    UpperCAmelCase     =	plt.imshow(_a  )
    fig.axes.get_xaxis().set_visible(_a  )
    fig.axes.get_yaxis().set_visible(_a  )
    plt.show()







def 		snake_case_							():
    UpperCAmelCase     =	datetime.now()
    UpperCAmelCase     =	current_time.strftime('''%H:%M:%S'''  )
    return timestamp




