
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       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       inspect.getfile(accelerate.test_utils			)
		lowercase	    =       os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ['scripts', 'test_script.py']			)
		lowercase	    =       os.path.sep.join(
		    mod_file.split(os.path.sep			)[:-1] + ['scripts', 'test_distributed_data_loop.py']			)
		lowercase	    =       os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ['scripts', 'test_ops.py']			)





	@require_multi_gpu
	def       SCREAMING_SNAKE_CASE__		(			self			):
		print(F'''Found {torch.cuda.device_count()} devices.'''			)
		lowercase	    =       ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
		with patch_environment(omp_num_threads=1			):
			execute_subprocess_async(snake_case						,       env=os.environ.copy()			)





	@require_multi_gpu
	def       SCREAMING_SNAKE_CASE__		(			self			):
		print(F'''Found {torch.cuda.device_count()} devices.'''			)
		lowercase	    =       ['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(snake_case						,       env=os.environ.copy()			)





	@require_multi_gpu
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__			)]
		with patch_environment(omp_num_threads=1			):
			execute_subprocess_async(snake_case						,       env=os.environ.copy()			)





	@require_multi_gpu
	def       SCREAMING_SNAKE_CASE__		(			self			):
		print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''			)
		lowercase	    =       ['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(snake_case						,       env=os.environ.copy()			)


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

	UpperCAmelCase       =					''''''

	UpperCAmelCase       =					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)."

	UpperCAmelCase       =					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."
	UpperCAmelCase       =					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)






import logging
import os

from .state import PartialState
class    __UpperCamelCase			(       logging.LoggerAdapter				):

 """simple docstring"""



 @staticmethod
 def 						_UpperCAmelCase       (	SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  a__												= PartialState()
  return not main_process_only or (main_process_only and state.is_main_process)



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  if PartialState._shared_state == {}:
   raise RuntimeError(
       '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''      )
  a__												= kwargs.pop('''main_process_only'''		,				SCREAMING_SNAKE_CASE      )
  a__												= kwargs.pop('''in_order'''		,				SCREAMING_SNAKE_CASE      )

  if self.isEnabledFor(SCREAMING_SNAKE_CASE      ):
   if self._should_log(SCREAMING_SNAKE_CASE      ):
    a__							,			a__												= self.process(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
    self.logger.log(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

   elif in_order:
    a__												= PartialState()
    for i in range(state.num_processes      ):
     if i == state.process_index:
      a__							,			a__												= self.process(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
      self.logger.log(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
     state.wait_for_everyone()






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None       ):
 if log_level is None:
  a__												= os.environ.get('''ACCELERATE_LOG_LEVEL'''							,  __UpperCAmelCase       )
 a__												= logging.getLogger(__UpperCAmelCase       )
 if log_level is not None:
  logger.setLevel(log_level.upper()       )
  logger.root.setLevel(log_level.upper()       )
 return MultiProcessAdapter(__UpperCAmelCase							,  {}       )





