




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class  __UpperCAmelCase					(	unittest.TestCase						):



   def 	__magic_name__   (							self							:					List[str]						):
     UpperCAmelCase	:							List[Any]						=			inspect.getfile(accelerate.test_utils						)
     UpperCAmelCase	:							Any						=			os.path.sep.join(
         mod_file.split(os.path.sep						)[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']						)

     from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

     UpperCAmelCase	:							int						=			test_metrics



   @require_cpu
   def 	__magic_name__   (							self							:					Optional[int]						):
     debug_launcher(self.test_metrics.main,		num_processes=1						)



   @require_cpu
   def 	__magic_name__   (							self							:					Any						):
     debug_launcher(self.test_metrics.main						)



   @require_single_gpu
   def 	__magic_name__   (							self							:					int						):
     self.test_metrics.main()





   @require_multi_gpu
   def 	__magic_name__   (							self							:					int						):
     print(F'''Found {torch.cuda.device_count()} devices.'''						)
     UpperCAmelCase	:							List[Any]						=			['''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()						)








import logging
import os

from .state import PartialState





class  __UpperCAmelCase					(	logging.LoggerAdapter						):



   @staticmethod
   def 	__magic_name__   (							__A							:					str						):
     UpperCAmelCase	:							Dict						=			PartialState()
     return not main_process_only or (main_process_only and state.is_main_process)





   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Union[str, Any],		__A							:					Union[str, Any],		*__A							:					Optional[int],		**__A							:					Tuple						):
     if PartialState._shared_state == {}:
       raise RuntimeError(
           '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''						)
     UpperCAmelCase	:							List[str]						=			kwargs.pop('''main_process_only''',		__A						)
     UpperCAmelCase	:							int						=			kwargs.pop('''in_order''',		__A						)

     if self.isEnabledFor(__A						):
       if self._should_log(__A						):
         UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			self.process(__A,		__A						)
         self.logger.log(__A,		__A,		*__A,		**__A						)

       elif in_order:
         UpperCAmelCase	:							Dict						=			PartialState()
         for i in range(state.num_processes						):
           if i == state.process_index:
             UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.process(__A,		__A						)
             self.logger.log(__A,		__A,		*__A,		**__A						)
           state.wait_for_everyone()






def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      str = None      )  ->						Dict:
  if log_level is None:
    UpperCAmelCase	:							Union[str, Any]						=			os.environ.get('''ACCELERATE_LOG_LEVEL'''    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Tuple						=			logging.getLogger(UpperCAmelCase      )
  if log_level is not None:
    logger.setLevel(log_level.upper()      )
    logger.root.setLevel(log_level.upper()      )
  return MultiProcessAdapter(UpperCAmelCase    ,			{}      )





