






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
       import torch

if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


a						:			Dict     	=					logging.get_logger(__name__)
@dataclass
class        a       (					lowercase__  ):



      """simple docstring"""


      a    :					Dict							=      [
          'no_inference',
          'no_cuda',
          'no_tpu',
          'no_speed',
          'no_memory',
          'no_env_print',
          'no_multi_process',
      ]
      def __init__( self   :     List[Any]						, **__lowercase   :     Dict   )				-> Tuple:
        for deprecated_arg in self.deprecated_args:
          if deprecated_arg in kwargs:
            __UpperCAmelCase				:      List[Any]	 =    deprecated_arg[3:]
            setattr(self						, __lowercase						, not kwargs.pop(__lowercase   )   )
            logger.warning(
                f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
                f""" {positive_arg}={kwargs[positive_arg]}"""   )

        __UpperCAmelCase				:      str	 =    kwargs.pop("""torchscript"""						, self.torchscript   )
        __UpperCAmelCase				:      Union[str, Any]	 =    kwargs.pop("""torch_xla_tpu_print_metrics"""						, self.torch_xla_tpu_print_metrics   )
        __UpperCAmelCase				:      Optional[Any]	 =    kwargs.pop("""fp16_opt_level"""						, self.fpaa_opt_level   )
        super().__init__(**__lowercase   )

      a    :					bool							=      field(default=lowercase__				,			metadata={'help': 'Trace the models using torchscript'}  )
      a    :					bool							=      field(default=lowercase__				,			metadata={'help': 'Print Xla/PyTorch tpu metrics'}  )
      a    :					str							=      field(
          default='O1'				,			metadata={
              'help': (
                  'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '
                  'See details at https://nvidia.github.io/apex/amp.html'
              )
          }				,			)
      @cached_property
      def       UpperCAmelCase ( self   :     Any   )				-> Tuple["torch.device", int]:
        requires_backends(self						, ["""torch"""]   )
        logger.info("""PyTorch: setting up devices"""   )
        if not self.cuda:
          __UpperCAmelCase				:      str	 =    torch.device("""cpu"""   )
          __UpperCAmelCase				:      int	 =    0
        elif is_torch_tpu_available():
          __UpperCAmelCase				:      Tuple	 =    xm.xla_device()
          __UpperCAmelCase				:      int	 =    0
        else:
          __UpperCAmelCase				:      Dict	 =    torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""   )
          __UpperCAmelCase				:      Optional[int]	 =    torch.cuda.device_count()
        return device, n_gpu
      @property
      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> str:
        return is_torch_tpu_available() and self.tpu
      @property
      def       UpperCAmelCase ( self   :     List[str]   )				-> int:
        requires_backends(self						, ["""torch"""]   )
        # TODO(PVP): currently only single GPU is supported
        return torch.cuda.current_device()
      @property
      def       UpperCAmelCase ( self   :     int   )				-> "torch.device":
        requires_backends(self						, ["""torch"""]   )
        return self._setup_devices[0]
      @property
      def       UpperCAmelCase ( self   :     int   )				-> List[Any]:
        requires_backends(self						, ["""torch"""]   )
        return self._setup_devices[1]




      @property
      def       UpperCAmelCase ( self   :     Tuple   )				-> List[str]:
        return self.n_gpu > 0






'''simple docstring'''
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   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :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__  :List[str]				   =					test_metrics




		@require_cpu
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							debug_launcher(self.test_metrics.main       ,  num_processes=1				)




		@require_cpu
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							debug_launcher(self.test_metrics.main				)




		@require_single_gpu
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							self.test_metrics.main()




		@require_multi_gpu
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							print(F'''Found {torch.cuda.device_count()} devices.'''				)
							UpperCamelCase__  :Optional[Any]				   =					['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
							with patch_environment(omp_num_threads=1				):
												execute_subprocess_async(UpperCamelCase_       ,  env=os.environ.copy()				)