
"""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_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment





class 							snake_case       (     unittest.TestCase		):







   def    UpperCAmelCase__	( self)   ->Optional[int]:
      a_						   =     inspect.getfile(accelerate.test_utils)
      a_						   =     os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ["scripts", "test_sync.py"])







   @require_cpu
   def    UpperCAmelCase__	( self)   ->Dict:
      debug_launcher(test_sync.main       ,							num_processes=1)







   @require_cpu
   def    UpperCAmelCase__	( self)   ->Any:
      debug_launcher(test_sync.main)







   @require_single_gpu
   def    UpperCAmelCase__	( self)   ->int:
      test_sync.main()







   @require_multi_gpu
   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      print(F'''Found {torch.cuda.device_count()} devices.''')
      a_						   =     ["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())

"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase_						   =   {
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_						   =   [
          'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MobileViTV2ForImageClassification',
          'MobileViTV2ForSemanticSegmentation',
          'MobileViTV2Model',
          'MobileViTV2PreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_mobilevitva import (
          MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileViTVaConfig,
          MobileViTVaOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevitva import (
                MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTVaForImageClassification,
                MobileViTVaForSemanticSegmentation,
                MobileViTVaModel,
                MobileViTVaPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase_						   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)