




"""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 			UpperCamelCase_       (    self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		int								=  inspect.getfile(accelerate.test_utils		)
   _snake_case							:		int								=  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

   _snake_case							:		Dict								=  test_metrics


 @require_cpu
 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



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


 @require_cpu
 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   debug_launcher(self.test_metrics.main		)


 @require_single_gpu
 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   self.test_metrics.main()


 @require_multi_gpu
 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   print(f"Found {torch.cuda.device_count()} devices."		)
   _snake_case							:		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(a_,				env=os.environ.copy()		)







"""simple docstring"""


from typing import TYPE_CHECKING

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


A_      =    {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    ['''MaskFormerFeatureExtractor''']
     A_      =    ['''MaskFormerImageProcessor''']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MaskFormerForInstanceSegmentation''',
         '''MaskFormerModel''',
         '''MaskFormerPreTrainedModel''',
     ]
     A_      =    [
         '''MaskFormerSwinBackbone''',
         '''MaskFormerSwinModel''',
         '''MaskFormerSwinPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
     from .configuration_maskformer_swin import MaskFormerSwinConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_maskformer import MaskFormerFeatureExtractor
          from .image_processing_maskformer import MaskFormerImageProcessor
     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_maskformer import (
              MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              MaskFormerForInstanceSegmentation,
              MaskFormerModel,
              MaskFormerPreTrainedModel,
          )
          from .modeling_maskformer_swin import (
              MaskFormerSwinBackbone,
              MaskFormerSwinModel,
              MaskFormerSwinPreTrainedModel,
          )


else:
     import sys

     A_      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)


