


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  snake_case_     (unittest.TestCase   ):
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					int:
												a__    =  inspect.getfile(accelerate.test_utils   )
												a__    =  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

												a__    =  test_metrics
					@require_cpu
					def 				lowerCamelCase__(   self     :int   )      ->					str:
												debug_launcher(self.test_metrics.main    ,num_processes=1   )
					@require_cpu
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					Optional[Any]:
												debug_launcher(self.test_metrics.main   )
					@require_single_gpu
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[Any]:
												self.test_metrics.main()



					@require_multi_gpu
					def 				lowerCamelCase__(   self     :int   )      ->					Tuple:
												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(__snake_case    ,env=os.environ.copy()   )








from typing import TYPE_CHECKING

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


snake_case							: Any      =   {
    '''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Union[str, Any]      =   ['''MobileViTFeatureExtractor''']
						snake_case							: int      =   ['''MobileViTImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Dict      =   [
						    '''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MobileViTForImageClassification''',
						    '''MobileViTForSemanticSegmentation''',
						    '''MobileViTModel''',
						    '''MobileViTPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Tuple      =   [
						    '''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFMobileViTForImageClassification''',
						    '''TFMobileViTForSemanticSegmentation''',
						    '''TFMobileViTModel''',
						    '''TFMobileViTPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilevit import MobileViTFeatureExtractor
												from .image_processing_mobilevit import MobileViTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilevit import (
												    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileViTForImageClassification,
												    MobileViTForSemanticSegmentation,
												    MobileViTModel,
												    MobileViTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilevit import (
												    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileViTForImageClassification,
												    TFMobileViTForSemanticSegmentation,
												    TFMobileViTModel,
												    TFMobileViTPreTrainedModel,
												)


else:
						import sys

						snake_case							: Optional[int]      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




