
'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCAmelCase_			:			Union[str, Any]			   =     {
    '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase_			:			Tuple			   =     [
       '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''VideoMAEForPreTraining''',
       '''VideoMAEModel''',
       '''VideoMAEPreTrainedModel''',
       '''VideoMAEForVideoClassification''',
   ]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase_			:			List[str]			   =     ['''VideoMAEFeatureExtractor''']
   lowerCAmelCase_			:			List[Any]			   =     ['''VideoMAEImageProcessor''']

if TYPE_CHECKING:
   from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_videomae import (
          VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          VideoMAEForPreTraining,
          VideoMAEForVideoClassification,
          VideoMAEModel,
          VideoMAEPreTrainedModel,
      )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_videomae import VideoMAEFeatureExtractor
      from .image_processing_videomae import VideoMAEImageProcessor

else:
   import sys

   lowerCAmelCase_			:			Tuple			   =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




'''simple docstring'''

import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
   import torch







class 	__lowerCAmelCase    (  unittest.TestCase       ,					__a				):



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Tuple         =    load_tool("""speech-to-text"""	)
          self.tool.setup()



     def     snake_case_ (self	):
          _UpperCAmelCase					:  str         =    self.tool(torch.ones(3_0_0_0	)	)
          self.assertEqual(lowerCAmelCase__						,	""" you"""	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Union[str, Any]         =    self.tool(audio=torch.ones(3_0_0_0	)	)
          self.assertEqual(lowerCAmelCase__						,	""" you"""	)


