






"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase					=     {
    """configuration_videomae""": ["""VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VideoMAEConfig"""],
}

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

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""VideoMAEFeatureExtractor"""]
  UpperCamelCase					=     ["""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

  UpperCamelCase					=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)














"""simple docstring"""





import doctest

import projectq
from projectq.ops import H, Measure






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							Dict         =     quantum_engine.allocate_qubit()
       H | qubit
       Measure | qubit
       return int(snake_case_						)


if __name__ == "__main__":
  doctest.testmod()

  # initialises a new quantum backend
  UpperCamelCase					=     projectq.MainEngine()

  # Generate a list of 10 random numbers
  UpperCamelCase					=     [get_random_number(quantum_engine) for _ in range(10)]

  # Flushes the quantum engine from memory
  quantum_engine.flush()

  print("""Random numbers""", random_numbers_list)






