from typing import TYPE_CHECKING

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


a=  {
    '''configuration_conditional_detr''': [
        '''CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConditionalDetrConfig''',
        '''ConditionalDetrOnnxConfig''',
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''ConditionalDetrFeatureExtractor''']
  a=  ['''ConditionalDetrImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''ConditionalDetrForObjectDetection''',
      '''ConditionalDetrForSegmentation''',
      '''ConditionalDetrModel''',
      '''ConditionalDetrPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ConditionalDetrConfig,
      ConditionalDetrOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
    from .image_processing_conditional_detr import ConditionalDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_conditional_detr import (
        CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConditionalDetrForObjectDetection,
        ConditionalDetrForSegmentation,
        ConditionalDetrModel,
        ConditionalDetrPreTrainedModel,
    )

else:
  import sys

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




'''simple docstring'''
def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count < 0:
        raise ValueError('The given input must be positive'			)

    # get the generated string sequence
    __UpperCamelCase	:	Dict		    =  gray_code_sequence_string(_a			)
    #
    # convert them to integers
    for i in range(len(_a			)			):
        __UpperCamelCase	:	int		    =  int(sequence[i]				,     2			)

    return sequence







def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count == 0:
        return ["0"]

    if bit_count == 1:
        return ["0", "1"]

    __UpperCamelCase	:	Dict		    =  1 << bit_count  # defines the length of the sequence
    # 1<< n is equivalent to 2^n

    # recursive answer will generate answer for n-1 bits
    __UpperCamelCase	:	Tuple		    =  gray_code_sequence_string(bit_count - 1			)

    __UpperCamelCase	:	str		    =  []

    # append 0 to first half of the smaller sequence generated
    for i in range(seq_len // 2			):
        __UpperCamelCase	:	Optional[Any]		    =  '0' + smaller_sequence[i]
        sequence.append(_a			)

    # append 1 to second half ... start from the end of the list
    for i in reversed(range(seq_len // 2			)			):
        __UpperCamelCase	:	List[Any]		    =  '1' + smaller_sequence[i]
        sequence.append(_a			)

    return sequence


if __name__ == "__main__":
  import doctest

  doctest.testmod()



