


'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowercase:		Tuple    						=  {
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		List[str]    						=  ["ImageGPTFeatureExtractor"]
       __lowercase:		Tuple    						=  ["ImageGPTImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		str    						=  [
           "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ImageGPTForCausalImageModeling",
           "ImageGPTForImageClassification",
           "ImageGPTModel",
           "ImageGPTPreTrainedModel",
           "load_tf_weights_in_imagegpt",
       ]


if TYPE_CHECKING:
       from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
              from .image_processing_imagegpt import ImageGPTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_imagegpt import (
                  IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ImageGPTForCausalImageModeling,
                  ImageGPTForImageClassification,
                  ImageGPTModel,
                  ImageGPTPreTrainedModel,
                  load_tf_weights_in_imagegpt,
              )

else:
       import sys

       __lowercase:		List[str]    						=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




def 						_lowerCAmelCase    (					__lowerCamelCase :  int  ):



 """simple docstring"""





 if not isinstance(__lowerCamelCase		,						__lowerCamelCase  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						(
      "operation can not be conducted on a object of type "
      F"""{type(__lowerCamelCase  ).__name__}"""
  )
  raise TypeError(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     List[str]							=						""
 for _ in range(0		,						32  ):
  bit_string += str(number % 2  )
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						number >> 1
 return bit_string






def 						_lowerCAmelCase    (					__lowerCamelCase :  int  ):



 """simple docstring"""





 if number < 0:
  raise ValueError("the value of input must be positive"  )
 elif isinstance(__lowerCamelCase		,						__lowerCamelCase  ):
  raise TypeError("Input value must be a 'int' type"  )
 elif isinstance(__lowerCamelCase		,						__lowerCamelCase  ):
  raise TypeError("'<' not supported between instances of 'str' and 'int'"  )
 __SCREAMING_SNAKE_CASE							:     Any							=						0
 # iterator over [1 to 32],since we are dealing with 32 bit integer
 for _ in range(1		,						33  ):
  # left shift the bits by unity
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						result << 1
  # get the end bit
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						number % 2
  # right shift the bits by unity
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						number >> 1
  # add that bit to our ans
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						result | end_bit
 return get_reverse_bit_string(__lowerCamelCase  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






