





"""simple docstring"""




from typing import TYPE_CHECKING

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


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

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_           =["""ImageGPTFeatureExtractor"""]
						UpperCamelCase_           =["""ImageGPTImageProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_           =[
						    """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

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










"""simple docstring"""




from __future__ import annotations





def        a_				(							_lowercase   ):
						assert (
						    isinstance(_lowercase       ,						_lowercase   ) and mask > 0
						), F"""mask needs to be positive integer, your input {mask}"""
						_UpperCamelCase			:					Union[str, Any]     =    []
						_UpperCamelCase			:					str     =    mask

						while submask:
												all_submasks.append(_lowercase   )
												_UpperCamelCase			:					int     =    (submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()



