
'''simple docstring'''





def   A			(							A_				:       str							):
						snake_case							:  Union[str, Any]			=     str(A_							).strip()
						if not bin_string:
												raise ValueError('''Empty string was passed to the function'''							)
						snake_case							:  Optional[int]			=     bin_string[0] == '''-'''
						if is_negative:
												snake_case							:  Any			=     bin_string[1:]
						if not all(char in '''01''' for char in bin_string							):
												raise ValueError('''Non-binary value was passed to the function'''							)
						snake_case							:  List[str]			=     0
						for char in bin_string:
												snake_case							:  Tuple			=     2 * decimal_number + int(A_							)
						return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
						from doctest import testmod

						testmod()




"""simple docstring"""
from typing import TYPE_CHECKING

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


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

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

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

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