



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__A   :							Union[str, Any]										=   {
    "configuration_efficientformer": [
        "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientFormerConfig",
    ]
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A   :							List[Any]										=   ["EfficientFormerImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A   :							str										=   [
					    "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "EfficientFormerForImageClassification",
					    "EfficientFormerForImageClassificationWithTeacher",
					    "EfficientFormerModel",
					    "EfficientFormerPreTrainedModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A   :							str										=   [
					    "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFEfficientFormerForImageClassification",
					    "TFEfficientFormerForImageClassificationWithTeacher",
					    "TFEfficientFormerModel",
					    "TFEfficientFormerPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_efficientformer import EfficientFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_efficientformer import (
										    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    EfficientFormerForImageClassification,
										    EfficientFormerForImageClassificationWithTeacher,
										    EfficientFormerModel,
										    EfficientFormerPreTrainedModel,
										)
					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_efficientformer import (
										    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFEfficientFormerForImageClassification,
										    TFEfficientFormerForImageClassificationWithTeacher,
										    TFEfficientFormerModel,
										    TFEfficientFormerPreTrainedModel,
										)

else:
					import sys

					__A   :							Union[str, Any]										=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








"""simple docstring"""


def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				if a < 0 or b < 0:
								raise ValueError('''the value of both inputs must be positive'''				)

				_UpperCAmelCase					=    str(bin(_SCREAMING_SNAKE_CASE				)				)[2:]  # remove the leading "0b"
				_UpperCAmelCase					=    str(bin(_SCREAMING_SNAKE_CASE				)				)[2:]  # remove the leading "0b"

				_UpperCAmelCase					=    max(len(_SCREAMING_SNAKE_CASE				)					,     len(_SCREAMING_SNAKE_CASE				)				)

				return "0b" + "".join(
				    str(int(char_a == '''1''' and char_b == '''1'''				)				)
				    for char_a, char_b in zip(a_binary.zfill(_SCREAMING_SNAKE_CASE				)					,     b_binary.zfill(_SCREAMING_SNAKE_CASE				)				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



