





from typing import TYPE_CHECKING

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


_lowercase		:  int			       ={"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  List[Any]			       =["""DeiTFeatureExtractor"""]
		_lowercase		:  Dict			       =["""DeiTImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  str			       =[
		    """DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """DeiTForImageClassification""",
		    """DeiTForImageClassificationWithTeacher""",
		    """DeiTForMaskedImageModeling""",
		    """DeiTModel""",
		    """DeiTPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Optional[int]			       =[
		    """TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFDeiTForImageClassification""",
		    """TFDeiTForImageClassificationWithTeacher""",
		    """TFDeiTForMaskedImageModeling""",
		    """TFDeiTModel""",
		    """TFDeiTPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		_lowercase		:  int			       =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'						)

		lowerCamelCase_	:     int 						=      str(bin(lowerCAmelCase__						)						)
		binary_number += "0" * shift_amount
		return binary_number


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'						)

		lowerCamelCase_	:     Union[str, Any] 						=      str(bin(lowerCAmelCase__						)						)[2:]
		if shift_amount >= len(lowerCAmelCase__						):
				return "0b0"
		lowerCamelCase_	:     List[str] 						=      binary_number[: len(lowerCAmelCase__						) - shift_amount]
		return "0b" + shifted_binary_number


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number >= 0:  # Get binary representation of positive number
				lowerCamelCase_	:     List[Any] 						=      '0' + str(bin(lowerCAmelCase__						)						).strip('-'						)[2:]
		else:  # Get binary (2's complement) representation of negative number
				lowerCamelCase_	:     Any 						=      len(bin(lowerCAmelCase__						)[3:]						)  # Find 2's complement of number
				lowerCamelCase_	:     List[str] 						=      bin(abs(lowerCAmelCase__						) - (1 << binary_number_length)						)[3:]
				lowerCamelCase_	:     List[str] 						=      (
				    '1' + '0' * (binary_number_length - len(lowerCAmelCase__						)) + binary_number
				)

		if shift_amount >= len(lowerCAmelCase__						):
				return "0b" + binary_number[0] * len(lowerCAmelCase__						)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(lowerCAmelCase__						) - shift_amount]
		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
