





from typing import TYPE_CHECKING

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


lowerCAmelCase_        =							{'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_        =							['''ViTFeatureExtractor''']
	lowerCAmelCase_        =							['''ViTImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_        =							[
	    '''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''ViTForImageClassification''',
	    '''ViTForMaskedImageModeling''',
	    '''ViTModel''',
	    '''ViTPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_        =							[
	    '''TFViTForImageClassification''',
	    '''TFViTModel''',
	    '''TFViTPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_        =							[
	    '''FlaxViTForImageClassification''',
	    '''FlaxViTModel''',
	    '''FlaxViTPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_vit import ViTFeatureExtractor
		from .image_processing_vit import ViTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_vit import (
		    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ViTForImageClassification,
		    ViTForMaskedImageModeling,
		    ViTModel,
		    ViTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
	import sys

	lowerCAmelCase_        =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



"""simple docstring"""





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

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

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

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

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

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()




