





from __future__ import annotations

import math

_SCREAMING_SNAKE_CASE      	=		"""2020.9.26"""
_SCREAMING_SNAKE_CASE      	=		"""xcodz-dot, cclaus, dhruvmanila"""


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a   ):
	if not all(isinstance(__a   , (float, int)   ) for val in locals().values()   ):
		snake_case_		:			Tuple											=	f"""Input values must either be float or int: {list(locals().values()   )}"""
		raise TypeError(__a   )
	snake_case_		:			Dict											=	((x * distance) / (z + distance)) * scale
	snake_case_		:			Optional[int]											=	((y * distance) / (z + distance)) * scale
	return projected_x, projected_y


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a   ):
	if not isinstance(__a   , __a   ):
		raise TypeError('Axis must be a str'   )
	snake_case_		:			List[str]											=	locals()
	del input_variables["axis"]
	if not all(isinstance(__a   , (float, int)   ) for val in input_variables.values()   ):
		snake_case_		:			int											=	(
		    'Input values except axis must either be float or int: '
		    f"""{list(input_variables.values()   )}"""
		)
		raise TypeError(__a   )
	snake_case_		:			Union[str, Any]											=	(angle % 3_60) / 4_50 * 1_80 / math.pi
	if axis == "z":
		snake_case_		:			Dict											=	x * math.cos(__a   ) - y * math.sin(__a   )
		snake_case_		:			List[Any]											=	y * math.cos(__a   ) + x * math.sin(__a   )
		snake_case_		:			Optional[int]											=	z
	elif axis == "x":
		snake_case_		:			Optional[Any]											=	y * math.cos(__a   ) - z * math.sin(__a   )
		snake_case_		:			Dict											=	z * math.cos(__a   ) + y * math.sin(__a   )
		snake_case_		:			str											=	x
	elif axis == "y":
		snake_case_		:			Tuple											=	x * math.cos(__a   ) - z * math.sin(__a   )
		snake_case_		:			Optional[Any]											=	z * math.cos(__a   ) + x * math.sin(__a   )
		snake_case_		:			int											=	y
	else:
		raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\''   )

	return new_x, new_y, new_z


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(F'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
						print(F'''{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }''')








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE      	=		{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """SwinForImageClassification""",
						    """SwinForMaskedImageModeling""",
						    """SwinModel""",
						    """SwinPreTrainedModel""",
						    """SwinBackbone""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFSwinForImageClassification""",
						    """TFSwinForMaskedImageModeling""",
						    """TFSwinModel""",
						    """TFSwinPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swin import (
												    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwinBackbone,
												    SwinForImageClassification,
												    SwinForMaskedImageModeling,
												    SwinModel,
												    SwinPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_swin import (
												    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSwinForImageClassification,
												    TFSwinForMaskedImageModeling,
												    TFSwinModel,
												    TFSwinPreTrainedModel,
												)

else:
						import sys

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

