





from __future__ import annotations

import math

lowercase				:	List[Any]      						=  """2020.9.26"""
lowercase				:	List[Any]      						=  """xcodz-dot, cclaus, dhruvmanila"""



def A_     (		A__		,				A__		,				A__		,				A__		,				A__    )  ->		tuple[float, float]:
       if not all(isinstance(A__		,				(float, int)    ) for val in locals().values()    ):
              a__						:     Dict			=    F'Input values must either be float or int: {list(locals().values()    )}'
              raise TypeError(A__    )
       a__						:     Union[str, Any]			=    ((x * distance) / (z + distance)) * scale
       a__						:     Optional[int]			=    ((y * distance) / (z + distance)) * scale
       return projected_x, projected_y



def A_     (		A__		,				A__		,				A__		,				A__		,				A__    )  ->		tuple[float, float, float]:
       if not isinstance(A__		,				A__    ):
              raise TypeError('Axis must be a str'    )
       a__						:     int			=    locals()
       del input_variables["axis"]
       if not all(isinstance(A__		,				(float, int)    ) for val in input_variables.values()    ):
              a__						:     List[str]			=    (
                  'Input values except axis must either be float or int: '
                  F'{list(input_variables.values()    )}'
              )
              raise TypeError(A__    )
       a__						:     List[str]			=    (angle % 360) / 450 * 180 / math.pi
       if axis == "z":
              a__						:     Tuple			=    x * math.cos(A__    ) - y * math.sin(A__    )
              a__						:     Union[str, Any]			=    y * math.cos(A__    ) + x * math.sin(A__    )
              a__						:     str			=    z
       elif axis == "x":
              a__						:     Tuple			=    y * math.cos(A__    ) - z * math.sin(A__    )
              a__						:     Dict			=    z * math.cos(A__    ) + y * math.sin(A__    )
              a__						:     List[Any]			=    x
       elif axis == "y":
              a__						:     int			=    x * math.cos(A__    ) - z * math.sin(A__    )
              a__						:     int			=    z * math.cos(A__    ) + x * math.sin(A__    )
              a__						:     List[Any]			=    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_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase				:	str      						=  {
    """configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[Any]      						=  ["""VisionEncoderDecoderModel"""]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[int]      						=  ["""TFVisionEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	List[str]      						=  ["""FlaxVisionEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
    import sys

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


