





"""simple docstring"""




from __future__ import annotations

import math

lowerCamelCase_     :						str						=					'2020.9.26'
lowerCamelCase_     :						Union[str, Any]						=					'xcodz-dot, cclaus, dhruvmanila'





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  if not all(isinstance(_UpperCAmelCase ,					(float, int)					) for val in locals().values()					):
    A_  :       Union[str, Any]										=  f"""Input values must either be float or int: {list(locals().values()					)}"""
    raise TypeError(_UpperCAmelCase					)
  A_  :       Dict										=  ((x * distance) / (z + distance)) * scale
  A_  :       Any										=  ((y * distance) / (z + distance)) * scale
  return projected_x, projected_y





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





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






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_     :						List[str]						=					{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						str						=					['XLNetTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						List[str]						=					['XLNetTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						int						=					[
       'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'XLNetForMultipleChoice',
       'XLNetForQuestionAnswering',
       'XLNetForQuestionAnsweringSimple',
       'XLNetForSequenceClassification',
       'XLNetForTokenClassification',
       'XLNetLMHeadModel',
       'XLNetModel',
       'XLNetPreTrainedModel',
       'load_tf_weights_in_xlnet',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						Union[str, Any]						=					[
       'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFXLNetForMultipleChoice',
       'TFXLNetForQuestionAnsweringSimple',
       'TFXLNetForSequenceClassification',
       'TFXLNetForTokenClassification',
       'TFXLNetLMHeadModel',
       'TFXLNetMainLayer',
       'TFXLNetModel',
       'TFXLNetPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet import XLNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet_fast import XLNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlnet import (
          XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLNetForMultipleChoice,
          XLNetForQuestionAnswering,
          XLNetForQuestionAnsweringSimple,
          XLNetForSequenceClassification,
          XLNetForTokenClassification,
          XLNetLMHeadModel,
          XLNetModel,
          XLNetPreTrainedModel,
          load_tf_weights_in_xlnet,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlnet import (
          TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLNetForMultipleChoice,
          TFXLNetForQuestionAnsweringSimple,
          TFXLNetForSequenceClassification,
          TFXLNetForTokenClassification,
          TFXLNetLMHeadModel,
          TFXLNetMainLayer,
          TFXLNetModel,
          TFXLNetPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_     :						List[Any]						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)