





from __future__ import annotations

import math

a__      :     Tuple         =		'2020.9.26'
a__      :     Optional[int]         =		'xcodz-dot, cclaus, dhruvmanila'

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float  ,						_UpperCAmelCase							:float  ,						_UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				tuple[float, float]:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float  ,						_UpperCAmelCase							:float  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:float				)	->				tuple[float, float, float]:
     '''simple docstring'''


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


a__      :     str         =		{
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[Any]         =		['RoFormerTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[str]         =		[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[Any]         =		[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     Dict         =		[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

      a__      :     Union[str, Any]         =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



