
"""simple docstring"""

from __future__ import annotations

import math

SCREAMING_SNAKE_CASE_            =    '''2020.9.26'''
SCREAMING_SNAKE_CASE_            =    '''xcodz-dot, cclaus, dhruvmanila'''




def      lowercase__    (       lowerCAmelCase					:       float			,					lowerCAmelCase					:       float			,					lowerCAmelCase					:       float			,					lowerCAmelCase					:       float			,					lowerCAmelCase					:       float			)			->   tuple[float, float]:






    """simple docstring"""




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




def      lowercase__    (       lowerCAmelCase					:       float			,					lowerCAmelCase					:       float			,					lowerCAmelCase					:       float			,					lowerCAmelCase					:       str			,					lowerCAmelCase					:       float			)			->   tuple[float, float, float]:






    """simple docstring"""




    if not isinstance(lowerCAmelCase			,					lowerCAmelCase			):
        raise TypeError('Axis must be a str'			)
    UpperCAmelCase          =       locals()
    del input_variables["axis"]
    if not all(isinstance(lowerCAmelCase			,					(float, int)			) for val in input_variables.values()			):
        UpperCAmelCase          =       (
            'Input values except axis must either be float or int: '
            F"{list(input_variables.values()			)}"
        )
        raise TypeError(lowerCAmelCase			)
    UpperCAmelCase          =       (angle % 360) / 450 * 180 / math.pi
    if axis == "z":
        UpperCAmelCase          =       x * math.cos(lowerCAmelCase			) - y * math.sin(lowerCAmelCase			)
        UpperCAmelCase          =       y * math.cos(lowerCAmelCase			) + x * math.sin(lowerCAmelCase			)
        UpperCAmelCase          =       z
    elif axis == "x":
        UpperCAmelCase          =       y * math.cos(lowerCAmelCase			) - z * math.sin(lowerCAmelCase			)
        UpperCAmelCase          =       z * math.cos(lowerCAmelCase			) + y * math.sin(lowerCAmelCase			)
        UpperCAmelCase          =       x
    elif axis == "y":
        UpperCAmelCase          =       x * math.cos(lowerCAmelCase			) - z * math.sin(lowerCAmelCase			)
        UpperCAmelCase          =       z * math.cos(lowerCAmelCase			) + x * math.sin(lowerCAmelCase			)
        UpperCAmelCase          =       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, 1_0.0, 1_0.0) = }')
   print(F'{rotate(1.0, 2.0, 3.0, "y", 9_0.0) = }')





"""simple docstring"""

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_            =    {
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

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



