






'''simple docstring'''



from __future__ import annotations

import math

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


def 							lowercase_				(    __A :       float					,      __A :       float					,      __A :       float					,      __A :       float					,      __A :       float			)       ->      tuple[float, float]:



 """simple docstring"""




 if not all(isinstance(__A					,      (float, int)			) for val in locals().values()			):
  lowercase					:   Dict				  =F'Input values must either be float or int: {list(locals().values()			)}'
  raise TypeError(__A			)
 lowercase					:   int				  =((x * distance) / (z + distance)) * scale
 lowercase					:   List[Any]				  =((y * distance) / (z + distance)) * scale
 return projected_x, projected_y


def 							lowercase_				(    __A :       float					,      __A :       float					,      __A :       float					,      __A :       str					,      __A :       float			)       ->      tuple[float, float, float]:



 """simple docstring"""




 if not isinstance(__A					,      __A			):
  raise TypeError('''Axis must be a str'''			)
 lowercase					:   Dict				  =locals()
 del input_variables["axis"]
 if not all(isinstance(__A					,      (float, int)			) for val in input_variables.values()			):
  lowercase					:   Tuple				  =(
      '''Input values except axis must either be float or int: '''
      F'{list(input_variables.values()			)}'
  )
  raise TypeError(__A			)
 lowercase					:   Optional[Any]				  =(angle % 3_6_0) / 4_5_0 * 1_8_0 / math.pi
 if axis == "z":
  lowercase					:   List[Any]				  =x * math.cos(__A			) - y * math.sin(__A			)
  lowercase					:   List[str]				  =y * math.cos(__A			) + x * math.sin(__A			)
  lowercase					:   Union[str, Any]				  =z
 elif axis == "x":
  lowercase					:   int				  =y * math.cos(__A			) - z * math.sin(__A			)
  lowercase					:   Optional[Any]				  =z * math.cos(__A			) + y * math.sin(__A			)
  lowercase					:   Any				  =x
 elif axis == "y":
  lowercase					:   Any				  =x * math.cos(__A			) - z * math.sin(__A			)
  lowercase					:   Any				  =z * math.cos(__A			) + x * math.sin(__A			)
  lowercase					:   Optional[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) = }""")











'''simple docstring'''



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE						=	{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	['RemBertTokenizer']

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	['RemBertTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RemBertForCausalLM',
          'RemBertForMaskedLM',
          'RemBertForMultipleChoice',
          'RemBertForQuestionAnswering',
          'RemBertForSequenceClassification',
          'RemBertForTokenClassification',
          'RemBertLayer',
          'RemBertModel',
          'RemBertPreTrainedModel',
          'load_tf_weights_in_rembert',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRemBertForCausalLM',
          'TFRemBertForMaskedLM',
          'TFRemBertForMultipleChoice',
          'TFRemBertForQuestionAnswering',
          'TFRemBertForSequenceClassification',
          'TFRemBertForTokenClassification',
          'TFRemBertLayer',
          'TFRemBertModel',
          'TFRemBertPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert import RemBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert_fast import RemBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rembert import (
                REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RemBertForCausalLM,
                RemBertForMaskedLM,
                RemBertForMultipleChoice,
                RemBertForQuestionAnswering,
                RemBertForSequenceClassification,
                RemBertForTokenClassification,
                RemBertLayer,
                RemBertModel,
                RemBertPreTrainedModel,
                load_tf_weights_in_rembert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rembert import (
                TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRemBertForCausalLM,
                TFRemBertForMaskedLM,
                TFRemBertForMultipleChoice,
                TFRemBertForQuestionAnswering,
                TFRemBertForSequenceClassification,
                TFRemBertForTokenClassification,
                TFRemBertLayer,
                TFRemBertModel,
                TFRemBertPreTrainedModel,
            )


else:
      import sys

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



