


'''simple docstring'''

from typing import TYPE_CHECKING

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


a_  :				Union[str, Any]						= {
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_  :				List[Any]						= [
         """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """XLMForMultipleChoice""",
         """XLMForQuestionAnswering""",
         """XLMForQuestionAnsweringSimple""",
         """XLMForSequenceClassification""",
         """XLMForTokenClassification""",
         """XLMModel""",
         """XLMPreTrainedModel""",
         """XLMWithLMHeadModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_  :				Tuple						= [
         """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFXLMForMultipleChoice""",
         """TFXLMForQuestionAnsweringSimple""",
         """TFXLMForSequenceClassification""",
         """TFXLMForTokenClassification""",
         """TFXLMMainLayer""",
         """TFXLMModel""",
         """TFXLMPreTrainedModel""",
         """TFXLMWithLMHeadModel""",
     ]


if TYPE_CHECKING:
     from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
     from .tokenization_xlm import XLMTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm import (
              XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMForMultipleChoice,
              XLMForQuestionAnswering,
              XLMForQuestionAnsweringSimple,
              XLMForSequenceClassification,
              XLMForTokenClassification,
              XLMModel,
              XLMPreTrainedModel,
              XLMWithLMHeadModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xlm import (
              TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXLMForMultipleChoice,
              TFXLMForQuestionAnsweringSimple,
              TFXLMForSequenceClassification,
              TFXLMForTokenClassification,
              TFXLMMainLayer,
              TFXLMModel,
              TFXLMPreTrainedModel,
              TFXLMWithLMHeadModel,
          )

else:
     import sys

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





from __future__ import annotations

import math

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




def 	_SCREAMING_SNAKE_CASE					(		_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	:   str			   =					F'Input values must either be float or int: {list(locals().values())}'
														raise TypeError(_lowerCamelCase)
							__UpperCamelCase	:   List[str]			   =					((x * distance) / (z + distance)) * scale
							__UpperCamelCase	:   List[Any]			   =					((y * distance) / (z + distance)) * scale
							return projected_x, projected_y




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