

from __future__ import annotations

import math

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					tuple[float, float]:
 if not all(isinstance(_UpperCamelCase     ,			(float, int)      ) for val in locals().values()      ):
  __lowerCamelCase    :    Optional[Any] 	=			F"Input values must either be float or int: {list(locals().values()      )}"
  raise TypeError(_UpperCamelCase      )
 __lowerCamelCase    :    Dict 	=			((x * distance) / (z + distance)) * scale
 __lowerCamelCase    :    Any 	=			((y * distance) / (z + distance)) * scale
 return projected_x, projected_y






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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """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		=[
					    """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		=[
					    """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		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




