




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__a							     =					{
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					['PerceiverFeatureExtractor']
     __a							     =					['PerceiverImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					[
         'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'PerceiverForImageClassificationConvProcessing',
         'PerceiverForImageClassificationFourier',
         'PerceiverForImageClassificationLearned',
         'PerceiverForMaskedLM',
         'PerceiverForMultimodalAutoencoding',
         'PerceiverForOpticalFlow',
         'PerceiverForSequenceClassification',
         'PerceiverLayer',
         'PerceiverModel',
         'PerceiverPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
     from .tokenization_perceiver import PerceiverTokenizer

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_perceiver import PerceiverFeatureExtractor
          from .image_processing_perceiver import PerceiverImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_perceiver import (
              PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
              PerceiverForImageClassificationConvProcessing,
              PerceiverForImageClassificationFourier,
              PerceiverForImageClassificationLearned,
              PerceiverForMaskedLM,
              PerceiverForMultimodalAutoencoding,
              PerceiverForOpticalFlow,
              PerceiverForSequenceClassification,
              PerceiverLayer,
              PerceiverModel,
              PerceiverPreTrainedModel,
          )

else:
     import sys

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





from __future__ import annotations

import math

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







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


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







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


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