

import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
    from ... import PreTrainedTokenizerBase, TensorType

lowerCAmelCase     :   Any						     =  logging.get_logger(__name__)
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[str]       =  '''vision-encoder-decoder'''
  SCREAMING_SNAKE_CASE    :      str       =  True

  def __init__(				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        super().__init__(**_SCREAMING_SNAKE_CASE					)
        if "encoder" not in kwargs or "decoder" not in kwargs:
              raise ValueError(
                  f"A configuraton of type {self.model_type} cannot be instantiated because "
                  f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     kwargs.pop('encoder'					)
        SCREAMING_SNAKE_CASE_				:  Any           =     encoder_config.pop('model_type'					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     kwargs.pop('decoder'					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     decoder_config.pop('model_type'					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     AutoConfig.for_model(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     AutoConfig.for_model(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     True

  @classmethod
  def  UpperCAmelCase    (				cls ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     True
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     True

        return cls(encoder=encoder_config.to_dict() ,					decoder=decoder_config.to_dict() ,					**_SCREAMING_SNAKE_CASE					)



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     copy.deepcopy(self.__dict__					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.encoder.to_dict()
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.decoder.to_dict()
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.__class__.model_type
        return output

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  version.parse('''1.11''')

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ]					)

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 1e-4



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}					)

class  _A		(  __magic_name__):

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     OrderedDict()
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     {0: 'batch', 1: 'encoder_sequence'}

        return common_inputs



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = -1 ,					_SCREAMING_SNAKE_CASE = -1 ,					_SCREAMING_SNAKE_CASE = False ,					_SCREAMING_SNAKE_CASE = None ,					):





        """simple docstring"""







        import torch

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     OrderedDict()

        SCREAMING_SNAKE_CASE_				:  int           =     super().generate_dummy_inputs(
            _SCREAMING_SNAKE_CASE ,					batch_size=_SCREAMING_SNAKE_CASE ,					seq_length=_SCREAMING_SNAKE_CASE ,					is_pair=_SCREAMING_SNAKE_CASE ,					framework=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  List[Any]           =     dummy_input['input_ids'].shape
        SCREAMING_SNAKE_CASE_				:  Dict           =     (batch, encoder_sequence, self._config.encoder_hidden_size)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     dummy_input.pop('input_ids'					)
        SCREAMING_SNAKE_CASE_				:  Any           =     dummy_input.pop('attention_mask'					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.zeros(_SCREAMING_SNAKE_CASE					)

        return common_inputs

class  _A		(  __magic_name__):

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        return VisionEncoderDecoderEncoderOnnxConfig(_SCREAMING_SNAKE_CASE					)



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = "default"					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     encoder_config.hidden_size
        return VisionEncoderDecoderDecoderOnnxConfig(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)









from __future__ import annotations

import math

lowerCAmelCase     :   List[str]						     =  '2020.9.26'
lowerCAmelCase     :   List[Any]						     =  'xcodz-dot, cclaus, dhruvmanila'

def    A_  (							a					,   a					,   a					,   a					,   a  ):

      """simple docstring"""



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

def    A_  (							a					,   a					,   a					,   a					,   a  ):

      """simple docstring"""



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






