


"""simple docstring"""


from __future__ import annotations

import math

__lowerCamelCase      :Tuple	    =		'2020.9.26'
__lowerCamelCase      :List[str]	    =		'xcodz-dot, cclaus, dhruvmanila'




def   snake_case		(     UpperCamelCase__       :						float			,				UpperCamelCase__       :						float			,				UpperCamelCase__       :						float			,				UpperCamelCase__       :						float			,				UpperCamelCase__       :						float    )   ->      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					:					int        =	((x * distance) / (z + distance)) * scale
      lowerCamelCase					:					Optional[int]        =	((y * distance) / (z + distance)) * scale
      return projected_x, projected_y




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








"""simple docstring"""


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Tuple													=(KDPMaDiscreteScheduler,)
     snake_case__	:					Tuple													=10

     def 		a__						(	self:						List[Any]     ,		**__a:						Optional[int]	)->    Union[str, Any]:
           lowerCamelCase					:					int        =	{
               """num_train_timesteps""": 1_100,
               """beta_start""": 0.00_01,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**__a	)
           return config

     def 		a__						(	self:						Union[str, Any]	)->    Any:
           for timesteps in [10, 50, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=__a	)

     def 		a__						(	self:						str	)->    int:
           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]     ,		[0.00_02, 0.0_02, 0.02]	):
                 self.check_over_configs(beta_start=__a     ,		beta_end=__a	)

     def 		a__						(	self:						int	)->    Union[str, Any]:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=__a	)

     def 		a__						(	self:						List[Any]	)->    List[Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__a	)

     def 		a__						(	self:						Union[str, Any]	)->    int:
           lowerCamelCase					:					List[str]        =	self.scheduler_classes[0]
           lowerCamelCase					:					Union[str, Any]        =	self.get_scheduler_config(prediction_type="""v_prediction"""	)
           lowerCamelCase					:					List[str]        =	scheduler_class(**__a	)

           scheduler.set_timesteps(self.num_inference_steps	)

           lowerCamelCase					:					Dict        =	self.dummy_model()
           lowerCamelCase					:					List[Any]        =	self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase					:					List[Any]        =	sample.to(__a	)

           for i, t in enumerate(scheduler.timesteps	):
                 lowerCamelCase					:					Optional[Any]        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[int]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					Tuple        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					Optional[Any]        =	output.prev_sample

           lowerCamelCase					:					List[str]        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					Tuple        =	torch.mean(torch.abs(__a	)	)

           if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 4.6_9_3_4e-0_7	) < 1e-2
                 assert abs(result_mean.item() - 6.1_1_1_2e-1_0	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7	) < 1e-2
                 assert abs(result_mean.item() - 0.00_02	) < 1e-3

     def 		a__						(	self:						Any	)->    Any:
           if torch_device == "mps":
                 return
           lowerCamelCase					:					Dict        =	self.scheduler_classes[0]
           lowerCamelCase					:					Dict        =	self.get_scheduler_config()
           lowerCamelCase					:					int        =	scheduler_class(**__a	)

           scheduler.set_timesteps(self.num_inference_steps	)

           lowerCamelCase					:					List[Any]        =	self.dummy_model()
           lowerCamelCase					:					Optional[Any]        =	self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase					:					Optional[int]        =	sample.to(__a	)

           for i, t in enumerate(scheduler.timesteps	):
                 lowerCamelCase					:					Dict        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[Any]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					Tuple        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					str        =	output.prev_sample

           lowerCamelCase					:					Tuple        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					Tuple        =	torch.mean(torch.abs(__a	)	)

           if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3







     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           if torch_device == "mps":
                 return
           lowerCamelCase					:					Any        =	self.scheduler_classes[0]
           lowerCamelCase					:					Union[str, Any]        =	self.get_scheduler_config()
           lowerCamelCase					:					Optional[Any]        =	scheduler_class(**__a	)

           scheduler.set_timesteps(self.num_inference_steps     ,		device=__a	)

           lowerCamelCase					:					Union[str, Any]        =	self.dummy_model()
           lowerCamelCase					:					List[str]        =	self.dummy_sample_deter.to(__a	) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 lowerCamelCase					:					Union[str, Any]        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[int]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					int        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					int        =	output.prev_sample

           lowerCamelCase					:					Union[str, Any]        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					int        =	torch.mean(torch.abs(__a	)	)

           if str(__a	).startswith("""cpu"""	):
                 # The following sum varies between 148 and 156 on mps. Why?
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3




