


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		(DEISMultistepScheduler,)
   __A :      Optional[Any]     		=		(("num_inference_steps", 25),)






   def _snake_case    (		self						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				{
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "solver_order": 2,
     }

     config.update(**__A )
     return config






   def _snake_case    (		self						,				__A=0						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				dict(self.forward_default_kwargs )
     lowerCamelCase :					List[str]    =				kwargs.pop("num_inference_steps"						,				__A )
     lowerCamelCase :					List[Any]    =				self.dummy_sample
     lowerCamelCase :					Any    =				0.1 * sample
     lowerCamelCase :					List[Any]    =				[residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
       lowerCamelCase :					Any    =				self.get_scheduler_config(**__A )
       lowerCamelCase :					List[str]    =				scheduler_class(**__A )
       scheduler.set_timesteps(__A )
       # copy over dummy past residuals
       lowerCamelCase :					int    =				dummy_past_residuals[: scheduler.config.solver_order]

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(__A )
         lowerCamelCase :					str    =				scheduler_class.from_pretrained(__A )
         new_scheduler.set_timesteps(__A )
         # copy over dummy past residuals
         lowerCamelCase :					Optional[Any]    =				dummy_past_residuals[: new_scheduler.config.solver_order]

       lowerCamelCase    ,	lowerCamelCase :					List[str]    =				sample, sample
       for t in range(__A						,				time_step + scheduler.config.solver_order + 1 ):
         lowerCamelCase :					str    =				scheduler.step(__A						,				__A						,				__A						,				**__A ).prev_sample
         lowerCamelCase :					List[str]    =				new_scheduler.step(__A						,				__A						,				__A						,				**__A ).prev_sample

         assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"






   def _snake_case    (		self ):






     """simple docstring"""





     pass






   def _snake_case    (		self						,				__A=0						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				dict(self.forward_default_kwargs )
     lowerCamelCase :					Optional[Any]    =				kwargs.pop("num_inference_steps"						,				__A )
     lowerCamelCase :					Optional[int]    =				self.dummy_sample
     lowerCamelCase :					List[str]    =				0.1 * sample
     lowerCamelCase :					Dict    =				[residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
       lowerCamelCase :					Tuple    =				self.get_scheduler_config()
       lowerCamelCase :					Tuple    =				scheduler_class(**__A )
       scheduler.set_timesteps(__A )

       # copy over dummy past residuals (must be after setting timesteps)
       lowerCamelCase :					Union[str, Any]    =				dummy_past_residuals[: scheduler.config.solver_order]

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(__A )
         lowerCamelCase :					Optional[int]    =				scheduler_class.from_pretrained(__A )
         # copy over dummy past residuals
         new_scheduler.set_timesteps(__A )

         # copy over dummy past residual (must be after setting timesteps)
         lowerCamelCase :					Any    =				dummy_past_residuals[: new_scheduler.config.solver_order]

       lowerCamelCase :					Tuple    =				scheduler.step(__A						,				__A						,				__A						,				**__A ).prev_sample
       lowerCamelCase :					List[Any]    =				new_scheduler.step(__A						,				__A						,				__A						,				**__A ).prev_sample

       assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"






   def _snake_case    (		self						,				__A=None						,				**__A ):






     """simple docstring"""





     if scheduler is None:
       lowerCamelCase :					Optional[Any]    =				self.scheduler_classes[0]
       lowerCamelCase :					Optional[int]    =				self.get_scheduler_config(**__A )
       lowerCamelCase :					Union[str, Any]    =				scheduler_class(**__A )

     lowerCamelCase :					List[Any]    =				self.scheduler_classes[0]
     lowerCamelCase :					int    =				self.get_scheduler_config(**__A )
     lowerCamelCase :					Union[str, Any]    =				scheduler_class(**__A )

     lowerCamelCase :					Union[str, Any]    =				10
     lowerCamelCase :					Any    =				self.dummy_model()
     lowerCamelCase :					Dict    =				self.dummy_sample_deter
     scheduler.set_timesteps(__A )

     for i, t in enumerate(scheduler.timesteps ):
       lowerCamelCase :					Union[str, Any]    =				model(__A						,				__A )
       lowerCamelCase :					Tuple    =				scheduler.step(__A						,				__A						,				__A ).prev_sample

     return sample






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				dict(self.forward_default_kwargs )

     lowerCamelCase :					Union[str, Any]    =				kwargs.pop("num_inference_steps"						,				__A )

     for scheduler_class in self.scheduler_classes:
       lowerCamelCase :					List[str]    =				self.get_scheduler_config()
       lowerCamelCase :					Dict    =				scheduler_class(**__A )

       lowerCamelCase :					List[Any]    =				self.dummy_sample
       lowerCamelCase :					Union[str, Any]    =				0.1 * sample

       if num_inference_steps is not None and hasattr(__A						,				"set_timesteps" ):
         scheduler.set_timesteps(__A )
       elif num_inference_steps is not None and not hasattr(__A						,				"set_timesteps" ):
         lowerCamelCase :					str    =				num_inference_steps

       # copy over dummy past residuals (must be done after set_timesteps)
       lowerCamelCase :					Union[str, Any]    =				[residual + 0.2, residual + 0.15, residual + 0.10]
       lowerCamelCase :					List[str]    =				dummy_past_residuals[: scheduler.config.solver_order]

       lowerCamelCase :					List[str]    =				scheduler.timesteps[5]
       lowerCamelCase :					Optional[int]    =				scheduler.timesteps[6]

       lowerCamelCase :					Tuple    =				scheduler.step(__A						,				__A						,				__A						,				**__A ).prev_sample
       lowerCamelCase :					Optional[int]    =				scheduler.step(__A						,				__A						,				__A						,				**__A ).prev_sample

       self.assertEqual(output_a.shape						,				sample.shape )
       self.assertEqual(output_a.shape						,				output_a.shape )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Any    =				DEISMultistepScheduler(**self.get_scheduler_config() )
     lowerCamelCase :					int    =				self.full_loop(scheduler=__A )
     lowerCamelCase :					List[Any]    =				torch.mean(torch.abs(__A ) )

     assert abs(result_mean.item() - 0.23916 ) < 1e-3

     lowerCamelCase :					Any    =				DPMSolverSinglestepScheduler.from_config(scheduler.config )
     lowerCamelCase :					List[Any]    =				DPMSolverMultistepScheduler.from_config(scheduler.config )
     lowerCamelCase :					Tuple    =				UniPCMultistepScheduler.from_config(scheduler.config )
     lowerCamelCase :					Dict    =				DEISMultistepScheduler.from_config(scheduler.config )

     lowerCamelCase :					List[Any]    =				self.full_loop(scheduler=__A )
     lowerCamelCase :					Tuple    =				torch.mean(torch.abs(__A ) )

     assert abs(result_mean.item() - 0.23916 ) < 1e-3






   def _snake_case    (		self ):






     """simple docstring"""





     for timesteps in [25, 50, 100, 999, 1000]:
       self.check_over_configs(num_train_timesteps=__A )






   def _snake_case    (		self ):






     """simple docstring"""





     self.check_over_configs(thresholding=__A )
     for order in [1, 2, 3]:
       for solver_type in ["logrho"]:
         for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample"]:
             self.check_over_configs(
                 thresholding=__A						,				prediction_type=__A						,				sample_max_value=__A						,				algorithm_type="deis"						,				solver_order=__A						,				solver_type=__A						,				)






   def _snake_case    (		self ):






     """simple docstring"""





     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=__A )






   def _snake_case    (		self ):






     """simple docstring"""





     for algorithm_type in ["deis"]:
       for solver_type in ["logrho"]:
         for order in [1, 2, 3]:
           for prediction_type in ["epsilon", "sample"]:
             self.check_over_configs(
                 solver_order=__A						,				solver_type=__A						,				prediction_type=__A						,				algorithm_type=__A						,				)
             lowerCamelCase :					Tuple    =				self.full_loop(
                 solver_order=__A						,				solver_type=__A						,				prediction_type=__A						,				algorithm_type=__A						,				)
             assert not torch.isnan(__A ).any(), "Samples have nan numbers"






   def _snake_case    (		self ):






     """simple docstring"""





     self.check_over_configs(lower_order_final=__A )
     self.check_over_configs(lower_order_final=__A )






   def _snake_case    (		self ):






     """simple docstring"""





     for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
       self.check_over_forward(num_inference_steps=__A						,				time_step=0 )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				self.full_loop()
     lowerCamelCase :					Optional[Any]    =				torch.mean(torch.abs(__A ) )

     assert abs(result_mean.item() - 0.23916 ) < 1e-3






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				self.full_loop(prediction_type="v_prediction" )
     lowerCamelCase :					List[Any]    =				torch.mean(torch.abs(__A ) )

     assert abs(result_mean.item() - 0.091 ) < 1e-3






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				self.scheduler_classes[0]
     lowerCamelCase :					Any    =				self.get_scheduler_config(thresholding=__A						,				dynamic_thresholding_ratio=0 )
     lowerCamelCase :					Any    =				scheduler_class(**__A )

     lowerCamelCase :					Union[str, Any]    =				10
     lowerCamelCase :					Optional[int]    =				self.dummy_model()
     lowerCamelCase :					List[Any]    =				self.dummy_sample_deter.half()
     scheduler.set_timesteps(__A )

     for i, t in enumerate(scheduler.timesteps ):
       lowerCamelCase :					Union[str, Any]    =				model(__A						,				__A )
       lowerCamelCase :					Optional[Any]    =				scheduler.step(__A						,				__A						,				__A ).prev_sample

     assert sample.dtype == torch.floataa







from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_snake_case             =     2_99_79_24_58

# Symbols
_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case             =     symbols('''ct x y z''')






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if velocity > c:
    raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"	)
  elif velocity < 1:
    # Usually the speed should be much higher than 1 (c order of magnitude)
    raise ValueError("Speed must be greater than or equal to 1!"	)

  return velocity / c






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE_	) ** 2	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return np.array(
      [
          [gamma(SCREAMING_SNAKE_CASE_	), -gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), 0, 0],
          [-gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), gamma(SCREAMING_SNAKE_CASE_	), 0, 0],
          [0, 0, 1, 0],
          [0, 0, 0, 1],
      ]	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = None	):


  '''simple docstring'''

  if event is None:
    lowerCamelCase :					Tuple    =				np.array([ct, x, y, z]	)  # Symbolic four vector
  else:
    event[0] *= c  # x0 is ct (speed of light * time)

  return transformation_matrix(SCREAMING_SNAKE_CASE_	) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   _snake_case             =     transform(29_97_92_45)
   print('''Example of four vector: ''')
   print(f'''ct\' = {four_vector[0]}''')
   print(f'''x\' = {four_vector[1]}''')
   print(f'''y\' = {four_vector[2]}''')
   print(f'''z\' = {four_vector[3]}''')

   # Substitute symbols with numerical values
   _snake_case             =     {ct: c, x: 1, y: 1, z: 1}
   _snake_case             =     [four_vector[i].subs(sub_dict) for i in range(4)]

   print(f'''\n{numerical_vector}''')



