



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 		UpperCAmelCase    (    _lowerCAmelCase					):
   SCREAMING_SNAKE_CASE__					      =  (EulerAncestralDiscreteScheduler,)
   SCREAMING_SNAKE_CASE__					      =  1_0





   def 						__lowerCAmelCase				(						self							,  **_lowerCAmelCase      ):
         _lowerCAmelCase			      =	{
             '''num_train_timesteps''': 1_100,
             '''beta_start''': 0.0_001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**_lowerCamelCase      )
         return config





   def 						__lowerCAmelCase				(						self      ):
         for timesteps in [10, 50, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=_lowerCamelCase      )





   def 						__lowerCAmelCase				(						self      ):
         for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]							,  [0.0_002, 0.002, 0.02]      ):
               self.check_over_configs(beta_start=_lowerCamelCase							,  beta_end=_lowerCamelCase      )





   def 						__lowerCAmelCase				(						self      ):
         for schedule in ["linear", "scaled_linear"]:
               self.check_over_configs(beta_schedule=_lowerCamelCase      )





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





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.scheduler_classes[0]
         _lowerCAmelCase			      =	self.get_scheduler_config()
         _lowerCAmelCase			      =	scheduler_class(**_lowerCamelCase      )

         scheduler.set_timesteps(self.num_inference_steps      )

         _lowerCAmelCase			      =	torch.manual_seed(0      )

         _lowerCAmelCase			      =	self.dummy_model()
         _lowerCAmelCase			      =	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         _lowerCAmelCase			      =	sample.to(_lowerCamelCase      )

         for i, t in enumerate(scheduler.timesteps      ):
               _lowerCAmelCase			      =	scheduler.scale_model_input(_lowerCamelCase							,  _lowerCamelCase      )

               _lowerCAmelCase			      =	model(_lowerCamelCase							,  _lowerCamelCase      )

               _lowerCAmelCase			      =	scheduler.step(_lowerCamelCase							,  _lowerCamelCase							,  _lowerCamelCase							,  generator=_lowerCamelCase      )
               _lowerCAmelCase			      =	output.prev_sample

         _lowerCAmelCase			      =	torch.sum(torch.abs(_lowerCamelCase      )      )
         _lowerCAmelCase			      =	torch.mean(torch.abs(_lowerCamelCase      )      )

         assert abs(result_sum.item() - 152.3_192      ) < 1E-2
         assert abs(result_mean.item() - 0.1_983      ) < 1E-3





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.scheduler_classes[0]
         _lowerCAmelCase			      =	self.get_scheduler_config(prediction_type='''v_prediction'''      )
         _lowerCAmelCase			      =	scheduler_class(**_lowerCamelCase      )

         scheduler.set_timesteps(self.num_inference_steps      )

         _lowerCAmelCase			      =	torch.manual_seed(0      )

         _lowerCAmelCase			      =	self.dummy_model()
         _lowerCAmelCase			      =	self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowerCAmelCase			      =	sample.to(_lowerCamelCase      )

         for i, t in enumerate(scheduler.timesteps      ):
               _lowerCAmelCase			      =	scheduler.scale_model_input(_lowerCamelCase							,  _lowerCamelCase      )

               _lowerCAmelCase			      =	model(_lowerCamelCase							,  _lowerCamelCase      )

               _lowerCAmelCase			      =	scheduler.step(_lowerCamelCase							,  _lowerCamelCase							,  _lowerCamelCase							,  generator=_lowerCamelCase      )
               _lowerCAmelCase			      =	output.prev_sample

         _lowerCAmelCase			      =	torch.sum(torch.abs(_lowerCamelCase      )      )
         _lowerCAmelCase			      =	torch.mean(torch.abs(_lowerCamelCase      )      )

         assert abs(result_sum.item() - 108.4_439      ) < 1E-2
         assert abs(result_mean.item() - 0.1_412      ) < 1E-3





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.scheduler_classes[0]
         _lowerCAmelCase			      =	self.get_scheduler_config()
         _lowerCAmelCase			      =	scheduler_class(**_lowerCamelCase      )

         scheduler.set_timesteps(self.num_inference_steps							,  device=_lowerCamelCase      )
         _lowerCAmelCase			      =	torch.manual_seed(0      )

         _lowerCAmelCase			      =	self.dummy_model()
         _lowerCAmelCase			      =	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         _lowerCAmelCase			      =	sample.to(_lowerCamelCase      )

         for t in scheduler.timesteps:
               _lowerCAmelCase			      =	scheduler.scale_model_input(_lowerCamelCase							,  _lowerCamelCase      )

               _lowerCAmelCase			      =	model(_lowerCamelCase							,  _lowerCamelCase      )

               _lowerCAmelCase			      =	scheduler.step(_lowerCamelCase							,  _lowerCamelCase							,  _lowerCamelCase							,  generator=_lowerCamelCase      )
               _lowerCAmelCase			      =	output.prev_sample

         _lowerCAmelCase			      =	torch.sum(torch.abs(_lowerCamelCase      )      )
         _lowerCAmelCase			      =	torch.mean(torch.abs(_lowerCamelCase      )      )

         assert abs(result_sum.item() - 152.3_192      ) < 1E-2
         assert abs(result_mean.item() - 0.1_983      ) < 1E-3







"""simple docstring"""


class    snake_case						:


   '''simple docstring'''

   def __init__(      self					:	Optional[Any]    ):

     '''simple docstring'''
     # numbermap keys are the number and its values are lists of indexes sorted
     # in ascending order
     __A		     =					{}
     # indexmap keys are an index and it's values are the number at that index
     __A		     =					{}


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	list | str | range,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     if isinstance(_lowerCamelCase,			(range, str)    ):
       __A		     =					list(_lowerCamelCase    )
     elif not isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       raise TypeError(
           '''binary_search_delete() only accepts either a list, range or str'''    )

     __A		     =					0
     __A		     =					len(_lowerCamelCase    ) - 1

     while low <= high:
       __A		     =					(low + high) // 2
       if array[mid] == item:
         array.pop(_lowerCamelCase    )
         return array
       elif array[mid] < item:
         __A		     =					mid + 1
       else:
         __A		     =					mid - 1
     raise ValueError(
         '''Either the item is not in the array or the array was unsorted'''    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	list | str | range,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     if isinstance(_lowerCamelCase,			(range, str)    ):
       __A		     =					list(_lowerCamelCase    )
     elif not isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       raise TypeError(
           '''binary_search_insert() only accepts either a list, range or str'''    )

     __A		     =					0
     __A		     =					len(_lowerCamelCase    ) - 1

     while low <= high:
       __A		     =					(low + high) // 2
       if array[mid] == index:
         # If the item already exists in the array,
         # insert it after the existing item
         array.insert(mid + 1,			_lowerCamelCase    )
         return array
       elif array[mid] < index:
         __A		     =					mid + 1
       else:
         __A		     =					mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
     array.insert(_lowerCamelCase,			_lowerCamelCase    )
     return array


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	int,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     # Remove previous index
     if index in self.indexmap:
       __A		     =					self.indexmap[index]
       if len(self.numbermap[n]    ) == 1:
         del self.numbermap[n]
       else:
         __A		     =					self.binary_search_delete(self.numbermap[n],			_lowerCamelCase    )

        # Set new index
     __A		     =					number

     # Number not seen before or empty so insert number value
     if number not in self.numbermap:
       __A		     =					[index]

     # Here we need to perform a binary search insertion in order to insert
     # The item in the correct place
     else:
       __A		     =					self.binary_search_insert(
           self.numbermap[number],			_lowerCamelCase    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     return self.numbermap.get(_lowerCamelCase,			[-1]    )[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()




