



"""simple docstring"""






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 			__snake_case			(      _lowercase):
  snake_case__							:  Any         =		(DDPMScheduler,)




  def 		SCREAMING_SNAKE_CASE							(					self	:		str		,   **__lowerCAmelCase	:		Dict						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[int]            =					{
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''variance_type''': '''fixed_small''',
             '''clip_sample''': True,
         }

         config.update(**__lowerCAmelCase						)
         return config




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		int						):






         """simple docstring"""






         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]		,   [0.0_02, 0.02, 0.2, 2]						):
                self.check_over_configs(beta_start=__lowerCAmelCase		,   beta_end=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Any						):






         """simple docstring"""






         for schedule in ["linear", "squaredcos_cap_v2"]:
                self.check_over_configs(beta_schedule=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		int						):






         """simple docstring"""






         for variance in ["fixed_small", "fixed_large", "other"]:
                self.check_over_configs(variance_type=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]						):






         """simple docstring"""






         for clip_sample in [True, False]:
                self.check_over_configs(clip_sample=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






         self.check_over_configs(thresholding=__lowerCAmelCase						)
         for threshold in [0.5, 1.0, 2.0]:
                for prediction_type in ["epsilon", "sample", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=__lowerCAmelCase		,   prediction_type=__lowerCAmelCase		,   sample_max_value=__lowerCAmelCase		,   )




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]						):






         """simple docstring"""






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]						):






         """simple docstring"""






         for t in [0, 5_0_0, 9_9_9]:
                self.check_over_forward(time_step=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		int						):






         """simple docstring"""






         _lowerCamelCase		:							Tuple            =					self.scheduler_classes[0]
         _lowerCamelCase		:							int            =					self.get_scheduler_config()
         _lowerCamelCase		:							Any            =					scheduler_class(**__lowerCAmelCase						)

         assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7						) - 0.0_09_79						)						) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9						) - 0.02						)						) < 1E-5




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							str            =					self.scheduler_classes[0]
         _lowerCamelCase		:							List[Any]            =					self.get_scheduler_config()
         _lowerCamelCase		:							Optional[int]            =					scheduler_class(**__lowerCAmelCase						)

         _lowerCamelCase		:							Tuple            =					len(__lowerCAmelCase						)

         _lowerCamelCase		:							Dict            =					self.dummy_model()
         _lowerCamelCase		:							str            =					self.dummy_sample_deter
         _lowerCamelCase		:							Any            =					torch.manual_seed(0						)

         for t in reversed(range(__lowerCAmelCase						)						):
                # 1. predict noise residual
                _lowerCamelCase		:							Optional[int]            =					model(__lowerCAmelCase		,   __lowerCAmelCase						)

                # 2. predict previous mean of sample x_t-1
                _lowerCamelCase		:							Dict            =					scheduler.step(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   generator=__lowerCAmelCase						).prev_sample

                # if t > 0:
                #     noise = self.dummy_sample_deter
                #     variance = scheduler.get_variance(t) ** (0.5) * noise
                #
                # sample = pred_prev_sample + variance
                _lowerCamelCase		:							Dict            =					pred_prev_sample

         _lowerCamelCase		:							str            =					torch.sum(torch.abs(__lowerCAmelCase						)						)
         _lowerCamelCase		:							str            =					torch.mean(torch.abs(__lowerCAmelCase						)						)

         assert abs(result_sum.item() - 2_58.96_06						) < 1E-2
         assert abs(result_mean.item() - 0.33_72						) < 1E-3




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					self.scheduler_classes[0]
         _lowerCamelCase		:							Union[str, Any]            =					self.get_scheduler_config(prediction_type='''v_prediction'''						)
         _lowerCamelCase		:							Union[str, Any]            =					scheduler_class(**__lowerCAmelCase						)

         _lowerCamelCase		:							Dict            =					len(__lowerCAmelCase						)

         _lowerCamelCase		:							str            =					self.dummy_model()
         _lowerCamelCase		:							Optional[Any]            =					self.dummy_sample_deter
         _lowerCamelCase		:							Dict            =					torch.manual_seed(0						)

         for t in reversed(range(__lowerCAmelCase						)						):
                # 1. predict noise residual
                _lowerCamelCase		:							Union[str, Any]            =					model(__lowerCAmelCase		,   __lowerCAmelCase						)

                # 2. predict previous mean of sample x_t-1
                _lowerCamelCase		:							List[Any]            =					scheduler.step(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   generator=__lowerCAmelCase						).prev_sample

                # if t > 0:
                #     noise = self.dummy_sample_deter
                #     variance = scheduler.get_variance(t) ** (0.5) * noise
                #
                # sample = pred_prev_sample + variance
                _lowerCamelCase		:							Any            =					pred_prev_sample

         _lowerCamelCase		:							List[str]            =					torch.sum(torch.abs(__lowerCAmelCase						)						)
         _lowerCamelCase		:							str            =					torch.mean(torch.abs(__lowerCAmelCase						)						)

         assert abs(result_sum.item() - 2_02.02_96						) < 1E-2
         assert abs(result_mean.item() - 0.26_31						) < 1E-3




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]						):






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					self.scheduler_classes[0]
         _lowerCamelCase		:							List[str]            =					self.get_scheduler_config()
         _lowerCamelCase		:							Dict            =					scheduler_class(**__lowerCAmelCase						)

         _lowerCamelCase		:							List[str]            =					[1_0_0, 8_7, 5_0, 1, 0]

         scheduler.set_timesteps(timesteps=__lowerCAmelCase						)

         _lowerCamelCase		:							Union[str, Any]            =					scheduler.timesteps

         for i, timestep in enumerate(__lowerCAmelCase						):
                if i == len(__lowerCAmelCase						) - 1:
                       _lowerCamelCase		:							str            =					-1
                else:
                       _lowerCamelCase		:							List[Any]            =					timesteps[i + 1]

                _lowerCamelCase		:							Optional[int]            =					scheduler.previous_timestep(__lowerCAmelCase						)
                _lowerCamelCase		:							Tuple            =					prev_t.item()

                self.assertEqual(__lowerCAmelCase		,   __lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		int						):






         """simple docstring"""






         _lowerCamelCase		:							str            =					self.scheduler_classes[0]
         _lowerCamelCase		:							Optional[int]            =					self.get_scheduler_config()
         _lowerCamelCase		:							str            =					scheduler_class(**__lowerCAmelCase						)

         _lowerCamelCase		:							Any            =					[1_0_0, 8_7, 5_0, 5_1, 0]

         with self.assertRaises(__lowerCAmelCase		,   msg='''`custom_timesteps` must be in descending order.'''						):
                scheduler.set_timesteps(timesteps=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		str						):






         """simple docstring"""






         _lowerCamelCase		:							Tuple            =					self.scheduler_classes[0]
         _lowerCamelCase		:							Optional[Any]            =					self.get_scheduler_config()
         _lowerCamelCase		:							List[str]            =					scheduler_class(**__lowerCAmelCase						)

         _lowerCamelCase		:							List[Any]            =					[1_0_0, 8_7, 5_0, 1, 0]
         _lowerCamelCase		:							List[str]            =					len(__lowerCAmelCase						)

         with self.assertRaises(__lowerCAmelCase		,   msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''						):
                scheduler.set_timesteps(num_inference_steps=__lowerCAmelCase		,   timesteps=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		str						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[Any]            =					self.scheduler_classes[0]
         _lowerCamelCase		:							str            =					self.get_scheduler_config()
         _lowerCamelCase		:							Optional[Any]            =					scheduler_class(**__lowerCAmelCase						)

         _lowerCamelCase		:							int            =					[scheduler.config.num_train_timesteps]

         with self.assertRaises(
             __lowerCAmelCase		,   msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''		,   ):
                scheduler.set_timesteps(timesteps=__lowerCAmelCase						)









'''simple docstring'''

from __future__ import annotations





def 		UpperCamelCase							(				_lowerCamelCase   :   list[int]			):  # This function is recursive
    A__							=     len(_lowerCamelCase			)
    # If the array contains only one element, we return it (it's the stop condition of
    # recursion)
    if array_length <= 1:
        return array
        # Else
    A__							=     array[0]
    A__							=     False
    A__							=     1
    A__							=     []
    while not is_found and i < array_length:
        if array[i] < pivot:
            A__							=     True
            A__							=     [element for element in array[i:] if element >= array[i]]
            A__							=     longest_subsequence(_lowerCamelCase			)
            if len(_lowerCamelCase			) > len(_lowerCamelCase			):
                A__							=     temp_array
        else:
            i += 1

    A__							=     [element for element in array[1:] if element >= pivot]
    A__							=     [pivot, *longest_subsequence(_lowerCamelCase			)]
    if len(_lowerCamelCase			) > len(_lowerCamelCase			):
        return temp_array
    else:
        return longest_subseq


if __name__ == "__main__":
  import doctest

  doctest.testmod()



