
import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest





class 						SCREAMING_SNAKE_CASE__		(_a ):
     lowercase_   :			List[str]   =   (DDIMScheduler,)
     lowercase_   :			Union[str, Any]   =   (("eta", 0.0), ("num_inference_steps", 50))
     def 					A__			(	self  :		Tuple					,	**__lowerCamelCase  :		Optional[int]	):



       """simple docstring"""
       lowerCAmelCase__								=			{
           '''num_train_timesteps''': 10_00,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''clip_sample''': True,
       }

       config.update(**A_	)
       return config
     def 					A__			(	self  :		Optional[Any]					,	**__lowerCamelCase  :		Any	):



       """simple docstring"""
       lowerCAmelCase__								=			self.scheduler_classes[0]
       lowerCAmelCase__								=			self.get_scheduler_config(**A_	)
       lowerCAmelCase__								=			scheduler_class(**A_	)

       lowerCAmelCase__							,			lowerCAmelCase__								=			10, 0.0

       lowerCAmelCase__								=			self.dummy_model()
       lowerCAmelCase__								=			self.dummy_sample_deter

       scheduler.set_timesteps(A_	)

       for t in scheduler.timesteps:
         lowerCAmelCase__								=			model(A_					,	A_	)
         lowerCAmelCase__								=			scheduler.step(A_					,	A_					,	A_					,	A_	).prev_sample

       return sample
     def 					A__			(	self  :		List[Any]	):



       """simple docstring"""
       for timesteps in [1_00, 5_00, 10_00]:
         self.check_over_configs(num_train_timesteps=A_	)
     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=A_	)

       lowerCAmelCase__								=			self.scheduler_classes[0]
       lowerCAmelCase__								=			self.get_scheduler_config(steps_offset=1	)
       lowerCAmelCase__								=			scheduler_class(**A_	)
       scheduler.set_timesteps(5	)
       assert torch.equal(scheduler.timesteps					,	torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]	)	)
     def 					A__			(	self  :		Union[str, Any]	):



       """simple docstring"""
       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					,	[0.002, 0.02, 0.2, 2]	):
         self.check_over_configs(beta_start=A_					,	beta_end=A_	)
     def 					A__			(	self  :		int	):



       """simple docstring"""
       for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=A_	)
     def 					A__			(	self  :		Any	):



       """simple docstring"""
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=A_	)
     def 					A__			(	self  :		int	):



       """simple docstring"""
       for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=A_	)
     def 					A__			(	self  :		int	):



       """simple docstring"""
       for timestep_spacing in ["trailing", "leading"]:
         self.check_over_configs(timestep_spacing=A_	)
     def 					A__			(	self  :		int	):



       """simple docstring"""
       for rescale_betas_zero_snr in [True, False]:
         self.check_over_configs(rescale_betas_zero_snr=A_	)
     def 					A__			(	self  :		List[str]	):



       """simple docstring"""
       self.check_over_configs(thresholding=A_	)
       for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(
               thresholding=A_					,	prediction_type=A_					,	sample_max_value=A_					,	)
     def 					A__			(	self  :		int	):



       """simple docstring"""
       for t in [1, 10, 49]:
         self.check_over_forward(time_step=A_	)
     def 					A__			(	self  :		str	):



       """simple docstring"""
       for t, num_inference_steps in zip([1, 10, 50]					,	[10, 50, 5_00]	):
         self.check_over_forward(time_step=A_					,	num_inference_steps=A_	)
     def 					A__			(	self  :		Optional[int]	):



       """simple docstring"""
       for t, eta in zip([1, 10, 49]					,	[0.0, 0.5, 1.0]	):
         self.check_over_forward(time_step=A_					,	eta=A_	)
     def 					A__			(	self  :		str	):



       """simple docstring"""
       lowerCAmelCase__								=			self.scheduler_classes[0]
       lowerCAmelCase__								=			self.get_scheduler_config()
       lowerCAmelCase__								=			scheduler_class(**A_	)

       assert torch.sum(torch.abs(scheduler._get_variance(0					,	0	) - 0.0	)	) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_20					,	4_00	) - 0.1_4771	)	) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_80					,	9_60	) - 0.3_2460	)	) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(0					,	0	) - 0.0	)	) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_87					,	4_86	) - 0.0_0979	)	) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_99					,	9_98	) - 0.02	)	) < 1e-5
     def 					A__			(	self  :		List[Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.full_loop()

       lowerCAmelCase__								=			torch.sum(torch.abs(A_	)	)
       lowerCAmelCase__								=			torch.mean(torch.abs(A_	)	)

       assert abs(result_sum.item() - 172.0067	) < 1e-2
       assert abs(result_mean.item() - 0.22_3967	) < 1e-3
     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       lowerCAmelCase__								=			self.full_loop(prediction_type='''v_prediction'''	)

       lowerCAmelCase__								=			torch.sum(torch.abs(A_	)	)
       lowerCAmelCase__								=			torch.mean(torch.abs(A_	)	)

       assert abs(result_sum.item() - 52.5302	) < 1e-2
       assert abs(result_mean.item() - 0.0684	) < 1e-3
     def 					A__			(	self  :		int	):



       """simple docstring"""
       # We specify different beta, so that the first alpha is 0.99
       lowerCAmelCase__								=			self.full_loop(set_alpha_to_one=A_					,	beta_start=0.01	)
       lowerCAmelCase__								=			torch.sum(torch.abs(A_	)	)
       lowerCAmelCase__								=			torch.mean(torch.abs(A_	)	)

       assert abs(result_sum.item() - 149.8295	) < 1e-2
       assert abs(result_mean.item() - 0.1951	) < 1e-3



     def 					A__			(	self  :		int	):



       """simple docstring"""
       # We specify different beta, so that the first alpha is 0.99
       lowerCAmelCase__								=			self.full_loop(set_alpha_to_one=A_					,	beta_start=0.01	)
       lowerCAmelCase__								=			torch.sum(torch.abs(A_	)	)
       lowerCAmelCase__								=			torch.mean(torch.abs(A_	)	)

       assert abs(result_sum.item() - 149.0784	) < 1e-2
       assert abs(result_mean.item() - 0.1941	) < 1e-3







from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class 						SCREAMING_SNAKE_CASE__		:
     def __init__(	self  :		int					,	__lowerCamelCase  :		Any	):



       """simple docstring"""
       lowerCAmelCase__								=			data
       lowerCAmelCase__								=			None



     def __str__(	self  :		int	):



       """simple docstring"""
       return F"""{self.data}"""





class 						SCREAMING_SNAKE_CASE__		:
     def __init__(	self  :		Union[str, Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			None
       lowerCAmelCase__								=			None
     def __iter__(	self  :		List[Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.front
       while node:
         yield node.data
         lowerCAmelCase__								=			node.next
     def __len__(	self  :		int	):



       """simple docstring"""
       return len(tuple(iter(self	)	)	)
     def __str__(	self  :		Optional[int]	):



       """simple docstring"""
       return " <- ".join(str(__lowerCamelCase	) for item in self	)
     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       return len(self	) == 0
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		Any	):



       """simple docstring"""
       lowerCAmelCase__								=			Node(__lowerCamelCase	)
       if self.is_empty():
         lowerCAmelCase__								=			lowerCAmelCase__								=			node
       else:
         assert isinstance(self.rear					,	__lowerCamelCase	)
         lowerCAmelCase__								=			node
         lowerCAmelCase__								=			node
     def 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       if self.is_empty():
         raise IndexError('''dequeue from empty queue'''	)
       assert isinstance(self.front					,	__lowerCamelCase	)
       lowerCAmelCase__								=			self.front
       lowerCAmelCase__								=			self.front.next
       if self.front is None:
         lowerCAmelCase__								=			None
       return node.data



     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       lowerCAmelCase__								=			lowerCAmelCase__								=			None


if __name__ == "__main__":
      from doctest import testmod

      testmod()

