



import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  torch.FloatTensor
   A    :  Optional[torch.FloatTensor]       				=    None

def   _lowerCAmelCase   (						A__						,  A__=0.9_99						,  A__="cosine"						,  ):
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(A__	):
                  return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2	) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(A__	):
                  return math.exp(t * -12.0	)

      else:
            raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''	)

      lowercase__     =  []
      for i in range(A__	):
            lowercase__     =  i / num_diffusion_timesteps
            lowercase__     =  (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(A__	) / alpha_bar_fn(A__	)						,  A__	)	)
      return torch.tensor(A__						,  dtype=torch.floataa	)



class       UpperCAmelCase__(							lowerCamelCase     ,							lowerCamelCase	):

   '''simple docstring'''




   @register_to_config
   def __init__(						self  :			List[str]			,				lowerCAmelCase  :			int = 10_00			,				lowerCAmelCase  :			str = "fixed_small_log"			,				lowerCAmelCase  :			bool = True			,				lowerCAmelCase  :			Optional[float] = 1.0			,				lowerCAmelCase  :			str = "epsilon"			,				lowerCAmelCase  :			str = "squaredcos_cap_v2"			,				)	->				List[str]:




         """simple docstring"""

         if beta_schedule != "squaredcos_cap_v2":
               raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'')

         lowercase__     =  betas_for_alpha_bar(lowerCAmelCase)

         lowercase__     =  1.0 - self.betas
         lowercase__     =  torch.cumprod(self.alphas			,				dim=0)
         lowercase__     =  torch.tensor(1.0)

         # standard deviation of the initial noise distribution
         lowercase__     =  1.0

         # setable values
         lowercase__     =  None
         lowercase__     =  torch.from_numpy(np.arange(0			,				lowerCAmelCase)[::-1].copy())

         lowercase__     =  variance_type


   def 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			torch.FloatTensor			,				lowerCAmelCase  :			Optional[int] = None)	->				torch.FloatTensor:




         """simple docstring"""

         return sample


   def 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			Union[str, torch.device] = None)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  num_inference_steps
         lowercase__     =  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         lowercase__     =  (np.arange(0			,				lowerCAmelCase) * step_ratio).round()[::-1].copy().astype(np.intaa)
         lowercase__     =  torch.from_numpy(lowerCAmelCase).to(lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			str=None			,				lowerCAmelCase  :			Union[str, Any]=None			,				lowerCAmelCase  :			Dict=None)	->				Tuple:




         """simple docstring"""

         if prev_timestep is None:
               lowercase__     =  t - 1

         lowercase__     =  self.alphas_cumprod[t]
         lowercase__     =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         lowercase__     =  1 - alpha_prod_t
         lowercase__     =  1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
               lowercase__     =  self.betas[t]
         else:
               lowercase__     =  1 - alpha_prod_t / alpha_prod_t_prev

         # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
         # and sample from it to get previous sample
         # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
         lowercase__     =  beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
               lowercase__     =  self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small_log":
               lowercase__     =  torch.log(torch.clamp(lowerCAmelCase			,				min=1E-2_0))
               lowercase__     =  torch.exp(0.5 * variance)
         elif variance_type == "learned_range":
               # NOTE difference with DDPM scheduler
               lowercase__     =  variance.log()
               lowercase__     =  beta.log()

               lowercase__     =  (predicted_variance + 1) / 2
               lowercase__     =  frac * max_log + (1 - frac) * min_log

         return variance


   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			torch.FloatTensor			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			torch.FloatTensor			,				lowerCAmelCase  :			Optional[int] = None			,				lowerCAmelCase  :			str=None			,				lowerCAmelCase  :			bool = True			,				)	->				Union[UnCLIPSchedulerOutput, Tuple]:




         """simple docstring"""

         lowercase__     =  timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
               lowercase__,	lowercase__     =  torch.split(lowerCAmelCase			,				sample.shape[1]			,				dim=1)
         else:
               lowercase__     =  None

         # 1. compute alphas, betas
         if prev_timestep is None:
               lowercase__     =  t - 1

         lowercase__     =  self.alphas_cumprod[t]
         lowercase__     =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         lowercase__     =  1 - alpha_prod_t
         lowercase__     =  1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
               lowercase__     =  self.betas[t]
               lowercase__     =  self.alphas[t]
         else:
               lowercase__     =  1 - alpha_prod_t / alpha_prod_t_prev
               lowercase__     =  1 - beta

         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
         if self.config.prediction_type == "epsilon":
               lowercase__     =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
               lowercase__     =  model_output
         else:
               raise ValueError(
                   f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
                   ' for the UnCLIPScheduler.')

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
               lowercase__     =  torch.clamp(
                   lowerCAmelCase			,				-self.config.clip_sample_range			,				self.config.clip_sample_range)

         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         lowercase__     =  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         lowercase__     =  alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

         # 5. Compute predicted previous sample µ_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         lowercase__     =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         lowercase__     =  0
         if t > 0:
               lowercase__     =  randn_tensor(
                   model_output.shape			,				dtype=model_output.dtype			,				generator=lowerCAmelCase			,				device=model_output.device)

               lowercase__     =  self._get_variance(
                   lowerCAmelCase			,				predicted_variance=lowerCAmelCase			,				prev_timestep=lowerCAmelCase			,				)

               if self.variance_type == "fixed_small_log":
                     lowercase__     =  variance
               elif self.variance_type == "learned_range":
                     lowercase__     =  (0.5 * variance).exp()
               else:
                     raise ValueError(
                         f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
                         ' for the UnCLIPScheduler.')

               lowercase__     =  variance * variance_noise

         lowercase__     =  pred_prev_sample + variance

         if not return_dict:
               return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase			,				pred_original_sample=lowerCAmelCase)



   def 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			torch.FloatTensor			,				lowerCAmelCase  :			torch.FloatTensor			,				lowerCAmelCase  :			torch.IntTensor			,				)	->				torch.FloatTensor:




         """simple docstring"""

         lowercase__     =  self.alphas_cumprod.to(device=original_samples.device			,				dtype=original_samples.dtype)
         lowercase__     =  timesteps.to(original_samples.device)

         lowercase__     =  alphas_cumprod[timesteps] ** 0.5
         lowercase__     =  sqrt_alpha_prod.flatten()
         while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
               lowercase__     =  sqrt_alpha_prod.unsqueeze(-1)

         lowercase__     =  (1 - alphas_cumprod[timesteps]) ** 0.5
         lowercase__     =  sqrt_one_minus_alpha_prod.flatten()
         while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
               lowercase__     =  sqrt_one_minus_alpha_prod.unsqueeze(-1)

         lowercase__     =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  str       				=    (DDIMParallelScheduler,)
   A    :  Any       				=    (("eta", 0.0), ("num_inference_steps", 50))


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				**lowerCAmelCase  :			Optional[int])	->				Dict:




         """simple docstring"""

         lowercase__     =  {
             'num_train_timesteps': 10_00,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'clip_sample': True,
         }

         config.update(**lowerCAmelCase)
         return config


   def 			UpperCAmelCase		(						self  :			int			,				**lowerCAmelCase  :			str)	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(**lowerCAmelCase)
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__,	lowercase__     =  10, 0.0

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter

         scheduler.set_timesteps(lowerCAmelCase)

         for t in scheduler.timesteps:
               lowercase__     =  model(lowerCAmelCase			,				lowerCAmelCase)
               lowercase__     =  scheduler.step(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase).prev_sample

         return sample


   def 			UpperCAmelCase		(						self  :			Tuple)	->				int:




         """simple docstring"""

         for timesteps in [1_00, 5_00, 10_00]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Tuple)	->				Any:




         """simple docstring"""

         for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=lowerCAmelCase)

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(steps_offset=1)
         lowercase__     =  scheduler_class(**lowerCAmelCase)
         scheduler.set_timesteps(5)
         assert torch.equal(scheduler.timesteps			,				torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]))


   def 			UpperCAmelCase		(						self  :			str)	->				Tuple:




         """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 			UpperCAmelCase		(						self  :			Optional[int])	->				str:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			List[str])	->				List[str]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			List[Any])	->				str:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				int:




         """simple docstring"""

         for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				List[str]:




         """simple docstring"""

         for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str])	->				Optional[int]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			int)	->				Optional[Any]:




         """simple docstring"""

         for t in [1, 10, 49]:
               self.check_over_forward(time_step=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				int:




         """simple docstring"""

         for t, num_inference_steps in zip([1, 10, 50]			,				[10, 50, 5_00]):
               self.check_over_forward(time_step=lowerCAmelCase			,				num_inference_steps=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				List[Any]:




         """simple docstring"""

         for t, eta in zip([1, 10, 49]			,				[0.0, 0.5, 1.0]):
               self.check_over_forward(time_step=lowerCAmelCase			,				eta=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         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_47_71)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_80			,				9_60) - 0.3_24_60)) < 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_09_79)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99			,				9_98) - 0.02)) < 1E-5


   def 			UpperCAmelCase		(						self  :			Dict)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__,	lowercase__     =  10, 0.0
         scheduler.set_timesteps(lowerCAmelCase)

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter
         lowercase__     =  self.dummy_sample_deter + 0.1
         lowercase__     =  self.dummy_sample_deter - 0.1

         lowercase__     =  samplea.shape[0]
         lowercase__     =  torch.stack([samplea, samplea, samplea]			,				dim=0)
         lowercase__     =  torch.arange(lowerCAmelCase)[0:3, None].repeat(1			,				lowerCAmelCase)

         lowercase__     =  model(samples.flatten(0			,				1)			,				timesteps.flatten(0			,				1))
         lowercase__     =  scheduler.batch_step_no_noise(lowerCAmelCase			,				timesteps.flatten(0			,				1)			,				samples.flatten(0			,				1)			,				lowerCAmelCase)

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 11_47.79_04) < 1E-2
         assert abs(result_mean.item() - 0.49_82) < 1E-3


   def 			UpperCAmelCase		(						self  :			Any)	->				int:




         """simple docstring"""

         lowercase__     =  self.full_loop()

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_72.00_67) < 1E-2
         assert abs(result_mean.item() - 0.22_39_67) < 1E-3


   def 			UpperCAmelCase		(						self  :			int)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.full_loop(prediction_type='v_prediction')

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 52.53_02) < 1E-2
         assert abs(result_mean.item() - 0.06_84) < 1E-3


   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=lowerCAmelCase			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_49.82_95) < 1E-2
         assert abs(result_mean.item() - 0.19_51) < 1E-3



   def 			UpperCAmelCase		(						self  :			str)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=lowerCAmelCase			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_49.07_84) < 1E-2
         assert abs(result_mean.item() - 0.19_41) < 1E-3
