


'''simple docstring'''







# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    42
     __magic_name__    			=    None






def 			lowercase				(_A   ,						_A=0.999   ,						_A="cosine"   ,						):







       """simple docstring"""







       if alpha_transform_type == "cosine":

              def alpha_bar_fn(_A					):
                     return math.cos((t + 0.008) / 1.008 * 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}'					)

       _lowerCAmelCase    :							Any   			=       []
       for i in range(_A					):
              _lowerCAmelCase    :							Optional[Any]   			=       i / num_diffusion_timesteps
              _lowerCAmelCase    :							Dict   			=       (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__					( SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    1







     @register_to_config
     def __init__( self      ,	snake_case__ = 1000      ,	snake_case__ = 0.0001      ,	snake_case__ = 0.02      ,	snake_case__ = "linear"      ,	snake_case__ = None      ,	snake_case__ = True      ,	snake_case__ = True      ,	snake_case__ = 0      ,	snake_case__ = "epsilon"      ,	snake_case__ = 1.0      ,	**snake_case__      ,	):

            '''simple docstring'''

            if kwargs.get('set_alpha_to_one'      ,	snake_case__   ) is not None:
                   _lowerCAmelCase    :							Any   			=       (
                       'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
                   )
                   deprecate('set_alpha_to_one'      ,	'1.0.0'      ,	snake_case__      ,	standard_warn=snake_case__   )
                   _lowerCAmelCase    :							Union[str, Any]   			=       kwargs['set_alpha_to_one']
            if trained_betas is not None:
                   _lowerCAmelCase    :							Union[str, Any]   			=       torch.tensor(snake_case__      ,	dtype=torch.floataa   )
            elif beta_schedule == "linear":
                   _lowerCAmelCase    :							Any   			=       torch.linspace(snake_case__      ,	snake_case__      ,	snake_case__      ,	dtype=torch.floataa   )
            elif beta_schedule == "scaled_linear":
                   # this schedule is very specific to the latent diffusion model.
                   _lowerCAmelCase    :							Tuple   			=       (
                       torch.linspace(beta_start**0.5      ,	beta_end**0.5      ,	snake_case__      ,	dtype=torch.floataa   ) ** 2
                   )
            elif beta_schedule == "squaredcos_cap_v2":
                   # Glide cosine schedule
                   _lowerCAmelCase    :							Optional[Any]   			=       betas_for_alpha_bar(snake_case__   )
            else:
                   raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'   )

            _lowerCAmelCase    :							str   			=       1.0 - self.betas
            _lowerCAmelCase    :							Dict   			=       torch.cumprod(self.alphas      ,	dim=0   )

            # At every step in inverted ddim, we are looking into the next alphas_cumprod
            # For the final step, there is no next alphas_cumprod, and the index is out of bounds
            # `set_alpha_to_zero` decides whether we set this parameter simply to zero
            # in this case, self.step() just output the predicted noise
            # or whether we use the final alpha of the "non-previous" one.
            _lowerCAmelCase    :							int   			=       torch.tensor(0.0   ) if set_alpha_to_zero else self.alphas_cumprod[-1]

            # standard deviation of the initial noise distribution
            _lowerCAmelCase    :							List[Any]   			=       1.0

            # setable values
            _lowerCAmelCase    :							Tuple   			=       None
            _lowerCAmelCase    :							Tuple   			=       torch.from_numpy(np.arange(0      ,	snake_case__   ).copy().astype(np.intaa   )   )







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            return sample







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            if num_inference_steps > self.config.num_train_timesteps:
                   raise ValueError(
                       F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                       F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                       F' maximal {self.config.num_train_timesteps} timesteps.'   )

            _lowerCAmelCase    :							List[Any]   			=       num_inference_steps
            _lowerCAmelCase    :							str   			=       self.config.num_train_timesteps // self.num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # casting to int to avoid issues when num_inference_step is power of 3
            _lowerCAmelCase    :							Tuple   			=       (np.arange(0      ,	snake_case__   ) * step_ratio).round().copy().astype(np.intaa   )
            _lowerCAmelCase    :							List[Any]   			=       torch.from_numpy(snake_case__   ).to(snake_case__   )
            self.timesteps += self.config.steps_offset







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__ = 0.0      ,	snake_case__ = False      ,	snake_case__ = None      ,	snake_case__ = True      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       timestep + self.config.num_train_timesteps // self.num_inference_steps

            # 2. compute alphas, betas
            # change original implementation to exactly match noise levels for analogous forward process
            _lowerCAmelCase    :							List[str]   			=       self.alphas_cumprod[timestep]
            _lowerCAmelCase    :							Tuple   			=       (
                self.alphas_cumprod[prev_timestep]
                if prev_timestep < self.config.num_train_timesteps
                else self.final_alpha_cumprod
            )

            _lowerCAmelCase    :							Dict   			=       1 - alpha_prod_t

            # 3. compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            if self.config.prediction_type == "epsilon":
                   _lowerCAmelCase    :							List[Any]   			=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                   _lowerCAmelCase    :							str   			=       model_output
            elif self.config.prediction_type == "sample":
                   _lowerCAmelCase    :							Optional[Any]   			=       model_output
                   _lowerCAmelCase    :							Optional[Any]   			=       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
            elif self.config.prediction_type == "v_prediction":
                   _lowerCAmelCase    :							Dict   			=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                   _lowerCAmelCase    :							str   			=       (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
            else:
                   raise ValueError(
                       F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                       ' `v_prediction`'   )

            # 4. Clip or threshold "predicted x_0"
            if self.config.clip_sample:
                   _lowerCAmelCase    :							Optional[Any]   			=       pred_original_sample.clamp(
                       -self.config.clip_sample_range      ,	self.config.clip_sample_range   )

            # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            _lowerCAmelCase    :							List[str]   			=       (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

            # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            _lowerCAmelCase    :							List[Any]   			=       alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

            if not return_dict:
                   return (prev_sample, pred_original_sample)
            return DDIMSchedulerOutput(prev_sample=snake_case__      ,	pred_original_sample=snake_case__   )







     def __len__( self   ):

            '''simple docstring'''

            return self.config.num_train_timesteps





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    (DDPMScheduler,)







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       {
                'num_train_timesteps': 1000,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'variance_type': 'fixed_small',
                'clip_sample': True,
            }

            config.update(**snake_case__   )
            return config







     def a	( self   ):

            '''simple docstring'''

            for timesteps in [1, 5, 100, 1000]:
                   self.check_over_configs(num_train_timesteps=snake_case__   )







     def a	( self   ):

            '''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=snake_case__      ,	beta_end=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            for t in [0, 500, 999]:
                   self.check_over_forward(time_step=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_0979   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.02   )   ) < 1E-5







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

            _lowerCAmelCase    :							str   			=       self.dummy_model()
            _lowerCAmelCase    :							str   			=       self.dummy_sample_deter
            _lowerCAmelCase    :							Union[str, Any]   			=       torch.manual_seed(0   )

            for t in reversed(range(snake_case__   )   ):
                   # 1. predict noise residual
                   _lowerCAmelCase    :							List[Any]   			=       model(snake_case__      ,	snake_case__   )

                   # 2. predict previous mean of sample x_t-1
                   _lowerCAmelCase    :							Any   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__      ,	generator=snake_case__   ).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    :							Dict   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							List[str]   			=       torch.mean(torch.abs(snake_case__   )   )

            assert abs(result_sum.item() - 258.9606   ) < 1E-2
            assert abs(result_mean.item() - 0.3372   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Any   			=       self.get_scheduler_config(prediction_type='v_prediction'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

            _lowerCAmelCase    :							Any   			=       self.dummy_model()
            _lowerCAmelCase    :							Tuple   			=       self.dummy_sample_deter
            _lowerCAmelCase    :							Optional[int]   			=       torch.manual_seed(0   )

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

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

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

            _lowerCAmelCase    :							Any   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							Dict   			=       torch.mean(torch.abs(snake_case__   )   )

            assert abs(result_sum.item() - 202.0296   ) < 1E-2
            assert abs(result_mean.item() - 0.2631   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[int]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       [100, 87, 50, 1, 0]

            scheduler.set_timesteps(timesteps=snake_case__   )

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

            for i, timestep in enumerate(snake_case__   ):
                   if i == len(snake_case__   ) - 1:
                          _lowerCAmelCase    :							str   			=       -1
                   else:
                          _lowerCAmelCase    :							Optional[Any]   			=       timesteps[i + 1]

                   _lowerCAmelCase    :							int   			=       scheduler.previous_timestep(snake_case__   )
                   _lowerCAmelCase    :							int   			=       prev_t.item()

                   self.assertEqual(snake_case__      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Tuple   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 51, 0]

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							List[str]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 1, 0]
            _lowerCAmelCase    :							int   			=       len(snake_case__   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							int   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Any   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Any   			=       [scheduler.config.num_train_timesteps]

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

