


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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class 	UpperCamelCase__					( _UpperCAmelCase	):



     """simple docstring"""






     __magic_name__    			=    42
     __magic_name__    			=    42
class 	UpperCamelCase__					( _UpperCAmelCase    ,					_UpperCAmelCase	):



     """simple docstring"""






     __magic_name__    			=    1







     @register_to_config
     def __init__( self      ,	snake_case__ = 2000      ,	snake_case__ = 0.15      ,	snake_case__ = 0.01      ,	snake_case__ = 1348.0      ,	snake_case__ = 1E-5      ,	snake_case__ = 1      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       sigma_max

            # setable values
            _lowerCAmelCase    :							List[Any]   			=       None

            self.set_sigmas(A_      ,	A_      ,	A_      ,	A_   )







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

            '''simple docstring'''

            return sample







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

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

            _lowerCAmelCase    :							Tuple   			=       torch.linspace(1      ,	A_      ,	A_      ,	device=A_   )







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

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       sigma_min if sigma_min is not None else self.config.sigma_min
            _lowerCAmelCase    :							Tuple   			=       sigma_max if sigma_max is not None else self.config.sigma_max
            _lowerCAmelCase    :							str   			=       sampling_eps if sampling_eps is not None else self.config.sampling_eps
            if self.timesteps is None:
                   self.set_timesteps(A_      ,	A_   )

            _lowerCAmelCase    :							Dict   			=       sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
            _lowerCAmelCase    :							List[Any]   			=       torch.exp(torch.linspace(math.log(A_   )      ,	math.log(A_   )      ,	A_   )   )
            _lowerCAmelCase    :							Optional[int]   			=       torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]   )







     def a	( self      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            return torch.where(
                timesteps == 0      ,	torch.zeros_like(t.to(timesteps.device   )   )      ,	self.discrete_sigmas[timesteps - 1].to(timesteps.device   )      ,	)







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

            '''simple docstring'''

            if self.timesteps is None:
                   raise ValueError(
                       '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'   )

            _lowerCAmelCase    :							int   			=       timestep * torch.ones(
                sample.shape[0]      ,	device=sample.device   )  # torch.repeat_interleave(timestep, sample.shape[0])
            _lowerCAmelCase    :							Optional[int]   			=       (timestep * (len(self.timesteps   ) - 1)).long()

            # mps requires indices to be in the same device, so we use cpu as is the default with cuda
            _lowerCAmelCase    :							Any   			=       timesteps.to(self.discrete_sigmas.device   )

            _lowerCAmelCase    :							Optional[int]   			=       self.discrete_sigmas[timesteps].to(sample.device   )
            _lowerCAmelCase    :							Optional[Any]   			=       self.get_adjacent_sigma(A_      ,	A_   ).to(sample.device   )
            _lowerCAmelCase    :							str   			=       torch.zeros_like(A_   )
            _lowerCAmelCase    :							str   			=       (sigma**2 - adjacent_sigma**2) ** 0.5

            # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
            # also equation 47 shows the analog from SDE models to ancestral sampling methods
            _lowerCAmelCase    :							Optional[Any]   			=       diffusion.flatten()
            while len(diffusion.shape   ) < len(sample.shape   ):
                   _lowerCAmelCase    :							Optional[Any]   			=       diffusion.unsqueeze(-1   )
            _lowerCAmelCase    :							List[Any]   			=       drift - diffusion**2 * model_output

            #  equation 6: sample noise for the diffusion term of
            _lowerCAmelCase    :							Optional[int]   			=       randn_tensor(
                sample.shape      ,	layout=sample.layout      ,	generator=A_      ,	device=sample.device      ,	dtype=sample.dtype   )
            _lowerCAmelCase    :							int   			=       sample - drift  # subtract because `dt` is a small negative timestep
            # TODO is the variable diffusion the correct scaling term for the noise?
            _lowerCAmelCase    :							Union[str, Any]   			=       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

            if not return_dict:
                   return (prev_sample, prev_sample_mean)

            return SdeVeOutput(prev_sample=A_      ,	prev_sample_mean=A_   )







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

            '''simple docstring'''

            if self.timesteps is None:
                   raise ValueError(
                       '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'   )

            # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
            # sample noise for correction
            _lowerCAmelCase    :							Dict   			=       randn_tensor(sample.shape      ,	layout=sample.layout      ,	generator=A_   ).to(sample.device   )

            # compute step size from the model_output, the noise, and the snr
            _lowerCAmelCase    :							Any   			=       torch.norm(model_output.reshape(model_output.shape[0]      ,	-1   )      ,	dim=-1   ).mean()
            _lowerCAmelCase    :							Optional[Any]   			=       torch.norm(noise.reshape(noise.shape[0]      ,	-1   )      ,	dim=-1   ).mean()
            _lowerCAmelCase    :							Tuple   			=       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
            _lowerCAmelCase    :							str   			=       step_size * torch.ones(sample.shape[0]   ).to(sample.device   )
            # self.repeat_scalar(step_size, sample.shape[0])

            # compute corrected sample: model_output term and noise term
            _lowerCAmelCase    :							List[Any]   			=       step_size.flatten()
            while len(step_size.shape   ) < len(sample.shape   ):
                   _lowerCAmelCase    :							Any   			=       step_size.unsqueeze(-1   )
            _lowerCAmelCase    :							Optional[Any]   			=       sample + step_size * model_output
            _lowerCAmelCase    :							Any   			=       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

            if not return_dict:
                   return (prev_sample,)

            return SchedulerOutput(prev_sample=A_   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       timesteps.to(original_samples.device   )
            _lowerCAmelCase    :							List[Any]   			=       self.discrete_sigmas.to(original_samples.device   )[timesteps]
            _lowerCAmelCase    :							Union[str, Any]   			=       (
                noise * sigmas[:, None, None, None]
                if noise is not None
                else torch.randn_like(A_   ) * sigmas[:, None, None, None]
            )
            _lowerCAmelCase    :							List[str]   			=       noise + original_samples
            return noisy_samples







     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__   )

