




'''simple docstring'''





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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'''



				__a							: torch.FloatTensor
				__a							: torch.FloatTensor




class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: int										=1



				@register_to_config
				def __init__( self       , UpperCAmelCase_ = 20_00       , UpperCAmelCase_ = 0.15       , UpperCAmelCase_ = 0.01       , UpperCAmelCase_ = 1348.0       , UpperCAmelCase_ = 1E-5       , UpperCAmelCase_ = 1       , ):
											# standard deviation of the initial noise distribution
											lowerCAmelCase   =		sigma_max

											# setable values
											lowerCAmelCase   =		None

											self.set_sigmas(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											return sample



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		sampling_eps if sampling_eps is not None else self.config.sampling_eps

											lowerCAmelCase   =		torch.linspace(1       , UpperCAmelCase_       , UpperCAmelCase_       , device=UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		sigma_min if sigma_min is not None else self.config.sigma_min
											lowerCAmelCase   =		sigma_max if sigma_max is not None else self.config.sigma_max
											lowerCAmelCase   =		sampling_eps if sampling_eps is not None else self.config.sampling_eps
											if self.timesteps is None:
																		self.set_timesteps(UpperCAmelCase_       , UpperCAmelCase_						)

											lowerCAmelCase   =		sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
											lowerCAmelCase   =		torch.exp(torch.linspace(math.log(UpperCAmelCase_						)       , math.log(UpperCAmelCase_						)       , UpperCAmelCase_						)						)
											lowerCAmelCase   =		torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)



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



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

											lowerCAmelCase   =		timestep * torch.ones(
											    sample.shape[0]       , device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
											lowerCAmelCase   =		(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   =		timesteps.to(self.discrete_sigmas.device						)

											lowerCAmelCase   =		self.discrete_sigmas[timesteps].to(sample.device						)
											lowerCAmelCase   =		self.get_adjacent_sigma(UpperCAmelCase_       , UpperCAmelCase_						).to(sample.device						)
											lowerCAmelCase   =		torch.zeros_like(UpperCAmelCase_						)
											lowerCAmelCase   =		(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   =		diffusion.flatten()
											while len(diffusion.shape						) < len(sample.shape						):
																		lowerCAmelCase   =		diffusion.unsqueeze(-1						)
											lowerCAmelCase   =		drift - diffusion**2 * model_output

											#  equation 6: sample noise for the diffusion term of
											lowerCAmelCase   =		randn_tensor(
											    sample.shape       , layout=sample.layout       , generator=UpperCAmelCase_       , device=sample.device       , dtype=sample.dtype						)
											lowerCAmelCase   =		sample - drift  # subtract because `dt` is a small negative timestep
											# TODO is the variable diffusion the correct scaling term for the noise?
											lowerCAmelCase   =		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=UpperCAmelCase_       , prev_sample_mean=UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , ):
											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   =		randn_tensor(sample.shape       , layout=sample.layout       , generator=UpperCAmelCase_						).to(sample.device						)

											# compute step size from the model_output, the noise, and the snr
											lowerCAmelCase   =		torch.norm(model_output.reshape(model_output.shape[0]       , -1						)       , dim=-1						).mean()
											lowerCAmelCase   =		torch.norm(noise.reshape(noise.shape[0]       , -1						)       , dim=-1						).mean()
											lowerCAmelCase   =		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
											lowerCAmelCase   =		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   =		step_size.flatten()
											while len(step_size.shape						) < len(sample.shape						):
																		lowerCAmelCase   =		step_size.unsqueeze(-1						)
											lowerCAmelCase   =		sample + step_size * model_output
											lowerCAmelCase   =		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

											if not return_dict:
																		return (prev_sample,)

											return SchedulerOutput(prev_sample=UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , ):
											# Make sure sigmas and timesteps have the same device and dtype as original_samples
											lowerCAmelCase   =		timesteps.to(original_samples.device						)
											lowerCAmelCase   =		self.discrete_sigmas.to(original_samples.device						)[timesteps]
											lowerCAmelCase   =		(
											    noise * sigmas[:, None, None, None]
											    if noise is not None
											    else torch.randn_like(UpperCAmelCase_						) * sigmas[:, None, None, None]
											)
											lowerCAmelCase   =		noise + original_samples
											return noisy_samples






				def __len__( self						):
											return self.config.num_train_timesteps









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_						={
    """configuration_jukebox""": [
        """JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """JukeboxConfig""",
        """JukeboxPriorConfig""",
        """JukeboxVQVAEConfig""",
    ],
    """tokenization_jukebox""": ["""JukeboxTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """JukeboxModel""",
		    """JukeboxPreTrainedModel""",
		    """JukeboxVQVAE""",
		    """JukeboxPrior""",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		UpperCAmelCase_						=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



