

import math
from typing import Union

import torch

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



class     _a						(_snake_case		,   _snake_case				):


					'''simple docstring'''






					UpperCAmelCase__:   str  							=       1






					@register_to_config
					def __init__( self    ,       A__=2000    ,       A__=0.1    ,       A__=20    ,       A__=1e-3			):
						A__							:    Union[str, Any]        =		None
						A__							:    Dict        =		None
						A__							:    Optional[Any]        =		None






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Any        =		torch.linspace(1    ,       self.config.sampling_eps    ,       __snake_case    ,       device=__snake_case			)






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

						# TODO(Patrick) better comments + non-PyTorch
						# postprocess model score
						A__							:    List[Any]        =		(
						    -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
						)
						A__							:    str        =		torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
						A__							:    List[str]        =		std.flatten()
						while len(std.shape			) < len(score.shape			):
							A__							:    int        =		std.unsqueeze(-1			)
						A__							:    Tuple        =		-score / std

						# compute
						A__							:    List[Any]        =		-1.0 / len(self.timesteps			)

						A__							:    str        =		self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
						A__							:    List[Any]        =		beta_t.flatten()
						while len(beta_t.shape			) < len(x.shape			):
							A__							:    Union[str, Any]        =		beta_t.unsqueeze(-1			)
						A__							:    Any        =		-0.5 * beta_t * x

						A__							:    Optional[int]        =		torch.sqrt(__snake_case			)
						A__							:    Optional[Any]        =		drift - diffusion**2 * score
						A__							:    str        =		x + drift * dt

						# add noise
						A__							:    Dict        =		randn_tensor(x.shape    ,       layout=x.layout    ,       generator=__snake_case    ,       device=x.device    ,       dtype=x.dtype			)
						A__							:    Union[str, Any]        =		x_mean + diffusion * math.sqrt(-dt			) * noise

						return x, x_mean






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







'''simple docstring'''


UpperCAmelCase_		:   Union[str, Any]							   =		'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

UpperCAmelCase_		:   Dict							   =		[{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase_		:   Any							   =		{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




