
import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput



class    a_ (			snake_case_					,			snake_case_    ):
			a	:			Union[str, Any]					   =							1






			@register_to_config
			def __init__(      self				,			__UpperCamelCase = 1_000				,			__UpperCamelCase = None  ):
									# set `betas`, `alphas`, `timesteps`
									self.set_timesteps(__snake_case  )

									# standard deviation of the initial noise distribution
									_lowercase					       =					1.0

									# For now we only support F-PNDM, i.e. the runge-kutta method
									# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
									# mainly at formula (9), (12), (13) and the Algorithm 2.
									_lowercase					       =					4

									# running values
									_lowercase					       =					[]






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = None  ):
									_lowercase					       =					num_inference_steps
									_lowercase					       =					torch.linspace(1				,			0				,			num_inference_steps + 1  )[:-1]
									_lowercase					       =					torch.cat([steps, torch.tensor([0.0]  )]  )

									if self.config.trained_betas is not None:
															_lowercase					       =					torch.tensor(self.config.trained_betas				,			dtype=torch.floataa  )
									else:
															_lowercase					       =					torch.sin(steps * math.pi / 2  ) ** 2

									_lowercase					       =					(1.0 - self.betas**2) ** 0.5

									_lowercase					       =					(torch.atana(self.betas				,			self.alphas  ) / math.pi * 2)[:-1]
									_lowercase					       =					timesteps.to(__snake_case  )

									_lowercase					       =					[]






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase = True				,			):
									if self.num_inference_steps is None:
															raise ValueError(
															    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )

									_lowercase					       =					(self.timesteps == timestep).nonzero().item()
									_lowercase					       =					timestep_index + 1

									_lowercase					       =					sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
									self.ets.append(__snake_case  )

									if len(self.ets  ) == 1:
															_lowercase					       =					self.ets[-1]
									elif len(self.ets  ) == 2:
															_lowercase					       =					(3 * self.ets[-1] - self.ets[-2]) / 2
									elif len(self.ets  ) == 3:
															_lowercase					       =					(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
									else:
															_lowercase					       =					(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

									_lowercase					       =					self._get_prev_sample(__snake_case				,			__snake_case				,			__snake_case				,			__snake_case  )

									if not return_dict:
															return (prev_sample,)

									return SchedulerOutput(prev_sample=__snake_case  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return sample






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					self.alphas[timestep_index]
									_lowercase					       =					self.betas[timestep_index]

									_lowercase					       =					self.alphas[prev_timestep_index]
									_lowercase					       =					self.betas[prev_timestep_index]

									_lowercase					       =					(sample - sigma * ets) / max(__snake_case				,			1E-8  )
									_lowercase					       =					next_alpha * pred + ets * next_sigma

									return prev_sample






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






'''simple docstring'''






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream




class 					UpperCAmelCase	(     snake_case_  ):




      def __init__(     self     :					Union[str, Any]     ,     __snake_case     :					Callable     ,     __snake_case     :					Optional[Features] = None     ,     __snake_case     :					str = None     ,     __snake_case     :					bool = False     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional[dict] = None     ,     __snake_case     :					Optional[int] = None     ,     **__snake_case     :					Optional[Any]     ,     )							-> Union[str, Any]:
            super().__init__(
                features=__snake_case     ,     cache_dir=__snake_case     ,     keep_in_memory=__snake_case     ,     streaming=__snake_case     ,     num_proc=__snake_case     ,     **__snake_case     ,     )
            _lowerCAmelCase					=				Generator(
                cache_dir=__snake_case     ,     features=__snake_case     ,     generator=__snake_case     ,     gen_kwargs=__snake_case     ,     **__snake_case     ,     )




      def 							lowercase__       (     self     :					str    )							-> Dict:
            # Build iterable dataset
            if self.streaming:
                  _lowerCAmelCase					=				self.builder.as_streaming_dataset(split="""train"""    )
            # Build regular (map-style) dataset
            else:
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None

                  self.builder.download_and_prepare(
                      download_config=__snake_case     ,     download_mode=__snake_case     ,     verification_mode=__snake_case     ,     base_path=__snake_case     ,     num_proc=self.num_proc     ,     )
                  _lowerCAmelCase					=				self.builder.as_dataset(
                      split="""train"""     ,     verification_mode=__snake_case     ,     in_memory=self.keep_in_memory    )
            return dataset






