






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       _UpperCAmelCase					(       A__		,A__	):

				"""simple docstring"""




				lowercase__											=       1




				@register_to_config
				def __init__(     self						:    Any,  lowerCamelCase						:    int = 1_000,  lowerCamelCase						:    Optional[Union[np.ndarray, List[float]]] = None  ):

								'''simple docstring'''
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(lowerCamelCase  )

								# 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 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    int,  lowerCamelCase						:    Union[str, torch.device] = None  ):

								'''simple docstring'''
								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(lowerCamelCase  )

								lowercase__    =			[]




				def 				lowercase__   (     self						:    Optional[int],  lowerCamelCase						:    torch.FloatTensor,  lowerCamelCase						:    int,  lowerCamelCase						:    torch.FloatTensor,  lowerCamelCase						:    bool = True,  ):

								'''simple docstring'''
								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(lowerCamelCase  )

								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(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase  )




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    torch.FloatTensor,  *lowerCamelCase						:    List[str],  **lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								return sample




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    List[Any],  lowerCamelCase						:    str,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								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(lowerCamelCase,  1E-8  )
								lowercase__    =			next_alpha * pred + ets * next_sigma

								return prev_sample




				def __len__(     self						:    List[Any]  ):

								'''simple docstring'''
								return self.config.num_train_timesteps












from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    Dict,  lowerCamelCase						:    pyspark.sql.DataFrame,  lowerCamelCase						:    Optional[NamedSplit] = None,  lowerCamelCase						:    Optional[Features] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = "arrow",  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(
								    split=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  keep_in_memory=lowerCamelCase,  streaming=lowerCamelCase,  **lowerCamelCase,  )
								lowercase__    =			load_from_cache_file
								lowercase__    =			file_format
								lowercase__    =			Spark(
								    df=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  working_dir=lowerCamelCase,  **lowerCamelCase,  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								if self.streaming:
												return self.builder.as_streaming_dataset(split=self.split  )
								lowercase__    =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
								self.builder.download_and_prepare(
								    download_mode=lowerCamelCase,  file_format=self._file_format,  )
								return self.builder.as_dataset(split=self.split  )




