"""simple docstring"""


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					(		lowerCAmelCase_						,				lowerCAmelCase_ ):
    _snake_case  :	Any  				=				1


    @register_to_config
    def __init__(       self						:	List[Any] ,					__lowerCAmelCase						:	int = 1000 ,					__lowerCAmelCase						:	Optional[Union[np.ndarray, List[float]]] = None       ):
      # set `betas`, `alphas`, `timesteps`
      self.set_timesteps(__lowerCAmelCase       )

      # standard deviation of the initial noise distribution
      _UpperCAmelCase    			=   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.
      _UpperCAmelCase    			=   4

      # running values
      _UpperCAmelCase    			=   []


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Union[str, torch.device] = None       ):
      _UpperCAmelCase    			=   num_inference_steps
      _UpperCAmelCase    			=   torch.linspace(1 ,					0 ,					num_inference_steps + 1       )[:-1]
      _UpperCAmelCase    			=   torch.cat([steps, torch.tensor([0.0]       )]       )

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

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

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

      _UpperCAmelCase    			=   []


    def   lowerCAmelCase_	(       self						:	Optional[int] ,					__lowerCAmelCase						:	torch.FloatTensor ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	torch.FloatTensor ,					__lowerCAmelCase						:	bool = 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"""       )

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

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

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

      _UpperCAmelCase    			=   self._get_prev_sample(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )

      if not return_dict:
        return (prev_sample,)

      return SchedulerOutput(prev_sample=__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Optional[int] ,					__lowerCAmelCase						:	torch.FloatTensor ,					*__lowerCAmelCase						:	Any ,					**__lowerCAmelCase						:	Dict       ):
      return sample


    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	str       ):
      _UpperCAmelCase    			=   self.alphas[timestep_index]
      _UpperCAmelCase    			=   self.betas[timestep_index]

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

      _UpperCAmelCase    			=   (sample - sigma * ets) / max(__lowerCAmelCase ,					1e-8       )
      _UpperCAmelCase    			=   next_alpha * pred + ets * next_sigma

      return prev_sample


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

"""simple docstring"""


import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs






@pytest.mark.parametrize(
    """kwargs, expected""" ,[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10		)]),
        ({"""num_shards""": 10, """max_num_jobs""": 10}, [range(lowercase ,i + 1		) for i in range(10		)]),
        ({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1		)]),
        ({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0 ,4		), range(4 ,7		), range(7 ,10		)]),
        ({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0 ,1		), range(1 ,2		), range(2 ,3		)]),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   _distribute_shards(**lowercase		)
  assert out == expected






@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected""" ,[
        ({"""foo""": 0}, 10, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   _split_gen_kwargs(lowercase ,lowercase		)
  assert out == expected






@pytest.mark.parametrize(
    """gen_kwargs, expected""" ,[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  if expected is RuntimeError:
    with pytest.raises(lowercase		):
      _number_of_shards_in_gen_kwargs(lowercase		)
  else:
    _UpperCAmelCase    			=   _number_of_shards_in_gen_kwargs(lowercase		)
    assert out == expected
