





from typing import List

import numpy as np


def _A       (   __magic_name__	):
   lowercase__			       =  {key: len(UpperCamelCase__	) for key, value in gen_kwargs.items() if isinstance(UpperCamelCase__  , UpperCamelCase__	)}
   if len(set(lists_lengths.values()	)	) > 1:
      raise RuntimeError(
          (
              "Sharding is ambiguous for this dataset: "
              + "we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n"
              + "\n".join(f'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()	)
              + "\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, "
              + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
          )	)
   lowercase__			       =  max(lists_lengths.values()  , default=0	)
   return max(1  , UpperCamelCase__	)


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  []
   for group_idx in range(UpperCamelCase__	):
      lowercase__			       =  num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
      if num_shards_to_add == 0:
         break
      lowercase__			       =  shards_indices_per_group[-1].stop if shards_indices_per_group else 0
      lowercase__			       =  range(UpperCamelCase__  , start + num_shards_to_add	)
      shards_indices_per_group.append(UpperCamelCase__	)
   return shards_indices_per_group


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  _number_of_shards_in_gen_kwargs(UpperCamelCase__	)
   if num_shards == 1:
      return [dict(UpperCamelCase__	)]
   else:
      lowercase__			       =  _distribute_shards(num_shards=UpperCamelCase__  , max_num_jobs=UpperCamelCase__	)
      return [
          {
              key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
              if isinstance(UpperCamelCase__  , UpperCamelCase__	)
              else value
              for key, value in gen_kwargs.items()
          }
          for group_idx in range(len(UpperCamelCase__	)	)
      ]


def _A       (   __magic_name__	):
   return {
       key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
       if isinstance(gen_kwargs_list[0][key]  , UpperCamelCase__	)
       else gen_kwargs_list[0][key]
       for key in gen_kwargs_list[0]
   }







def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  {len(UpperCamelCase__	) for value in gen_kwargs.values() if isinstance(UpperCamelCase__  , UpperCamelCase__	)}
   lowercase__			       =  {}
   for size in list_sizes:
      lowercase__			       =  list(range(UpperCamelCase__	)	)
      rng.shuffle(indices_per_size[size]	)
   # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
   lowercase__			       =  dict(UpperCamelCase__	)
   for key, value in shuffled_kwargs.items():
      if isinstance(UpperCamelCase__  , UpperCamelCase__	):
         lowercase__			       =  [value[i] for i in indices_per_size[len(UpperCamelCase__	)]]
   return shuffled_kwargs












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  lowerCAmelCase							(       lowercase_							,							lowercase_ ):
  __lowerCamelCase		=      1







  @register_to_config
  def __init__(      self  :Dict   ,							_lowercase  :int = 10_00   ,							_lowercase  :Optional[Union[np.ndarray, List[float]]] = None ):






     '''simple docstring'''





     self.set_timesteps(_lowercase )

     # 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  :str   ,							_lowercase  :int   ,							_lowercase  :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(_lowercase )

     lowercase__			       =  []







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :torch.FloatTensor   ,							_lowercase  :int   ,							_lowercase  :torch.FloatTensor   ,							_lowercase  :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(_lowercase )

     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(_lowercase   ,							_lowercase   ,							_lowercase   ,							_lowercase )

     if not return_dict:
        return (prev_sample,)

     return SchedulerOutput(prev_sample=_lowercase )







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :torch.FloatTensor   ,							*_lowercase  :int   ,							**_lowercase  :int ):






     '''simple docstring'''





     return sample







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :Tuple   ,							_lowercase  :int   ,							_lowercase  :Optional[Any]   ,							_lowercase  :List[str] ):






     '''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(_lowercase   ,							1e-8 )
     lowercase__			       =  next_alpha * pred + ets * next_sigma

     return prev_sample







  def __len__(      self  :Union[str, Any] ):






     '''simple docstring'''





     return self.config.num_train_timesteps






