





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     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase				):

   '''simple docstring'''


   a						:							Optional[Any]   		=		1




   @register_to_config
   def __init__(    self,		__magic_name__ = 1000,		__magic_name__ = None							) ->      Tuple:






       """simple docstring"""
       # set `betas`, `alphas`, `timesteps`
       self.set_timesteps(__magic_name__							)

       # standard deviation of the initial noise distribution
       UpperCamelCase__							:				List[Any]          =      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__							:				Optional[int]          =      4

       # running values
       UpperCamelCase__							:				Optional[Any]          =      []




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      num_inference_steps
       UpperCamelCase__							:				Tuple          =      torch.linspace(1,		0,		num_inference_steps + 1							)[:-1]
       UpperCamelCase__							:				Any          =      torch.cat([steps, torch.tensor([0.0]							)]							)

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

       UpperCamelCase__							:				str          =      (1.0 - self.betas**2) ** 0.5

       UpperCamelCase__							:				List[str]          =      (torch.atana(self.betas,		self.alphas							) / math.pi * 2)[:-1]
       UpperCamelCase__							:				int          =      timesteps.to(__magic_name__							)

       UpperCamelCase__							:				Tuple          =      []




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = True,		) ->      Union[SchedulerOutput, Tuple]:






       """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'''							)

       UpperCamelCase__							:				str          =      (self.timesteps == timestep).nonzero().item()
       UpperCamelCase__							:				int          =      timestep_index + 1

       UpperCamelCase__							:				Any          =      sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
       self.ets.append(__magic_name__							)

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

       UpperCamelCase__							:				Optional[int]          =      self._get_prev_sample(__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							)

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		*__magic_name__,		**__magic_name__							) ->      torch.FloatTensor:






       """simple docstring"""
       return sample




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      self.alphas[timestep_index]
       UpperCamelCase__							:				Optional[int]          =      self.betas[timestep_index]

       UpperCamelCase__							:				int          =      self.alphas[prev_timestep_index]
       UpperCamelCase__							:				Dict          =      self.betas[prev_timestep_index]

       UpperCamelCase__							:				Any          =      (sample - sigma * ets) / max(__magic_name__,		1E-8							)
       UpperCamelCase__							:				Any          =      next_alpha * pred + ets * next_sigma

       return prev_sample




   def __len__(    self							) ->      Dict:






       """simple docstring"""
       return self.config.num_train_timesteps








import re
from pathlib import Path
from unittest import TestCase

import pytest






@pytest.mark.integration
class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Union[str, Any]:






       """simple docstring"""

       with open(__magic_name__,		encoding='''utf-8'''							) as input_file:
           UpperCamelCase__							:				Tuple          =      re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''							)
           UpperCamelCase__							:				str          =      input_file.read()
           UpperCamelCase__							:				List[Any]          =      regexp.search(__magic_name__							)

       return match




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Any:






       """simple docstring"""

       with open(__magic_name__,		encoding='''utf-8'''							) as input_file:
           UpperCamelCase__							:				Dict          =      re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()''',		re.DOTALL							)
           UpperCamelCase__							:				Any          =      input_file.read()
           # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
           UpperCamelCase__							:				Tuple          =      regexp.finditer(__magic_name__							)

       UpperCamelCase__							:				Dict          =      [match for match in matches if match is not None and match.group(1							) is not None]
       return matches[0] if matches else None




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      Path('''./datasets'''							)
       UpperCamelCase__							:				Any          =      list(dataset_paths.absolute().glob('''**/*.py'''							)							)

       for dataset in dataset_files:
           if self._no_encoding_on_file_open(str(__magic_name__							)							):
               raise AssertionError(f"open(...) must use utf-8 encoding in {dataset}"							)




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      Path('''./datasets'''							)
       UpperCamelCase__							:				Optional[Any]          =      list(dataset_paths.absolute().glob('''**/*.py'''							)							)

       for dataset in dataset_files:
           if self._no_print_statements(str(__magic_name__							)							):
               raise AssertionError(f"print statement found in {dataset}. Use datasets.logger/logging instead."							)

