'''simple docstring'''


import copy
import re






class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		'''hp'''
  lowerCamelCase_		:Optional[int]						   =		{}
  lowerCamelCase_		:str						   =		None


  @classmethod
  def _UpperCamelCase					(			cls				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				prefix
       UpperCAmelCase_							:       int       =				defaults
       cls.build_naming_info()


  @staticmethod
  def _UpperCamelCase					(			snake_case_				,		snake_case_						):




       '''simple docstring'''

       if len(snake_case_						) == 0:
            return ""
       UpperCAmelCase_							:       Any       =				None
       if any(char.isdigit() for char in word						):
            raise Exception(F'''Parameters should not contain numbers: \'{word}\' contains a number'''						)
       if word in info["short_word"]:
            return info["short_word"][word]
       for prefix_len in range(1				,		len(snake_case_						) + 1						):
            UpperCAmelCase_							:       str       =				word[:prefix_len]
            if prefix in info["reverse_short_word"]:
                 continue
            else:
                 UpperCAmelCase_							:       Dict       =				prefix
                 break

       if short_word is None:
            # Paranoid fallback
            def int_to_alphabetic(snake_case_						):
                 UpperCAmelCase_							:       Any       =				''
                 while integer != 0:
                      UpperCAmelCase_							:       str       =				chr(ord('A'						) + integer % 1_0						) + s
                      integer //= 1_0
                 return s

            UpperCAmelCase_							:       Optional[int]       =				0
            while True:
                 UpperCAmelCase_							:       Tuple       =				word + '#' + int_to_alphabetic(snake_case_						)
                 if sword in info["reverse_short_word"]:
                      continue
                 else:
                      UpperCAmelCase_							:       Optional[Any]       =				sword
                      break

       UpperCAmelCase_							:       Union[str, Any]       =				short_word
       UpperCAmelCase_							:       str       =				word
       return short_word


  @staticmethod
  def _UpperCamelCase					(			snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				param_name.split('_'						)

       UpperCAmelCase_							:       Tuple       =				[TrialShortNamer.shortname_for_word(snake_case_				,		snake_case_						) for word in words]

       # We try to create a separatorless short name, but if there is a collision we have to fallback
       # to a separated short name
       UpperCAmelCase_							:       List[str]       =				['', '_']

       for separator in separators:
            UpperCAmelCase_							:       str       =				separator.join(snake_case_						)
            if shortname not in info["reverse_short_param"]:
                 UpperCAmelCase_							:       Dict       =				shortname
                 UpperCAmelCase_							:       str       =				param_name
                 return shortname

       return param_name


  @staticmethod
  def _UpperCamelCase					(			snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				TrialShortNamer.shortname_for_key(snake_case_				,		snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				short_name
       UpperCAmelCase_							:       List[str]       =				param_name


  @classmethod
  def _UpperCamelCase					(			cls						):




       '''simple docstring'''

       if cls.NAMING_INFO is not None:
            return

       UpperCAmelCase_							:       Any       =				{
           'short_word': {},
           'reverse_short_word': {},
           'short_param': {},
           'reverse_short_param': {},
       }

       UpperCAmelCase_							:       Union[str, Any]       =				list(cls.DEFAULTS.keys()						)

       for k in field_keys:
            cls.add_new_param_name(snake_case_				,		snake_case_						)

       UpperCAmelCase_							:       Union[str, Any]       =				info


  @classmethod
  def _UpperCamelCase					(			cls				,		snake_case_						):




       '''simple docstring'''

       cls.build_naming_info()
       assert cls.PREFIX is not None
       UpperCAmelCase_							:       Any       =				[copy.copy(cls.PREFIX						)]

       for k, v in params.items():
            if k not in cls.DEFAULTS:
                 raise Exception(F'''You should provide a default value for the param name {k} with value {v}'''						)
            if v == cls.DEFAULTS[k]:
                 # The default value is not added to the name
                 continue

            UpperCAmelCase_							:       Dict       =				cls.NAMING_INFO['short_param'][k]

            if isinstance(snake_case_				,		snake_case_						):
                 UpperCAmelCase_							:       Tuple       =				1 if v else 0

            UpperCAmelCase_							:       Dict       =				'' if isinstance(snake_case_				,		(int, float)						) else '-'
            UpperCAmelCase_							:       str       =				F'''{key}{sep}{v}'''
            name.append(snake_case_						)

       return "_".join(snake_case_						)






  @classmethod
  def _UpperCamelCase					(			cls				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				repr[len(cls.PREFIX						) + 1 :]
       if repr == "":
            UpperCAmelCase_							:       List[Any]       =				[]
       else:
            UpperCAmelCase_							:       List[Any]       =				repr.split('_'						)

       UpperCAmelCase_							:       Optional[Any]       =				{}

       for value in values:
            if "-" in value:
                 UpperCAmelCase_       ,      UpperCAmelCase_							:       Dict       =				value.split('-'						)
            else:
                 UpperCAmelCase_							:       Union[str, Any]       =				re.sub('[0-9.]'				,		''				,		snake_case_						)
                 UpperCAmelCase_							:       List[Any]       =				float(re.sub('[^0-9.]'				,		''				,		snake_case_						)						)

            UpperCAmelCase_							:       Optional[int]       =				cls.NAMING_INFO['reverse_short_param'][p_k]

            UpperCAmelCase_							:       Tuple       =				p_v

       for k in cls.DEFAULTS:
            if k not in parameters:
                 UpperCAmelCase_							:       Optional[Any]       =				cls.DEFAULTS[k]

       return parameters







'''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 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Union[str, Any]						   =		1


  @register_to_config
  def __init__(			self				,		snake_case_ = 1_0_0_0				,		snake_case_ = None						):




       '''simple docstring'''

       self.set_timesteps(snake_case_						)

       # standard deviation of the initial noise distribution
       UpperCAmelCase_							:       Union[str, 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_							:       int       =				4

       # running values
       UpperCAmelCase_							:       str       =				[]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				num_inference_steps
       UpperCAmelCase_							:       int       =				torch.linspace(1				,		0				,		num_inference_steps + 1						)[:-1]
       UpperCAmelCase_							:       Tuple       =				torch.cat([steps, torch.tensor([0.0]						)]						)

       if self.config.trained_betas is not None:
            UpperCAmelCase_							:       Optional[int]       =				torch.tensor(self.config.trained_betas				,		dtype=torch.floataa						)
       else:
            UpperCAmelCase_							:       Tuple       =				torch.sin(steps * math.pi / 2						) ** 2

       UpperCAmelCase_							:       Dict       =				(1.0 - self.betas**2) ** 0.5

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

       UpperCAmelCase_							:       Any       =				[]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_ = 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'						)

       UpperCAmelCase_							:       Any       =				(self.timesteps == timestep).nonzero().item()
       UpperCAmelCase_							:       Optional[Any]       =				timestep_index + 1

       UpperCAmelCase_							:       Dict       =				sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
       self.ets.append(snake_case_						)

       if len(self.ets						) == 1:
            UpperCAmelCase_							:       Tuple       =				self.ets[-1]
       elif len(self.ets						) == 2:
            UpperCAmelCase_							:       Any       =				(3 * self.ets[-1] - self.ets[-2]) / 2
       elif len(self.ets						) == 3:
            UpperCAmelCase_							:       List[str]       =				(2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
       else:
            UpperCAmelCase_							:       Union[str, Any]       =				(1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

       UpperCAmelCase_							:       Union[str, Any]       =				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				,		snake_case_				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return sample


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.alphas[timestep_index]
       UpperCAmelCase_							:       Union[str, Any]       =				self.betas[timestep_index]

       UpperCAmelCase_							:       Any       =				self.alphas[prev_timestep_index]
       UpperCAmelCase_							:       Dict       =				self.betas[prev_timestep_index]

       UpperCAmelCase_							:       List[Any]       =				(sample - sigma * ets) / max(snake_case_				,		1E-8						)
       UpperCAmelCase_							:       Tuple       =				next_alpha * pred + ets * next_sigma

       return prev_sample






  def __len__(			self						):




       '''simple docstring'''

       return self.config.num_train_timesteps






