



"""simple docstring"""




import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


_UpperCamelCase:				Any    							=  logging.get_logger(__name__)

_UpperCamelCase:				int    							=  {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_UpperCamelCase:				int    							=  {
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
}

_UpperCamelCase:				Dict    							=  {
    'openai-gpt': 5_1_2,
}


def   lowercase__     (			_UpperCAmelCase				)     ->			Any:






      '''simple docstring'''


      lowercase  :      Tuple					=   text.strip()
      if not text:
            return []
      lowercase  :      Optional[int]					=   text.split()
      return tokens


class   a__		(						SCREAMING_SNAKE_CASE__     ):


   def __init__(							self      :							Any,     lowerCAmelCase      :							List[Any]=True,     lowerCAmelCase      :							int=None,     lowerCAmelCase      :							Dict=True,     lowerCAmelCase      :							Optional[Any]=None,     lowerCAmelCase      :							Optional[Any]=True,     )    ->       Dict:
         if never_split is None:
               lowercase  :      Any					=   []
         lowercase  :      List[Any]					=   do_lower_case
         lowercase  :      Optional[Any]					=   set(lowerCAmelCase			)
         lowercase  :      int					=   tokenize_chinese_chars
         lowercase  :      int					=   strip_accents
         lowercase  :      str					=   do_split_on_punc


   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Tuple=None			)    ->       Optional[int]:
         lowercase  :      Tuple					=   self.never_split.union(set(lowerCAmelCase			)			) if never_split else self.never_split
         lowercase  :      int					=   self._clean_text(lowerCAmelCase			)

         # This was added on November 1st, 2018 for the multilingual and Chinese
         # models. This is also applied to the English models now, but it doesn't
         # matter since the English models were not trained on any Chinese data
         # and generally don't have any Chinese data in them (there are Chinese
         # characters in the vocabulary because Wikipedia does have some Chinese
         # words in the English Wikipedia.).
         if self.tokenize_chinese_chars:
               lowercase  :      str					=   self._tokenize_chinese_chars(lowerCAmelCase			)
         # prevents treating the same character with different unicode codepoints as different characters
         lowercase  :      List[Any]					=   unicodedata.normalize('NFC',     lowerCAmelCase			)
         lowercase  :      Dict					=   whitespace_tokenize(lowerCAmelCase			)
         lowercase  :      str					=   []
         for token in orig_tokens:
               if token not in never_split:
                     if self.do_lower_case:
                           lowercase  :      Any					=   token.lower()
                           if self.strip_accents is not False:
                                 lowercase  :      str					=   self._run_strip_accents(lowerCAmelCase			)
                     elif self.strip_accents:
                           lowercase  :      int					=   self._run_strip_accents(lowerCAmelCase			)
               split_tokens.extend(self._run_split_on_punc(lowerCAmelCase,     lowerCAmelCase			)			)

         lowercase  :      Any					=   whitespace_tokenize(' '.join(lowerCAmelCase			)			)
         return output_tokens


   def        lowercase				(							self      :							str,     lowerCAmelCase      :							Optional[Any]			)    ->       int:
         lowercase  :      List[Any]					=   unicodedata.normalize('NFD',     lowerCAmelCase			)
         lowercase  :      List[Any]					=   []
         for char in text:
               lowercase  :      Optional[Any]					=   unicodedata.category(lowerCAmelCase			)
               if cat == "Mn":
                     continue
               output.append(lowerCAmelCase			)
         return "".join(lowerCAmelCase			)


   def        lowercase				(							self      :							List[Any],     lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							Any=None			)    ->       Tuple:
         if not self.do_split_on_punc or (never_split is not None and text in never_split):
               return [text]
         lowercase  :      Optional[int]					=   list(lowerCAmelCase			)
         lowercase  :      int					=   0
         lowercase  :      Tuple					=   True
         lowercase  :      Optional[Any]					=   []
         while i < len(lowerCAmelCase			):
               lowercase  :      Any					=   chars[i]
               if _is_punctuation(lowerCAmelCase			):
                     output.append([char]			)
                     lowercase  :      Any					=   True
               else:
                     if start_new_word:
                           output.append([]			)
                     lowercase  :      Optional[int]					=   False
                     output[-1].append(lowerCAmelCase			)
               i += 1

         return ["".join(lowerCAmelCase			) for x in output]


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							Dict			)    ->       Tuple:
         lowercase  :      Union[str, Any]					=   []
         for char in text:
               lowercase  :      Any					=   ord(lowerCAmelCase			)
               if self._is_chinese_char(lowerCAmelCase			):
                     output.append(' '			)
                     output.append(lowerCAmelCase			)
                     output.append(' '			)
               else:
                     output.append(lowerCAmelCase			)
         return "".join(lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							int			)    ->       List[str]:
         # This defines a "chinese character" as anything in the CJK Unicode block:
         #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
         #
         # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
         # despite its name. The modern Korean Hangul alphabet is a different block,
         # as is Japanese Hiragana and Katakana. Those alphabets are used to write
         # space-separated words, so they are not treated specially and handled
         # like the all of the other languages.
         if (
             (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
             or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
             or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
             or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
             or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
             or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
             or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
             or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
         ):  #
               return True

         return False






   def        lowercase				(							self      :							str,     lowerCAmelCase      :							Optional[Any]			)    ->       Tuple:
         lowercase  :      List[Any]					=   []
         for char in text:
               lowercase  :      Any					=   ord(lowerCAmelCase			)
               if cp == 0 or cp == 0xF_F_F_D or _is_control(lowerCAmelCase			):
                     continue
               if _is_whitespace(lowerCAmelCase			):
                     output.append(' '			)
               else:
                     output.append(lowerCAmelCase			)
         return "".join(lowerCAmelCase			)




def   lowercase__     (			_UpperCAmelCase				)     ->			Optional[Any]:






      '''simple docstring'''


      lowercase  :      str					=   set()
      lowercase  :      Optional[int]					=   word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)				)
            lowercase  :      List[Any]					=   char
      return pairs


def   lowercase__     (			_UpperCAmelCase				)     ->			Optional[int]:






      '''simple docstring'''


      lowercase  :      str					=   text.replace('—'	,      '-'				)
      lowercase  :      Any					=   text.replace('–'	,      '-'				)
      lowercase  :      Union[str, Any]					=   text.replace('―'	,      '-'				)
      lowercase  :      List[str]					=   text.replace('…'	,      '...'				)
      lowercase  :      Optional[int]					=   text.replace('´'	,      '\''				)
      lowercase  :      List[Any]					=   re.sub(R'(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'	,      R' \1 '	,      _UpperCAmelCase				)
      lowercase  :      Optional[int]					=   re.sub(R'\s*\n\s*'	,      ' \n '	,      _UpperCAmelCase				)
      lowercase  :      List[str]					=   re.sub(R'[^\S\n]+'	,      ' '	,      _UpperCAmelCase				)
      return text.strip()





class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				VOCAB_FILES_NAMES
   _lowerCamelCase												=				PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase												=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowerCamelCase												=				['input_ids', 'attention_mask']


   def __init__(							self      :							Optional[int],     lowerCAmelCase      :							Optional[int],     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							int="<unk>",     **lowerCAmelCase      :							Optional[Any]			)    ->       List[Any]:
         super().__init__(unk_token=lowerCAmelCase,     **lowerCAmelCase			)

         try:
               import ftfy
               from spacy.lang.en import English

               lowercase  :      List[Any]					=   English()
               lowercase  :      int					=   _nlp.tokenizer
               lowercase  :      Union[str, Any]					=   ftfy.fix_text
         except ImportError:
               logger.warning('ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'			)
               lowercase  :      int					=   BasicTokenizer(do_lower_case=lowerCAmelCase			)
               lowercase  :      Optional[int]					=   None

         with open(lowerCAmelCase,     encoding='utf-8'			) as vocab_handle:
               lowercase  :      int					=   json.load(lowerCAmelCase			)
         lowercase  :      Optional[Any]					=   {v: k for k, v in self.encoder.items()}
         with open(lowerCAmelCase,     encoding='utf-8'			) as merges_handle:
               lowercase  :      int					=   merges_handle.read().split('\n'			)[1:-1]
         lowercase  :      int					=   [tuple(merge.split()			) for merge in merges]
         lowercase  :      List[Any]					=   dict(zip(lowerCAmelCase,     range(len(lowerCAmelCase			)			)			)			)
         lowercase  :      int					=   {}


   @property
   def        lowercase				(							self      :							Tuple			)    ->       List[Any]:
         return True


   @property
   def        lowercase				(							self      :							List[Any]			)    ->       List[str]:
         return len(self.encoder			)


   def        lowercase				(							self      :							Union[str, Any]			)    ->       Union[str, Any]:
         return dict(self.encoder,     **self.added_tokens_encoder			)


   def        lowercase				(							self      :							Dict,     lowerCAmelCase      :							Any			)    ->       int:
         lowercase  :      Optional[int]					=   tuple(token[:-1]			) + (token[-1] + '</w>',)
         if token in self.cache:
               return self.cache[token]
         lowercase  :      Any					=   get_pairs(lowerCAmelCase			)

         if not pairs:
               return token + "</w>"

         while True:
               lowercase  :      str					=   min(lowerCAmelCase,     key=lambda lowerCAmelCase			: self.bpe_ranks.get(lowerCAmelCase,     float('inf'			)			)			)
               if bigram not in self.bpe_ranks:
                     break
               lowercase	,    lowercase  :      Any					=   bigram
               lowercase  :      int					=   []
               lowercase  :      Optional[Any]					=   0
               while i < len(lowerCAmelCase			):
                     try:
                           lowercase  :      List[Any]					=   word.index(lowerCAmelCase,     lowerCAmelCase			)
                     except ValueError:
                           new_word.extend(word[i:]			)
                           break
                     else:
                           new_word.extend(word[i:j]			)
                           lowercase  :      Tuple					=   j

                     if word[i] == first and i < len(lowerCAmelCase			) - 1 and word[i + 1] == second:
                           new_word.append(first + second			)
                           i += 2
                     else:
                           new_word.append(word[i]			)
                           i += 1
               lowercase  :      str					=   tuple(lowerCAmelCase			)
               lowercase  :      Dict					=   new_word
               if len(lowerCAmelCase			) == 1:
                     break
               else:
                     lowercase  :      Dict					=   get_pairs(lowerCAmelCase			)
         lowercase  :      Any					=   ' '.join(lowerCAmelCase			)
         if word == "\n  </w>":
               lowercase  :      Optional[Any]					=   '\n</w>'
         lowercase  :      Optional[int]					=   word
         return word


   def        lowercase				(							self      :							Dict,     lowerCAmelCase      :							List[str]			)    ->       Tuple:
         lowercase  :      Optional[Any]					=   []
         if self.fix_text is None:
               # Using BERT's BasicTokenizer
               lowercase  :      Dict					=   self.nlp.tokenize(lowerCAmelCase			)
               for token in text:
                     split_tokens.extend(list(self.bpe(lowerCAmelCase			).split(' '			)			)			)
         else:
               # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
               lowercase  :      Any					=   self.nlp(text_standardize(self.fix_text(lowerCAmelCase			)			)			)
               for token in text:
                     split_tokens.extend(list(self.bpe(token.text.lower()			).split(' '			)			)			)
         return split_tokens


   def        lowercase				(							self      :							Union[str, Any],     lowerCAmelCase      :							List[Any]			)    ->       str:
         return self.encoder.get(lowerCAmelCase,     self.encoder.get(self.unk_token			)			)


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							Optional[int]			)    ->       int:
         return self.decoder.get(lowerCAmelCase,     self.unk_token			)


   def        lowercase				(							self      :							Optional[int],     lowerCAmelCase      :							Tuple			)    ->       Dict:
         lowercase  :      Optional[Any]					=   ''.join(lowerCAmelCase			).replace('</w>',     ' '			).strip()
         return out_string






   def        lowercase				(							self      :							Dict,     lowerCAmelCase      :							str,     lowerCAmelCase      :							Optional[str] = None			)    ->       Tuple[str]:
         if not os.path.isdir(lowerCAmelCase			):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
               return
         lowercase  :      Dict					=   os.path.join(
             lowerCAmelCase,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
         lowercase  :      Any					=   os.path.join(
             lowerCAmelCase,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']			)

         with open(lowerCAmelCase,     'w',     encoding='utf-8'			) as f:
               f.write(json.dumps(self.encoder,     indent=2,     sort_keys=lowerCAmelCase,     ensure_ascii=lowerCAmelCase			) + '\n'			)

         lowercase  :      List[str]					=   0
         with open(lowerCAmelCase,     'w',     encoding='utf-8'			) as writer:
               writer.write('#version: 0.2\n'			)
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),     key=lambda lowerCAmelCase			: kv[1]			):
                     if index != token_index:
                           logger.warning(
                               f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                               ' Please check that the tokenizer is not corrupted!'			)
                           lowercase  :      List[str]					=   token_index
                     writer.write(' '.join(lowerCAmelCase			) + '\n'			)
                     index += 1

         return vocab_file, merge_file





"""simple docstring"""




import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_UpperCamelCase:				str    							=  logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				42
   _lowerCamelCase												=				None




def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase=0.9_9_9	,      _UpperCAmelCase="cosine"	,      )     ->			Optional[Any]:






      '''simple docstring'''


      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_UpperCAmelCase				):
                  return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_UpperCAmelCase				):
                  return math.exp(t * -1_2.0				)

      else:
            raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

      lowercase  :      Any					=   []
      for i in range(_UpperCAmelCase				):
            lowercase  :      List[str]					=   i / num_diffusion_timesteps
            lowercase  :      str					=   (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase				) / alpha_bar_fn(_UpperCAmelCase				)	,      _UpperCAmelCase				)				)
      return torch.tensor(_UpperCAmelCase	,      dtype=torch.floataa				)





class   a__		(						SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				[e.name for e in KarrasDiffusionSchedulers]
   _lowerCamelCase												=				1


   @register_to_config
   def __init__(							self      :							str,     lowerCAmelCase      :							int = 1000,     lowerCAmelCase      :							float = 0.0001,     lowerCAmelCase      :							float = 0.02,     lowerCAmelCase      :							str = "linear",     lowerCAmelCase      :							Optional[Union[np.ndarray, List[float]]] = None,     lowerCAmelCase      :							str = "epsilon",     lowerCAmelCase      :							str = "linspace",     lowerCAmelCase      :							int = 0,     )    ->       List[Any]:
         if trained_betas is not None:
               lowercase  :      Union[str, Any]					=   torch.tensor(lowerCAmelCase,     dtype=torch.floataa			)
         elif beta_schedule == "linear":
               lowercase  :      Union[str, Any]					=   torch.linspace(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     dtype=torch.floataa			)
         elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               lowercase  :      str					=   (
                   torch.linspace(beta_start**0.5,     beta_end**0.5,     lowerCAmelCase,     dtype=torch.floataa			) ** 2
               )
         elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               lowercase  :      List[Any]					=   betas_for_alpha_bar(lowerCAmelCase			)
         else:
               raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

         lowercase  :      Any					=   1.0 - self.betas
         lowercase  :      Optional[int]					=   torch.cumprod(self.alphas,     dim=0			)

         lowercase  :      Optional[Any]					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
         lowercase  :      Optional[int]					=   np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
         lowercase  :      int					=   torch.from_numpy(lowerCAmelCase			)

         # setable values
         lowercase  :      List[str]					=   None
         lowercase  :      Tuple					=   np.linspace(0,     num_train_timesteps - 1,     lowerCAmelCase,     dtype=lowerCAmelCase			)[::-1].copy()
         lowercase  :      Dict					=   torch.from_numpy(lowerCAmelCase			)
         lowercase  :      str					=   False


   @property
   def        lowercase				(							self      :							Tuple			)    ->       Tuple:
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5


   def        lowercase				(							self      :							Any,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Union[float, torch.FloatTensor]			)    ->       torch.FloatTensor:
         if isinstance(lowerCAmelCase,     torch.Tensor			):
               lowercase  :      Any					=   timestep.to(self.timesteps.device			)
         lowercase  :      List[Any]					=   (self.timesteps == timestep).nonzero().item()
         lowercase  :      Union[str, Any]					=   self.sigmas[step_index]
         lowercase  :      Dict					=   sample / ((sigma**2 + 1) ** 0.5)
         lowercase  :      Union[str, Any]					=   True
         return sample


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							int,     lowerCAmelCase      :							Union[str, torch.device] = None			)    ->       List[Any]:
         lowercase  :      Union[str, Any]					=   num_inference_steps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
               lowercase  :      int					=   np.linspace(0,     self.config.num_train_timesteps - 1,     lowerCAmelCase,     dtype=lowerCAmelCase			)[
                   ::-1
               ].copy()
         elif self.config.timestep_spacing == "leading":
               lowercase  :      List[str]					=   self.config.num_train_timesteps // self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               lowercase  :      Any					=   (np.arange(0,     lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase			)
               timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
               lowercase  :      Optional[Any]					=   self.config.num_train_timesteps / self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               lowercase  :      Optional[int]					=   (np.arange(self.config.num_train_timesteps,     0,     -step_ratio			)).round().copy().astype(lowerCAmelCase			)
               timesteps -= 1
         else:
               raise ValueError(
                   f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''			)

         lowercase  :      int					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
         lowercase  :      List[str]					=   np.interp(lowerCAmelCase,     np.arange(0,     len(lowerCAmelCase			)			),     lowerCAmelCase			)
         lowercase  :      Any					=   np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
         lowercase  :      Optional[Any]					=   torch.from_numpy(lowerCAmelCase			).to(device=lowerCAmelCase			)
         if str(lowerCAmelCase			).startswith('mps'			):
               # mps does not support float64
               lowercase  :      List[Any]					=   torch.from_numpy(lowerCAmelCase			).to(lowerCAmelCase,     dtype=torch.floataa			)
         else:
               lowercase  :      List[str]					=   torch.from_numpy(lowerCAmelCase			).to(device=lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Union[float, torch.FloatTensor],     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Optional[torch.Generator] = None,     lowerCAmelCase      :							bool = True,     )    ->       Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

         if (
             isinstance(lowerCAmelCase,     lowerCAmelCase			)
             or isinstance(lowerCAmelCase,     torch.IntTensor			)
             or isinstance(lowerCAmelCase,     torch.LongTensor			)
         ):
               raise ValueError(
                   (
                       'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                       ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
                       ' one of the `scheduler.timesteps` as a timestep.'
                   ),     )

         if not self.is_scale_input_called:
               logger.warning(
                   'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                   'See `StableDiffusionPipeline` for a usage example.'			)

         if isinstance(lowerCAmelCase,     torch.Tensor			):
               lowercase  :      List[str]					=   timestep.to(self.timesteps.device			)

         lowercase  :      List[str]					=   (self.timesteps == timestep).nonzero().item()
         lowercase  :      Optional[int]					=   self.sigmas[step_index]

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
               lowercase  :      Optional[Any]					=   sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               lowercase  :      int					=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         elif self.config.prediction_type == "sample":
               raise NotImplementedError('prediction_type not implemented yet: sample'			)
         else:
               raise ValueError(
                   f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''			)

         lowercase  :      List[Any]					=   self.sigmas[step_index]
         lowercase  :      str					=   self.sigmas[step_index + 1]
         lowercase  :      Optional[int]					=   (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
         lowercase  :      Optional[int]					=   (sigma_to**2 - sigma_up**2) ** 0.5

         # 2. Convert to an ODE derivative
         lowercase  :      Tuple					=   (sample - pred_original_sample) / sigma

         lowercase  :      str					=   sigma_down - sigma

         lowercase  :      int					=   sample + derivative * dt

         lowercase  :      Dict					=   model_output.device
         lowercase  :      List[Any]					=   randn_tensor(model_output.shape,     dtype=model_output.dtype,     device=lowerCAmelCase,     generator=lowerCAmelCase			)

         lowercase  :      List[str]					=   prev_sample + noise * sigma_up

         if not return_dict:
               return (prev_sample,)

         return EulerAncestralDiscreteSchedulerOutput(
             prev_sample=lowerCAmelCase,     pred_original_sample=lowerCAmelCase			)


   def        lowercase				(							self      :							int,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							torch.FloatTensor,     )    ->       torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         lowercase  :      Tuple					=   self.sigmas.to(device=original_samples.device,     dtype=original_samples.dtype			)
         if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase			):
               # mps does not support float64
               lowercase  :      Dict					=   self.timesteps.to(original_samples.device,     dtype=torch.floataa			)
               lowercase  :      Union[str, Any]					=   timesteps.to(original_samples.device,     dtype=torch.floataa			)
         else:
               lowercase  :      List[str]					=   self.timesteps.to(original_samples.device			)
               lowercase  :      Tuple					=   timesteps.to(original_samples.device			)

         lowercase  :      Union[str, Any]					=   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

         lowercase  :      Union[str, Any]					=   sigmas[step_indices].flatten()
         while len(sigma.shape			) < len(original_samples.shape			):
               lowercase  :      Dict					=   sigma.unsqueeze(-1			)

         lowercase  :      Optional[Any]					=   original_samples + noise * sigma
         return noisy_samples






   def __len__(							self      :							int			)    ->       List[str]:
         return self.config.num_train_timesteps
