



"""simple docstring"""




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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase    							=  logging.get_logger(__name__)

_UpperCamelCase    							=  {
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

_UpperCamelCase    							=  {
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

_UpperCamelCase    							=  {'stas/tiny-wmt19-en-de': 1_0_2_4}
_UpperCamelCase    							=  {
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 1_0_2_4,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}


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






      '''simple docstring'''


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


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






      '''simple docstring'''


      lowercase  :      List[Any]					=   text.replace('，'	,      ','				)
      lowercase  :      Union[str, Any]					=   re.sub(R'。\s*'	,      '. '	,      _UpperCAmelCase				)
      lowercase  :      Optional[Any]					=   text.replace('、'	,      ','				)
      lowercase  :      Optional[Any]					=   text.replace('”'	,      '"'				)
      lowercase  :      int					=   text.replace('“'	,      '"'				)
      lowercase  :      Optional[int]					=   text.replace('∶'	,      ':'				)
      lowercase  :      List[str]					=   text.replace('：'	,      ':'				)
      lowercase  :      Dict					=   text.replace('？'	,      '?'				)
      lowercase  :      Union[str, Any]					=   text.replace('《'	,      '"'				)
      lowercase  :      List[Any]					=   text.replace('》'	,      '"'				)
      lowercase  :      Dict					=   text.replace('）'	,      ')'				)
      lowercase  :      int					=   text.replace('！'	,      '!'				)
      lowercase  :      Tuple					=   text.replace('（'	,      '('				)
      lowercase  :      int					=   text.replace('；'	,      ';'				)
      lowercase  :      int					=   text.replace('１'	,      '1'				)
      lowercase  :      Optional[Any]					=   text.replace('」'	,      '"'				)
      lowercase  :      Union[str, Any]					=   text.replace('「'	,      '"'				)
      lowercase  :      Optional[int]					=   text.replace('０'	,      '0'				)
      lowercase  :      int					=   text.replace('３'	,      '3'				)
      lowercase  :      Optional[Any]					=   text.replace('２'	,      '2'				)
      lowercase  :      Optional[int]					=   text.replace('５'	,      '5'				)
      lowercase  :      Tuple					=   text.replace('６'	,      '6'				)
      lowercase  :      List[Any]					=   text.replace('９'	,      '9'				)
      lowercase  :      Dict					=   text.replace('７'	,      '7'				)
      lowercase  :      Tuple					=   text.replace('８'	,      '8'				)
      lowercase  :      Union[str, Any]					=   text.replace('４'	,      '4'				)
      lowercase  :      str					=   re.sub(R'．\s*'	,      '. '	,      _UpperCAmelCase				)
      lowercase  :      Any					=   text.replace('～'	,      '~'				)
      lowercase  :      str					=   text.replace('’'	,      '\''				)
      lowercase  :      Tuple					=   text.replace('…'	,      '...'				)
      lowercase  :      str					=   text.replace('━'	,      '-'				)
      lowercase  :      List[Any]					=   text.replace('〈'	,      '<'				)
      lowercase  :      Tuple					=   text.replace('〉'	,      '>'				)
      lowercase  :      int					=   text.replace('【'	,      '['				)
      lowercase  :      Optional[int]					=   text.replace('】'	,      ']'				)
      lowercase  :      List[Any]					=   text.replace('％'	,      '%'				)
      return text


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






      '''simple docstring'''


      lowercase  :      List[Any]					=   []
      for char in text:
            lowercase  :      Optional[Any]					=   unicodedata.category(_UpperCAmelCase				)
            if cat.startswith('C'				):
                  continue
            output.append(_UpperCAmelCase				)
      return "".join(_UpperCAmelCase				)





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


   def __init__(							self      :							int,     lowerCAmelCase      :							Tuple=None,     lowerCAmelCase      :							Tuple=None,     lowerCAmelCase      :							Dict=None,     lowerCAmelCase      :							int=None,     lowerCAmelCase      :							List[str]=False,     lowerCAmelCase      :							Dict="<unk>",     lowerCAmelCase      :							Optional[Any]="<s>",     lowerCAmelCase      :							str="</s>",     lowerCAmelCase      :							Optional[int]="<pad>",     **lowerCAmelCase      :							Union[str, Any],     )    ->       Any:
         super().__init__(
             langs=lowerCAmelCase,     src_vocab_file=lowerCAmelCase,     tgt_vocab_file=lowerCAmelCase,     merges_file=lowerCAmelCase,     do_lower_case=lowerCAmelCase,     unk_token=lowerCAmelCase,     bos_token=lowerCAmelCase,     sep_token=lowerCAmelCase,     pad_token=lowerCAmelCase,     **lowerCAmelCase,     )

         try:
               import sacremoses
         except ImportError:
               raise ImportError(
                   'You need to install sacremoses to use XLMTokenizer. '
                   'See https://pypi.org/project/sacremoses/ for installation.'			)

         lowercase  :      Optional[Any]					=   sacremoses

         lowercase  :      List[Any]					=   src_vocab_file
         lowercase  :      str					=   tgt_vocab_file
         lowercase  :      Union[str, Any]					=   merges_file
         lowercase  :      Tuple					=   do_lower_case

         # cache of sm.MosesPunctNormalizer instance
         lowercase  :      Optional[int]					=   {}
         # cache of sm.MosesTokenizer instance
         lowercase  :      Tuple					=   {}
         lowercase  :      List[Any]					=   {}

         if langs and len(lowerCAmelCase			) == 2:
               lowercase  :      str					=   langs
         else:
               raise ValueError(
                   f'''arg `langs` needs to be a list of 2 langs, e.g. [\'en\', \'ru\'], but got {langs}. '''
                   'Usually that means that tokenizer can\'t find a mapping for the given model path '
                   'in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer.'			)

         with open(lowerCAmelCase,     encoding='utf-8'			) as src_vocab_handle:
               lowercase  :      Union[str, Any]					=   json.load(lowerCAmelCase			)
         with open(lowerCAmelCase,     encoding='utf-8'			) as tgt_vocab_handle:
               lowercase  :      Any					=   json.load(lowerCAmelCase			)
               lowercase  :      Union[str, Any]					=   {v: k for k, v in tgt_vocab.items()}
         with open(lowerCAmelCase,     encoding='utf-8'			) as merges_handle:
               lowercase  :      List[Any]					=   merges_handle.read().split('\n'			)[:-1]
         lowercase  :      Tuple					=   [tuple(merge.split()[:2]			) for merge in merges]
         lowercase  :      Dict					=   dict(zip(lowerCAmelCase,     range(len(lowerCAmelCase			)			)			)			)
         lowercase  :      Optional[Any]					=   {}


   def        lowercase				(							self      :							Optional[Any]			)    ->       Dict[str, int]:
         return self.get_src_vocab()


   @property
   def        lowercase				(							self      :							Dict			)    ->       int:
         return self.src_vocab_size


   def        lowercase				(							self      :							Optional[int],     lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							List[str]			)    ->       List[str]:
         if lang not in self.cache_moses_punct_normalizer:
               lowercase  :      List[Any]					=   self.sm.MosesPunctNormalizer(lang=lowerCAmelCase			)
               lowercase  :      List[str]					=   punct_normalizer
         return self.cache_moses_punct_normalizer[lang].normalize(lowerCAmelCase			)


   def        lowercase				(							self      :							Any,     lowerCAmelCase      :							Union[str, Any],     lowerCAmelCase      :							Union[str, Any]			)    ->       Dict:
         if lang not in self.cache_moses_tokenizer:
               lowercase  :      Dict					=   self.sm.MosesTokenizer(lang=lowerCAmelCase			)
               lowercase  :      List[str]					=   moses_tokenizer
         return self.cache_moses_tokenizer[lang].tokenize(
             lowerCAmelCase,     aggressive_dash_splits=lowerCAmelCase,     return_str=lowerCAmelCase,     escape=lowerCAmelCase			)


   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							Union[str, Any],     lowerCAmelCase      :							Any			)    ->       Union[str, Any]:
         if lang not in self.cache_moses_detokenizer:
               lowercase  :      Dict					=   self.sm.MosesDetokenizer(lang=lowerCAmelCase			)
               lowercase  :      str					=   moses_detokenizer
         return self.cache_moses_detokenizer[lang].detokenize(lowerCAmelCase			)


   def        lowercase				(							self      :							int,     lowerCAmelCase      :							Dict,     lowerCAmelCase      :							Tuple			)    ->       Union[str, Any]:
         lowercase  :      Tuple					=   replace_unicode_punct(lowerCAmelCase			)
         lowercase  :      int					=   self.moses_punct_norm(lowerCAmelCase,     lowerCAmelCase			)
         lowercase  :      Union[str, Any]					=   remove_non_printing_char(lowerCAmelCase			)
         return text


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


   @property
   def        lowercase				(							self      :							int			)    ->       Dict:
         return len(self.decoder			)


   def        lowercase				(							self      :							int			)    ->       str:
         return dict(self.encoder,     **self.added_tokens_encoder			)


   def        lowercase				(							self      :							int			)    ->       Optional[int]:
         return dict(self.decoder,     **self.added_tokens_decoder			)


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

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

         while True:
               lowercase  :      List[str]					=   min(lowerCAmelCase,     key=lambda lowerCAmelCase			: self.bpe_ranks.get(lowerCAmelCase,     float('inf'			)			)			)
               if bigram not in self.bpe_ranks:
                     break
               lowercase  :      Any					=   bigram
               lowercase  :      int					=   []
               lowercase  :      Optional[int]					=   0
               while i < len(lowerCAmelCase			):
                     try:
                           lowercase  :      Dict					=   word.index(lowerCAmelCase,     lowerCAmelCase			)
                     except ValueError:
                           new_word.extend(word[i:]			)
                           break
                     else:
                           new_word.extend(word[i:j]			)
                           lowercase  :      Optional[int]					=   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  :      Union[str, Any]					=   tuple(lowerCAmelCase			)
               lowercase  :      Any					=   new_word
               if len(lowerCAmelCase			) == 1:
                     break
               else:
                     lowercase  :      Optional[Any]					=   get_pairs(lowerCAmelCase			)
         lowercase  :      Optional[int]					=   ' '.join(lowerCAmelCase			)
         if word == "\n  </w>":
               lowercase  :      List[str]					=   '\n</w>'
         lowercase  :      Optional[int]					=   word
         return word


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Optional[int]="en",     lowerCAmelCase      :							List[str]=False			)    ->       Any:
         lowercase  :      Dict					=   self.src_lang

         if self.do_lower_case:
               lowercase  :      Union[str, Any]					=   text.lower()

         if bypass_tokenizer:
               lowercase  :      str					=   text.split()
         else:
               lowercase  :      List[Any]					=   self.moses_pipeline(lowerCAmelCase,     lang=lowerCAmelCase			)
               lowercase  :      int					=   self.moses_tokenize(lowerCAmelCase,     lang=lowerCAmelCase			)

         lowercase  :      Union[str, Any]					=   []
         for token in text:
               if token:
                     split_tokens.extend(list(self.bpe(lowerCAmelCase			).split(' '			)			)			)

         return split_tokens


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


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


   def        lowercase				(							self      :							List[Any],     lowerCAmelCase      :							List[str]			)    ->       List[Any]:
         lowercase  :      Dict					=   [t.replace(' ',     ''			).replace('</w>',     ' '			) for t in tokens]
         lowercase  :      List[Any]					=   ''.join(lowerCAmelCase			).split()
         # detokenize
         lowercase  :      Optional[int]					=   self.moses_detokenize(lowerCAmelCase,     self.tgt_lang			)
         return text


   def        lowercase				(							self      :							List[Any],     lowerCAmelCase      :							List[int],     lowerCAmelCase      :							Optional[List[int]] = None			)    ->       List[int]:
         lowercase  :      Tuple					=   [self.sep_token_id]

         # no bos used in fairseq
         if token_ids_a is None:
               return token_ids_a + sep
         return token_ids_a + sep + token_ids_a + sep


   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							List[int],     lowerCAmelCase      :							Optional[List[int]] = None,     lowerCAmelCase      :							bool = False			)    ->       List[int]:

         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCAmelCase,     token_ids_a=lowerCAmelCase,     already_has_special_tokens=lowerCAmelCase			)
         # no bos used in fairseq
         if token_ids_a is not None:
               return ([0] * len(lowerCAmelCase			)) + [1] + ([0] * len(lowerCAmelCase			)) + [1]
         return ([0] * len(lowerCAmelCase			)) + [1]


   def        lowercase				(							self      :							Union[str, Any],     lowerCAmelCase      :							List[int],     lowerCAmelCase      :							Optional[List[int]] = None			)    ->       List[int]:
         lowercase  :      str					=   [self.sep_token_id]

         # no bos used in fairseq
         if token_ids_a is None:
               return len(token_ids_a + sep			) * [0]
         return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]


   def        lowercase				(							self      :							Tuple,     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  :      Tuple					=   os.path.join(
             lowerCAmelCase,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['src_vocab_file']			)
         lowercase  :      List[Any]					=   os.path.join(
             lowerCAmelCase,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['tgt_vocab_file']			)
         lowercase  :      List[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'			)

         with open(lowerCAmelCase,     'w',     encoding='utf-8'			) as f:
               lowercase  :      Dict					=   {v: k for k, v in self.decoder.items()}
               f.write(json.dumps(lowerCAmelCase,     indent=2,     sort_keys=lowerCAmelCase,     ensure_ascii=lowerCAmelCase			) + '\n'			)

         lowercase  :      List[Any]					=   0
         with open(lowerCAmelCase,     'w',     encoding='utf-8'			) as writer:
               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 {merges_file}: BPE merge indices are not consecutive.'''
                               ' Please check that the tokenizer is not corrupted!'			)
                           lowercase  :      List[Any]					=   token_index
                     writer.write(' '.join(lowerCAmelCase			) + '\n'			)
                     index += 1

         return src_vocab_file, tgt_vocab_file, merges_file


   def __getstate__(							self      :							str			)    ->       List[str]:
         lowercase  :      Optional[int]					=   self.__dict__.copy()
         lowercase  :      int					=   None
         return state






   def __setstate__(							self      :							int,     lowerCAmelCase      :							List[str]			)    ->       Optional[Any]:
         lowercase  :      int					=   d

         try:
               import sacremoses
         except ImportError:
               raise ImportError(
                   'You need to install sacremoses to use XLMTokenizer. '
                   'See https://pypi.org/project/sacremoses/ for installation.'			)

         lowercase  :      Union[str, Any]					=   sacremoses





"""simple docstring"""




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				(HeunDiscreteScheduler,)
   _lowerCamelCase												=				10


   def        lowercase				(							self      :							List[Any],     **lowerCAmelCase      :							Optional[int]			)    ->       Dict:
         lowercase  :      List[Any]					=   {
             'num_train_timesteps': 1100,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**lowerCAmelCase			)
         return config


   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:
         for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase			)


   def        lowercase				(							self      :							Any			)    ->       Any:
         for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001],     [0.0002, 0.002, 0.02]			):
               self.check_over_configs(beta_start=lowerCAmelCase,     beta_end=lowerCAmelCase			)


   def        lowercase				(							self      :							Union[str, Any]			)    ->       Any:
         for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[int]			)    ->       Dict:
         for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCAmelCase,     clip_sample=lowerCAmelCase			)


   def        lowercase				(							self      :							Dict			)    ->       Union[str, Any]:
         for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=lowerCAmelCase			)


   def        lowercase				(							self      :							str			)    ->       Tuple:
         lowercase  :      Tuple					=   self.scheduler_classes[0]
         lowercase  :      int					=   self.get_scheduler_config()
         lowercase  :      str					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      Tuple					=   self.dummy_model()
         lowercase  :      Dict					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      Optional[Any]					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      Any					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Union[str, Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Tuple					=   output.prev_sample

         lowercase  :      Tuple					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Tuple			)    ->       List[str]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      Any					=   self.get_scheduler_config(prediction_type='v_prediction'			)
         lowercase  :      List[str]					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      List[Any]					=   self.dummy_model()
         lowercase  :      List[Any]					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      List[str]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Any					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      List[str]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6934e-07			) < 1e-2
               assert abs(result_mean.item() - 6.1112e-10			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 4.693_4286_5017_0972e-07			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Optional[Any]			)    ->       Tuple:
         lowercase  :      Optional[int]					=   self.scheduler_classes[0]
         lowercase  :      Optional[int]					=   self.get_scheduler_config()
         lowercase  :      Dict					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps,     device=lowerCAmelCase			)

         lowercase  :      int					=   self.dummy_model()
         lowercase  :      Tuple					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
               lowercase  :      Optional[Any]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Optional[int]					=   output.prev_sample

         lowercase  :      str					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if str(lowerCAmelCase			).startswith('cpu'			):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         elif str(lowerCAmelCase			).startswith('mps'			):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.0002			) < 1e-2
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      str					=   self.get_scheduler_config()
         lowercase  :      Tuple					=   scheduler_class(**lowerCAmelCase,     use_karras_sigmas=lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps,     device=lowerCAmelCase			)

         lowercase  :      Dict					=   self.dummy_model()
         lowercase  :      Any					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for t in scheduler.timesteps:
               lowercase  :      int					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      int					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      str					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Union[str, Any]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         assert abs(result_sum.item() - 0.0_0015			) < 1e-2
         assert abs(result_mean.item() - 1.9869_5545_3503_4695e-07			) < 1e-2
