code: 
  
  """simple docstring"""
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
      from .tokenization_xlm_roberta import XLMRobertaTokenizer
  else:
      _A						=     None
  
  
  _A						=     logging.get_logger(__name__)
  
  _A						=     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
  
  _A						=     {
      """vocab_file""": {
          """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
          """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
          """xlm-roberta-large-finetuned-conll02-dutch""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
          ),
          """xlm-roberta-large-finetuned-conll02-spanish""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
          ),
          """xlm-roberta-large-finetuned-conll03-english""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
          ),
          """xlm-roberta-large-finetuned-conll03-german""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
          ),
      },
      """tokenizer_file""": {
          """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json""",
          """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json""",
          """xlm-roberta-large-finetuned-conll02-dutch""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"""
          ),
          """xlm-roberta-large-finetuned-conll02-spanish""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"""
          ),
          """xlm-roberta-large-finetuned-conll03-english""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"""
          ),
          """xlm-roberta-large-finetuned-conll03-german""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"""
          ),
      },
  }
  
  _A						=     {
      """xlm-roberta-base""": 5_12,
      """xlm-roberta-large""": 5_12,
      """xlm-roberta-large-finetuned-conll02-dutch""": 5_12,
      """xlm-roberta-large-finetuned-conll02-spanish""": 5_12,
      """xlm-roberta-large-finetuned-conll03-english""": 5_12,
      """xlm-roberta-large-finetuned-conll03-german""": 5_12,
  }
  
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE							=     PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE							=     ['input_ids', 'attention_mask']
    SCREAMING_SNAKE_CASE							=     XLMRobertaTokenizer
  
  
  
    def __init__(self       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase="<s>"       ,					_lowerCamelCase="</s>"       ,					_lowerCamelCase="</s>"       ,					_lowerCamelCase="<s>"       ,					_lowerCamelCase="<unk>"       ,					_lowerCamelCase="<pad>"       ,					_lowerCamelCase="<mask>"       ,					**_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						AddedToken(_lowerCamelCase       ,					lstrip=_lowerCamelCase       ,					rstrip=_lowerCamelCase  ) if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) else mask_token
  
          super().__init__(
              _lowerCamelCase       ,					tokenizer_file=_lowerCamelCase       ,					bos_token=_lowerCamelCase       ,					eos_token=_lowerCamelCase       ,					sep_token=_lowerCamelCase       ,					cls_token=_lowerCamelCase       ,					unk_token=_lowerCamelCase       ,					pad_token=_lowerCamelCase       ,					mask_token=_lowerCamelCase       ,					**_lowerCamelCase       ,					)
  
          UpperCAmelCase__ :       Any						=						vocab_file
          UpperCAmelCase__ :       Union[str, Any]						=						False if not self.vocab_file else True
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None  ):
  
  
          """simple docstring"""
  
          if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCAmelCase__ :       Dict						=						[self.cls_token_id]
          UpperCAmelCase__ :       Tuple						=						[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						[self.sep_token_id]
          UpperCAmelCase__ :       Dict						=						[self.cls_token_id]
  
          if token_ids_a is None:
                return len(cls + token_ids_a + sep  ) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None  ):
  
  
          """simple docstring"""
  
          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                    """tokenizer."""  )
  
          if not os.path.isdir(_lowerCamelCase  ):
                logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""  )
                return
          UpperCAmelCase__ :       str						=						os.path.join(
              _lowerCamelCase       ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
  
          if os.path.abspath(self.vocab_file  ) != os.path.abspath(_lowerCamelCase  ):
                copyfile(self.vocab_file       ,					_lowerCamelCase  )
  
          return (out_vocab_file,)
  
  
style_context: 
  
  """simple docstring"""
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     (ScoreSdeVeScheduler,)
    SCREAMING_SNAKE_CASE							=     ()
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						4
          UpperCAmelCase__ :       Tuple						=						3
          UpperCAmelCase__ :       Optional[Any]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[Any]						=						torch.rand((batch_size, num_channels, height, width)  )
  
          return sample
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						4
          UpperCAmelCase__ :       Optional[Any]						=						3
          UpperCAmelCase__ :       List[str]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[str]						=						batch_size * num_channels * height * width
          UpperCAmelCase__ :       Any						=						torch.arange(_lowerCamelCase  )
          UpperCAmelCase__ :       Any						=						sample.reshape(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
          UpperCAmelCase__ :       Dict						=						sample / num_elems
          UpperCAmelCase__ :       Optional[int]						=						sample.permute(3       ,					0       ,					1       ,					2  )
  
          return sample
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          def model(_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
                return sample * t / (t + 1)
  
          return model
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						{
              """num_train_timesteps""": 2000,
              """snr""": 0.15,
              """sigma_min""": 0.01,
              """sigma_max""": 1348,
              """sampling_eps""": 1e-5,
          }
  
          config.update(**_lowerCamelCase  )
          return config
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						dict(self.forward_default_kwargs  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       int						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						0.1 * sample
  
                UpperCAmelCase__ :       int						=						self.get_scheduler_config(**_lowerCamelCase  )
                UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Tuple						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Optional[int]						=						new_scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Union[str, Any]						=						new_scheduler.step_correct(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
          kwargs.update(_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       List[str]						=						self.dummy_sample
                UpperCAmelCase__ :       List[str]						=						0.1 * sample
  
                UpperCAmelCase__ :       Optional[Any]						=						self.get_scheduler_config()
                UpperCAmelCase__ :       Optional[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Any						=						new_scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
  
                UpperCAmelCase__ :       Optional[Any]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Dict						=						new_scheduler.step_correct(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for timesteps in [10, 100, 1000]:
                self.check_over_configs(num_train_timesteps=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,					[1, 100, 1000]  ):
                self.check_over_configs(sigma_min=_lowerCamelCase       ,					sigma_max=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for t in [0.1, 0.5, 0.75]:
                self.check_over_forward(time_step=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       int						=						self.scheduler_classes[0]
          UpperCAmelCase__ :       int						=						self.get_scheduler_config()
          UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						3
  
          UpperCAmelCase__ :       List[Any]						=						self.dummy_model()
          UpperCAmelCase__ :       int						=						self.dummy_sample_deter
  
          scheduler.set_sigmas(_lowerCamelCase  )
          scheduler.set_timesteps(_lowerCamelCase  )
          UpperCAmelCase__ :       Tuple						=						torch.manual_seed(0  )
  
          for i, t in enumerate(scheduler.timesteps  ):
                UpperCAmelCase__ :       Tuple						=						scheduler.sigmas[i]
  
                for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                            UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
                      UpperCAmelCase__ :       List[str]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  ).prev_sample
  
                with torch.no_grad():
                      UpperCAmelCase__ :       Tuple						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  )
                UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						output.prev_sample, output.prev_sample_mean
  
          UpperCAmelCase__ :       int						=						torch.sum(torch.abs(_lowerCamelCase  )  )
          UpperCAmelCase__ :       str						=						torch.mean(torch.abs(_lowerCamelCase  )  )
  
          assert np.isclose(result_sum.item()       ,					14_372_758_528.0  )
          assert np.isclose(result_mean.item()       ,					18_714_530.0  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						kwargs.pop("""num_inference_steps"""       ,					_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       Dict						=						self.get_scheduler_config()
                UpperCAmelCase__ :       List[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						0.1 * sample
  
                if num_inference_steps is not None and hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                      scheduler.set_timesteps(_lowerCamelCase  )
                elif num_inference_steps is not None and not hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                      UpperCAmelCase__ :       Dict						=						num_inference_steps
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					0       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       int						=						scheduler.step_pred(_lowerCamelCase       ,					1       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                self.assertEqual(output_a.shape       ,					sample.shape  )
                self.assertEqual(output_a.shape       ,					output_a.shape  )
  
  