code: 
  
  
  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 SchedulerMixin
  
  
  SCREAMING_SNAKE_CASE__				:    Optional[Any]         =    logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  @dataclass
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
       __SCREAMING_SNAKE_CASE    				=						42
  
  
  class 		_SCREAMING_SNAKE_CASE    (	A       ,  A    ):
       __SCREAMING_SNAKE_CASE    				=						1
  
  
       @register_to_config
       def __init__(     self       ,				A_ = 40       ,				A_ = 0.0_0_2       ,				A_ = 8_0.0       ,				A_ = 0.5       ,				A_ = 1.0       ,				A_ = 7.0       ,				A_ = True       ,				):
         # standard deviation of the initial noise distribution
         _UpperCAmelCase		:       List[Any]       =    sigma_max
  
         _UpperCAmelCase		:       Optional[int]       =    np.linspace(0       ,				1       ,				A_						)
         _UpperCAmelCase		:       Dict       =    self._convert_to_karras(A_						)
         _UpperCAmelCase		:       int       =    self.sigma_to_t(A_						)
  
         # setable values
         _UpperCAmelCase		:       str       =    None
         _UpperCAmelCase		:       Tuple       =    torch.from_numpy(A_						)
         _UpperCAmelCase		:       Optional[int]       =    torch.from_numpy(A_						)
         _UpperCAmelCase		:       Tuple       =    False
         _UpperCAmelCase		:       Dict       =    False
  
  
       def 					__snake_case(     self       ,				A_       ,				A_=None						):
         if schedule_timesteps is None:
           _UpperCAmelCase		:       Tuple       =    self.timesteps
  
         _UpperCAmelCase		:       Optional[Any]       =    (schedule_timesteps == timestep).nonzero()
         return indices.item()
  
  
       def 					__snake_case(     self       ,				A_       ,				A_						):
         # Get sigma corresponding to timestep
         if isinstance(A_       ,				torch.Tensor						):
           _UpperCAmelCase		:       List[str]       =    timestep.to(self.timesteps.device						)
         _UpperCAmelCase		:       Optional[Any]       =    self.index_for_timestep(A_						)
         _UpperCAmelCase		:       List[str]       =    self.sigmas[step_idx]
  
         _UpperCAmelCase		:       Optional[Any]       =    sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
         _UpperCAmelCase		:       int       =    True
         return sample
  
  
       def 					__snake_case(     self       ,				A_						):
         if not isinstance(A_       ,				np.ndarray						):
           _UpperCAmelCase		:       Union[str, Any]       =    np.array(A_       ,				dtype=np.floataa						)
  
         _UpperCAmelCase		:       List[str]       =    10_00 * 0.2_5 * np.log(sigmas + 1e-44						)
  
         return timesteps
  
  
       def 					__snake_case(     self       ,				A_ = None       ,				A_ = None       ,				A_ = None       ,				):
         if num_inference_steps is None and timesteps is None:
           raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""						)
  
         if num_inference_steps is not None and timesteps is not None:
           raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""						)
  
         # Follow DDPMScheduler custom timesteps logic
         if timesteps is not None:
           for i in range(1       ,				len(A_						)						):
             if timesteps[i] >= timesteps[i - 1]:
               raise ValueError("""`timesteps` must be in descending order."""						)
  
           if timesteps[0] >= self.config.num_train_timesteps:
             raise ValueError(
                 F'''`timesteps` must start before `self.config.train_timesteps`:'''
                 F''' {self.config.num_train_timesteps}.'''						)
  
           _UpperCAmelCase		:       Union[str, Any]       =    np.array(A_       ,				dtype=np.intaa						)
           _UpperCAmelCase		:       int       =    True
         else:
           if num_inference_steps > self.config.num_train_timesteps:
             raise ValueError(
                 F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                 F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                 F''' maximal {self.config.num_train_timesteps} timesteps.'''						)
  
           _UpperCAmelCase		:       List[str]       =    num_inference_steps
  
           _UpperCAmelCase		:       Union[str, Any]       =    self.config.num_train_timesteps // self.num_inference_steps
           _UpperCAmelCase		:       int       =    (np.arange(0       ,				A_						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
           _UpperCAmelCase		:       int       =    False
  
         # Map timesteps to Karras sigmas directly for multistep sampling
         # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
         _UpperCAmelCase		:       Any       =    self.config.num_train_timesteps
         _UpperCAmelCase		:       Tuple       =    timesteps[::-1].copy()
         _UpperCAmelCase		:       Any       =    ramp / (num_train_timesteps - 1)
         _UpperCAmelCase		:       int       =    self._convert_to_karras(A_						)
         _UpperCAmelCase		:       Tuple       =    self.sigma_to_t(A_						)
  
         _UpperCAmelCase		:       Optional[Any]       =    np.concatenate([sigmas, [self.sigma_min]]						).astype(np.floataa						)
         _UpperCAmelCase		:       str       =    torch.from_numpy(A_						).to(device=A_						)
  
         if str(A_						).startswith("""mps"""						):
           # mps does not support float64
           _UpperCAmelCase		:       List[str]       =    torch.from_numpy(A_						).to(A_       ,				dtype=torch.floataa						)
         else:
           _UpperCAmelCase		:       Any       =    torch.from_numpy(A_						).to(device=A_						)
  
  
       def 					__snake_case(     self       ,				A_						):
         _UpperCAmelCase		:       float       =    self.config.sigma_min
         _UpperCAmelCase		:       float       =    self.config.sigma_max
  
         _UpperCAmelCase		:       List[str]       =    self.config.rho
         _UpperCAmelCase		:       Union[str, Any]       =    sigma_min ** (1 / rho)
         _UpperCAmelCase		:       int       =    sigma_max ** (1 / rho)
         _UpperCAmelCase		:       List[str]       =    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas
  
  
       def 					__snake_case(     self       ,				A_						):
         _UpperCAmelCase		:       str       =    self.config.sigma_data
  
         _UpperCAmelCase		:       Dict       =    sigma_data**2 / (sigma**2 + sigma_data**2)
         _UpperCAmelCase		:       Tuple       =    sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out
  
  
       def 					__snake_case(     self       ,				A_						):
         _UpperCAmelCase		:       Optional[Any]       =    self.config.sigma_min
         _UpperCAmelCase		:       int       =    self.config.sigma_data
  
         _UpperCAmelCase		:       str       =    sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
         _UpperCAmelCase		:       int       =    (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_ = None       ,				A_ = True       ,				):
  
         if (
             isinstance(A_       ,				A_						)
             or isinstance(A_       ,				torch.IntTensor						)
             or isinstance(A_       ,				torch.LongTensor						)
         ):
           raise ValueError(
               (
                   """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                   F''' `{self.__class__}.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(A_       ,				torch.Tensor						):
           _UpperCAmelCase		:       int       =    timestep.to(self.timesteps.device						)
  
         _UpperCAmelCase		:       List[Any]       =    self.config.sigma_min
         _UpperCAmelCase		:       Optional[Any]       =    self.config.sigma_max
  
         _UpperCAmelCase		:       str       =    self.index_for_timestep(A_						)
  
         # sigma_next corresponds to next_t in original implementation
         _UpperCAmelCase		:       int       =    self.sigmas[step_index]
         if step_index + 1 < self.config.num_train_timesteps:
           _UpperCAmelCase		:       List[str]       =    self.sigmas[step_index + 1]
         else:
           # Set sigma_next to sigma_min
           _UpperCAmelCase		:       Dict       =    self.sigmas[-1]
  
         # Get scalings for boundary conditions
         _UpperCAmelCase,_UpperCAmelCase		:       Optional[Any]       =    self.get_scalings_for_boundary_condition(A_						)
  
         # 1. Denoise model output using boundary conditions
         _UpperCAmelCase		:       Any       =    c_out * model_output + c_skip * sample
         if self.config.clip_denoised:
           _UpperCAmelCase		:       Optional[Any]       =    denoised.clamp(-1       ,				1						)
  
         # 2. Sample z ~ N(0, s_noise^2 * I)
         # Noise is not used for onestep sampling.
         if len(self.timesteps						) > 1:
           _UpperCAmelCase		:       Tuple       =    randn_tensor(
               model_output.shape       ,				dtype=model_output.dtype       ,				device=model_output.device       ,				generator=A_						)
         else:
           _UpperCAmelCase		:       Union[str, Any]       =    torch.zeros_like(A_						)
         _UpperCAmelCase		:       Dict       =    noise * self.config.s_noise
  
         _UpperCAmelCase		:       Dict       =    sigma_next.clamp(min=A_       ,				max=A_						)
  
         # 3. Return noisy sample
         # tau = sigma_hat, eps = sigma_min
         _UpperCAmelCase		:       str       =    denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
         if not return_dict:
           return (prev_sample,)
  
         return CMStochasticIterativeSchedulerOutput(prev_sample=A_						)
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				):
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         _UpperCAmelCase		:       List[str]       =    self.sigmas.to(device=original_samples.device       ,				dtype=original_samples.dtype						)
         if original_samples.device.type == "mps" and torch.is_floating_point(A_						):
           # mps does not support float64
           _UpperCAmelCase		:       Union[str, Any]       =    self.timesteps.to(original_samples.device       ,				dtype=torch.floataa						)
           _UpperCAmelCase		:       Optional[int]       =    timesteps.to(original_samples.device       ,				dtype=torch.floataa						)
         else:
           _UpperCAmelCase		:       List[str]       =    self.timesteps.to(original_samples.device						)
           _UpperCAmelCase		:       Optional[Any]       =    timesteps.to(original_samples.device						)
  
         _UpperCAmelCase		:       Union[str, Any]       =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
         _UpperCAmelCase		:       Dict       =    sigmas[step_indices].flatten()
         while len(sigma.shape						) < len(original_samples.shape						):
           _UpperCAmelCase		:       Dict       =    sigma.unsqueeze(-1						)
  
         _UpperCAmelCase		:       List[Any]       =    original_samples + noise * sigma
         return noisy_samples
  
  
  
  
       def __len__(     self						):
         return self.config.num_train_timesteps
  
  
  
  
  
  
style_context: 
  
  
  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:
   SCREAMING_SNAKE_CASE__				:    Optional[Any]         =    None
  
  
  SCREAMING_SNAKE_CASE__				:    Dict         =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__				:    List[Any]         =    {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
  
  SCREAMING_SNAKE_CASE__				:    Any         =    {
      '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'
          ),
      },
  }
  
  SCREAMING_SNAKE_CASE__				:    Optional[int]         =    {
      'xlm-roberta-base': 512,
      'xlm-roberta-large': 512,
      'xlm-roberta-large-finetuned-conll02-dutch': 512,
      'xlm-roberta-large-finetuned-conll02-spanish': 512,
      'xlm-roberta-large-finetuned-conll03-english': 512,
      'xlm-roberta-large-finetuned-conll03-german': 512,
  }
  
  
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
       __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       ,				A_=None       ,				A_=None       ,				A_="<s>"       ,				A_="</s>"       ,				A_="</s>"       ,				A_="<s>"       ,				A_="<unk>"       ,				A_="<pad>"       ,				A_="<mask>"       ,				**A_       ,				):
         # Mask token behave like a normal word, i.e. include the space before it
         _UpperCAmelCase		:       int       =    AddedToken(A_       ,				lstrip=A_       ,				rstrip=A_						) if isinstance(A_       ,				A_						) else mask_token
  
         super().__init__(
             A_       ,				tokenizer_file=A_       ,				bos_token=A_       ,				eos_token=A_       ,				sep_token=A_       ,				cls_token=A_       ,				unk_token=A_       ,				pad_token=A_       ,				mask_token=A_       ,				**A_       ,				)
  
         _UpperCAmelCase		:       Union[str, Any]       =    vocab_file
         _UpperCAmelCase		:       List[str]       =    False if not self.vocab_file else True
  
  
       def 					__snake_case(     self       ,				A_       ,				A_ = None						):
  
         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _UpperCAmelCase		:       Optional[Any]       =    [self.cls_token_id]
         _UpperCAmelCase		:       Union[str, Any]       =    [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
       def 					__snake_case(     self       ,				A_       ,				A_ = None						):
         _UpperCAmelCase		:       Tuple       =    [self.sep_token_id]
         _UpperCAmelCase		:       Optional[int]       =    [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 					__snake_case(     self       ,				A_       ,				A_ = None						):
         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(A_						):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''						)
           return
         _UpperCAmelCase		:       List[str]       =    os.path.join(
             A_       ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
  
         if os.path.abspath(self.vocab_file						) != os.path.abspath(A_						):
           copyfile(self.vocab_file       ,				A_						)
  
         return (out_vocab_file,)
  
  
  
  
  
  