

'''simple docstring'''




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


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






@dataclass
class   _SCREAMING_SNAKE_CASE    (    __a	):
 __SCREAMING_SNAKE_CASE     :int			       =     42






class   _SCREAMING_SNAKE_CASE    (    __a		,__a	):
 __SCREAMING_SNAKE_CASE     :Any			       =     1



 @register_to_config
 def __init__(    self		:    Union[str, Any] ,      a__		:    Dict = 40 ,      a__		:    Optional[int] = 0.002 ,      a__		:    Union[str, Any] = 80.0 ,      a__		:    Optional[int] = 0.5 ,      a__		:    List[str] = 1.0 ,      a__		:    List[Any] = 7.0 ,      a__		:    int = True ,      ):
      # standard deviation of the initial noise distribution
      __magic_name__						   =				sigma_max

      __magic_name__						   =				np.linspace(0 ,      1 ,      UpperCAmelCase_			)
      __magic_name__						   =				self._convert_to_karras(UpperCAmelCase_			)
      __magic_name__						   =				self.sigma_to_t(UpperCAmelCase_			)

      # setable values
      __magic_name__						   =				None
      __magic_name__						   =				torch.from_numpy(UpperCAmelCase_			)
      __magic_name__						   =				torch.from_numpy(UpperCAmelCase_			)
      __magic_name__						   =				False
      __magic_name__						   =				False



 def  snake_case__  (    self		:    Optional[int] ,      a__		:    Any ,      a__		:    Union[str, Any]=None			):
      if schedule_timesteps is None:
           __magic_name__						   =				self.timesteps

      __magic_name__						   =				(schedule_timesteps == timestep).nonzero()
      return indices.item()



 def  snake_case__  (    self		:    Tuple ,      a__		:    List[str] ,      a__		:    Any			):
      # Get sigma corresponding to timestep
      if isinstance(UpperCAmelCase_ ,      torch.Tensor			):
           __magic_name__						   =				timestep.to(self.timesteps.device			)
      __magic_name__						   =				self.index_for_timestep(UpperCAmelCase_			)
      __magic_name__						   =				self.sigmas[step_idx]

      __magic_name__						   =				sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

      __magic_name__						   =				True
      return sample



 def  snake_case__  (    self		:    int ,      a__		:    List[str]			):
      if not isinstance(UpperCAmelCase_ ,      np.ndarray			):
           __magic_name__						   =				np.array(UpperCAmelCase_ ,      dtype=np.floataa			)

      __magic_name__						   =				1000 * 0.25 * np.log(sigmas + 1E-44			)

      return timesteps



 def  snake_case__  (    self		:    Dict ,      a__		:    Tuple = None ,      a__		:    List[Any] = None ,      a__		:    List[str] = 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(UpperCAmelCase_			)			):
                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}.'''			)

           __magic_name__						   =				np.array(UpperCAmelCase_ ,      dtype=np.intaa			)
           __magic_name__						   =				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.'''			)

           __magic_name__						   =				num_inference_steps

           __magic_name__						   =				self.config.num_train_timesteps // self.num_inference_steps
           __magic_name__						   =				(np.arange(0 ,      UpperCAmelCase_			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
           __magic_name__						   =				False

      # Map timesteps to Karras sigmas directly for multistep sampling
      # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
      __magic_name__						   =				self.config.num_train_timesteps
      __magic_name__						   =				timesteps[::-1].copy()
      __magic_name__						   =				ramp / (num_train_timesteps - 1)
      __magic_name__						   =				self._convert_to_karras(UpperCAmelCase_			)
      __magic_name__						   =				self.sigma_to_t(UpperCAmelCase_			)

      __magic_name__						   =				np.concatenate([sigmas, [self.sigma_min]]			).astype(np.floataa			)
      __magic_name__						   =				torch.from_numpy(UpperCAmelCase_			).to(device=UpperCAmelCase_			)

      if str(UpperCAmelCase_			).startswith('''mps'''			):
           # mps does not support float64
           __magic_name__						   =				torch.from_numpy(UpperCAmelCase_			).to(UpperCAmelCase_ ,      dtype=torch.floataa			)
      else:
           __magic_name__						   =				torch.from_numpy(UpperCAmelCase_			).to(device=UpperCAmelCase_			)



 def  snake_case__  (    self		:    List[str] ,      a__		:    Optional[int]			):
      __magic_name__						   =				self.config.sigma_min
      __magic_name__						   =				self.config.sigma_max

      __magic_name__						   =				self.config.rho
      __magic_name__						   =				sigma_min ** (1 / rho)
      __magic_name__						   =				sigma_max ** (1 / rho)
      __magic_name__						   =				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas



 def  snake_case__  (    self		:    str ,      a__		:    Optional[Any]			):
      __magic_name__						   =				self.config.sigma_data

      __magic_name__						   =				sigma_data**2 / (sigma**2 + sigma_data**2)
      __magic_name__						   =				sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
      return c_skip, c_out



 def  snake_case__  (    self		:    Optional[int] ,      a__		:    Dict			):
      __magic_name__						   =				self.config.sigma_min
      __magic_name__						   =				self.config.sigma_data

      __magic_name__						   =				sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
      __magic_name__						   =				(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
      return c_skip, c_out



 def  snake_case__  (    self		:    int ,      a__		:    Tuple ,      a__		:    Union[str, Any] ,      a__		:    Optional[int] ,      a__		:    Dict = None ,      a__		:    Optional[Any] = True ,      ):

      if (
          isinstance(UpperCAmelCase_ ,      UpperCAmelCase_			)
          or isinstance(UpperCAmelCase_ ,      torch.IntTensor			)
          or isinstance(UpperCAmelCase_ ,      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(UpperCAmelCase_ ,      torch.Tensor			):
           __magic_name__						   =				timestep.to(self.timesteps.device			)

      __magic_name__						   =				self.config.sigma_min
      __magic_name__						   =				self.config.sigma_max

      __magic_name__						   =				self.index_for_timestep(UpperCAmelCase_			)

      # sigma_next corresponds to next_t in original implementation
      __magic_name__						   =				self.sigmas[step_index]
      if step_index + 1 < self.config.num_train_timesteps:
           __magic_name__						   =				self.sigmas[step_index + 1]
      else:
           # Set sigma_next to sigma_min
           __magic_name__						   =				self.sigmas[-1]

      # Get scalings for boundary conditions
      __magic_name__   ,  __magic_name__						   =				self.get_scalings_for_boundary_condition(UpperCAmelCase_			)

      # 1. Denoise model output using boundary conditions
      __magic_name__						   =				c_out * model_output + c_skip * sample
      if self.config.clip_denoised:
           __magic_name__						   =				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:
           __magic_name__						   =				randn_tensor(
               model_output.shape ,      dtype=model_output.dtype ,      device=model_output.device ,      generator=UpperCAmelCase_			)
      else:
           __magic_name__						   =				torch.zeros_like(UpperCAmelCase_			)
      __magic_name__						   =				noise * self.config.s_noise

      __magic_name__						   =				sigma_next.clamp(min=UpperCAmelCase_ ,      max=UpperCAmelCase_			)

      # 3. Return noisy sample
      # tau = sigma_hat, eps = sigma_min
      __magic_name__						   =				denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

      if not return_dict:
           return (prev_sample,)

      return CMStochasticIterativeSchedulerOutput(prev_sample=UpperCAmelCase_			)



 def  snake_case__  (    self		:    Union[str, Any] ,      a__		:    Optional[Any] ,      a__		:    Tuple ,      a__		:    List[str] ,      ):
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      __magic_name__						   =				self.sigmas.to(device=original_samples.device ,      dtype=original_samples.dtype			)
      if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_			):
           # mps does not support float64
           __magic_name__						   =				self.timesteps.to(original_samples.device ,      dtype=torch.floataa			)
           __magic_name__						   =				timesteps.to(original_samples.device ,      dtype=torch.floataa			)
      else:
           __magic_name__						   =				self.timesteps.to(original_samples.device			)
           __magic_name__						   =				timesteps.to(original_samples.device			)

      __magic_name__						   =				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

      __magic_name__						   =				sigmas[step_indices].flatten()
      while len(sigma.shape			) < len(original_samples.shape			):
           __magic_name__						   =				sigma.unsqueeze(-1			)

      __magic_name__						   =				original_samples + noise * sigma
      return noisy_samples



 def __len__(    self		:    Optional[int]			):
      return self.config.num_train_timesteps













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:
							UpperCAmelCase__  :	Union[str, Any]     =None


UpperCAmelCase__  :	str     =logging.get_logger(__name__)

UpperCAmelCase__  :	Optional[int]     ={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__  :	str     ={
    '''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'''
        ),
    },
}

UpperCAmelCase__  :	int     ={
    '''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 					__A   (  a			):
				__A            = VOCAB_FILES_NAMES
				__A            = PRETRAINED_VOCAB_FILES_MAP
				__A            = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A            = ["""input_ids""", """attention_mask"""]
				__A            = XLMRobertaTokenizer


				def __init__(						self      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_="<s>"      ,       UpperCAmelCase_="</s>"      ,       UpperCAmelCase_="</s>"      ,       UpperCAmelCase_="<s>"      ,       UpperCAmelCase_="<unk>"      ,       UpperCAmelCase_="<pad>"      ,       UpperCAmelCase_="<mask>"      ,       **UpperCAmelCase_      ,       ):
								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase		       =AddedToken(UpperCAmelCase_      ,       lstrip=UpperCAmelCase_      ,       rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_      ,       UpperCAmelCase_							) else mask_token

								super().__init__(
								    UpperCAmelCase_      ,       tokenizer_file=UpperCAmelCase_      ,       bos_token=UpperCAmelCase_      ,       eos_token=UpperCAmelCase_      ,       sep_token=UpperCAmelCase_      ,       cls_token=UpperCAmelCase_      ,       unk_token=UpperCAmelCase_      ,       pad_token=UpperCAmelCase_      ,       mask_token=UpperCAmelCase_      ,       **UpperCAmelCase_      ,       )

								lowerCamelCase		       =vocab_file
								lowerCamelCase		       =False if not self.vocab_file else True


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):

								if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowerCamelCase		       =[self.cls_token_id]
								lowerCamelCase		       =[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):
								lowerCamelCase		       =[self.sep_token_id]
								lowerCamelCase		       =[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      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = 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(UpperCAmelCase_							):
												logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""							)
												return
								lowerCamelCase		       =os.path.join(
								    UpperCAmelCase_      ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

								if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCAmelCase_							):
												copyfile(self.vocab_file      ,       UpperCAmelCase_							)

								return (out_vocab_file,)





