



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


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







@dataclass
class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : torch.FloatTensor







class       _snake_case				(     __snake_case ,							__snake_case ):






   '''simple docstring'''






   A__      : List[str]       =						1


   @register_to_config
   def __init__(    self:     Optional[int]  ,lowerCamelCase_:     int = 40  ,lowerCamelCase_:     float = 0.0_0_2  ,lowerCamelCase_:     float = 8_0.0  ,lowerCamelCase_:     float = 0.5  ,lowerCamelCase_:     float = 1.0  ,lowerCamelCase_:     float = 7.0  ,lowerCamelCase_:     bool = True  ,)	->      List[Any]:
       # standard deviation of the initial noise distribution
       UpperCAmelCase_     :       Tuple  		=					sigma_max

       UpperCAmelCase_     :       int  		=					np.linspace(0  ,1  ,lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[int]  		=					self._convert_to_karras(lowerCamelCase_							)
       UpperCAmelCase_     :       List[Any]  		=					self.sigma_to_t(lowerCamelCase_							)

       # setable values
       UpperCAmelCase_     :       Optional[Any]  		=					None
       UpperCAmelCase_     :       Any  		=					torch.from_numpy(lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[Any]  		=					torch.from_numpy(lowerCamelCase_							)
       UpperCAmelCase_     :       Tuple  		=					False
       UpperCAmelCase_     :       Dict  		=					False


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     List[str]=None							)	->      Optional[Any]:
       if schedule_timesteps is None:
           UpperCAmelCase_     :       str  		=					self.timesteps

       UpperCAmelCase_     :       str  		=					(schedule_timesteps == timestep).nonzero()
       return indices.item()


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     Union[float, torch.FloatTensor]							)	->      torch.FloatTensor:
       # Get sigma corresponding to timestep
       if isinstance(lowerCamelCase_  ,torch.Tensor							):
           UpperCAmelCase_     :       Any  		=					timestep.to(self.timesteps.device							)
       UpperCAmelCase_     :       Optional[Any]  		=					self.index_for_timestep(lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					self.sigmas[step_idx]

       UpperCAmelCase_     :       Tuple  		=					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

       UpperCAmelCase_     :       Any  		=					True
       return sample


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Union[float, np.ndarray]							)	->      Tuple:
       if not isinstance(lowerCamelCase_  ,np.ndarray							):
           UpperCAmelCase_     :       str  		=					np.array(lowerCamelCase_  ,dtype=np.floataa							)

       UpperCAmelCase_     :       Dict  		=					1000 * 0.2_5 * np.log(sigmas + 1e-44							)

       return timesteps


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Optional[int] = None  ,lowerCamelCase_:     Union[str, torch.device] = None  ,lowerCamelCase_:     Optional[List[int]] = None  ,)	->      Optional[Any]:
       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(lowerCamelCase_							)							):
               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_     :       List[Any]  		=					np.array(lowerCamelCase_  ,dtype=np.intaa							)
           UpperCAmelCase_     :       Tuple  		=					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_     :       Any  		=					num_inference_steps

           UpperCAmelCase_     :       Any  		=					self.config.num_train_timesteps // self.num_inference_steps
           UpperCAmelCase_     :       Optional[int]  		=					(np.arange(0  ,lowerCamelCase_							) * 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_     :       List[str]  		=					self.config.num_train_timesteps
       UpperCAmelCase_     :       Any  		=					timesteps[::-1].copy()
       UpperCAmelCase_     :       int  		=					ramp / (num_train_timesteps - 1)
       UpperCAmelCase_     :       Tuple  		=					self._convert_to_karras(lowerCamelCase_							)
       UpperCAmelCase_     :       Dict  		=					self.sigma_to_t(lowerCamelCase_							)

       UpperCAmelCase_     :       List[str]  		=					np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
       UpperCAmelCase_     :       Union[str, Any]  		=					torch.from_numpy(lowerCamelCase_							).to(device=lowerCamelCase_							)

       if str(lowerCamelCase_							).startswith("""mps"""							):
           # mps does not support float64
           UpperCAmelCase_     :       Optional[Any]  		=					torch.from_numpy(lowerCamelCase_							).to(lowerCamelCase_  ,dtype=torch.floataa							)
       else:
           UpperCAmelCase_     :       Optional[int]  		=					torch.from_numpy(lowerCamelCase_							).to(device=lowerCamelCase_							)


   def       A__    (    self:     str  ,lowerCamelCase_:     Any							)	->      Optional[Any]:
       UpperCAmelCase_     :       float  		=					self.config.sigma_min
       UpperCAmelCase_     :       float  		=					self.config.sigma_max

       UpperCAmelCase_     :       Tuple  		=					self.config.rho
       UpperCAmelCase_     :       str  		=					sigma_min ** (1 / rho)
       UpperCAmelCase_     :       Optional[int]  		=					sigma_max ** (1 / rho)
       UpperCAmelCase_     :       str  		=					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     int							)	->      Tuple:
       UpperCAmelCase_     :       List[Any]  		=					self.config.sigma_data

       UpperCAmelCase_     :       int  		=					sigma_data**2 / (sigma**2 + sigma_data**2)
       UpperCAmelCase_     :       str  		=					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     List[Any]							)	->      List[Any]:
       UpperCAmelCase_     :       List[Any]  		=					self.config.sigma_min
       UpperCAmelCase_     :       List[str]  		=					self.config.sigma_data

       UpperCAmelCase_     :       Union[str, Any]  		=					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       UpperCAmelCase_     :       Any  		=					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     Union[float, torch.FloatTensor]  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     Optional[torch.Generator] = None  ,lowerCamelCase_:     bool = True  ,)	->      Union[CMStochasticIterativeSchedulerOutput, 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"""
                   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(lowerCamelCase_  ,torch.Tensor							):
           UpperCAmelCase_     :       List[str]  		=					timestep.to(self.timesteps.device							)

       UpperCAmelCase_     :       Optional[Any]  		=					self.config.sigma_min
       UpperCAmelCase_     :       List[str]  		=					self.config.sigma_max

       UpperCAmelCase_     :       int  		=					self.index_for_timestep(lowerCamelCase_							)

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

       # Get scalings for boundary conditions
       UpperCAmelCase_		, UpperCAmelCase_     :       Any  		=					self.get_scalings_for_boundary_condition(lowerCamelCase_							)

       # 1. Denoise model output using boundary conditions
       UpperCAmelCase_     :       Optional[Any]  		=					c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
           UpperCAmelCase_     :       int  		=					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_     :       Optional[int]  		=					randn_tensor(
               model_output.shape  ,dtype=model_output.dtype  ,device=model_output.device  ,generator=lowerCamelCase_							)
       else:
           UpperCAmelCase_     :       Union[str, Any]  		=					torch.zeros_like(lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					noise * self.config.s_noise

       UpperCAmelCase_     :       str  		=					sigma_next.clamp(min=lowerCamelCase_  ,max=lowerCamelCase_							)

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

       if not return_dict:
           return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=lowerCamelCase_							)


   def       A__    (    self:     str  ,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
       UpperCAmelCase_     :       int  		=					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
           UpperCAmelCase_     :       List[str]  		=					self.timesteps.to(original_samples.device  ,dtype=torch.floataa							)
           UpperCAmelCase_     :       Dict  		=					timesteps.to(original_samples.device  ,dtype=torch.floataa							)
       else:
           UpperCAmelCase_     :       int  		=					self.timesteps.to(original_samples.device							)
           UpperCAmelCase_     :       Tuple  		=					timesteps.to(original_samples.device							)

       UpperCAmelCase_     :       Tuple  		=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

       UpperCAmelCase_     :       List[str]  		=					original_samples + noise * sigma
       return noisy_samples






   def __len__(    self:     Tuple							)	->      Optional[Any]:
       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:
					snake_case_   :	Optional[int]							      =None


snake_case_   :	List[str]							      =logging.get_logger(__name__)

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

snake_case_   :	Union[str, 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'''
        ),
    },
}

snake_case_   :	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        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       int 					= VOCAB_FILES_NAMES
		UpperCAmelCase_							:       str 					= PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase_							:       Union[str, Any] 					= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase_							:       int 					= ['input_ids', 'attention_mask']
		UpperCAmelCase_							:       Optional[int] 					= XLMRobertaTokenizer






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

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

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:
				__A    =							[self.sep_token_id]
				__A    =							[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 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				Tuple[str]:
				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(lowercase__						):
						logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""						)
						return
				__A    =							os.path.join(
				    lowercase__							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

				return (out_vocab_file,)

