


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









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,)





