


import math
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 KarrasDiffusionSchedulers, SchedulerMixin


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class lowerCAmelCase_						( lowerCamelCase__       ):

 '''simple docstring'''

 __snake_case												=  42
 __snake_case												=  None






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE=0.9_9_9					,					SCREAMING_SNAKE_CASE="cosine"					,					)->  str:




      """simple docstring"""

      if alpha_transform_type == "cosine":

            def alpha_bar_fn(SCREAMING_SNAKE_CASE					):
                  return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(SCREAMING_SNAKE_CASE					):
                  return math.exp(t * -1_2.0					)

      else:
            raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''					)

      snake_case_       =      []
      for i in range(SCREAMING_SNAKE_CASE					):
            snake_case_       =      i / num_diffusion_timesteps
            snake_case_       =      (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE					) / alpha_bar_fn(SCREAMING_SNAKE_CASE					)					,					SCREAMING_SNAKE_CASE					)					)
      return torch.tensor(SCREAMING_SNAKE_CASE					,					dtype=torch.floataa					)







class lowerCAmelCase_						( lowerCamelCase__					,						lowerCamelCase__       ):

 '''simple docstring'''

 __snake_case												=  [e.name for e in KarrasDiffusionSchedulers]
 __snake_case												=  1



 @register_to_config
 def __init__(	self		,      _UpperCAmelCase = 10_00		,      _UpperCAmelCase = 0.0_001		,      _UpperCAmelCase = 0.02		,      _UpperCAmelCase = "linear"		,      _UpperCAmelCase = None		,      _UpperCAmelCase = "epsilon"		,      _UpperCAmelCase = "linspace"		,      _UpperCAmelCase = 0		,      ):
       if trained_betas is not None:
             snake_case_       =      torch.tensor(UpperCAmelCase_		,      dtype=torch.floataa					)
       elif beta_schedule == "linear":
             snake_case_       =      torch.linspace(UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      dtype=torch.floataa					)
       elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             snake_case_       =      (
                 torch.linspace(beta_start**0.5		,      beta_end**0.5		,      UpperCAmelCase_		,      dtype=torch.floataa					) ** 2
             )
       elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             snake_case_       =      betas_for_alpha_bar(UpperCAmelCase_					)
       else:
             raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''					)

       snake_case_       =      1.0 - self.betas
       snake_case_       =      torch.cumprod(self.alphas		,      dim=0					)

       snake_case_       =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
       snake_case_       =      np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
       snake_case_       =      torch.from_numpy(UpperCAmelCase_					)

       # setable values
       snake_case_       =      None
       snake_case_       =      np.linspace(0		,      num_train_timesteps - 1		,      UpperCAmelCase_		,      dtype=UpperCAmelCase_					)[::-1].copy()
       snake_case_       =      torch.from_numpy(UpperCAmelCase_					)
       snake_case_       =      False



 @property
 def 						UpperCamelCase__     (	self					):
       # standard deviation of the initial noise distribution
       if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase					):
       if isinstance(UpperCAmelCase_		,      torch.Tensor					):
             snake_case_       =      timestep.to(self.timesteps.device					)
       snake_case_       =      (self.timesteps == timestep).nonzero().item()
       snake_case_       =      self.sigmas[step_index]
       snake_case_       =      sample / ((sigma**2 + 1) ** 0.5)
       snake_case_       =      True
       return sample



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase = None					):
       snake_case_       =      num_inference_steps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
             snake_case_       =      np.linspace(0		,      self.config.num_train_timesteps - 1		,      UpperCAmelCase_		,      dtype=UpperCAmelCase_					)[
                 ::-1
             ].copy()
       elif self.config.timestep_spacing == "leading":
             snake_case_       =      self.config.num_train_timesteps // self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             snake_case_       =      (np.arange(0		,      UpperCAmelCase_					) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_					)
             timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
             snake_case_       =      self.config.num_train_timesteps / self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             snake_case_       =      (np.arange(self.config.num_train_timesteps		,      0		,      -step_ratio					)).round().copy().astype(UpperCAmelCase_					)
             timesteps -= 1
       else:
             raise ValueError(
                 F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

       snake_case_       =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
       snake_case_       =      np.interp(UpperCAmelCase_		,      np.arange(0		,      len(UpperCAmelCase_					)					)		,      UpperCAmelCase_					)
       snake_case_       =      np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
       snake_case_       =      torch.from_numpy(UpperCAmelCase_					).to(device=UpperCAmelCase_					)
       if str(UpperCAmelCase_					).startswith('''mps'''					):
             # mps does not support float64
             snake_case_       =      torch.from_numpy(UpperCAmelCase_					).to(UpperCAmelCase_		,      dtype=torch.floataa					)
       else:
             snake_case_       =      torch.from_numpy(UpperCAmelCase_					).to(device=UpperCAmelCase_					)



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 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'''
                     ''' `EulerDiscreteScheduler.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					):
             snake_case_       =      timestep.to(self.timesteps.device					)

       snake_case_       =      (self.timesteps == timestep).nonzero().item()
       snake_case_       =      self.sigmas[step_index]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
             snake_case_       =      sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             snake_case_       =      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       elif self.config.prediction_type == "sample":
             raise NotImplementedError('''prediction_type not implemented yet: sample'''					)
       else:
             raise ValueError(
                 F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

       snake_case_       =      self.sigmas[step_index]
       snake_case_       =      self.sigmas[step_index + 1]
       snake_case_       =      (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
       snake_case_       =      (sigma_to**2 - sigma_up**2) ** 0.5

       # 2. Convert to an ODE derivative
       snake_case_       =      (sample - pred_original_sample) / sigma

       snake_case_       =      sigma_down - sigma

       snake_case_       =      sample + derivative * dt

       snake_case_       =      model_output.device
       snake_case_       =      randn_tensor(model_output.shape		,      dtype=model_output.dtype		,      device=UpperCAmelCase_		,      generator=UpperCAmelCase_					)

       snake_case_       =      prev_sample + noise * sigma_up

       if not return_dict:
             return (prev_sample,)

       return EulerAncestralDiscreteSchedulerOutput(
           prev_sample=UpperCAmelCase_		,      pred_original_sample=UpperCAmelCase_					)



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      ):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       snake_case_       =      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
             snake_case_       =      self.timesteps.to(original_samples.device		,      dtype=torch.floataa					)
             snake_case_       =      timesteps.to(original_samples.device		,      dtype=torch.floataa					)
       else:
             snake_case_       =      self.timesteps.to(original_samples.device					)
             snake_case_       =      timesteps.to(original_samples.device					)

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

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

       snake_case_       =      original_samples + noise * sigma
       return noisy_samples





 def __len__(	self					):
       return self.config.num_train_timesteps







from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case			 =						"""\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
"""

snake_case			 =						"""\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


snake_case			 =						"""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Dict		  =			[rgram for rgrams in rgramslist for rgram in rgrams]
  SCREAMING_SNAKE_CASE				:					Dict		  =			Counter(lowercase )

  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			Counter(lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			Counter()
  for sgram, scount in sgramcounter.items():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			scount * numref

  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			Counter(lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			Counter()
  for cgram, ccount in cgramcounter.items():
    SCREAMING_SNAKE_CASE				:					Tuple		  =			ccount * numref

  # KEEP
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			sgramcounter_rep & cgramcounter_rep
  SCREAMING_SNAKE_CASE				:					Dict		  =			keepgramcounter_rep & rgramcounter
  SCREAMING_SNAKE_CASE				:					Tuple		  =			sgramcounter_rep & rgramcounter

  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			0
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			0
  for keepgram in keepgramcountergood_rep:
    keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
    # Fix an alleged bug [2] in the keep score computation.
    # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
    keeptmpscorea += keepgramcountergood_rep[keepgram]
  # Define 0/0=1 instead of 0 to give higher scores for predictions that match
  #      a target exactly.
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			1
  SCREAMING_SNAKE_CASE				:					int		  =			1
  if len(lowercase ) > 0:
    SCREAMING_SNAKE_CASE				:					Optional[int]		  =			keeptmpscorea / len(lowercase )
  if len(lowercase ) > 0:
    # Fix an alleged bug [2] in the keep score computation.
    # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			keeptmpscorea / sum(keepgramcounterall_rep.values() )
  SCREAMING_SNAKE_CASE				:					Any		  =			0
  if keepscore_precision > 0 or keepscore_recall > 0:
    SCREAMING_SNAKE_CASE				:					int		  =			2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

  # DELETION
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			sgramcounter_rep - cgramcounter_rep
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			delgramcounter_rep - rgramcounter
  SCREAMING_SNAKE_CASE				:					Dict		  =			sgramcounter_rep - rgramcounter
  SCREAMING_SNAKE_CASE				:					List[str]		  =			0
  SCREAMING_SNAKE_CASE				:					int		  =			0
  for delgram in delgramcountergood_rep:
    deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
    deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
  # Define 0/0=1 instead of 0 to give higher scores for predictions that match
  # a target exactly.
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			1
  if len(lowercase ) > 0:
    SCREAMING_SNAKE_CASE				:					str		  =			deltmpscorea / len(lowercase )

  # ADDITION
  SCREAMING_SNAKE_CASE				:					Dict		  =			set(lowercase ) - set(lowercase )
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			set(lowercase ) & set(lowercase )
  SCREAMING_SNAKE_CASE				:					int		  =			set(lowercase ) - set(lowercase )

  SCREAMING_SNAKE_CASE				:					Any		  =			0
  for addgram in addgramcountergood:
    addtmpscore += 1

  # Define 0/0=1 instead of 0 to give higher scores for predictions that match
  # a target exactly.
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			1
  SCREAMING_SNAKE_CASE				:					Tuple		  =			1
  if len(lowercase ) > 0:
    SCREAMING_SNAKE_CASE				:					str		  =			addtmpscore / len(lowercase )
  if len(lowercase ) > 0:
    SCREAMING_SNAKE_CASE				:					List[str]		  =			addtmpscore / len(lowercase )
  SCREAMING_SNAKE_CASE				:					Any		  =			0
  if addscore_precision > 0 or addscore_recall > 0:
    SCREAMING_SNAKE_CASE				:					str		  =			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

  return (keepscore, delscore_precision, addscore)


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[Any]		  =			len(lowercase )

  SCREAMING_SNAKE_CASE				:					Dict		  =			ssent.split(" " )
  SCREAMING_SNAKE_CASE				:					str		  =			csent.split(" " )
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[]
  SCREAMING_SNAKE_CASE				:					Any		  =			[]
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[]
  SCREAMING_SNAKE_CASE				:					Any		  =			[]
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[]
  SCREAMING_SNAKE_CASE				:					Any		  =			[]

  SCREAMING_SNAKE_CASE				:					List[str]		  =			[]
  SCREAMING_SNAKE_CASE				:					int		  =			[]
  SCREAMING_SNAKE_CASE				:					Dict		  =			[]
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[]
  for rsent in rsents:
    SCREAMING_SNAKE_CASE				:					Any		  =			rsent.split(" " )
    SCREAMING_SNAKE_CASE				:					int		  =			[]
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[]
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[]
    ragramslist.append(lowercase )
    for i in range(0  ,   len(lowercase ) - 1 ):
      if i < len(lowercase ) - 1:
        SCREAMING_SNAKE_CASE				:					Tuple		  =			ragrams[i] + " " + ragrams[i + 1]
        ragrams.append(lowercase )
      if i < len(lowercase ) - 2:
        SCREAMING_SNAKE_CASE				:					Optional[int]		  =			ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
        ragrams.append(lowercase )
      if i < len(lowercase ) - 3:
        SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
        ragrams.append(lowercase )
    ragramslist.append(lowercase )
    ragramslist.append(lowercase )
    ragramslist.append(lowercase )

  for i in range(0  ,   len(lowercase ) - 1 ):
    if i < len(lowercase ) - 1:
      SCREAMING_SNAKE_CASE				:					List[Any]		  =			sagrams[i] + " " + sagrams[i + 1]
      sagrams.append(lowercase )
    if i < len(lowercase ) - 2:
      SCREAMING_SNAKE_CASE				:					int		  =			sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
      sagrams.append(lowercase )
    if i < len(lowercase ) - 3:
      SCREAMING_SNAKE_CASE				:					Dict		  =			sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
      sagrams.append(lowercase )

  for i in range(0  ,   len(lowercase ) - 1 ):
    if i < len(lowercase ) - 1:
      SCREAMING_SNAKE_CASE				:					Optional[int]		  =			cagrams[i] + " " + cagrams[i + 1]
      cagrams.append(lowercase )
    if i < len(lowercase ) - 2:
      SCREAMING_SNAKE_CASE				:					str		  =			cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
      cagrams.append(lowercase )
    if i < len(lowercase ) - 3:
      SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
      cagrams.append(lowercase )

  ((SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE))				:					Any		  =			SARIngram(lowercase  ,   lowercase  ,   lowercase  ,   lowercase )
  ((SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE))				:					List[Any]		  =			SARIngram(lowercase  ,   lowercase  ,   lowercase  ,   lowercase )
  ((SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE))				:					List[str]		  =			SARIngram(lowercase  ,   lowercase  ,   lowercase  ,   lowercase )
  ((SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE))				:					Optional[Any]		  =			SARIngram(lowercase  ,   lowercase  ,   lowercase  ,   lowercase )
  SCREAMING_SNAKE_CASE				:					int		  =			sum([keepascore, keepascore, keepascore, keepascore] ) / 4
  SCREAMING_SNAKE_CASE				:					List[str]		  =			sum([delascore, delascore, delascore, delascore] ) / 4
  SCREAMING_SNAKE_CASE				:					Any		  =			sum([addascore, addascore, addascore, addascore] ) / 4
  SCREAMING_SNAKE_CASE				:					int		  =			(avgkeepscore + avgdelscore + avgaddscore) / 3
  return finalscore


def      lowerCamelCase__	(   lowercase  ,   lowercase = True  ,   lowercase = "13a"  ,   lowercase = True ):

  """simple docstring"""






  if lowercase:
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			sentence.lower()

  if tokenizer in ["13a", "intl"]:
    if version.parse(sacrebleu.__version__ ).major >= 2:
      SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			sacrebleu.metrics.bleu._get_tokenizer(lowercase )()(lowercase )
    else:
      SCREAMING_SNAKE_CASE				:					Tuple		  =			sacrebleu.TOKENIZERS[tokenizer]()(lowercase )
  elif tokenizer == "moses":
    SCREAMING_SNAKE_CASE				:					Dict		  =			sacremoses.MosesTokenizer().tokenize(lowercase  ,   return_str=lowercase  ,   escape=lowercase )
  elif tokenizer == "penn":
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			sacremoses.MosesTokenizer().penn_tokenize(lowercase  ,   return_str=lowercase )
  else:
    SCREAMING_SNAKE_CASE				:					str		  =			sentence

  if not return_str:
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			normalized_sent.split()

  return normalized_sent


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,						_KWARGS_DESCRIPTION    )
class        SCREAMING_SNAKE_CASE	(	datasets.Metric    ):
     '''simple docstring'''


     def  _A				( self				:		Optional[int]					):
       return datasets.MetricInfo(
           description=_DESCRIPTION    ,       citation=_CITATION    ,       inputs_description=_KWARGS_DESCRIPTION    ,       features=datasets.Features(
               {
                   "sources": datasets.Value("string"    ,       id="sequence"					),
                   "predictions": datasets.Value("string"    ,       id="sequence"					),
                   "references": datasets.Sequence(datasets.Value("string"    ,       id="sequence"					)    ,       id="references"					),
               }					)    ,       codebase_urls=[
               "https://github.com/cocoxu/simplification/blob/master/SARI.py",
               "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
           ]    ,       reference_urls=["https://www.aclweb.org/anthology/Q16-1029.pdf"]    ,       )






     def  _A				( self				:		str    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		Optional[int]					):
       if not (len(UpperCAmelCase_					) == len(UpperCAmelCase_					) == len(UpperCAmelCase_					)):
         raise ValueError("Sources length must match predictions and references lengths."					)
       SCREAMING_SNAKE_CASE				:					Any		  =			0
       for src, pred, refs in zip(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					):
         sari_score += SARIsent(normalize(UpperCAmelCase_					)    ,       normalize(UpperCAmelCase_					)    ,       [normalize(UpperCAmelCase_					) for sent in refs]					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			sari_score / len(UpperCAmelCase_					)
       return {"sari": 100 * sari_score}






