






import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
   import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     def __init__( self				:		int					):
       self.register_to_config()
       super().__init__()


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		List[Any]    ,       UpperCAmelCase_				:		Optional[Any]					):
       if dicta == dicta:
         return True
       else:
         SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					int		  =			self._remove_meta_keys(UpperCAmelCase_					)
         SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[Any]		  =			self._remove_meta_keys(UpperCAmelCase_					)
         if configa == configa:
           print(f'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''					)
           return True
       return False


     def  _A				( self				:		str    ,       UpperCAmelCase_				:		Dict					):
       SCREAMING_SNAKE_CASE				:					Any		  =			[]
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			config_dict.copy()
       for key in config_dict.keys():
         if key.startswith("_"					):
           temp_dict.pop(UpperCAmelCase_					)
           meta_keys.append(UpperCAmelCase_					)
       return (temp_dict, meta_keys)


     @torch.no_grad()
     def  _A				( self				:		str    ,       UpperCAmelCase_				:		List[Union[str, os.PathLike]]    ,       **UpperCAmelCase_				:		str					):
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			kwargs.pop("cache_dir"    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			kwargs.pop("resume_download"    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			kwargs.pop("force_download"    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			kwargs.pop("proxies"    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Dict		  =			kwargs.pop("local_files_only"    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					str		  =			kwargs.pop("use_auth_token"    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			kwargs.pop("revision"    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Any		  =			kwargs.pop("torch_dtype"    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			kwargs.pop("device_map"    ,       UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					int		  =			kwargs.pop("alpha"    ,       0.5					)
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			kwargs.pop("interp"    ,       UpperCAmelCase_					)

       print("Received list"    ,       UpperCAmelCase_					)
       print(f'''Combining with alpha={alpha}, interpolation mode={interp}'''					)

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			len(UpperCAmelCase_					)
       # Ignore result from model_index_json comparision of the two checkpoints
       SCREAMING_SNAKE_CASE				:					List[str]		  =			kwargs.pop("force"    ,       UpperCAmelCase_					)

       # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
       if checkpoint_count > 3 or checkpoint_count < 2:
         raise ValueError(
             "Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"
             " passed."					)

       print("Received the right number of checkpoints"					)
       # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
       # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

       # Validate that the checkpoints can be merged
       # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[]
       for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
         SCREAMING_SNAKE_CASE				:					str		  =			DiffusionPipeline.load_config(
             UpperCAmelCase_    ,       cache_dir=UpperCAmelCase_    ,       resume_download=UpperCAmelCase_    ,       force_download=UpperCAmelCase_    ,       proxies=UpperCAmelCase_    ,       local_files_only=UpperCAmelCase_    ,       use_auth_token=UpperCAmelCase_    ,       revision=UpperCAmelCase_    ,       )
         config_dicts.append(UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					List[str]		  =			True
       for idx in range(1    ,       len(UpperCAmelCase_					)					):
         comparison_result &= self._compare_model_configs(config_dicts[idx - 1]    ,       config_dicts[idx]					)
         if not force and comparison_result is False:
           raise ValueError("Incompatible checkpoints. Please check model_index.json for the models."					)
           print(config_dicts[0]    ,       config_dicts[1]					)
       print("Compatible model_index.json files found"					)
       # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
       SCREAMING_SNAKE_CASE				:					str		  =			[]
       for pretrained_model_name_or_path, config_dict in zip(UpperCAmelCase_    ,       UpperCAmelCase_					):
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			[k for k in config_dict.keys() if not k.startswith("_"					)]
         SCREAMING_SNAKE_CASE				:					Any		  =			[os.path.join(UpperCAmelCase_    ,       "*"					) for k in folder_names]
         allow_patterns += [
             WEIGHTS_NAME,
             SCHEDULER_CONFIG_NAME,
             CONFIG_NAME,
             ONNX_WEIGHTS_NAME,
             DiffusionPipeline.config_name,
         ]
         SCREAMING_SNAKE_CASE				:					List[str]		  =			config_dict.get("_class_name"					)
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			{"diffusers": __version__, "pipeline_class": requested_pipeline_class}

         SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
             pretrained_model_name_or_path
             if os.path.isdir(UpperCAmelCase_					)
             else snapshot_download(
                 UpperCAmelCase_    ,       cache_dir=UpperCAmelCase_    ,       resume_download=UpperCAmelCase_    ,       proxies=UpperCAmelCase_    ,       local_files_only=UpperCAmelCase_    ,       use_auth_token=UpperCAmelCase_    ,       revision=UpperCAmelCase_    ,       allow_patterns=UpperCAmelCase_    ,       user_agent=UpperCAmelCase_    ,       )
         )
         print("Cached Folder"    ,       UpperCAmelCase_					)
         cached_folders.append(UpperCAmelCase_					)

       # Step 3:-
       # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			DiffusionPipeline.from_pretrained(
           cached_folders[0]    ,       torch_dtype=UpperCAmelCase_    ,       device_map=UpperCAmelCase_					)
       final_pipe.to(self.device					)

       SCREAMING_SNAKE_CASE				:					List[Any]		  =			None
       if len(UpperCAmelCase_					) > 2:
         SCREAMING_SNAKE_CASE				:					Any		  =			os.path.join(cached_folders[2]					)

       if interp == "sigmoid":
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CheckpointMergerPipeline.sigmoid
       elif interp == "inv_sigmoid":
         SCREAMING_SNAKE_CASE				:					int		  =			CheckpointMergerPipeline.inv_sigmoid
       elif interp == "add_diff":
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			CheckpointMergerPipeline.add_difference
       else:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CheckpointMergerPipeline.weighted_sum

       # Find each module's state dict.
       for attr in final_pipe.config.keys():
         if not attr.startswith("_"					):
           SCREAMING_SNAKE_CASE				:					str		  =			os.path.join(cached_folders[1]    ,       UpperCAmelCase_					)
           if os.path.exists(UpperCAmelCase_					):
             SCREAMING_SNAKE_CASE				:					Any		  =			[
                 *glob.glob(os.path.join(UpperCAmelCase_    ,       "*.safetensors"					)					),
                 *glob.glob(os.path.join(UpperCAmelCase_    ,       "*.bin"					)					),
             ]
             SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			files[0] if len(UpperCAmelCase_					) > 0 else None
           if len(UpperCAmelCase_					) < 3:
             SCREAMING_SNAKE_CASE				:					Any		  =			None
           else:
             SCREAMING_SNAKE_CASE				:					Any		  =			os.path.join(cached_folders[2]    ,       UpperCAmelCase_					)
             if os.path.exists(UpperCAmelCase_					):
               SCREAMING_SNAKE_CASE				:					int		  =			[
                   *glob.glob(os.path.join(UpperCAmelCase_    ,       "*.safetensors"					)					),
                   *glob.glob(os.path.join(UpperCAmelCase_    ,       "*.bin"					)					),
               ]
               SCREAMING_SNAKE_CASE				:					int		  =			files[0] if len(UpperCAmelCase_					) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
           if checkpoint_path_a is None and checkpoint_path_a is None:
             print(f'''Skipping {attr}: not present in 2nd or 3d model'''					)
             continue
           try:
             SCREAMING_SNAKE_CASE				:					List[str]		  =			getattr(UpperCAmelCase_    ,       UpperCAmelCase_					)
             if isinstance(UpperCAmelCase_    ,       UpperCAmelCase_					):  # ignore requires_safety_checker boolean
               continue
             SCREAMING_SNAKE_CASE				:					Dict		  =			getattr(UpperCAmelCase_    ,       "state_dict"					)
             SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			theta_a()

             SCREAMING_SNAKE_CASE				:					List[Any]		  =			getattr(UpperCAmelCase_    ,       "load_state_dict"					)
             SCREAMING_SNAKE_CASE				:					Tuple		  =			(
                 safetensors.torch.load_file(UpperCAmelCase_					)
                 if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"					))
                 else torch.load(UpperCAmelCase_    ,       map_location="cpu"					)
             )
             SCREAMING_SNAKE_CASE				:					List[Any]		  =			None
             if checkpoint_path_a:
               SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
                   safetensors.torch.load_file(UpperCAmelCase_					)
                   if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"					))
                   else torch.load(UpperCAmelCase_    ,       map_location="cpu"					)
               )

             if not theta_a.keys() == theta_a.keys():
               print(f'''Skipping {attr}: key mismatch'''					)
               continue
             if theta_a and not theta_a.keys() == theta_a.keys():
               print(f'''Skipping {attr}:y mismatch'''					)
           except Exception as e:
             print(f'''Skipping {attr} do to an unexpected error: {str(UpperCAmelCase_					)}'''					)
             continue
           print(f'''MERGING {attr}'''					)

           for key in theta_a.keys():
             if theta_a:
               SCREAMING_SNAKE_CASE				:					List[Any]		  =			theta_func(theta_a[key]    ,       theta_a[key]    ,       theta_a[key]    ,       UpperCAmelCase_					)
             else:
               SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			theta_func(theta_a[key]    ,       theta_a[key]    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)

           del theta_a
           del theta_a
           update_theta_a(UpperCAmelCase_					)

           del theta_a
       return final_pipe


     @staticmethod
     def  _A				( UpperCAmelCase_				:		Dict    ,       UpperCAmelCase_				:		Any    ,       UpperCAmelCase_				:		str    ,       UpperCAmelCase_				:		Optional[int]					):
       return ((1 - alpha) * thetaa) + (alpha * thetaa)


     @staticmethod
     def  _A				( UpperCAmelCase_				:		List[str]    ,       UpperCAmelCase_				:		List[str]    ,       UpperCAmelCase_				:		Union[str, Any]    ,       UpperCAmelCase_				:		Any					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			alpha * alpha * (3 - (2 * alpha))
       return thetaa + ((thetaa - thetaa) * alpha)


     @staticmethod
     def  _A				( UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		Tuple    ,       UpperCAmelCase_				:		Any    ,       UpperCAmelCase_				:		Any					):
       import math

       SCREAMING_SNAKE_CASE				:					Tuple		  =			0.5 - math.sin(math.asin(1.0 - 2.0 * alpha					) / 3.0					)
       return thetaa + ((thetaa - thetaa) * alpha)






     @staticmethod
     def  _A				( UpperCAmelCase_				:		Optional[int]    ,       UpperCAmelCase_				:		Any    ,       UpperCAmelCase_				:		Any    ,       UpperCAmelCase_				:		Union[str, Any]					):
       return thetaa + (thetaa - thetaa) * (1.0 - alpha)














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}






