




'''simple docstring'''






import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
      from comet_ml import OfflineExperiment

if is_tensorboard_available():
      import struct

      import tensorboard.compat.proto.event_pba as event_pba

A_ 			=		logging.getLogger(__name__)






@require_tensorboard
class 					lowercase_			(	unittest.TestCase       ):





     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           snake_case__					:      int  			=						'test_project_with_config'
           with tempfile.TemporaryDirectory() as dirpath:
                 snake_case__					:      List[str]  			=						Accelerator(log_with='tensorboard'    ,       project_dir=__lowerCamelCase )
                 snake_case__					:      Dict  			=						{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
                 accelerator.init_trackers(__lowerCamelCase    ,       __lowerCamelCase )
                 accelerator.end_training()
                 for child in Path(F"{dirpath}/{project_name}" ).glob('*/**' ):
                       snake_case__					:      List[Any]  			=						list(filter(lambda __lowerCamelCase : x.is_file()    ,       child.iterdir() ) )[0]
                 self.assertNotEqual(str(__lowerCamelCase )    ,       '' )





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__					:      Optional[Any]  			=						'test_project_with_log'
           with tempfile.TemporaryDirectory() as dirpath:
                 snake_case__					:      Union[str, Any]  			=						Accelerator(log_with='tensorboard'    ,       project_dir=__lowerCamelCase )
                 accelerator.init_trackers(__lowerCamelCase )
                 snake_case__					:      Tuple  			=						{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
                 accelerator.log(__lowerCamelCase    ,       step=0 )
                 accelerator.end_training()
                 # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
                 # Names are randomly generated each time
                 snake_case__					:      Any  			=						list(filter(lambda __lowerCamelCase : x.is_file()    ,       Path(F"{dirpath}/{project_name}" ).iterdir() ) )[0]
                 self.assertNotEqual(str(__lowerCamelCase )    ,       '' )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__					:      str  			=						'test_project_with_log'
           with tempfile.TemporaryDirectory() as dirpath:
                 snake_case__					:      Optional[int]  			=						Accelerator(log_with='tensorboard'    ,       project_dir=__lowerCamelCase )
                 accelerator.init_trackers(__lowerCamelCase )
                 snake_case__					:      Union[str, Any]  			=						{'tensor': torch.tensor(1 )}
                 accelerator.log(__lowerCamelCase    ,       step=0 )
                 accelerator.end_training()
                 # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
                 # Names are randomly generated each time
                 snake_case__					:      Tuple  			=						list(filter(lambda __lowerCamelCase : x.is_file()    ,       Path(F"{dirpath}/{project_name}" ).iterdir() ) )[0]
                 # Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
                 with open(__lowerCamelCase    ,       'rb' ) as f:
                       snake_case__					:      List[str]  			=						f.read()
                 snake_case__					:      Optional[Any]  			=						False
                 while data:
                       snake_case__					:      Dict  			=						struct.unpack('Q'    ,       data[:8] )

                       snake_case__					:      Dict  			=						data[12 : 12 + int(header[0] )]  # 8+4
                       snake_case__					:      Any  			=						data[12 + int(header[0] ) + 4 :]
                       snake_case__					:      Union[str, Any]  			=						event_pba.Event()

                       event.ParseFromString(__lowerCamelCase )
                       if event.HasField('summary' ):
                             for value in event.summary.value:
                                   if value.simple_value == 1.0 and value.tag == "tensor":
                                         snake_case__					:      int  			=						True
                 self.assertTrue(__lowerCamelCase    ,       'Converted tensor was not found in the log file!' )





     def  _lowerCAmelCase			(    self    :				Dict ):
           with self.assertRaisesRegex(__lowerCamelCase    ,       'Logging with `tensorboard` requires a `logging_dir`' ):
                 snake_case__					:      Any  			=						Accelerator(log_with='tensorboard' )
           with tempfile.TemporaryDirectory() as dirpath:
                 snake_case__					:      List[Any]  			=						Accelerator(log_with='tensorboard'    ,       project_dir=__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__					:      List[str]  			=						ProjectConfiguration(total_limit=30 )
           with tempfile.TemporaryDirectory() as dirpath:
                 snake_case__					:      List[str]  			=						Accelerator(log_with='tensorboard'    ,       project_dir=__lowerCamelCase    ,       project_config=__lowerCamelCase )






@require_wandb
@mock.patch.dict(os.environ	,					{"WANDB_MODE": "offline"}       )
class 					lowercase_			(	lowerCAmelCase_	,					lowerCAmelCase_       ):





     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           super().setUp()
           # wandb let's us override where logs are stored to via the WANDB_DIR env var
           self.add_mocks(mock.patch.dict(os.environ    ,       {'WANDB_DIR': self.tmpdir} ) )





     @staticmethod
     def  _lowerCAmelCase			(    __lowerCamelCase    :				str    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				bool = True ):
           snake_case__					:      Dict  			=						rF"{section} ([\S\s]*?)\n\n"
           if record:
                 snake_case__					:      str  			=						rF"Record: {pattern}"
           snake_case__					:      str  			=						re.findall(__lowerCamelCase    ,       __lowerCamelCase )[0]
           # A config
           if section == "config" or section == "history":
                 snake_case__					:      int  			=						re.findall(r'"([a-zA-Z0-9_.,]+)'    ,       __lowerCamelCase )
                 return {key: val for key, val in zip(cleaned_record[0::2]    ,       cleaned_record[1::2] )}
           # Everything else
           else:
                 return dict(re.findall(r'(\w+): "([^\s]+)"'    ,       __lowerCamelCase ) )





     @skip
     def  _lowerCAmelCase			(    self    :				Any ):
           snake_case__					:      Any  			=						'test_project_with_config'
           snake_case__					:      List[str]  			=						Accelerator(log_with='wandb' )
           snake_case__					:      List[str]  			=						{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
           snake_case__					:      Dict  			=						{'wandb': {'tags': ['my_tag']}}
           accelerator.init_trackers(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )
           snake_case__					:      List[Any]  			=						{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
           accelerator.log(__lowerCamelCase    ,       step=0 )
           accelerator.end_training()
           # The latest offline log is stored at wandb/latest-run/*.wandb
           for child in Path(F"{self.tmpdir}/wandb/latest-run" ).glob('*' ):
                 if child.is_file() and child.suffix == ".wandb":
                       snake_case__					:      Dict  			=						subprocess.check_output(
                           ['wandb', 'sync', '--view', '--verbose', str(__lowerCamelCase )]    ,       env=os.environ.copy() ).decode('utf8'    ,       'ignore' )
                       break

        # Check HPS through careful parsing and cleaning
           snake_case__					:      int  			=						self.parse_log(__lowerCamelCase    ,       'config' )
           self.assertEqual(logged_items['num_iterations']    ,       '12' )
           self.assertEqual(logged_items['learning_rate']    ,       '0.01' )
           self.assertEqual(logged_items['some_boolean']    ,       'false' )
           self.assertEqual(logged_items['some_string']    ,       'some_value' )
           self.assertEqual(logged_items['some_string']    ,       'some_value' )

           # Run tags
           snake_case__					:      Any  			=						self.parse_log(__lowerCamelCase    ,       'run'    ,       __lowerCamelCase )
           self.assertEqual(logged_items['tags']    ,       'my_tag' )

           # Actual logging
           snake_case__					:      Union[str, Any]  			=						self.parse_log(__lowerCamelCase    ,       'history' )
           self.assertEqual(logged_items['total_loss']    ,       '0.1' )
           self.assertEqual(logged_items['iteration']    ,       '1' )
           self.assertEqual(logged_items['my_text']    ,       'some_value' )
           self.assertEqual(logged_items['_step']    ,       '0' )






def 		UpperCamelCase__			(		self							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Union[str, Any]:
      snake_case__					:      List[Any]  			=						run_name
      snake_case__					:      Optional[int]  			=						OfflineExperiment(project_name=__SCREAMING_SNAKE_CASE							,			offline_directory=__SCREAMING_SNAKE_CASE		)
      logger.info(f"Initialized offline CometML project {self.run_name}"		)
      logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!'		)






@require_comet_ml
@mock.patch.object(lowerCAmelCase_	,					"__init__"	,					lowerCAmelCase_       )
class 					lowercase_			(	unittest.TestCase       ):





     @staticmethod
     def  _lowerCAmelCase			(    __lowerCamelCase    :				List[str]    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				bool = False ):
           for log in log_list:
                 snake_case__					:      List[Any]  			=						json.loads(__lowerCamelCase )['payload']
                 if is_param and "param" in j.keys():
                       if j["param"]["paramName"] == key:
                             return j["param"]["paramValue"]
                 if "log_other" in j.keys():
                       if j["log_other"]["key"] == key:
                             return j["log_other"]["val"]
                 if "metric" in j.keys():
                       if j["metric"]["metricName"] == key:
                             return j["metric"]["metricValue"]





     def  _lowerCAmelCase			(    self    :				str ):
           with tempfile.TemporaryDirectory() as d:
                 snake_case__					:      List[str]  			=						CometMLTracker('test_project_with_config'    ,       __lowerCamelCase )
                 snake_case__					:      Tuple  			=						Accelerator(log_with=__lowerCamelCase )
                 snake_case__					:      Optional[int]  			=						{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
                 accelerator.init_trackers(__lowerCamelCase    ,       __lowerCamelCase )
                 accelerator.end_training()
                 snake_case__					:      Any  			=						os.listdir(__lowerCamelCase )[0]  # Comet is nice, it's just a zip file here
                 # We parse the raw logs
                 snake_case__					:      Optional[int]  			=						os.path.join(__lowerCamelCase    ,       __lowerCamelCase )
                 snake_case__					:      str  			=						zipfile.ZipFile(__lowerCamelCase    ,       'r' )
                 snake_case__					:      Any  			=						archive.open('messages.json' ).read().decode('utf-8' )
           snake_case__					:      Optional[int]  			=						log.split('\n' )[:-1]
           self.assertEqual(self.get_value_from_key(__lowerCamelCase    ,       'num_iterations'    ,       __lowerCamelCase )    ,       12 )
           self.assertEqual(self.get_value_from_key(__lowerCamelCase    ,       'learning_rate'    ,       __lowerCamelCase )    ,       0.0_1 )
           self.assertEqual(self.get_value_from_key(__lowerCamelCase    ,       'some_boolean'    ,       __lowerCamelCase )    ,       __lowerCamelCase )
           self.assertEqual(self.get_value_from_key(__lowerCamelCase    ,       'some_string'    ,       __lowerCamelCase )    ,       'some_value' )





     def  _lowerCAmelCase			(    self    :				Tuple ):
           with tempfile.TemporaryDirectory() as d:
                 snake_case__					:      List[str]  			=						CometMLTracker('test_project_with_config'    ,       __lowerCamelCase )
                 snake_case__					:      Optional[Any]  			=						Accelerator(log_with=__lowerCamelCase )
                 accelerator.init_trackers(__lowerCamelCase )
                 snake_case__					:      List[Any]  			=						{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
                 accelerator.log(__lowerCamelCase    ,       step=0 )
                 accelerator.end_training()
                 snake_case__					:      Union[str, Any]  			=						os.listdir(__lowerCamelCase )[0]  # Comet is nice, it's just a zip file here
                 # We parse the raw logs
                 snake_case__					:      Tuple  			=						os.path.join(__lowerCamelCase    ,       __lowerCamelCase )
                 snake_case__					:      List[str]  			=						zipfile.ZipFile(__lowerCamelCase    ,       'r' )
                 snake_case__					:      Optional[Any]  			=						archive.open('messages.json' ).read().decode('utf-8' )
           snake_case__					:      Optional[Any]  			=						log.split('\n' )[:-1]
           self.assertEqual(self.get_value_from_key(__lowerCamelCase    ,       'curr_step'    ,       __lowerCamelCase )    ,       0 )
           self.assertEqual(self.get_value_from_key(__lowerCamelCase    ,       'total_loss' )    ,       0.1 )
           self.assertEqual(self.get_value_from_key(__lowerCamelCase    ,       'iteration' )    ,       1 )
           self.assertEqual(self.get_value_from_key(__lowerCamelCase    ,       'my_text' )    ,       'some_value' )






class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= [
         "total_loss",
         "iteration",
         "my_text",
         "learning_rate",
         "num_iterations",
         "some_boolean",
         "some_string",
     ]

     A_       							= "my_custom_tracker"
     A_       							= False





     def __init__(    self    :				str    ,       __lowerCamelCase    :				str ):
           snake_case__					:      Union[str, Any]  			=						open(F"{dir}/log.csv"    ,       'w+' )
           snake_case__					:      Optional[int]  			=						csv.DictWriter(self.f    ,       fieldnames=self._col_names )
           self.writer.writeheader()





     @property
     def  _lowerCAmelCase			(    self    :				List[Any] ):
           return self.writer





     def  _lowerCAmelCase			(    self    :				Optional[int]    ,       __lowerCamelCase    :				dict ):
           logger.info('Call init' )
           self.writer.writerow(__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Optional[Any]    ,       __lowerCamelCase    :				dict    ,       __lowerCamelCase    :				Optional[int] ):
           logger.info('Call log' )
           self.writer.writerow(__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           self.f.close()






class 					lowercase_			(	unittest.TestCase       ):





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           with tempfile.TemporaryDirectory() as d:
                 snake_case__					:      Tuple  			=						MyCustomTracker(__lowerCamelCase )
                 snake_case__					:      List[str]  			=						Accelerator(log_with=__lowerCamelCase )
                 snake_case__					:      List[Any]  			=						{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
                 accelerator.init_trackers('Some name'    ,       __lowerCamelCase )
                 accelerator.end_training()
                 with open(F"{d}/log.csv"    ,       'r' ) as f:
                       snake_case__					:      Optional[int]  			=						csv.DictReader(__lowerCamelCase )
                       snake_case__					:      Tuple  			=						next(__lowerCamelCase )
                       snake_case__					:      List[Any]  			=						{
                           'total_loss': '',
                           'iteration': '',
                           'my_text': '',
                           'learning_rate': '0.01',
                           'num_iterations': '12',
                           'some_boolean': 'False',
                           'some_string': 'some_value',
                       }
                       self.assertDictEqual(__lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Tuple ):
           with tempfile.TemporaryDirectory() as d:
                 snake_case__					:      Optional[Any]  			=						MyCustomTracker(__lowerCamelCase )
                 snake_case__					:      Tuple  			=						Accelerator(log_with=__lowerCamelCase )
                 accelerator.init_trackers('Some name' )
                 snake_case__					:      int  			=						{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
                 accelerator.log(__lowerCamelCase    ,       step=0 )
                 accelerator.end_training()
                 with open(F"{d}/log.csv"    ,       'r' ) as f:
                       snake_case__					:      Dict  			=						csv.DictReader(__lowerCamelCase )
                       snake_case__					:      Optional[Any]  			=						next(__lowerCamelCase )
                       snake_case__					:      Union[str, Any]  			=						{
                           'total_loss': '0.1',
                           'iteration': '1',
                           'my_text': 'some_value',
                           'learning_rate': '',
                           'num_iterations': '',
                           'some_boolean': '',
                           'some_string': '',
                       }
                       self.assertDictEqual(__lowerCamelCase    ,       __lowerCamelCase )






'''simple docstring'''






from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A_ 			=		"\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n"

A_ 			=		"\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow's tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n"


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






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Dict:
      snake_case__					:      Dict  			=						[rgram for rgrams in rgramslist for rgram in rgrams]
      snake_case__					:      int  			=						Counter(__SCREAMING_SNAKE_CASE		)

      snake_case__					:      Tuple  			=						Counter(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Union[str, Any]  			=						Counter()
      for sgram, scount in sgramcounter.items():
            snake_case__					:      Any  			=						scount * numref

      snake_case__					:      Any  			=						Counter(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Dict  			=						Counter()
      for cgram, ccount in cgramcounter.items():
            snake_case__					:      Optional[int]  			=						ccount * numref

      # KEEP
      snake_case__					:      Union[str, Any]  			=						sgramcounter_rep & cgramcounter_rep
      snake_case__					:      Optional[int]  			=						keepgramcounter_rep & rgramcounter
      snake_case__					:      Tuple  			=						sgramcounter_rep & rgramcounter

      snake_case__					:      Optional[int]  			=						0
      snake_case__					:      Tuple  			=						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.
      snake_case__					:      List[Any]  			=						1
      snake_case__					:      Union[str, Any]  			=						1
      if len(__SCREAMING_SNAKE_CASE		) > 0:
            snake_case__					:      List[Any]  			=						keeptmpscorea / len(__SCREAMING_SNAKE_CASE		)
      if len(__SCREAMING_SNAKE_CASE		) > 0:
            # Fix an alleged bug [2] in the keep score computation.
            # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
            snake_case__					:      Dict  			=						keeptmpscorea / sum(keepgramcounterall_rep.values()		)
      snake_case__					:      Optional[Any]  			=						0
      if keepscore_precision > 0 or keepscore_recall > 0:
            snake_case__					:      str  			=						2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

      # DELETION
      snake_case__					:      Optional[Any]  			=						sgramcounter_rep - cgramcounter_rep
      snake_case__					:      int  			=						delgramcounter_rep - rgramcounter
      snake_case__					:      Tuple  			=						sgramcounter_rep - rgramcounter
      snake_case__					:      List[str]  			=						0
      snake_case__					:      List[str]  			=						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.
      snake_case__					:      Optional[Any]  			=						1
      if len(__SCREAMING_SNAKE_CASE		) > 0:
            snake_case__					:      str  			=						deltmpscorea / len(__SCREAMING_SNAKE_CASE		)

      # ADDITION
      snake_case__					:      Optional[int]  			=						set(__SCREAMING_SNAKE_CASE		) - set(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      str  			=						set(__SCREAMING_SNAKE_CASE		) & set(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Any  			=						set(__SCREAMING_SNAKE_CASE		) - set(__SCREAMING_SNAKE_CASE		)

      snake_case__					:      List[str]  			=						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.
      snake_case__					:      Optional[Any]  			=						1
      snake_case__					:      Union[str, Any]  			=						1
      if len(__SCREAMING_SNAKE_CASE		) > 0:
            snake_case__					:      str  			=						addtmpscore / len(__SCREAMING_SNAKE_CASE		)
      if len(__SCREAMING_SNAKE_CASE		) > 0:
            snake_case__					:      List[Any]  			=						addtmpscore / len(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      str  			=						0
      if addscore_precision > 0 or addscore_recall > 0:
            snake_case__					:      int  			=						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

      return (keepscore, delscore_precision, addscore)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Tuple:
      snake_case__					:      Any  			=						len(__SCREAMING_SNAKE_CASE		)

      snake_case__					:      str  			=						ssent.split(' '		)
      snake_case__					:      Optional[int]  			=						csent.split(' '		)
      snake_case__					:      Optional[Any]  			=						[]
      snake_case__					:      int  			=						[]
      snake_case__					:      Optional[int]  			=						[]
      snake_case__					:      Union[str, Any]  			=						[]
      snake_case__					:      Tuple  			=						[]
      snake_case__					:      Tuple  			=						[]

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

      for i in range(0							,			len(__SCREAMING_SNAKE_CASE		) - 1		):
            if i < len(__SCREAMING_SNAKE_CASE		) - 1:
                  snake_case__					:      Optional[int]  			=						sagrams[i] + ' ' + sagrams[i + 1]
                  sagrams.append(__SCREAMING_SNAKE_CASE		)
            if i < len(__SCREAMING_SNAKE_CASE		) - 2:
                  snake_case__					:      Dict  			=						sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
                  sagrams.append(__SCREAMING_SNAKE_CASE		)
            if i < len(__SCREAMING_SNAKE_CASE		) - 3:
                  snake_case__					:      List[Any]  			=						sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
                  sagrams.append(__SCREAMING_SNAKE_CASE		)

      for i in range(0							,			len(__SCREAMING_SNAKE_CASE		) - 1		):
            if i < len(__SCREAMING_SNAKE_CASE		) - 1:
                  snake_case__					:      Any  			=						cagrams[i] + ' ' + cagrams[i + 1]
                  cagrams.append(__SCREAMING_SNAKE_CASE		)
            if i < len(__SCREAMING_SNAKE_CASE		) - 2:
                  snake_case__					:      Any  			=						cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
                  cagrams.append(__SCREAMING_SNAKE_CASE		)
            if i < len(__SCREAMING_SNAKE_CASE		) - 3:
                  snake_case__					:      Tuple  			=						cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
                  cagrams.append(__SCREAMING_SNAKE_CASE		)

      ((snake_case__)			,       (snake_case__)			,       (snake_case__))					:      List[str]  			=						SARIngram(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      ((snake_case__)			,       (snake_case__)			,       (snake_case__))					:      str  			=						SARIngram(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      ((snake_case__)			,       (snake_case__)			,       (snake_case__))					:      Dict  			=						SARIngram(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      ((snake_case__)			,       (snake_case__)			,       (snake_case__))					:      Tuple  			=						SARIngram(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Tuple  			=						sum([keepascore, keepascore, keepascore, keepascore]		) / 4
      snake_case__					:      Optional[int]  			=						sum([delascore, delascore, delascore, delascore]		) / 4
      snake_case__					:      Optional[int]  			=						sum([addascore, addascore, addascore, addascore]		) / 4
      snake_case__					:      Union[str, Any]  			=						(avgkeepscore + avgdelscore + avgaddscore) / 3
      return finalscore






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE = True							,			__SCREAMING_SNAKE_CASE = "13a"							,			__SCREAMING_SNAKE_CASE = True		)						->      Optional[int]:
      # Normalization is requried for the ASSET dataset (one of the primary
      # datasets in sentence simplification) to allow using space
      # to split the sentence. Even though Wiki-Auto and TURK datasets,
      # do not require normalization, we do it for consistency.
      # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
      # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

      if lowercase:
            snake_case__					:      Dict  			=						sentence.lower()

      if tokenizer in ["13a", "intl"]:
            if version.parse(sacrebleu.__version__		).major >= 2:
                  snake_case__					:      List[str]  			=						sacrebleu.metrics.bleu._get_tokenizer(__SCREAMING_SNAKE_CASE		)()(__SCREAMING_SNAKE_CASE		)
            else:
                  snake_case__					:      Any  			=						sacrebleu.TOKENIZERS[tokenizer]()(__SCREAMING_SNAKE_CASE		)
      elif tokenizer == "moses":
            snake_case__					:      List[str]  			=						sacremoses.MosesTokenizer().tokenize(__SCREAMING_SNAKE_CASE							,			return_str=__SCREAMING_SNAKE_CASE							,			escape=__SCREAMING_SNAKE_CASE		)
      elif tokenizer == "penn":
            snake_case__					:      int  			=						sacremoses.MosesTokenizer().penn_tokenize(__SCREAMING_SNAKE_CASE							,			return_str=__SCREAMING_SNAKE_CASE		)
      else:
            snake_case__					:      List[Any]  			=						sentence

      if not return_str:
            snake_case__					:      List[Any]  			=						normalized_sent.split()

      return normalized_sent






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,					_KWARGS_DESCRIPTION       )
class 					lowercase_			(	datasets.Metric       ):





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