






import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       CpmAntConfig,
       CpmAntForCausalLM,
       CpmAntModel,
       CpmAntTokenizer,
   )


@require_torch
class        SCREAMING_SNAKE_CASE	:
     '''simple docstring'''


     def __init__( self				:		List[Any]    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		Tuple=2    ,       UpperCAmelCase_				:		Optional[int]=8    ,       UpperCAmelCase_				:		int=True    ,       UpperCAmelCase_				:		Any=False    ,       UpperCAmelCase_				:		Optional[int]=False    ,       UpperCAmelCase_				:		Tuple=False    ,       UpperCAmelCase_				:		Any=False    ,       UpperCAmelCase_				:		str=99    ,       UpperCAmelCase_				:		Tuple=32    ,       UpperCAmelCase_				:		Any=3    ,       UpperCAmelCase_				:		Tuple=4    ,       UpperCAmelCase_				:		int=37    ,       UpperCAmelCase_				:		Optional[Any]=32    ,       UpperCAmelCase_				:		Tuple=128    ,       UpperCAmelCase_				:		Dict=8    ,       UpperCAmelCase_				:		Tuple=8    ,       UpperCAmelCase_				:		Any=8    ,       UpperCAmelCase_				:		Union[str, Any]=1.0    ,       UpperCAmelCase_				:		Dict=True    ,       ):
       SCREAMING_SNAKE_CASE				:					List[str]		  =			parent
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			batch_size
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			seq_length
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			is_training
       SCREAMING_SNAKE_CASE				:					str		  =			use_token_type_ids
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			use_input_mask
       SCREAMING_SNAKE_CASE				:					int		  =			use_labels
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			use_mc_token_ids
       SCREAMING_SNAKE_CASE				:					Any		  =			vocab_size
       SCREAMING_SNAKE_CASE				:					List[str]		  =			hidden_size
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			num_hidden_layers
       SCREAMING_SNAKE_CASE				:					str		  =			num_attention_heads
       SCREAMING_SNAKE_CASE				:					Tuple		  =			intermediate_size
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			num_buckets
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			max_distance
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			prompt_length
       SCREAMING_SNAKE_CASE				:					str		  =			prompt_types
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			segment_types
       SCREAMING_SNAKE_CASE				:					Dict		  =			init_std
       SCREAMING_SNAKE_CASE				:					Tuple		  =			return_dict


     def  _A				( self				:		List[str]					):
       SCREAMING_SNAKE_CASE				:					Any		  =			{}
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size					).type(torch.intaa					)
       SCREAMING_SNAKE_CASE				:					Dict		  =			False

       SCREAMING_SNAKE_CASE				:					List[Any]		  =			self.get_config()

       return (config, input_ids)


     def  _A				( self				:		List[Any]					):
       return CpmAntConfig(
           vocab_size=self.vocab_size    ,       hidden_size=self.hidden_size    ,       num_hidden_layers=self.num_hidden_layers    ,       num_attention_heads=self.num_attention_heads    ,       dim_ff=self.intermediate_size    ,       position_bias_num_buckets=self.num_buckets    ,       position_bias_max_distance=self.max_distance    ,       prompt_types=self.prompt_types    ,       prompt_length=self.prompt_length    ,       segment_types=self.segment_types    ,       use_cache=UpperCAmelCase_    ,       init_std=self.init_std    ,       return_dict=self.return_dict    ,       )


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		str    ,       UpperCAmelCase_				:		int    ,       *UpperCAmelCase_				:		Dict					):
       SCREAMING_SNAKE_CASE				:					List[str]		  =			CpmAntModel(config=UpperCAmelCase_					)
       model.to(UpperCAmelCase_					)
       model.eval()

       SCREAMING_SNAKE_CASE				:					str		  =			model(**UpperCAmelCase_					).last_hidden_state

       self.parent.assertEqual(hidden_states.shape    ,       (self.batch_size, self.seq_length, config.hidden_size)					)


     def  _A				( self				:		Dict    ,       UpperCAmelCase_				:		List[str]    ,       UpperCAmelCase_				:		Any    ,       *UpperCAmelCase_				:		List[str]					):
       SCREAMING_SNAKE_CASE				:					Any		  =			CpmAntForCausalLM(UpperCAmelCase_					)
       model.to(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Dict		  =			input_ids["input_ids"].to(UpperCAmelCase_					)
       model.eval()

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			model(**UpperCAmelCase_					)
       self.parent.assertEqual(
           model_output.logits.shape    ,       (self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)    ,       )






     def  _A				( self				:		Dict					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Tuple		  =			self.prepare_config_and_inputs()
       return config, inputs_dict





@require_torch
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase					,						lowerCAmelCase					,						unittest.TestCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						Any								=		(CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
     UpperCamelCase_							:						Dict								=		(
         {'''feature-extraction''': CpmAntModel, '''text-generation''': CpmAntForCausalLM} if is_torch_available() else {}
     )

     UpperCamelCase_							:						Optional[Any]								=		False
     UpperCamelCase_							:						Union[str, Any]								=		False
     UpperCamelCase_							:						List[str]								=		False
     UpperCamelCase_							:						Tuple								=		False
     UpperCamelCase_							:						Optional[int]								=		False


     def  _A				( self				:		int					):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			CpmAntModelTester(self					)
       SCREAMING_SNAKE_CASE				:					int		  =			ConfigTester(self    ,       config_class=UpperCAmelCase_					)


     def  _A				( self				:		List[Any]					):
       self.config_tester.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()


     def  _A				( self				:		Tuple					):
       unittest.skip("CPMAnt doesn't support input_embeds."					)(self.test_inputs_embeds					)


     def  _A				( self				:		int					):
       unittest.skip(
           "CPMAnt doesn't support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model."					)(self.test_retain_grad_hidden_states_attentions					)


     def  _A				( self				:		int					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[Any]		  =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_cpmant_model(UpperCAmelCase_    ,       UpperCAmelCase_					)






     def  _A				( self				:		Any					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Any		  =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head_model(UpperCAmelCase_    ,       UpperCAmelCase_					)





@require_torch
class        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     @tooslow
     def  _A				( self				:		int					):
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			"今天天气真好！"
       SCREAMING_SNAKE_CASE				:					Any		  =			"openbmb/cpm-ant-10b"
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			CpmAntModel.from_pretrained(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CpmAntTokenizer.from_pretrained(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			tokenizer(UpperCAmelCase_    ,       return_tensors="pt"					)
       SCREAMING_SNAKE_CASE				:					int		  =			model(**UpperCAmelCase_					).last_hidden_state

       SCREAMING_SNAKE_CASE				:					List[str]		  =			torch.tensor(
           [[[6.1_708, 5.9_244, 1.0_835], [6.5_207, 6.2_893, -11.3_324], [-1.0_107, -0.0_576, -5.9_577]]]    ,       )
       self.assertTrue(torch.allclose(hidden_states[:, :3, :3]    ,       UpperCAmelCase_    ,       atol=1E-2					)					)





@require_torch
class        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     @tooslow
     def  _A				( self				:		Any					):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			"今天天气真好！"
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			"openbmb/cpm-ant-10b"
       SCREAMING_SNAKE_CASE				:					Tuple		  =			CpmAntForCausalLM.from_pretrained(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					str		  =			CpmAntTokenizer.from_pretrained(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tokenizer(UpperCAmelCase_    ,       return_tensors="pt"					)
       SCREAMING_SNAKE_CASE				:					Dict		  =			model(**UpperCAmelCase_					).logits

       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			torch.tensor(
           [[[-6.4_267, -6.4_083, -6.3_958], [-5.8_802, -5.9_447, -5.7_811], [-5.3_896, -5.4_820, -5.4_295]]]    ,       )
       self.assertTrue(torch.allclose(hidden_states[:, :3, :3]    ,       UpperCAmelCase_    ,       atol=1E-2					)					)


     @tooslow
     def  _A				( self				:		Tuple					):
       SCREAMING_SNAKE_CASE				:					int		  =			"openbmb/cpm-ant-10b"
       SCREAMING_SNAKE_CASE				:					str		  =			CpmAntForCausalLM.from_pretrained(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Any		  =			CpmAntTokenizer.from_pretrained(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					int		  =			"今天天气不错，"
       SCREAMING_SNAKE_CASE				:					Any		  =			"今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的"
       SCREAMING_SNAKE_CASE				:					int		  =			tokenizer(UpperCAmelCase_    ,       return_tensors="pt"					)
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			model.generate(**UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					int		  =			tokenizer.batch_decode(UpperCAmelCase_					)
       self.assertEqual(UpperCAmelCase_    ,       UpperCAmelCase_					)






     @tooslow
     def  _A				( self				:		List[Any]					):
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			"openbmb/cpm-ant-10b"
       SCREAMING_SNAKE_CASE				:					Tuple		  =			CpmAntForCausalLM.from_pretrained(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CpmAntTokenizer.from_pretrained(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			["今天天气不错，", "新年快乐，万事如意！"]
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[
           "今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的",
           "新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的",
       ]
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tokenizer(UpperCAmelCase_    ,       return_tensors="pt"    ,       padding=UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Any		  =			model.generate(**UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					str		  =			tokenizer.batch_decode(UpperCAmelCase_					)
       self.assertEqual(UpperCAmelCase_    ,       UpperCAmelCase_					)














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}






