
import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_PRETRAINING_MAPPING,
     MobileBertForMaskedLM,
     MobileBertForMultipleChoice,
     MobileBertForNextSentencePrediction,
     MobileBertForPreTraining,
     MobileBertForQuestionAnswering,
     MobileBertForSequenceClassification,
     MobileBertForTokenClassification,
     MobileBertModel,
 )


class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       int				,       _snake_case		:       Tuple				,       _snake_case		:       Tuple=13				,       _snake_case		:       Optional[int]=7				,       _snake_case		:       str=True				,       _snake_case		:       Dict=True				,       _snake_case		:       Union[str, Any]=True				,       _snake_case		:       Tuple=True				,       _snake_case		:       List[Any]=99				,       _snake_case		:       Optional[int]=64				,       _snake_case		:       Dict=32				,       _snake_case		:       Any=5				,       _snake_case		:       Optional[int]=4				,       _snake_case		:       Union[str, Any]=37				,       _snake_case		:       Optional[int]="gelu"				,       _snake_case		:       str=0.1				,       _snake_case		:       str=0.1				,       _snake_case		:       Any=512				,       _snake_case		:       Dict=16				,       _snake_case		:       Any=2				,       _snake_case		:       str=0.02				,       _snake_case		:       Optional[int]=3				,       _snake_case		:       int=4				,       _snake_case		:       Optional[int]=None				,       ):
          __lowercase		:			Optional[Any]	    =       parent
          __lowercase		:			Dict	    =       batch_size
          __lowercase		:			Dict	    =       seq_length
          __lowercase		:			Any	    =       is_training
          __lowercase		:			Optional[int]	    =       use_input_mask
          __lowercase		:			int	    =       use_token_type_ids
          __lowercase		:			Optional[Any]	    =       use_labels
          __lowercase		:			Dict	    =       vocab_size
          __lowercase		:			str	    =       hidden_size
          __lowercase		:			int	    =       embedding_size
          __lowercase		:			int	    =       num_hidden_layers
          __lowercase		:			Optional[int]	    =       num_attention_heads
          __lowercase		:			List[Any]	    =       intermediate_size
          __lowercase		:			List[str]	    =       hidden_act
          __lowercase		:			Tuple	    =       hidden_dropout_prob
          __lowercase		:			Any	    =       attention_probs_dropout_prob
          __lowercase		:			Dict	    =       max_position_embeddings
          __lowercase		:			Any	    =       type_vocab_size
          __lowercase		:			Union[str, Any]	    =       type_sequence_label_size
          __lowercase		:			Tuple	    =       initializer_range
          __lowercase		:			List[str]	    =       num_labels
          __lowercase		:			List[Any]	    =       num_choices
          __lowercase		:			List[Any]	    =       scope




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Union[str, Any]	    =       ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size				)

          __lowercase		:			Optional[Any]	    =       None
          if self.use_input_mask:
              __lowercase		:			str	    =       random_attention_mask([self.batch_size, self.seq_length]				)

          __lowercase		:			List[str]	    =       None
          if self.use_token_type_ids:
              __lowercase		:			Dict	    =       ids_tensor([self.batch_size, self.seq_length]				,       self.type_vocab_size				)

          __lowercase		:			Optional[int]	    =       None
          __lowercase		:			Union[str, Any]	    =       None
          __lowercase		:			Any	    =       None
          if self.use_labels:
              __lowercase		:			List[str]	    =       ids_tensor([self.batch_size]				,       self.type_sequence_label_size				)
              __lowercase		:			Optional[Any]	    =       ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels				)
              __lowercase		:			List[str]	    =       ids_tensor([self.batch_size]				,       self.num_choices				)

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

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




      def 		snake_case_			(      self		:       List[Any]				):
          return MobileBertConfig(
              vocab_size=self.vocab_size				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       intermediate_size=self.intermediate_size				,       embedding_size=self.embedding_size				,       hidden_act=self.hidden_act				,       hidden_dropout_prob=self.hidden_dropout_prob				,       attention_probs_dropout_prob=self.attention_probs_dropout_prob				,       max_position_embeddings=self.max_position_embeddings				,       type_vocab_size=self.type_vocab_size				,       is_decoder=_snake_case				,       initializer_range=self.initializer_range				,       )




      def 		snake_case_			(      self		:       Any				,       _snake_case		:       int				,       _snake_case		:       List[Any]				,       _snake_case		:       Any				,       _snake_case		:       Optional[int]				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Dict				,       _snake_case		:       List[str]				):
          __lowercase		:			Any	    =       MobileBertModel(config=_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			Optional[int]	    =       model(_snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case				)
          __lowercase		:			Dict	    =       model(_snake_case				,       token_type_ids=_snake_case				)
          __lowercase		:			str	    =       model(_snake_case				)

          self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)				)
          self.parent.assertEqual(result.pooler_output.shape				,       (self.batch_size, self.hidden_size)				)




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       str				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       List[Any]				,       _snake_case		:       Optional[int]				,       _snake_case		:       Any				,       _snake_case		:       Tuple				,       _snake_case		:       Union[str, Any]				):
          __lowercase		:			Union[str, Any]	    =       MobileBertForMaskedLM(config=_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			Tuple	    =       model(_snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case				,       labels=_snake_case				)
          self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)				)




      def 		snake_case_			(      self		:       Optional[int]				,       _snake_case		:       List[Any]				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Any				,       _snake_case		:       List[Any]				,       _snake_case		:       int				,       _snake_case		:       Optional[Any]				,       _snake_case		:       List[str]				):
          __lowercase		:			List[str]	    =       MobileBertForNextSentencePrediction(config=_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			str	    =       model(
              _snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case				,       labels=_snake_case				,       )
          self.parent.assertEqual(result.logits.shape				,       (self.batch_size, 2)				)




      def 		snake_case_			(      self		:       Optional[int]				,       _snake_case		:       str				,       _snake_case		:       Any				,       _snake_case		:       Dict				,       _snake_case		:       Tuple				,       _snake_case		:       Optional[Any]				,       _snake_case		:       str				,       _snake_case		:       Dict				):
          __lowercase		:			int	    =       MobileBertForPreTraining(config=_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			str	    =       model(
              _snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case				,       labels=_snake_case				,       next_sentence_label=_snake_case				,       )
          self.parent.assertEqual(result.prediction_logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)				)
          self.parent.assertEqual(result.seq_relationship_logits.shape				,       (self.batch_size, 2)				)




      def 		snake_case_			(      self		:       Union[str, Any]				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       str				,       _snake_case		:       List[Any]				,       _snake_case		:       Optional[Any]				,       _snake_case		:       List[Any]				,       _snake_case		:       Any				,       _snake_case		:       str				):
          __lowercase		:			Any	    =       MobileBertForQuestionAnswering(config=_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			Union[str, Any]	    =       model(
              _snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case				,       start_positions=_snake_case				,       end_positions=_snake_case				,       )
          self.parent.assertEqual(result.start_logits.shape				,       (self.batch_size, self.seq_length)				)
          self.parent.assertEqual(result.end_logits.shape				,       (self.batch_size, self.seq_length)				)




      def 		snake_case_			(      self		:       List[Any]				,       _snake_case		:       Tuple				,       _snake_case		:       Tuple				,       _snake_case		:       Any				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Optional[int]				,       _snake_case		:       Optional[int]				,       _snake_case		:       Any				):
          __lowercase		:			List[Any]	    =       self.num_labels
          __lowercase		:			int	    =       MobileBertForSequenceClassification(_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			Any	    =       model(_snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case				,       labels=_snake_case				)
          self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)				)




      def 		snake_case_			(      self		:       Dict				,       _snake_case		:       Any				,       _snake_case		:       Any				,       _snake_case		:       int				,       _snake_case		:       int				,       _snake_case		:       str				,       _snake_case		:       Tuple				,       _snake_case		:       int				):
          __lowercase		:			List[Any]	    =       self.num_labels
          __lowercase		:			Tuple	    =       MobileBertForTokenClassification(config=_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			int	    =       model(_snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case				,       labels=_snake_case				)
          self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)				)




      def 		snake_case_			(      self		:       Any				,       _snake_case		:       Dict				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Optional[int]				,       _snake_case		:       Tuple				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Optional[Any]				):
          __lowercase		:			str	    =       self.num_choices
          __lowercase		:			int	    =       MobileBertForMultipleChoice(config=_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			str	    =       input_ids.unsqueeze(1				).expand(-1				,       self.num_choices				,       -1				).contiguous()
          __lowercase		:			int	    =       token_type_ids.unsqueeze(1				).expand(-1				,       self.num_choices				,       -1				).contiguous()
          __lowercase		:			Dict	    =       input_mask.unsqueeze(1				).expand(-1				,       self.num_choices				,       -1				).contiguous()
          __lowercase		:			Tuple	    =       model(
              _snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case				,       labels=_snake_case				,       )
          self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_choices)				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			str	    =       self.prepare_config_and_inputs()
          (
              (
              __lowercase		
          )    ,      (
              __lowercase		
          )    ,      (
              __lowercase		
          )    ,      (
              __lowercase		
          )    ,      (
              __lowercase		
          )    ,      (
              __lowercase		
          )    ,      (
              __lowercase		
          )    ,      		
          )		:			Optional[Any]	    =       config_and_inputs
          __lowercase		:			Any	    =       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict






@require_torch
class 						__lowerCAmelCase	(	__UpperCAmelCase , __UpperCAmelCase , unittest.TestCase      ):



      """simple docstring"""







      A__	:			List[Any]        =    (
          (
              MobileBertModel,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      A__	:			str        =    (
          {
              '''feature-extraction''': MobileBertModel,
              '''fill-mask''': MobileBertForMaskedLM,
              '''question-answering''': MobileBertForQuestionAnswering,
              '''text-classification''': MobileBertForSequenceClassification,
              '''token-classification''': MobileBertForTokenClassification,
              '''zero-shot''': MobileBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      A__	:			Optional[int]        =    True




      def 		snake_case_			(      self		:       int				,       _snake_case		:       List[Any]				,       _snake_case		:       List[Any]				,       _snake_case		:       List[str]=False				):
          __lowercase		:			int	    =       super()._prepare_for_class(_snake_case				,       _snake_case				,       return_labels=_snake_case				)

          if return_labels:
              if model_class in get_values(_snake_case				):
                  __lowercase		:			Union[str, Any]	    =       torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.seq_length)				,       dtype=torch.long				,       device=_snake_case				)
                  __lowercase		:			Dict	    =       torch.zeros(
                      self.model_tester.batch_size				,       dtype=torch.long				,       device=_snake_case				)
          return inputs_dict




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			str	    =       MobileBertModelTester(self				)
          __lowercase		:			Tuple	    =       ConfigTester(self				,       config_class=_snake_case				,       hidden_size=37				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          self.config_tester.run_common_tests()




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Optional[int]	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_model(*_snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Any	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_masked_lm(*_snake_case				)




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			Tuple	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Union[str, Any]	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			List[Any]	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_pretraining(*_snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			Dict	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_question_answering(*_snake_case				)




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			Dict	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Any	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_token_classification(*_snake_case				)






def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						List[Any]:

    '''simple docstring'''





    return torch.tensor(
        __lowerCAmelCase	,					dtype=torch.long	,					device=__lowerCAmelCase	,					)


__lowerCAmelCase					:			Optional[Any]			      =		1e-3





@require_torch
@require_sentencepiece
@require_tokenizers
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      @slow
      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Union[str, Any]	    =       MobileBertModel.from_pretrained('''google/mobilebert-uncased'''				).to(_snake_case				)
          __lowercase		:			Union[str, Any]	    =       _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]				)
          with torch.no_grad():
              __lowercase		:			List[Any]	    =       model(_snake_case				)[0]
          __lowercase		:			str	    =       torch.Size((1, 9, 512)				)
          self.assertEqual(output.shape				,       _snake_case				)
          __lowercase		:			List[str]	    =       torch.tensor(
              [
                  [
                      [-2.4_7_3_6_5_2_6E0_7, 8.2_6_9_1_6_5_6E0_4, 1.6_5_2_1_8_3_8E0_5],
                      [-5.7_5_4_1_7_0_4E-0_1, 3.9_0_5_6_0_2_2E0_0, 4.4_0_1_1_5_0_7E0_0],
                      [2.6_0_4_7_3_5_9E0_0, 1.5_6_7_7_6_5_2E0_0, -1.7_3_2_4_1_8_8E-0_1],
                  ]
              ]				,       device=_snake_case				,       )

          # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
          # ~1 difference, it's therefore not a good idea to measure using addition.
          # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
          # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
          __lowercase		:			Optional[int]	    =       torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE				)
          __lowercase		:			Union[str, Any]	    =       torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE				)

          self.assertTrue(lower_bound and upper_bound				)




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCAmelCase					:			Tuple			      =		"\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__lowerCAmelCase					:			Dict			      =		"\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


__lowerCAmelCase					:			Optional[int]			      =		"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\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\n    sacrebleu: sacrebleu score\n    exact: exact score\n\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 .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						str:

    def remove_articles(__lowerCAmelCase	):
        __lowercase		:			int	    =       re.compile(R'''\b(a|an|the)\b'''	,					re.UNICODE	)
        return re.sub(__lowerCAmelCase	,					''' '''	,					__lowerCAmelCase	)

    def white_space_fix(__lowerCAmelCase	):
        return " ".join(text.split()	)

    def remove_punc(__lowerCAmelCase	):
        __lowercase		:			int	    =       set(string.punctuation	)
        return "".join(ch for ch in text if ch not in exclude	)

    def lower(__lowerCAmelCase	):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase	)	)	)	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						List[str]:
    return int(normalize_answer(__lowerCAmelCase	) == normalize_answer(__lowerCAmelCase	)	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[int]:
    __lowercase		:			Union[str, Any]	    =       [any(compute_exact(__lowerCAmelCase	,					__lowerCAmelCase	) for ref in refs	) for pred, refs in zip(__lowerCAmelCase	,					__lowerCAmelCase	)]
    return (sum(__lowerCAmelCase	) / len(__lowerCAmelCase	)) * 100


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[int]:
    __lowercase		:			Union[str, Any]	    =       [rgram for rgrams in rgramslist for rgram in rgrams]
    __lowercase		:			Dict	    =       Counter(__lowerCAmelCase	)

    __lowercase		:			Union[str, Any]	    =       Counter(__lowerCAmelCase	)
    __lowercase		:			str	    =       Counter()
    for sgram, scount in sgramcounter.items():
        __lowercase		:			List[Any]	    =       scount * numref

    __lowercase		:			Optional[Any]	    =       Counter(__lowerCAmelCase	)
    __lowercase		:			Union[str, Any]	    =       Counter()
    for cgram, ccount in cgramcounter.items():
        __lowercase		:			Union[str, Any]	    =       ccount * numref

    # KEEP
    __lowercase		:			Optional[Any]	    =       sgramcounter_rep & cgramcounter_rep
    __lowercase		:			Dict	    =       keepgramcounter_rep & rgramcounter
    __lowercase		:			str	    =       sgramcounter_rep & rgramcounter

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

    # DELETION
    __lowercase		:			Tuple	    =       sgramcounter_rep - cgramcounter_rep
    __lowercase		:			str	    =       delgramcounter_rep - rgramcounter
    __lowercase		:			int	    =       sgramcounter_rep - rgramcounter
    __lowercase		:			Dict	    =       0
    __lowercase		:			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.
    __lowercase		:			Union[str, Any]	    =       1
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			Union[str, Any]	    =       deltmpscorea / len(__lowerCAmelCase	)

    # ADDITION
    __lowercase		:			int	    =       set(__lowerCAmelCase	) - set(__lowerCAmelCase	)
    __lowercase		:			Tuple	    =       set(__lowerCAmelCase	) & set(__lowerCAmelCase	)
    __lowercase		:			Any	    =       set(__lowerCAmelCase	) - set(__lowerCAmelCase	)

    __lowercase		:			Tuple	    =       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.
    __lowercase		:			int	    =       1
    __lowercase		:			Any	    =       1
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			Any	    =       addtmpscore / len(__lowerCAmelCase	)
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			str	    =       addtmpscore / len(__lowerCAmelCase	)
    __lowercase		:			int	    =       0
    if addscore_precision > 0 or addscore_recall > 0:
        __lowercase		:			Any	    =       2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						Union[str, Any]:
    __lowercase		:			Tuple	    =       len(__lowerCAmelCase	)

    __lowercase		:			str	    =       ssent.split(''' '''	)
    __lowercase		:			List[str]	    =       csent.split(''' '''	)
    __lowercase		:			Tuple	    =       []
    __lowercase		:			Any	    =       []
    __lowercase		:			Optional[Any]	    =       []
    __lowercase		:			Optional[int]	    =       []
    __lowercase		:			Dict	    =       []
    __lowercase		:			Optional[int]	    =       []

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

    for i in range(0	,					len(__lowerCAmelCase	) - 1	):
        if i < len(__lowerCAmelCase	) - 1:
            __lowercase		:			Dict	    =       sagrams[i] + ''' ''' + sagrams[i + 1]
            sagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 2:
            __lowercase		:			int	    =       sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
            sagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 3:
            __lowercase		:			str	    =       sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
            sagrams.append(__lowerCAmelCase	)

    for i in range(0	,					len(__lowerCAmelCase	) - 1	):
        if i < len(__lowerCAmelCase	) - 1:
            __lowercase		:			int	    =       cagrams[i] + ''' ''' + cagrams[i + 1]
            cagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 2:
            __lowercase		:			Any	    =       cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
            cagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 3:
            __lowercase		:			Optional[int]	    =       cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
            cagrams.append(__lowerCAmelCase	)

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


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase = True	,					__lowerCAmelCase = "13a"	,					__lowerCAmelCase = True	) ->						List[str]:
    # 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:
        __lowercase		:			List[Any]	    =       sentence.lower()

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

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

    return normalized_sent


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						Union[str, Any]:
    if not (len(__lowerCAmelCase	) == len(__lowerCAmelCase	) == len(__lowerCAmelCase	)):
        raise ValueError('''Sources length must match predictions and references lengths.'''	)
    __lowercase		:			str	    =       0
    for src, pred, refs in zip(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	):
        sari_score += SARIsent(normalize(__lowerCAmelCase	)	,					normalize(__lowerCAmelCase	)	,					[normalize(__lowerCAmelCase	) for sent in refs]	)
    __lowercase		:			int	    =       sari_score / len(__lowerCAmelCase	)
    return 100 * sari_score


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase="exp"	,					__lowerCAmelCase=None	,					__lowerCAmelCase=False	,					__lowerCAmelCase=False	,					__lowerCAmelCase=False	,					) ->						str:
    __lowercase		:			List[Any]	    =       len(references[0]	)
    if any(len(__lowerCAmelCase	) != references_per_prediction for refs in references	):
        raise ValueError('''Sacrebleu requires the same number of references for each prediction'''	)
    __lowercase		:			int	    =       [[refs[i] for refs in references] for i in range(__lowerCAmelCase	)]
    __lowercase		:			List[str]	    =       sacrebleu.corpus_bleu(
        __lowerCAmelCase	,					__lowerCAmelCase	,					smooth_method=__lowerCAmelCase	,					smooth_value=__lowerCAmelCase	,					force=__lowerCAmelCase	,					lowercase=__lowerCAmelCase	,					use_effective_order=__lowerCAmelCase	,					)
    return output.score





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



      """simple docstring"""







      def 		snake_case_			(      self		:       Dict				):
          return datasets.MetricInfo(
              description=_DESCRIPTION				,       citation=_CITATION				,       inputs_description=_KWARGS_DESCRIPTION				,       features=datasets.Features(
                  {
                      '''predictions''': datasets.Value('''string'''				,       id='''sequence'''				),
                      '''references''': datasets.Sequence(datasets.Value('''string'''				,       id='''sequence'''				)				,       id='''references'''				),
                  }				)				,       codebase_urls=[
                  '''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
                  '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
                  '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
                  '''https://github.com/mjpost/sacreBLEU''',
              ]				,       reference_urls=[
                  '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
                  '''https://github.com/mjpost/sacreBLEU''',
                  '''https://en.wikipedia.org/wiki/BLEU''',
                  '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
              ]				,       )




      def 		snake_case_			(      self		:       Optional[int]				,       _snake_case		:       Optional[Any]				,       _snake_case		:       List[Any]				,       _snake_case		:       List[Any]				):
          __lowercase		:			List[Any]	    =       {}
          result.update({'''sari''': compute_sari(sources=_snake_case				,       predictions=_snake_case				,       references=_snake_case				)}				)
          result.update({'''sacrebleu''': compute_sacrebleu(predictions=_snake_case				,       references=_snake_case				)}				)
          result.update({'''exact''': compute_em(predictions=_snake_case				,       references=_snake_case				)}				)
          return result


