




'''simple docstring'''






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


A_ 			=		logging.get_logger(__name__)

A_ 			=		{
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}






class 					lowercase_			(	__UpperCAmelCase       ):
   A_       							= "mbart"
   A_       							= ["past_key_values"]
   A_       							= {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





   def __init__(    self    :				List[str]    ,       __lowerCamelCase    :				Optional[int]=50265    ,       __lowerCamelCase    :				List[str]=1024    ,       __lowerCamelCase    :				List[Any]=12    ,       __lowerCamelCase    :				Any=4096    ,       __lowerCamelCase    :				str=16    ,       __lowerCamelCase    :				Any=12    ,       __lowerCamelCase    :				List[Any]=4096    ,       __lowerCamelCase    :				Tuple=16    ,       __lowerCamelCase    :				str=0.0    ,       __lowerCamelCase    :				List[Any]=0.0    ,       __lowerCamelCase    :				Tuple=True    ,       __lowerCamelCase    :				Any=True    ,       __lowerCamelCase    :				Optional[int]="gelu"    ,       __lowerCamelCase    :				List[Any]=1024    ,       __lowerCamelCase    :				str=0.1    ,       __lowerCamelCase    :				List[Any]=0.0    ,       __lowerCamelCase    :				str=0.0    ,       __lowerCamelCase    :				List[Any]=0.0_2    ,       __lowerCamelCase    :				int=0.0    ,       __lowerCamelCase    :				List[str]=False    ,       __lowerCamelCase    :				Any=1    ,       __lowerCamelCase    :				Tuple=0    ,       __lowerCamelCase    :				Optional[int]=2    ,       __lowerCamelCase    :				str=2    ,       **__lowerCamelCase    :				Any    ,       ):
         snake_case__					:      Dict  			=						vocab_size
         snake_case__					:      Dict  			=						max_position_embeddings
         snake_case__					:      Any  			=						d_model
         snake_case__					:      List[str]  			=						encoder_ffn_dim
         snake_case__					:      Union[str, Any]  			=						encoder_layers
         snake_case__					:      List[str]  			=						encoder_attention_heads
         snake_case__					:      List[Any]  			=						decoder_ffn_dim
         snake_case__					:      Optional[int]  			=						decoder_layers
         snake_case__					:      List[str]  			=						decoder_attention_heads
         snake_case__					:      Any  			=						dropout
         snake_case__					:      int  			=						attention_dropout
         snake_case__					:      str  			=						activation_dropout
         snake_case__					:      List[str]  			=						activation_function
         snake_case__					:      Tuple  			=						init_std
         snake_case__					:      str  			=						encoder_layerdrop
         snake_case__					:      Tuple  			=						decoder_layerdrop
         snake_case__					:      Optional[Any]  			=						classifier_dropout
         snake_case__					:      List[Any]  			=						use_cache
         snake_case__					:      List[str]  			=						encoder_layers
         snake_case__					:      Any  			=						scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=lowercase_    ,       bos_token_id=lowercase_    ,       eos_token_id=lowercase_    ,       is_encoder_decoder=lowercase_    ,       forced_eos_token_id=lowercase_    ,       **lowercase_    ,       )






class 					lowercase_			(	__UpperCAmelCase       ):





   @property
   def  _lowerCAmelCase			(    self    :				Any ):

         if self.task in ["default", "seq2seq-lm"]:
               snake_case__					:      str  			=						OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                   ] )

               if self.use_past:
                     snake_case__					:      List[Any]  			=						{0: '''batch'''}
                     snake_case__					:      List[str]  			=						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
               else:
                     snake_case__					:      Optional[int]  			=						{0: '''batch''', 1: '''decoder_sequence'''}
                     snake_case__					:      List[str]  			=						{0: '''batch''', 1: '''decoder_sequence'''}

               if self.use_past:
                     self.fill_with_past_key_values_(lowercase_    ,       direction='inputs' )
         elif self.task == "causal-lm":
               # TODO: figure this case out.
               snake_case__					:      int  			=						OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                   ] )
               if self.use_past:
                     snake_case__					:      List[Any]  			=						self.num_layers
                     for i in range(lowercase_ ):
                           snake_case__					:      int  			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
                           snake_case__					:      Dict  			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
         else:
               snake_case__					:      Any  			=						OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                       ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                       ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                   ] )

         return common_inputs





   @property
   def  _lowerCAmelCase			(    self    :				Dict ):

         if self.task in ["default", "seq2seq-lm"]:
               snake_case__					:      List[Any]  			=						super().outputs
         else:
               snake_case__					:      Optional[int]  			=						super(lowercase_    ,       self ).outputs
               if self.use_past:
                     snake_case__					:      Union[str, Any]  			=						self.num_layers
                     for i in range(lowercase_ ):
                           snake_case__					:      List[str]  			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
                           snake_case__					:      int  			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
         return common_outputs





   def  _lowerCAmelCase			(    self    :				Optional[Any]    ,       __lowerCamelCase    :				List[Any]    ,       __lowerCamelCase    :				Tuple = -1    ,       __lowerCamelCase    :				Any = -1    ,       __lowerCamelCase    :				List[Any] = False    ,       __lowerCamelCase    :				Optional[int] = None    ,       ):
         snake_case__					:      Optional[int]  			=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_ )

         # Generate decoder inputs
         snake_case__					:      Optional[Any]  			=						seq_length if not self.use_past else 1
         snake_case__					:      Optional[int]  			=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_ )
         snake_case__					:      Any  			=						{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
         snake_case__					:      Optional[Any]  			=						dict(**lowercase_    ,       **lowercase_ )

         if self.use_past:
               if not is_torch_available():
                     raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
               else:
                     import torch
               snake_case__					:      Dict  			=						common_inputs['''input_ids'''].shape
               snake_case__					:      Dict  			=						common_inputs['''decoder_input_ids'''].shape[1]
               snake_case__					:      List[str]  			=						self.num_attention_heads
               snake_case__					:      Optional[int]  			=						(
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               snake_case__					:      List[Any]  			=						decoder_seq_length + 3
               snake_case__					:      Optional[Any]  			=						(
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               snake_case__					:      List[Any]  			=						torch.cat(
                   [common_inputs['decoder_attention_mask'], torch.ones(lowercase_    ,       lowercase_ )]    ,       dim=1 )

               snake_case__					:      List[str]  			=						[]
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               snake_case__					:      Tuple  			=						self.num_layers
               snake_case__					:      Union[str, Any]  			=						min(lowercase_    ,       lowercase_ )
               snake_case__					:      Optional[int]  			=						max(lowercase_    ,       lowercase_ ) - min_num_layers
               snake_case__					:      Union[str, Any]  			=						'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

               for _ in range(lowercase_ ):
                     common_inputs["past_key_values"].append(
                         (
                             torch.zeros(lowercase_ ),
                             torch.zeros(lowercase_ ),
                             torch.zeros(lowercase_ ),
                             torch.zeros(lowercase_ ),
                         ) )
               # TODO: test this.
               snake_case__					:      Optional[int]  			=						encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
               for _ in range(lowercase_    ,       lowercase_ ):
                     common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
         return common_inputs





   def  _lowerCAmelCase			(    self    :				List[Any]    ,       __lowerCamelCase    :				Optional[Any]    ,       __lowerCamelCase    :				Any = -1    ,       __lowerCamelCase    :				Dict = -1    ,       __lowerCamelCase    :				Optional[int] = False    ,       __lowerCamelCase    :				Union[str, Any] = None    ,       ):
         snake_case__					:      Any  			=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_ )

         if self.use_past:
               if not is_torch_available():
                     raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
               else:
                     import torch
               snake_case__					:      Dict  			=						common_inputs['''input_ids'''].shape
               # Not using the same length for past_key_values
               snake_case__					:      Optional[int]  			=						seqlen + 2
               snake_case__					:      str  			=						self.num_layers
               snake_case__					:      Optional[int]  			=						self.num_attention_heads
               snake_case__					:      Union[str, Any]  			=						(
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               snake_case__					:      Optional[Any]  			=						common_inputs['''attention_mask'''].dtype
               snake_case__					:      List[Any]  			=						torch.cat(
                   [common_inputs['attention_mask'], torch.ones(lowercase_    ,       lowercase_    ,       dtype=lowercase_ )]    ,       dim=1 )
               snake_case__					:      List[str]  			=						[
                   (torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
               ]
         return common_inputs





   def  _lowerCAmelCase			(    self    :				Optional[int]    ,       __lowerCamelCase    :				Optional[int]    ,       __lowerCamelCase    :				List[str] = -1    ,       __lowerCamelCase    :				Dict = -1    ,       __lowerCamelCase    :				Dict = False    ,       __lowerCamelCase    :				Dict = None    ,       ):
         snake_case__					:      str  			=						compute_effective_axis_dimension(
             lowercase_    ,       fixed_dimension=OnnxConfig.default_fixed_batch    ,       num_token_to_add=0 )

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         snake_case__					:      List[str]  			=						tokenizer.num_special_tokens_to_add(lowercase_ )
         snake_case__					:      List[Any]  			=						compute_effective_axis_dimension(
             lowercase_    ,       fixed_dimension=OnnxConfig.default_fixed_sequence    ,       num_token_to_add=lowercase_ )

         # Generate dummy inputs according to compute batch and sequence
         snake_case__					:      List[Any]  			=						[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
         snake_case__					:      int  			=						dict(tokenizer(lowercase_    ,       return_tensors=lowercase_ ) )
         return common_inputs





   def  _lowerCAmelCase			(    self    :				List[Any]    ,       __lowerCamelCase    :				List[Any]    ,       __lowerCamelCase    :				List[str] = -1    ,       __lowerCamelCase    :				Any = -1    ,       __lowerCamelCase    :				List[str] = False    ,       __lowerCamelCase    :				Optional[Any] = None    ,       ):

         if self.task in ["default", "seq2seq-lm"]:
               snake_case__					:      Union[str, Any]  			=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   lowercase_    ,       batch_size=lowercase_    ,       seq_length=lowercase_    ,       is_pair=lowercase_    ,       framework=lowercase_ )

         elif self.task == "causal-lm":
               snake_case__					:      Optional[int]  			=						self._generate_dummy_inputs_for_causal_lm(
                   lowercase_    ,       batch_size=lowercase_    ,       seq_length=lowercase_    ,       is_pair=lowercase_    ,       framework=lowercase_ )
         else:
               snake_case__					:      List[str]  			=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                   lowercase_    ,       batch_size=lowercase_    ,       seq_length=lowercase_    ,       is_pair=lowercase_    ,       framework=lowercase_ )

         return common_inputs





   def  _lowerCAmelCase			(    self    :				List[Any]    ,       __lowerCamelCase    :				Dict    ,       __lowerCamelCase    :				int    ,       __lowerCamelCase    :				Tuple    ,       __lowerCamelCase    :				List[str] ):

         if self.task in ["default", "seq2seq-lm"]:
               snake_case__					:      Optional[int]  			=						super()._flatten_past_key_values_(lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_ )
         else:
               snake_case__					:      Union[str, Any]  			=						super(lowercase_    ,       self )._flatten_past_key_values_(
                   lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_ )






'''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}
