




'''simple docstring'''






from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import (
          TF_MODEL_FOR_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_MASKED_LM_MAPPING,
          TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
          TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          TFSharedEmbeddings,
      )

      if _tf_gpu_memory_limit is not None:
            A_ 			=		tf.config.list_physical_devices("GPU")
            for gpu in gpus:
                  # Restrict TensorFlow to only allocate x GB of memory on the GPUs
                  try:
                        tf.config.set_logical_device_configuration(
                            gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                        )
                        A_ 			=		tf.config.list_logical_devices("GPU")
                        print("Logical GPUs", logical_gpus)
                  except RuntimeError as e:
                        # Virtual devices must be set before GPUs have been initialized
                        print(e)






@require_tf
class 					lowercase_			:
   A_       							= None
   A_       							= ()
   A_       							= ()
   A_       							= True
   A_       							= True
   A_       							= True
   A_       							= False





   def  _lowerCAmelCase			(    self    :				str    ,       __lowerCamelCase    :				List[Any]    ,       __lowerCamelCase    :				List[str]    ,       __lowerCamelCase    :				Tuple=False ):
         snake_case__					:      Optional[Any]  			=						copy.deepcopy(_lowerCAmelCase )

         if model_class in get_values(_lowerCAmelCase ):
               snake_case__					:      Any  			=						{
                   k: tf.tile(tf.expand_dims(_lowerCAmelCase    ,       1 )    ,       (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
                   if isinstance(_lowerCAmelCase    ,       tf.Tensor ) and v.ndim > 0
                   else v
                   for k, v in inputs_dict.items()
               }

         if return_labels:
               if model_class in get_values(_lowerCAmelCase ):
                     snake_case__					:      Any  			=						tf.ones(self.model_tester.batch_size    ,       dtype=tf.intaa )
               elif model_class in get_values(_lowerCAmelCase ):
                     snake_case__					:      Dict  			=						tf.zeros(self.model_tester.batch_size    ,       dtype=tf.intaa )
                     snake_case__					:      int  			=						tf.zeros(self.model_tester.batch_size    ,       dtype=tf.intaa )
               elif model_class in [
                   *get_values(_lowerCAmelCase ),
                   *get_values(_lowerCAmelCase ),
               ]:
                     snake_case__					:      Union[str, Any]  			=						tf.zeros(self.model_tester.batch_size    ,       dtype=tf.intaa )
               elif model_class in get_values(_lowerCAmelCase ):
                     snake_case__					:      Optional[Any]  			=						tf.zeros(self.model_tester.batch_size    ,       dtype=tf.intaa )
               elif model_class in [
                   *get_values(_lowerCAmelCase ),
                   *get_values(_lowerCAmelCase ),
                   *get_values(_lowerCAmelCase ),
                   *get_values(_lowerCAmelCase ),
                   *get_values(_lowerCAmelCase ),
               ]:
                     snake_case__					:      Optional[int]  			=						tf.zeros(
                         (self.model_tester.batch_size, self.model_tester.seq_length)    ,       dtype=tf.intaa )
         return inputs_dict





   @slow
   def  _lowerCAmelCase			(    self    :				Optional[Any] ):
         snake_case__			,       snake_case__					:      Optional[int]  			=						self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes[:2]:
               snake_case__					:      Optional[Any]  			=						self._prepare_for_class(_lowerCAmelCase    ,       _lowerCAmelCase )
               snake_case__					:      Dict  			=						model_class(_lowerCAmelCase )

               @tf.function
               def run_in_graph_mode():
                     return model(_lowerCAmelCase )

               snake_case__					:      Any  			=						run_in_graph_mode()
               self.assertIsNotNone(_lowerCAmelCase )





   @slow
   def  _lowerCAmelCase			(    self    :				List[str] ):
         snake_case__			,       snake_case__					:      Tuple  			=						self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes[:2]:
               snake_case__					:      List[Any]  			=						self._prepare_for_class(_lowerCAmelCase    ,       _lowerCAmelCase )
               snake_case__					:      Optional[Any]  			=						model_class(_lowerCAmelCase )

               @tf.function(experimental_compile=_lowerCAmelCase )
               def run_in_graph_mode():
                     return model(_lowerCAmelCase )

               snake_case__					:      Tuple  			=						run_in_graph_mode()
               self.assertIsNotNone(_lowerCAmelCase )





   @slow
   def  _lowerCAmelCase			(    self    :				Tuple ):
         # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
         snake_case__			,       snake_case__					:      Any  			=						self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes[:2]:
               snake_case__					:      Any  			=						model_class(_lowerCAmelCase )
               if getattr(_lowerCAmelCase    ,       'hf_compute_loss'    ,       _lowerCAmelCase ):
                     # Test that model correctly compute the loss with kwargs
                     snake_case__					:      int  			=						self._prepare_for_class(inputs_dict.copy()    ,       _lowerCAmelCase    ,       return_labels=_lowerCAmelCase )
                     # Is there a better way to remove these decoder inputs?
                     snake_case__					:      str  			=						{
                         key: val
                         for key, val in prepared_for_class.items()
                         if key not in ('head_mask', 'decoder_head_mask', 'cross_attn_head_mask', 'decoder_input_ids')
                     }

                     snake_case__					:      Union[str, Any]  			=						{
                         'labels',
                         'label',
                         'label_ids',
                         'start_positions',
                         'start_position',
                         'end_positions',
                         'end_position',
                         'next_sentence_label',
                     }
                     snake_case__					:      Any  			=						possible_label_cols.intersection(set(_lowerCAmelCase ) )
                     self.assertGreater(len(_lowerCAmelCase )    ,       0    ,       msg='No matching label names found!' )
                     snake_case__					:      Any  			=						{key: val for key, val in prepared_for_class.items() if key in label_names}
                     snake_case__					:      int  			=						{key: val for key, val in prepared_for_class.items() if key not in label_names}
                     self.assertGreater(len(_lowerCAmelCase )    ,       0 )

                     # Make sure it works with XLA!
                     model.compile(optimizer=tf.keras.optimizers.SGD(0.0 )    ,       jit_compile=_lowerCAmelCase )
                     # Make sure the model fits without crashing regardless of where we pass the labels
                     snake_case__					:      List[str]  			=						model.fit(
                         _lowerCAmelCase    ,       validation_data=_lowerCAmelCase    ,       steps_per_epoch=1    ,       validation_steps=1    ,       shuffle=_lowerCAmelCase    ,       verbose=0    ,       )
                     snake_case__					:      Dict  			=						history.history['loss'][0]
                     self.assertTrue(not isnan(_lowerCAmelCase ) )
                     snake_case__					:      Dict  			=						history.history['val_loss'][0]
                     self.assertTrue(not isnan(_lowerCAmelCase ) )

                     # Now test it with separate labels, to make sure that path works in XLA too.
                     snake_case__					:      str  			=						model_class(_lowerCAmelCase )
                     model.compile(optimizer=tf.keras.optimizers.SGD(0.0 )    ,       jit_compile=_lowerCAmelCase )
                     snake_case__					:      Tuple  			=						model.fit(
                         _lowerCAmelCase    ,       _lowerCAmelCase    ,       validation_data=(inputs_minus_labels, labels)    ,       steps_per_epoch=1    ,       validation_steps=1    ,       shuffle=_lowerCAmelCase    ,       verbose=0    ,       )

                     snake_case__					:      str  			=						history.history['loss'][0]
                     self.assertTrue(not isnan(_lowerCAmelCase ) )
                     snake_case__					:      Dict  			=						history.history['val_loss'][0]
                     self.assertTrue(not isnan(_lowerCAmelCase ) )





   @slow
   def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
         snake_case__			,       snake_case__					:      Tuple  			=						self.model_tester.prepare_config_and_inputs_for_common()
         snake_case__					:      Tuple  			=						True
         snake_case__					:      Tuple  			=						True

         if hasattr(_lowerCAmelCase    ,       'use_cache' ):
               snake_case__					:      int  			=						True

         snake_case__					:      Tuple  			=						getattr(self.model_tester    ,       'encoder_seq_length'    ,       self.model_tester.seq_length )
         snake_case__					:      Dict  			=						getattr(self.model_tester    ,       'key_length'    ,       _lowerCAmelCase )

         for model_class in self.all_model_classes[:2]:
               snake_case__					:      Tuple  			=						self._prepare_for_class(_lowerCAmelCase    ,       _lowerCAmelCase )
               snake_case__					:      Dict  			=						model_class(_lowerCAmelCase )
               model.build()
               snake_case__					:      str  			=						len(model(_lowerCAmelCase ) )

               for key in list(class_inputs_dict.keys() ):
                     # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
                     if key not in model.input_signature:
                           del class_inputs_dict[key]
                     # Check it's a tensor, in case the inputs dict has some bools in it too
                     elif isinstance(class_inputs_dict[key]    ,       tf.Tensor ) and class_inputs_dict[key].dtype.is_integer:
                           snake_case__					:      List[str]  			=						tf.cast(class_inputs_dict[key]    ,       tf.intaa )

               if set(class_inputs_dict.keys() ) != set(model.input_signature.keys() ):
                     continue  # Some models have inputs that the preparation functions don't create, we skip those

               with tempfile.TemporaryDirectory() as tmpdirname:
                     model.save_pretrained(_lowerCAmelCase    ,       saved_model=_lowerCAmelCase )
                     snake_case__					:      Optional[Any]  			=						os.path.join(_lowerCAmelCase    ,       'saved_model'    ,       '1' )
                     snake_case__					:      int  			=						tf.keras.models.load_model(_lowerCAmelCase )
                     snake_case__					:      Dict  			=						model(_lowerCAmelCase )

                     if self.is_encoder_decoder:
                           snake_case__					:      Any  			=						outputs['encoder_hidden_states']
                           snake_case__					:      List[str]  			=						outputs['encoder_attentions']
                     else:
                           snake_case__					:      Union[str, Any]  			=						outputs['hidden_states']
                           snake_case__					:      Union[str, Any]  			=						outputs['attentions']

                     self.assertEqual(len(_lowerCAmelCase )    ,       _lowerCAmelCase )

                     snake_case__					:      Dict  			=						getattr(
                         self.model_tester    ,       'expected_num_hidden_layers'    ,       self.model_tester.num_hidden_layers + 1 )

                     self.assertEqual(len(_lowerCAmelCase )    ,       _lowerCAmelCase )
                     self.assertListEqual(
                         list(output_hidden_states[0].shape[-2:] )    ,       [self.model_tester.seq_length, self.model_tester.hidden_size]    ,       )

                     self.assertEqual(len(_lowerCAmelCase )    ,       self.model_tester.num_hidden_layers )
                     self.assertListEqual(
                         list(output_attentions[0].shape[-3:] )    ,       [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]    ,       )





   @slow
   def  _lowerCAmelCase			(    self    :				Dict ):
         tf.keras.mixed_precision.set_global_policy('mixed_float16' )

         # try/finally block to ensure subsequent tests run in float32
         try:
               snake_case__			,       snake_case__					:      List[str]  			=						self.model_tester.prepare_config_and_inputs_for_common()
               for model_class in self.all_model_classes[:2]:
                     snake_case__					:      List[str]  			=						self._prepare_for_class(_lowerCAmelCase    ,       _lowerCAmelCase )
                     snake_case__					:      int  			=						model_class(_lowerCAmelCase )
                     snake_case__					:      Optional[int]  			=						model(_lowerCAmelCase )

                     self.assertIsNotNone(_lowerCAmelCase )
         finally:
               tf.keras.mixed_precision.set_global_policy('float32' )





   @slow
   def  _lowerCAmelCase			(    self    :				int ):
         snake_case__			,       snake_case__					:      Any  			=						self.model_tester.prepare_config_and_inputs_for_common()
         # head_mask and decoder_head_mask has different shapes than other input args
         if "head_mask" in inputs_dict:
               del inputs_dict["head_mask"]
         if "decoder_head_mask" in inputs_dict:
               del inputs_dict["decoder_head_mask"]
         if "cross_attn_head_mask" in inputs_dict:
               del inputs_dict["cross_attn_head_mask"]
         snake_case__					:      str  			=						{
             module_member
             for model_class in self.all_model_classes
             for module in (import_module(model_class.__module__ ),)
             for module_member_name in dir(_lowerCAmelCase )
             if module_member_name.endswith('MainLayer' )
             for module_member in (getattr(_lowerCAmelCase    ,       _lowerCAmelCase ),)
             if isinstance(_lowerCAmelCase    ,       _lowerCAmelCase )
             and tf.keras.layers.Layer in module_member.__bases__
             and getattr(_lowerCAmelCase    ,       '_keras_serializable'    ,       _lowerCAmelCase )
         }

         for main_layer_class in tf_main_layer_classes:
               # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
               if "T5" in main_layer_class.__name__:
                     # Take the same values than in TFT5ModelTester for this shared layer
                     snake_case__					:      Union[str, Any]  			=						TFSharedEmbeddings(self.model_tester.vocab_size    ,       self.model_tester.hidden_size    ,       name='shared' )
                     snake_case__					:      str  			=						False
                     snake_case__					:      Optional[Any]  			=						main_layer_class(_lowerCAmelCase    ,       embed_tokens=_lowerCAmelCase )
               else:
                     snake_case__					:      Any  			=						main_layer_class(_lowerCAmelCase )

               snake_case__					:      str  			=						{
                   name: tf.keras.Input(tensor.shape[1:]    ,       dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
               }

               if hasattr(self.model_tester    ,       'num_labels' ):
                     snake_case__					:      Optional[Any]  			=						self.model_tester.num_labels
               else:
                     snake_case__					:      int  			=						2

               snake_case__					:      Any  			=						tf.data.Dataset.from_tensor_slices(
                   (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1) )) ).batch(1 )

               snake_case__					:      int  			=						main_layer(_lowerCAmelCase )[0]
               snake_case__					:      Any  			=						tf.keras.layers.Dense(_lowerCAmelCase    ,       activation='softmax'    ,       name='outputs' )(_lowerCAmelCase )
               snake_case__					:      Optional[int]  			=						tf.keras.models.Model(inputs=_lowerCAmelCase    ,       outputs=[outputs] )

               model.compile(loss='binary_crossentropy'    ,       optimizer='adam'    ,       metrics=['binary_accuracy'] )
               model.fit(_lowerCAmelCase    ,       epochs=1 )

               with tempfile.TemporaryDirectory() as tmpdirname:
                     snake_case__					:      Dict  			=						os.path.join(_lowerCAmelCase    ,       'keras_model.h5' )
                     model.save(_lowerCAmelCase )
                     if "T5" in main_layer_class.__name__:
                           snake_case__					:      str  			=						tf.keras.models.load_model(
                               _lowerCAmelCase    ,       custom_objects={
                                   main_layer_class.__name__: main_layer_class,
                                   'TFSharedEmbeddings': TFSharedEmbeddings,
                               }    ,       )
                     else:
                           snake_case__					:      Dict  			=						tf.keras.models.load_model(
                               _lowerCAmelCase    ,       custom_objects={main_layer_class.__name__: main_layer_class} )
                     assert isinstance(_lowerCAmelCase    ,       tf.keras.Model )
                     model(_lowerCAmelCase )





   @slow
   def  _lowerCAmelCase			(    self    :				int ):
         snake_case__			,       snake_case__					:      str  			=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes[:2]:
               snake_case__					:      Optional[int]  			=						model_class(_lowerCAmelCase )

               snake_case__					:      str  			=						copy.deepcopy(_lowerCAmelCase )

               if not self.is_encoder_decoder:
                     snake_case__					:      Dict  			=						inputs['input_ids']
                     del inputs["input_ids"]
               else:
                     snake_case__					:      Optional[Any]  			=						inputs['input_ids']
                     snake_case__					:      Tuple  			=						inputs.get('decoder_input_ids'    ,       _lowerCAmelCase )
                     del inputs["input_ids"]
                     inputs.pop('decoder_input_ids'    ,       _lowerCAmelCase )

               if not self.is_encoder_decoder:
                     snake_case__					:      Optional[int]  			=						model.get_input_embeddings()(_lowerCAmelCase )
               else:
                     snake_case__					:      Optional[Any]  			=						model.get_input_embeddings()(_lowerCAmelCase )
                     snake_case__					:      str  			=						model.get_input_embeddings()(_lowerCAmelCase )

               snake_case__					:      Any  			=						self._prepare_for_class(_lowerCAmelCase    ,       _lowerCAmelCase )

               @tf.function
               def run_in_graph_mode():
                     return model(_lowerCAmelCase )

               snake_case__					:      Optional[Any]  			=						run_in_graph_mode()
               self.assertIsNotNone(_lowerCAmelCase )





   def  _lowerCAmelCase			(    self    :				Union[str, Any]    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				Tuple ):
         # special tokens cannot be bad tokens
         snake_case__					:      Tuple  			=						[]
         if model.config.bos_token_id is not None:
               special_tokens.append(model.config.bos_token_id )
         if model.config.pad_token_id is not None:
               special_tokens.append(model.config.pad_token_id )
         if model.config.eos_token_id is not None:
               special_tokens.append(model.config.eos_token_id )

         # create random bad tokens that are not special tokens
         snake_case__					:      Any  			=						[]
         while len(_lowerCAmelCase ) < num_bad_tokens:
               snake_case__					:      str  			=						tf.squeeze(ids_tensor((1, 1)    ,       self.model_tester.vocab_size )    ,       0 ).numpy()[0]
               if token not in special_tokens:
                     bad_tokens.append(_lowerCAmelCase )
         return bad_tokens





   def  _lowerCAmelCase			(    self    :				Tuple    ,       __lowerCamelCase    :				Optional[int] ):
         for token_id in output_ids[0].numpy().tolist():
               self.assertGreaterEqual(_lowerCAmelCase    ,       0 )
               self.assertLess(_lowerCAmelCase    ,       self.model_tester.vocab_size )





   def  _lowerCAmelCase			(    self    :				Optional[int]    ,       __lowerCamelCase    :				Optional[int]    ,       __lowerCamelCase    :				List[Any] ):
         # for all bad word tokens
         for bad_word_ids in bad_words_ids:
               # for all slices in batch
               for generated_ids_slice in generated_ids:
                     # for all word idx
                     for i in range(len(_lowerCAmelCase )    ,       len(_lowerCAmelCase ) ):
                           # if tokens match
                           if generated_ids_slice[i - len(_lowerCAmelCase ) : i] == bad_word_ids:
                                 return True
         return False






'''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}
