




'''simple docstring'''






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      A_ 			=		"platform"

      import jax
      import jax.numpy as jnp

      from transformers import MarianTokenizer
      from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			)						->      Union[str, Any]:
      if attention_mask is None:
            snake_case__					:      Union[str, Any]  			=						np.where(input_ids != config.pad_token_id							,			1							,			0		)
      if decoder_attention_mask is None:
            snake_case__					:      int  			=						np.where(decoder_input_ids != config.pad_token_id							,			1							,			0		)
      if head_mask is None:
            snake_case__					:      Optional[int]  			=						np.ones((config.encoder_layers, config.encoder_attention_heads)		)
      if decoder_head_mask is None:
            snake_case__					:      str  			=						np.ones((config.decoder_layers, config.decoder_attention_heads)		)
      if cross_attn_head_mask is None:
            snake_case__					:      int  			=						np.ones((config.decoder_layers, config.decoder_attention_heads)		)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }






class 					lowercase_			:





     def __init__(    self    :				Any    ,       __lowerCamelCase    :				Optional[Any]    ,       __lowerCamelCase    :				Optional[Any]=13    ,       __lowerCamelCase    :				str=7    ,       __lowerCamelCase    :				Optional[int]=True    ,       __lowerCamelCase    :				List[str]=False    ,       __lowerCamelCase    :				str=99    ,       __lowerCamelCase    :				List[str]=16    ,       __lowerCamelCase    :				Optional[Any]=2    ,       __lowerCamelCase    :				Tuple=4    ,       __lowerCamelCase    :				Any=4    ,       __lowerCamelCase    :				Optional[int]="gelu"    ,       __lowerCamelCase    :				Optional[Any]=0.1    ,       __lowerCamelCase    :				List[Any]=0.1    ,       __lowerCamelCase    :				Optional[int]=32    ,       __lowerCamelCase    :				Optional[Any]=2    ,       __lowerCamelCase    :				Union[str, Any]=1    ,       __lowerCamelCase    :				Dict=0    ,       __lowerCamelCase    :				Tuple=0.0_2    ,       ):
           snake_case__					:      Tuple  			=						parent
           snake_case__					:      Any  			=						batch_size
           snake_case__					:      Union[str, Any]  			=						seq_length
           snake_case__					:      Optional[Any]  			=						is_training
           snake_case__					:      Union[str, Any]  			=						use_labels
           snake_case__					:      Union[str, Any]  			=						vocab_size
           snake_case__					:      Any  			=						hidden_size
           snake_case__					:      Tuple  			=						num_hidden_layers
           snake_case__					:      Tuple  			=						num_attention_heads
           snake_case__					:      str  			=						intermediate_size
           snake_case__					:      Union[str, Any]  			=						hidden_act
           snake_case__					:      Optional[Any]  			=						hidden_dropout_prob
           snake_case__					:      str  			=						attention_probs_dropout_prob
           snake_case__					:      Optional[int]  			=						max_position_embeddings
           snake_case__					:      Union[str, Any]  			=						eos_token_id
           snake_case__					:      str  			=						pad_token_id
           snake_case__					:      Tuple  			=						bos_token_id
           snake_case__					:      str  			=						initializer_range





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__					:      List[Any]  			=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,       self.vocab_size )    ,       3    ,       self.vocab_size )
           snake_case__					:      Tuple  			=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,       dtype=np.intaa ))    ,       -1 )

           snake_case__					:      Optional[int]  			=						shift_tokens_right(__lowerCamelCase    ,       1    ,       2 )

           snake_case__					:      Union[str, Any]  			=						MarianConfig(
               vocab_size=self.vocab_size    ,       d_model=self.hidden_size    ,       encoder_layers=self.num_hidden_layers    ,       decoder_layers=self.num_hidden_layers    ,       encoder_attention_heads=self.num_attention_heads    ,       decoder_attention_heads=self.num_attention_heads    ,       encoder_ffn_dim=self.intermediate_size    ,       decoder_ffn_dim=self.intermediate_size    ,       dropout=self.hidden_dropout_prob    ,       attention_dropout=self.attention_probs_dropout_prob    ,       max_position_embeddings=self.max_position_embeddings    ,       eos_token_id=self.eos_token_id    ,       bos_token_id=self.bos_token_id    ,       pad_token_id=self.pad_token_id    ,       initializer_range=self.initializer_range    ,       use_cache=__lowerCamelCase    ,       )
           snake_case__					:      Union[str, Any]  			=						prepare_marian_inputs_dict(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )
           return config, inputs_dict





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__			,       snake_case__					:      Optional[int]  			=						self.prepare_config_and_inputs()
           return config, inputs_dict





     def  _lowerCAmelCase			(    self    :				Tuple    ,       __lowerCamelCase    :				Union[str, Any]    ,       __lowerCamelCase    :				Dict    ,       __lowerCamelCase    :				Dict ):
           snake_case__					:      Optional[int]  			=						20
           snake_case__					:      str  			=						model_class_name(__lowerCamelCase )

           snake_case__					:      Dict  			=						model.encode(inputs_dict['input_ids'] )

           snake_case__			,       snake_case__					:      int  			=						(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

           snake_case__					:      Dict  			=						model.init_cache(decoder_input_ids.shape[0]    ,       __lowerCamelCase    ,       __lowerCamelCase )
           snake_case__					:      Optional[int]  			=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,       dtype='i4' )

           snake_case__					:      List[str]  			=						jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,       )
           snake_case__					:      Dict  			=						model.decode(
               decoder_input_ids[:, :-1]    ,       __lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase    ,       past_key_values=__lowerCamelCase    ,       decoder_position_ids=__lowerCamelCase    ,       )

           snake_case__					:      List[str]  			=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,       dtype='i4' )
           snake_case__					:      Tuple  			=						model.decode(
               decoder_input_ids[:, -1:]    ,       __lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase    ,       past_key_values=outputs_cache.past_key_values    ,       decoder_position_ids=__lowerCamelCase    ,       )

           snake_case__					:      Union[str, Any]  			=						model.decode(__lowerCamelCase    ,       __lowerCamelCase )

           snake_case__					:      Tuple  			=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
           self.parent.assertTrue(diff < 1E-3    ,       msg=F"Max diff is {diff}" )





     def  _lowerCAmelCase			(    self    :				Tuple    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				Dict    ,       __lowerCamelCase    :				Optional[int] ):
           snake_case__					:      int  			=						20
           snake_case__					:      Tuple  			=						model_class_name(__lowerCamelCase )

           snake_case__					:      Optional[int]  			=						model.encode(inputs_dict['input_ids'] )

           snake_case__			,       snake_case__					:      Dict  			=						(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

           snake_case__					:      List[str]  			=						jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
               ]    ,       axis=-1    ,       )

           snake_case__					:      Union[str, Any]  			=						model.init_cache(decoder_input_ids.shape[0]    ,       __lowerCamelCase    ,       __lowerCamelCase )
           snake_case__					:      List[str]  			=						jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,       )

           snake_case__					:      List[Any]  			=						model.decode(
               decoder_input_ids[:, :-1]    ,       __lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase    ,       past_key_values=__lowerCamelCase    ,       decoder_position_ids=__lowerCamelCase    ,       )
           snake_case__					:      Tuple  			=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,       dtype='i4' )
           snake_case__					:      int  			=						model.decode(
               decoder_input_ids[:, -1:]    ,       __lowerCamelCase    ,       past_key_values=outputs_cache.past_key_values    ,       decoder_attention_mask=__lowerCamelCase    ,       decoder_position_ids=__lowerCamelCase    ,       )

           snake_case__					:      Dict  			=						model.decode(__lowerCamelCase    ,       __lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase )

           snake_case__					:      Optional[int]  			=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
           self.parent.assertTrue(diff < 1E-3    ,       msg=F"Max diff is {diff}" )






@require_flax
class 					lowercase_			(	lowerCAmelCase_	,					unittest.TestCase	,					lowerCAmelCase_       ):
     A_       							= True
     A_       							= (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
     A_       							= (FlaxMarianMTModel,) if is_flax_available() else ()





     def  _lowerCAmelCase			(    self    :				int ):
           snake_case__					:      Any  			=						FlaxMarianModelTester(self )





     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           snake_case__			,       snake_case__					:      Optional[Any]  			=						self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				str ):
           snake_case__			,       snake_case__					:      str  			=						self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__			,       snake_case__					:      Optional[Any]  			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__ ):
                       snake_case__					:      Union[str, Any]  			=						self._prepare_for_class(__lowerCamelCase    ,       __lowerCamelCase )
                       snake_case__					:      List[str]  			=						model_class(__lowerCamelCase )

                       @jax.jit
                       def encode_jitted(__lowerCamelCase    :				Dict    ,       __lowerCamelCase    :				Optional[Any]=None    ,       **__lowerCamelCase    :				List[str] ):
                             return model.encode(input_ids=__lowerCamelCase    ,       attention_mask=__lowerCamelCase )

                       with self.subTest('JIT Enabled' ):
                             snake_case__					:      List[Any]  			=						encode_jitted(**__lowerCamelCase ).to_tuple()

                       with self.subTest('JIT Disabled' ):
                             with jax.disable_jit():
                                   snake_case__					:      Dict  			=						encode_jitted(**__lowerCamelCase ).to_tuple()

                       self.assertEqual(len(__lowerCamelCase )    ,       len(__lowerCamelCase ) )
                       for jitted_output, output in zip(__lowerCamelCase    ,       __lowerCamelCase ):
                             self.assertEqual(jitted_output.shape    ,       output.shape )





     def  _lowerCAmelCase			(    self    :				int ):
           snake_case__			,       snake_case__					:      Dict  			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__ ):
                       snake_case__					:      List[str]  			=						model_class(__lowerCamelCase )
                       snake_case__					:      Union[str, Any]  			=						model.encode(inputs_dict['input_ids']    ,       inputs_dict['attention_mask'] )

                       snake_case__					:      str  			=						{
                           'decoder_input_ids': inputs_dict['decoder_input_ids'],
                           'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                           'encoder_outputs': encoder_outputs,
                       }

                       @jax.jit
                       def decode_jitted(__lowerCamelCase    :				Tuple    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				Optional[Any] ):
                             return model.decode(
                                 decoder_input_ids=__lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase    ,       encoder_outputs=__lowerCamelCase    ,       )

                       with self.subTest('JIT Enabled' ):
                             snake_case__					:      List[str]  			=						decode_jitted(**__lowerCamelCase ).to_tuple()

                       with self.subTest('JIT Disabled' ):
                             with jax.disable_jit():
                                   snake_case__					:      str  			=						decode_jitted(**__lowerCamelCase ).to_tuple()

                       self.assertEqual(len(__lowerCamelCase )    ,       len(__lowerCamelCase ) )
                       for jitted_output, output in zip(__lowerCamelCase    ,       __lowerCamelCase ):
                             self.assertEqual(jitted_output.shape    ,       output.shape )





     @slow
     def  _lowerCAmelCase			(    self    :				List[str] ):
           for model_class_name in self.all_model_classes:
                 snake_case__					:      Any  			=						model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de' )
                 # FlaxMarianForSequenceClassification expects eos token in input_ids
                 snake_case__					:      List[Any]  			=						np.ones((1, 1) ) * model.config.eos_token_id
                 snake_case__					:      Union[str, Any]  			=						model(__lowerCamelCase )
                 self.assertIsNotNone(__lowerCamelCase )






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	unittest.TestCase       ):
     A_       							= None
     A_       							= None





     @classmethod
     def  _lowerCAmelCase			(    cls    :				Union[str, Any] ):
           snake_case__					:      List[Any]  			=						F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
           return cls





     @cached_property
     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           return MarianTokenizer.from_pretrained(self.model_name )





     @property
     def  _lowerCAmelCase			(    self    :				int ):
           return self.tokenizer.eos_token_id





     @cached_property
     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      FlaxMarianMTModel  			=						FlaxMarianMTModel.from_pretrained(self.model_name )
           self.assertEqual(model.config.decoder_start_token_id    ,       model.config.pad_token_id )
           return model





     def  _lowerCAmelCase			(    self    :				Tuple    ,       **__lowerCamelCase    :				List[Any] ):
           snake_case__					:      Dict  			=						self.translate_src_text(**__lowerCamelCase )
           self.assertListEqual(self.expected_text    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[str]    ,       **__lowerCamelCase    :				Dict ):
           snake_case__					:      str  			=						self.tokenizer(self.src_text    ,       padding=__lowerCamelCase    ,       return_tensors='np'    ,       **__lowerCamelCase )
           snake_case__					:      Any  			=						self.model.generate(
               model_inputs.input_ids    ,       attention_mask=model_inputs.attention_mask    ,       num_beams=2    ,       max_length=128    ,       ).sequences
           snake_case__					:      List[str]  			=						self.tokenizer.batch_decode(__lowerCamelCase    ,       skip_special_tokens=__lowerCamelCase )
           return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "en"
     A_       							= "fr"
     A_       							= [
         "I am a small frog.",
         "Now I can forget the 100 words of german that I know.",
     ]
     A_       							= [
         "Je suis une petite grenouille.",
         "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
     ]





     @slow
     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "fr"
     A_       							= "en"
     A_       							= [
         "Donnez moi le micro.",
         "Tom et Mary étaient assis à une table.",  # Accents
     ]
     A_       							= [
         "Give me the microphone.",
         "Tom and Mary were sitting at a table.",
     ]





     @slow
     def  _lowerCAmelCase			(    self    :				List[Any] ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "mt"
     A_       							= "en"
     A_       							= ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
     A_       							= ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]





     @slow
     def  _lowerCAmelCase			(    self    :				Dict ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "en"
     A_       							= "de"
     A_       							= [
         "I am a small frog.",
         "Now I can forget the 100 words of german that I know.",
         "Tom asked his teacher for advice.",
         "That's how I would do it.",
         "Tom really admired Mary's courage.",
         "Turn around and close your eyes.",
     ]
     A_       							= [
         "Ich bin ein kleiner Frosch.",
         "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
         "Tom bat seinen Lehrer um Rat.",
         "So würde ich das machen.",
         "Tom bewunderte Marias Mut wirklich.",
         "Drehen Sie sich um und schließen Sie die Augen.",
     ]





     @slow
     def  _lowerCAmelCase			(    self    :				Dict ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "en"
     A_       							= "zh"
     A_       							= ["My name is Wolfgang and I live in Berlin"]
     A_       							= ["我叫沃尔夫冈 我住在柏林"]





     @slow
     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "ru"
     A_       							= "fr"
     A_       							= ["Он показал мне рукопись своей новой пьесы."]
     A_       							= ["Il m'a montré le manuscrit de sa nouvelle pièce."]





     @slow
     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "en"
     A_       							= "ROMANCE"
     A_       							= [
         ">>fr<< Don't spend so much time watching TV.",
         ">>pt<< Your message has been sent.",
         ">>es<< He's two years older than me.",
     ]
     A_       							= [
         "Ne passez pas autant de temps à regarder la télé.",
         "A sua mensagem foi enviada.",
         "Es dos años más viejo que yo.",
     ]





     @slow
     def  _lowerCAmelCase			(    self    :				int ):
           self._assert_generated_batch_equal_expected()






'''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}
