




'''simple docstring'''






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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 FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None		)						->      Tuple:
      if attention_mask is None:
            snake_case__					:      List[str]  			=						np.where(input_ids != config.pad_token_id							,			1							,			0		)
      return {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
      }






@require_flax
class 					lowercase_			:





     def __init__(    self    :				List[Any]    ,       __lowerCamelCase    :				Optional[int]    ,       __lowerCamelCase    :				Any=13    ,       __lowerCamelCase    :				Tuple=7    ,       __lowerCamelCase    :				Tuple=True    ,       __lowerCamelCase    :				int=False    ,       __lowerCamelCase    :				List[Any]=99    ,       __lowerCamelCase    :				List[Any]=16    ,       __lowerCamelCase    :				List[Any]=2    ,       __lowerCamelCase    :				str=4    ,       __lowerCamelCase    :				Dict=4    ,       __lowerCamelCase    :				List[Any]="gelu"    ,       __lowerCamelCase    :				Dict=0.1    ,       __lowerCamelCase    :				str=0.1    ,       __lowerCamelCase    :				List[str]=20    ,       __lowerCamelCase    :				Optional[Any]=2    ,       __lowerCamelCase    :				Any=1    ,       __lowerCamelCase    :				str=0    ,       __lowerCamelCase    :				Optional[Any]=16    ,       __lowerCamelCase    :				List[str]=16    ,       __lowerCamelCase    :				Any=0.0_2    ,       ):
           snake_case__					:      Union[str, Any]  			=						parent
           snake_case__					:      List[str]  			=						batch_size
           snake_case__					:      Any  			=						seq_length
           snake_case__					:      str  			=						is_training
           snake_case__					:      List[str]  			=						use_labels
           snake_case__					:      List[str]  			=						vocab_size
           snake_case__					:      List[Any]  			=						hidden_size
           snake_case__					:      Union[str, Any]  			=						num_hidden_layers
           snake_case__					:      Optional[int]  			=						num_attention_heads
           snake_case__					:      Any  			=						intermediate_size
           snake_case__					:      Any  			=						hidden_act
           snake_case__					:      Tuple  			=						hidden_dropout_prob
           snake_case__					:      Optional[Any]  			=						attention_probs_dropout_prob
           snake_case__					:      str  			=						max_position_embeddings
           snake_case__					:      str  			=						eos_token_id
           snake_case__					:      List[str]  			=						pad_token_id
           snake_case__					:      Optional[int]  			=						bos_token_id
           snake_case__					:      Dict  			=						embed_dim
           snake_case__					:      Union[str, Any]  			=						word_embed_proj_dim
           snake_case__					:      Any  			=						initializer_range
           snake_case__					:      str  			=						False





     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__					:      Optional[int]  			=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,       dtype=np.intaa ))    ,       -1 )

           snake_case__					:      Tuple  			=						OPTConfig(
               vocab_size=self.vocab_size    ,       hidden_size=self.hidden_size    ,       num_hidden_layers=self.num_hidden_layers    ,       num_attention_heads=self.num_attention_heads    ,       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    ,       embed_dim=self.embed_dim    ,       is_encoder_decoder=__lowerCamelCase    ,       word_embed_proj_dim=self.word_embed_proj_dim    ,       initializer_range=self.initializer_range    ,       use_cache=__lowerCamelCase    ,       )
           snake_case__					:      Optional[int]  			=						prepare_opt_inputs_dict(__lowerCamelCase    ,       __lowerCamelCase )
           return config, inputs_dict





     def  _lowerCAmelCase			(    self    :				Any ):
           snake_case__			,       snake_case__					:      Tuple  			=						self.prepare_config_and_inputs()
           return config, inputs_dict





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

           snake_case__					:      Union[str, Any]  			=						inputs_dict['input_ids']
           snake_case__					:      Optional[Any]  			=						inputs_dict['attention_mask']

           snake_case__					:      List[str]  			=						model.init_cache(input_ids.shape[0]    ,       __lowerCamelCase )
           snake_case__					:      Optional[Any]  			=						jnp.ones((input_ids.shape[0], max_length)    ,       dtype='i4' )

           snake_case__					:      Optional[Any]  			=						jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1 )[None, :]    ,       (input_ids.shape[0], input_ids.shape[-1] - 1)    ,       )
           snake_case__					:      Dict  			=						model(
               input_ids[:, :-1]    ,       attention_mask=__lowerCamelCase    ,       past_key_values=__lowerCamelCase    ,       position_ids=__lowerCamelCase    ,       )

           snake_case__					:      int  			=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,       dtype='i4' )
           snake_case__					:      List[str]  			=						model(
               input_ids[:, -1:]    ,       attention_mask=__lowerCamelCase    ,       past_key_values=outputs_cache.past_key_values    ,       position_ids=__lowerCamelCase    ,       )

           snake_case__					:      int  			=						model(__lowerCamelCase )

           snake_case__					:      Optional[Any]  			=						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    :				List[Any]    ,       __lowerCamelCase    :				Tuple    ,       __lowerCamelCase    :				Union[str, Any] ):
           snake_case__					:      Dict  			=						20
           snake_case__					:      List[str]  			=						model_class_name(__lowerCamelCase )

           snake_case__			,       snake_case__					:      Any  			=						(
               inputs_dict['input_ids'],
               inputs_dict['attention_mask'],
           )

           snake_case__					:      Tuple  			=						jnp.concatenate(
               [
                   attention_mask,
                   jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1]) ),
               ]    ,       axis=-1    ,       )

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

           snake_case__					:      Union[str, Any]  			=						model(
               input_ids[:, :-1]    ,       attention_mask=__lowerCamelCase    ,       past_key_values=__lowerCamelCase    ,       position_ids=__lowerCamelCase    ,       )
           snake_case__					:      Union[str, Any]  			=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,       dtype='i4' )
           snake_case__					:      Dict  			=						model(
               input_ids[:, -1:]    ,       past_key_values=outputs_cache.past_key_values    ,       attention_mask=__lowerCamelCase    ,       position_ids=__lowerCamelCase    ,       )

           snake_case__					:      List[Any]  			=						model(__lowerCamelCase    ,       attention_mask=__lowerCamelCase )

           snake_case__					:      Dict  			=						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_       							= (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
     A_       							= () if is_flax_available() else ()





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__					:      Union[str, Any]  			=						FlaxOPTModelTester(self )





     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__			,       snake_case__					:      List[str]  			=						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    :				Optional[Any] ):
           snake_case__			,       snake_case__					:      Dict  			=						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 )





     @slow
     def  _lowerCAmelCase			(    self    :				int ):
           for model_class_name in self.all_model_classes:
                 snake_case__					:      Tuple  			=						model_class_name.from_pretrained('facebook/opt-125m' )
                 snake_case__					:      Any  			=						np.ones((1, 1) ) * model.config.eos_token_id
                 snake_case__					:      Optional[Any]  			=						model(__lowerCamelCase )
                 self.assertIsNotNone(__lowerCamelCase )






@require_sentencepiece
@require_flax
class 					lowercase_			(	unittest.TestCase       ):





     @slow
     def  _lowerCAmelCase			(    self    :				Tuple ):
           snake_case__					:      str  			=						FlaxOPTModel.from_pretrained('facebook/opt-350m' )
           snake_case__					:      Any  			=						jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
           snake_case__					:      Optional[Any]  			=						model(input_ids=__lowerCamelCase ).last_hidden_state
           snake_case__					:      int  			=						(1, 11, 512)
           self.assertEqual(output.shape    ,       __lowerCamelCase )
           snake_case__					:      Any  			=						jnp.array(
               [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]] )
           self.assertTrue(jnp.allclose(output[:, :3, :3]    ,       __lowerCamelCase    ,       atol=4E-2 ) )






@require_flax
@slow
class 					lowercase_			(	unittest.TestCase       ):





     def  _lowerCAmelCase			(    self    :				Dict ):
           super().setUp()
           snake_case__					:      List[Any]  			=						'facebook/opt-350m'





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      Tuple  			=						FlaxOPTForCausalLM.from_pretrained(self.path_model )
           snake_case__					:      List[Any]  			=						GPTaTokenizer.from_pretrained(self.path_model )

           snake_case__					:      Dict  			=						[
               'Today is a beautiful day and I want to',
               'In the city of',
               'Paris is the capital of France and',
               'Computers and mobile phones have taken',
           ]
           # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
           snake_case__					:      Union[str, Any]  			=						tokenizer(__lowerCamelCase    ,       return_tensors='jax'    ,       padding=__lowerCamelCase    ,       add_special_tokens=__lowerCamelCase )
           snake_case__					:      int  			=						model(inputs.input_ids    ,       attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
           snake_case__					:      Union[str, Any]  			=						jnp.array(
               [
                   [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                   [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
                   [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
                   [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
               ] )
           self.assertTrue(jnp.allclose(__lowerCamelCase    ,       __lowerCamelCase    ,       atol=4E-2 ) )

           snake_case__					:      Optional[int]  			=						jax.jit(__lowerCamelCase )
           snake_case__					:      List[str]  			=						model(inputs.input_ids    ,       attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
           self.assertTrue(jnp.allclose(__lowerCamelCase    ,       __lowerCamelCase    ,       atol=4E-2 ) )






@require_flax
@slow
class 					lowercase_			(	unittest.TestCase       ):





     @property
     def  _lowerCAmelCase			(    self    :				int ):
           return [
               "Today is a beautiful day and I want",
               "In the city of",
               "Paris is the capital of France and",
               "Computers and mobile phones have taken",
           ]





     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      Union[str, Any]  			=						'facebook/opt-125m'

           snake_case__					:      Tuple  			=						[
               'Today is a beautiful day and I want to',
               'In the city of New York, the city',
               'Paris is the capital of France and the capital',
               'Computers and mobile phones have taken over the',
           ]

           snake_case__					:      List[Any]  			=						[]

           snake_case__					:      List[str]  			=						FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase )
           snake_case__					:      Optional[Any]  			=						GPTaTokenizer.from_pretrained(__lowerCamelCase )

           for prompt in self.prompts:
                 snake_case__					:      Optional[Any]  			=						tokenizer(__lowerCamelCase    ,       return_tensors='jax' ).input_ids

                 snake_case__					:      int  			=						model.generate(__lowerCamelCase    ,       max_length=10 )
                 snake_case__					:      Any  			=						generated_ids[0]

                 snake_case__					:      Optional[int]  			=						tokenizer.batch_decode(__lowerCamelCase    ,       skip_special_tokens=__lowerCamelCase )
                 predicted_outputs += generated_string

           self.assertListEqual(__lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[str] ):
           snake_case__					:      Optional[Any]  			=						'facebook/opt-350m'

           snake_case__					:      str  			=						[
               'Today is a beautiful day and I want to',
               'In the city of San Francisco, the city',
               'Paris is the capital of France and the capital',
               'Computers and mobile phones have taken over the',
           ]

           snake_case__					:      str  			=						[]
           snake_case__					:      List[Any]  			=						FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase )
           snake_case__					:      Optional[int]  			=						GPTaTokenizer.from_pretrained(__lowerCamelCase )

           for prompt in self.prompts:
                 snake_case__					:      Any  			=						tokenizer(__lowerCamelCase    ,       return_tensors='jax' ).input_ids

                 snake_case__					:      Optional[int]  			=						model.generate(__lowerCamelCase    ,       max_length=10 )
                 snake_case__					:      Union[str, Any]  			=						generated_ids[0]

                 snake_case__					:      str  			=						tokenizer.batch_decode(__lowerCamelCase    ,       skip_special_tokens=__lowerCamelCase )
                 predicted_outputs += generated_string

           self.assertListEqual(__lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      Optional[Any]  			=						'facebook/opt-125m'
           snake_case__					:      Optional[Any]  			=						[
               'Today is a beautiful day and I want to thank',
               'In the city of Rome Canaver Canaver Canaver Canaver',
           ]
           snake_case__					:      List[Any]  			=						FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase )
           snake_case__					:      Tuple  			=						GPTaTokenizer.from_pretrained(__lowerCamelCase )
           snake_case__					:      Any  			=						tokenizer(
               [
                   'Today is a beautiful day and I want to',
                   'In the city of',
               ]    ,       return_tensors='jax'    ,       padding=__lowerCamelCase    ,       )

           snake_case__					:      Optional[int]  			=						jax.jit(model.generate )

           snake_case__					:      Dict  			=						jit_generate(inputs['input_ids']    ,       attention_mask=inputs['attention_mask'] ).sequences

           snake_case__					:      Optional[int]  			=						tokenizer.batch_decode(__lowerCamelCase    ,       skip_special_tokens=__lowerCamelCase )

           self.assertIsNotNone(__lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      List[str]  			=						'facebook/opt-350m'

           snake_case__					:      str  			=						GPTaTokenizer.from_pretrained(__lowerCamelCase )
           snake_case__					:      int  			=						FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase )

           snake_case__					:      List[Any]  			=						'left'

           # use different length sentences to test batching
           snake_case__					:      Union[str, Any]  			=						[
               'Hello, my dog is a little',
               'Today, I',
           ]

           snake_case__					:      List[str]  			=						tokenizer(__lowerCamelCase    ,       return_tensors='jax'    ,       padding=__lowerCamelCase )
           snake_case__					:      List[Any]  			=						inputs['input_ids']

           snake_case__					:      Union[str, Any]  			=						model.generate(input_ids=__lowerCamelCase    ,       attention_mask=inputs['attention_mask']    ,       trace=__lowerCamelCase )

           snake_case__					:      List[Any]  			=						tokenizer(sentences[0]    ,       return_tensors='jax' ).input_ids
           snake_case__					:      Any  			=						model.generate(input_ids=__lowerCamelCase )

           snake_case__					:      Optional[int]  			=						inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
           snake_case__					:      List[Any]  			=						tokenizer(sentences[1]    ,       return_tensors='jax' ).input_ids
           snake_case__					:      Optional[int]  			=						model.generate(input_ids=__lowerCamelCase    ,       max_length=model.config.max_length - num_paddings )

           snake_case__					:      Dict  			=						tokenizer.batch_decode(outputs[0]    ,       skip_special_tokens=__lowerCamelCase )
           snake_case__					:      Union[str, Any]  			=						tokenizer.decode(output_non_padded[0][0]    ,       skip_special_tokens=__lowerCamelCase )
           snake_case__					:      Union[str, Any]  			=						tokenizer.decode(output_padded[0][0]    ,       skip_special_tokens=__lowerCamelCase )

           snake_case__					:      Optional[Any]  			=						[
               'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
               'Today, I was in the middle of a conversation with a friend about the',
           ]
           self.assertListEqual(__lowerCamelCase    ,       __lowerCamelCase )
           self.assertListEqual(__lowerCamelCase    ,       [non_padded_sentence, padded_sentence] )






'''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}
