


'''simple docstring'''
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
							import torch






class 				__lowerCAmelCase      :

						"""simple docstring"""






						def __init__(     self       :			Tuple   ,     lowerCAmelCase       :			Any   ,     lowerCAmelCase       :			Optional[Any]=14   ,     lowerCAmelCase       :			Any=7   ,     lowerCAmelCase       :			List[str]=True   ,     lowerCAmelCase       :			int=True   ,     lowerCAmelCase       :			str=False   ,     lowerCAmelCase       :			str=True   ,     lowerCAmelCase       :			Optional[int]=99   ,     lowerCAmelCase       :			Optional[int]=32   ,     lowerCAmelCase       :			Dict=5   ,     lowerCAmelCase       :			int=4   ,     lowerCAmelCase       :			List[str]=37   ,     lowerCAmelCase       :			List[Any]="gelu"   ,     lowerCAmelCase       :			Any=0.1   ,     lowerCAmelCase       :			Optional[int]=0.1   ,     lowerCAmelCase       :			List[Any]=5_12   ,     lowerCAmelCase       :			List[str]=0.0_2   ,     ):
													A_ 						=		parent
													A_ 						=		batch_size
													A_ 						=		seq_length
													A_ 						=		is_training
													A_ 						=		use_input_mask
													A_ 						=		use_token_type_ids
													A_ 						=		use_labels
													A_ 						=		vocab_size
													A_ 						=		hidden_size
													A_ 						=		num_hidden_layers
													A_ 						=		num_attention_heads
													A_ 						=		intermediate_size
													A_ 						=		hidden_act
													A_ 						=		hidden_dropout_prob
													A_ 						=		attention_probs_dropout_prob
													A_ 						=		max_position_embeddings
													A_ 						=		initializer_range
													A_ 						=		None
													A_ 						=		vocab_size - 1
													A_ 						=		vocab_size - 1
													A_ 						=		vocab_size - 1






						def 							_UpperCAmelCase					(     self       :			str  ):
													A_ 						=		ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size  )

													A_ 						=		None
													if self.use_input_mask:
																				A_ 						=		random_attention_mask([self.batch_size, self.seq_length]  )

													A_ 						=		GPTaConfig(
													    vocab_size=self.vocab_size   ,     n_embd=self.hidden_size   ,     n_layer=self.num_hidden_layers   ,     n_head=self.num_attention_heads   ,     n_positions=self.max_position_embeddings   ,     use_cache=lowerCAmelCase   ,     bos_token_id=self.bos_token_id   ,     eos_token_id=self.eos_token_id   ,     pad_token_id=self.pad_token_id   ,     )

													return (config, input_ids, input_mask)






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		self.prepare_config_and_inputs()
													A_						,				A_						,				A_ 						=		config_and_inputs
													A_ 						=		{"input_ids": input_ids, "attention_mask": attention_mask}
													return config, inputs_dict






						def 							_UpperCAmelCase					(     self       :			Any  ):
													A_						,				A_						,				A_ 						=		self.prepare_config_and_inputs()

													A_ 						=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
													A_ 						=		ids_tensor([self.batch_size, self.seq_length]   ,     vocab_size=2  )

													return (
													    config,
													    input_ids,
													    attention_mask,
													    encoder_hidden_states,
													    encoder_attention_mask,
													)






						def 							_UpperCAmelCase					(     self       :			Optional[Any]   ,     lowerCAmelCase       :			Dict   ,     lowerCAmelCase       :			Optional[Any]   ,     lowerCAmelCase       :			Optional[Any]   ,     lowerCAmelCase       :			Tuple  ):
													A_ 						=		20
													A_ 						=		model_class_name(lowerCAmelCase  )

													A_ 						=		model.init_cache(input_ids.shape[0]   ,     lowerCAmelCase  )
													A_ 						=		jnp.ones((input_ids.shape[0], max_decoder_length)   ,     dtype="i4"  )

													A_ 						=		jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1  )[None, :]   ,     (input_ids.shape[0], input_ids.shape[-1] - 1)  )
													A_ 						=		model(
													    input_ids[:, :-1]   ,     attention_mask=lowerCAmelCase   ,     past_key_values=lowerCAmelCase   ,     position_ids=lowerCAmelCase   ,     )

													A_ 						=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,     dtype="i4"  )
													A_ 						=		model(
													    input_ids[:, -1:]   ,     attention_mask=lowerCAmelCase   ,     past_key_values=outputs_cache.past_key_values   ,     position_ids=lowerCAmelCase   ,     )

													A_ 						=		model(lowerCAmelCase  )

													A_ 						=		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 							_UpperCAmelCase					(     self       :			List[Any]   ,     lowerCAmelCase       :			Optional[Any]   ,     lowerCAmelCase       :			Optional[Any]   ,     lowerCAmelCase       :			Union[str, Any]   ,     lowerCAmelCase       :			Any  ):
													A_ 						=		20
													A_ 						=		model_class_name(lowerCAmelCase  )

													A_ 						=		jnp.concatenate(
													    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]   ,     axis=-1   ,     )

													A_ 						=		model.init_cache(input_ids.shape[0]   ,     lowerCAmelCase  )
													A_ 						=		jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1  )[None, :]   ,     (input_ids.shape[0], input_ids.shape[-1] - 1)  )

													A_ 						=		model(
													    input_ids[:, :-1]   ,     attention_mask=lowerCAmelCase   ,     past_key_values=lowerCAmelCase   ,     position_ids=lowerCAmelCase   ,     )
													A_ 						=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,     dtype="i4"  )
													A_ 						=		model(
													    input_ids[:, -1:]   ,     past_key_values=outputs_cache.past_key_values   ,     attention_mask=lowerCAmelCase   ,     position_ids=lowerCAmelCase   ,     )

													A_ 						=		model(lowerCAmelCase   ,     attention_mask=lowerCAmelCase  )

													A_ 						=		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 				__lowerCAmelCase      (      lowercase							, lowercase							, unittest.TestCase	):

						"""simple docstring"""






						_UpperCAmelCase		:					List[Any]      	=(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
						_UpperCAmelCase		:					Optional[int]      	=(FlaxGPTaLMHeadModel,) if is_flax_available() else ()






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													A_ 						=		FlaxGPTaModelTester(self  )






						def 							_UpperCAmelCase					(     self       :			Optional[Any]  ):
													for model_class_name in self.all_model_classes:
																				A_						,				A_						,				A_ 						=		self.model_tester.prepare_config_and_inputs()
																				self.model_tester.check_use_cache_forward(lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Any  ):
													for model_class_name in self.all_model_classes:
																				A_						,				A_						,				A_ 						=		self.model_tester.prepare_config_and_inputs()
																				self.model_tester.check_use_cache_forward_with_attn_mask(
																				    lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase  )






						@slow
						def 							_UpperCAmelCase					(     self       :			str  ):
													A_ 						=		GPTaTokenizer.from_pretrained("gpt2"   ,     pad_token="</s>"   ,     padding_side="left"  )
													A_ 						=		tokenizer(["Hello this is a long string", "Hey"]   ,     return_tensors="np"   ,     padding=lowerCAmelCase   ,     truncation=lowerCAmelCase  )

													A_ 						=		FlaxGPTaLMHeadModel.from_pretrained("gpt2"  )
													A_ 						=		False
													A_ 						=		model.config.eos_token_id

													A_ 						=		jax.jit(model.generate  )

													A_ 						=		jit_generate(inputs["input_ids"]   ,     attention_mask=inputs["attention_mask"]  ).sequences

													A_ 						=		tokenizer.batch_decode(lowerCAmelCase   ,     skip_special_tokens=lowerCAmelCase  )

													A_ 						=		[
													    "Hello this is a long string of words. I'm going to start with the first one.\n",
													    "Hey, I'm not sure if I'm going to be able to do",
													]

													self.assertListEqual(lowerCAmelCase   ,     lowerCAmelCase  )






						@is_pt_flax_cross_test
						def 							_UpperCAmelCase					(     self       :			Any  ):
													A_						,				A_ 						=		self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__  ):
																											# prepare inputs
																											A_ 						=		self._prepare_for_class(lowerCAmelCase   ,     lowerCAmelCase  )
																											A_ 						=		{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

																											# load corresponding PyTorch class
																											A_ 						=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
																											A_ 						=		getattr(lowerCAmelCase   ,     lowerCAmelCase  )

																											A_						,				A_ 						=		pt_inputs["input_ids"].shape
																											A_ 						=		np.random.randint(0   ,     seq_length - 1   ,     size=(batch_size,)  )
																											for batch_idx, start_index in enumerate(lowerCAmelCase  ):
																																		A_ 						=		0
																																		A_ 						=		1
																																		A_ 						=		0
																																		A_ 						=		1
																											A_ 						=		pt_model_class(lowerCAmelCase  ).eval()
																											A_ 						=		model_class(lowerCAmelCase   ,     dtype=jnp.floataa  )

																											A_ 						=		convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,     lowerCAmelCase  )
																											A_ 						=		fx_state

																											with torch.no_grad():
																																		A_ 						=		pt_model(**lowerCAmelCase  ).to_tuple()

																											A_ 						=		fx_model(**lowerCAmelCase  ).to_tuple()
																											self.assertEqual(len(lowerCAmelCase  )   ,     len(lowerCAmelCase  )   ,     "Output lengths differ between Flax and PyTorch"  )
																											for fx_output, pt_output in zip(lowerCAmelCase   ,     lowerCAmelCase  ):
																																		self.assert_almost_equals(fx_output[:, -1]   ,     pt_output[:, -1].numpy()   ,     4e-2  )

																											with tempfile.TemporaryDirectory() as tmpdirname:
																																		pt_model.save_pretrained(lowerCAmelCase  )
																																		A_ 						=		model_class.from_pretrained(lowerCAmelCase   ,     from_pt=lowerCAmelCase  )

																											A_ 						=		fx_model_loaded(**lowerCAmelCase  ).to_tuple()
																											self.assertEqual(
																											    len(lowerCAmelCase  )   ,     len(lowerCAmelCase  )   ,     "Output lengths differ between Flax and PyTorch"  )
																											for fx_output_loaded, pt_output in zip(lowerCAmelCase   ,     lowerCAmelCase  ):
																																		self.assert_almost_equals(fx_output_loaded[:, -1]   ,     pt_output[:, -1].numpy()   ,     4e-2  )






						@is_pt_flax_cross_test
						def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													A_						,				A_ 						=		self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__  ):
																											# prepare inputs
																											A_ 						=		self._prepare_for_class(lowerCAmelCase   ,     lowerCAmelCase  )
																											A_ 						=		{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

																											# load corresponding PyTorch class
																											A_ 						=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
																											A_ 						=		getattr(lowerCAmelCase   ,     lowerCAmelCase  )

																											A_ 						=		pt_model_class(lowerCAmelCase  ).eval()
																											A_ 						=		model_class(lowerCAmelCase   ,     dtype=jnp.floataa  )

																											A_ 						=		load_flax_weights_in_pytorch_model(lowerCAmelCase   ,     fx_model.params  )
																											A_						,				A_ 						=		pt_inputs["input_ids"].shape
																											A_ 						=		np.random.randint(0   ,     seq_length - 1   ,     size=(batch_size,)  )
																											for batch_idx, start_index in enumerate(lowerCAmelCase  ):
																																		A_ 						=		0
																																		A_ 						=		1
																																		A_ 						=		0
																																		A_ 						=		1

																											# make sure weights are tied in PyTorch
																											pt_model.tie_weights()

																											with torch.no_grad():
																																		A_ 						=		pt_model(**lowerCAmelCase  ).to_tuple()

																											A_ 						=		fx_model(**lowerCAmelCase  ).to_tuple()
																											self.assertEqual(len(lowerCAmelCase  )   ,     len(lowerCAmelCase  )   ,     "Output lengths differ between Flax and PyTorch"  )
																											for fx_output, pt_output in zip(lowerCAmelCase   ,     lowerCAmelCase  ):
																																		self.assert_almost_equals(fx_output[:, -1]   ,     pt_output[:, -1].numpy()   ,     4e-2  )

																											with tempfile.TemporaryDirectory() as tmpdirname:
																																		fx_model.save_pretrained(lowerCAmelCase  )
																																		A_ 						=		pt_model_class.from_pretrained(lowerCAmelCase   ,     from_flax=lowerCAmelCase  )

																											with torch.no_grad():
																																		A_ 						=		pt_model_loaded(**lowerCAmelCase  ).to_tuple()

																											self.assertEqual(
																											    len(lowerCAmelCase  )   ,     len(lowerCAmelCase  )   ,     "Output lengths differ between Flax and PyTorch"  )
																											for fx_output, pt_output in zip(lowerCAmelCase   ,     lowerCAmelCase  ):
																																		self.assert_almost_equals(fx_output[:, -1]   ,     pt_output[:, -1].numpy()   ,     4e-2  )






						@slow
						def 							_UpperCAmelCase					(     self       :			str  ):
													for model_class_name in self.all_model_classes:
																				A_ 						=		model_class_name.from_pretrained("gpt2"   ,     from_pt=lowerCAmelCase  )
																				A_ 						=		model(np.ones((1, 1)  )  )
																				self.assertIsNotNone(lowerCAmelCase  )









'''simple docstring'''
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		'''\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
'''

__SCREAMING_SNAKE_CASE				:							List[Any]		       =		'''\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
'''


__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		'''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]
    >>> sari = datasets.load_metric("sari")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 26.953601953601954}
'''







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		[rgram for rgrams in rgramslist for rgram in rgrams]
							A_ 						=		Counter(UpperCamelCase_   )

							A_ 						=		Counter(UpperCamelCase_   )
							A_ 						=		Counter()
							for sgram, scount in sgramcounter.items():
														A_ 						=		scount * numref

							A_ 						=		Counter(UpperCamelCase_   )
							A_ 						=		Counter()
							for cgram, ccount in cgramcounter.items():
														A_ 						=		ccount * numref

							# KEEP
							A_ 						=		sgramcounter_rep & cgramcounter_rep
							A_ 						=		keepgramcounter_rep & rgramcounter
							A_ 						=		sgramcounter_rep & rgramcounter

							A_ 						=		0
							A_ 						=		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.
							A_ 						=		1
							A_ 						=		1
							if len(UpperCamelCase_   ) > 0:
														A_ 						=		keeptmpscorea / len(UpperCamelCase_   )
							if len(UpperCamelCase_   ) > 0:
														# Fix an alleged bug [2] in the keep score computation.
														# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
														A_ 						=		keeptmpscorea / sum(keepgramcounterall_rep.values()   )
							A_ 						=		0
							if keepscore_precision > 0 or keepscore_recall > 0:
														A_ 						=		2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

							# DELETION
							A_ 						=		sgramcounter_rep - cgramcounter_rep
							A_ 						=		delgramcounter_rep - rgramcounter
							A_ 						=		sgramcounter_rep - rgramcounter
							A_ 						=		0
							A_ 						=		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.
							A_ 						=		1
							if len(UpperCamelCase_   ) > 0:
														A_ 						=		deltmpscorea / len(UpperCamelCase_   )

							# ADDITION
							A_ 						=		set(UpperCamelCase_   ) - set(UpperCamelCase_   )
							A_ 						=		set(UpperCamelCase_   ) & set(UpperCamelCase_   )
							A_ 						=		set(UpperCamelCase_   ) - set(UpperCamelCase_   )

							A_ 						=		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.
							A_ 						=		1
							A_ 						=		1
							if len(UpperCamelCase_   ) > 0:
														A_ 						=		addtmpscore / len(UpperCamelCase_   )
							if len(UpperCamelCase_   ) > 0:
														A_ 						=		addtmpscore / len(UpperCamelCase_   )
							A_ 						=		0
							if addscore_precision > 0 or addscore_recall > 0:
														A_ 						=		2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		len(UpperCamelCase_   )

							A_ 						=		ssent.split(" "   )
							A_ 						=		csent.split(" "   )
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]

							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							for rsent in rsents:
														A_ 						=		rsent.split(" "   )
														A_ 						=		[]
														A_ 						=		[]
														A_ 						=		[]
														ragramslist.append(UpperCamelCase_   )
														for i in range(0	,     len(UpperCamelCase_   ) - 1   ):
																					if i < len(UpperCamelCase_   ) - 1:
																												A_ 						=		ragrams[i] + " " + ragrams[i + 1]
																												ragrams.append(UpperCamelCase_   )
																					if i < len(UpperCamelCase_   ) - 2:
																												A_ 						=		ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
																												ragrams.append(UpperCamelCase_   )
																					if i < len(UpperCamelCase_   ) - 3:
																												A_ 						=		ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
																												ragrams.append(UpperCamelCase_   )
														ragramslist.append(UpperCamelCase_   )
														ragramslist.append(UpperCamelCase_   )
														ragramslist.append(UpperCamelCase_   )

							for i in range(0	,     len(UpperCamelCase_   ) - 1   ):
														if i < len(UpperCamelCase_   ) - 1:
																					A_ 						=		sagrams[i] + " " + sagrams[i + 1]
																					sagrams.append(UpperCamelCase_   )
														if i < len(UpperCamelCase_   ) - 2:
																					A_ 						=		sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
																					sagrams.append(UpperCamelCase_   )
														if i < len(UpperCamelCase_   ) - 3:
																					A_ 						=		sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
																					sagrams.append(UpperCamelCase_   )

							for i in range(0	,     len(UpperCamelCase_   ) - 1   ):
														if i < len(UpperCamelCase_   ) - 1:
																					A_ 						=		cagrams[i] + " " + cagrams[i + 1]
																					cagrams.append(UpperCamelCase_   )
														if i < len(UpperCamelCase_   ) - 2:
																					A_ 						=		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
																					cagrams.append(UpperCamelCase_   )
														if i < len(UpperCamelCase_   ) - 3:
																					A_ 						=		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
																					cagrams.append(UpperCamelCase_   )

							((A_)						,				(A_)						,				(A_)) 						=		SARIngram(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							((A_)						,				(A_)						,				(A_)) 						=		SARIngram(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							((A_)						,				(A_)						,				(A_)) 						=		SARIngram(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							((A_)						,				(A_)						,				(A_)) 						=		SARIngram(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							A_ 						=		sum([keepascore, keepascore, keepascore, keepascore]   ) / 4
							A_ 						=		sum([delascore, delascore, delascore, delascore]   ) / 4
							A_ 						=		sum([addascore, addascore, addascore, addascore]   ) / 4
							A_ 						=		(avgkeepscore + avgdelscore + avgaddscore) / 3
							return finalscore







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_ = True	,     UpperCamelCase_ = "13a"	,     UpperCamelCase_ = True   ):
							# 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:
														A_ 						=		sentence.lower()

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

							if not return_str:
														A_ 						=		normalized_sent.split()

							return normalized_sent







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

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													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 							_UpperCAmelCase					(     self       :			Tuple   ,     lowerCAmelCase       :			Optional[Any]   ,     lowerCAmelCase       :			Dict   ,     lowerCAmelCase       :			List[Any]  ):
													if not (len(lowerCAmelCase  ) == len(lowerCAmelCase  ) == len(lowerCAmelCase  )):
																				raise ValueError("Sources length must match predictions and references lengths."  )
													A_ 						=		0
													for src, pred, refs in zip(lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase  ):
																				sari_score += SARIsent(normalize(lowerCAmelCase  )   ,     normalize(lowerCAmelCase  )   ,     [normalize(lowerCAmelCase  ) for sent in refs]  )
													A_ 						=		sari_score / len(lowerCAmelCase  )
													return {"sari": 1_00 * sari_score}





