



import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, 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
	import numpy as np

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
	import torch


@require_flax
class    _lowercase :
				'''simple docstring'''







				def __init__(			self  ,			snake_case__  ,			snake_case__=14  ,			snake_case__=7  ,			snake_case__=True  ,			snake_case__=True  ,			snake_case__=True  ,			snake_case__=99  ,			snake_case__=32  ,			snake_case__=5  ,			snake_case__=4  ,			snake_case__=37  ,			snake_case__="gelu"  ,			snake_case__=0.1  ,			snake_case__=0.1  ,			snake_case__=512  ,			snake_case__=0.02  ,			snake_case__=None  ,			):

									'''simple docstring'''


									UpperCamelCase_			= parent
									UpperCamelCase_			= batch_size
									UpperCamelCase_			= seq_length
									UpperCamelCase_			= is_training
									UpperCamelCase_			= use_input_mask
									UpperCamelCase_			= use_labels
									UpperCamelCase_			= vocab_size
									UpperCamelCase_			= d_model
									UpperCamelCase_			= num_hidden_layers
									UpperCamelCase_			= num_attention_heads
									UpperCamelCase_			= ffn_dim
									UpperCamelCase_			= activation_function
									UpperCamelCase_			= activation_dropout
									UpperCamelCase_			= attention_dropout
									UpperCamelCase_			= max_position_embeddings
									UpperCamelCase_			= initializer_range
									UpperCamelCase_			= None
									UpperCamelCase_			= 0
									UpperCamelCase_			= 2
									UpperCamelCase_			= 1

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= np.clip(ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size     )  ,			3  ,			self.vocab_size     )

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

									UpperCamelCase_			= XGLMConfig(
									    vocab_size=self.vocab_size  ,			d_model=self.hidden_size  ,			num_layers=self.num_hidden_layers  ,			attention_heads=self.num_attention_heads  ,			ffn_dim=self.ffn_dim  ,			activation_function=self.activation_function  ,			activation_dropout=self.activation_dropout  ,			attention_dropout=self.attention_dropout  ,			max_position_embeddings=self.max_position_embeddings  ,			initializer_range=self.initializer_range  ,			use_cache=snake_case__  ,			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    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.prepare_config_and_inputs()
									UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= config_and_inputs
									UpperCamelCase_			= {"input_ids": input_ids, "attention_mask": attention_mask}
									return config, inputs_dict

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= self.prepare_config_and_inputs()

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

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

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= 20
									UpperCamelCase_			= model_class_name(snake_case__     )

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

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

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

									UpperCamelCase_			= model(snake_case__     )

									UpperCamelCase_			= 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  ,			snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= 20
									UpperCamelCase_			= model_class_name(snake_case__     )

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

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

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

									UpperCamelCase_			= model(snake_case__  ,			attention_mask=snake_case__     )
									UpperCamelCase_			= 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_sentencepiece
@require_flax
class    _lowercase (a_   , a_   , unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
				lowercase__     =    (FlaxXGLMForCausalLM,) if is_flax_available() else ()

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= FlaxXGLMModelTester(self     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									for model_class_name in self.all_model_classes:
														UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward(snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									for model_class_name in self.all_model_classes:
														UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward_with_attn_mask(
														    snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     )

				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= XGLMTokenizer.from_pretrained("XGLM"  ,			padding_side="left"     )
									UpperCamelCase_			= tokenizer(["Hello this is a long string", "Hey"]  ,			return_tensors="np"  ,			padding=snake_case__  ,			truncation=snake_case__     )

									UpperCamelCase_			= FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"     )
									UpperCamelCase_			= 1
									UpperCamelCase_			= False

									UpperCamelCase_			= jax.jit(model.generate     )

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

									UpperCamelCase_			= tokenizer.batch_decode(snake_case__  ,			skip_special_tokens=snake_case__     )

									UpperCamelCase_			= [
									    "Hello this is a long string of questions, but I'm not sure if I'm",
									    "Hey, I'm a newbie to the forum and I'",
									]

									self.assertListEqual(snake_case__  ,			snake_case__     )

				@is_pt_flax_cross_test
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= 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
																			UpperCamelCase_			= self._prepare_for_class(snake_case__  ,			snake_case__     )
																			UpperCamelCase_			= {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

																			UpperCamelCase_	,     UpperCamelCase_			= pt_inputs["input_ids"].shape
																			UpperCamelCase_			= np.random.randint(0  ,			seq_length - 1  ,			size=(batch_size,)     )
																			for batch_idx, start_index in enumerate(snake_case__     ):
																								UpperCamelCase_			= 0
																								UpperCamelCase_			= 1
																								UpperCamelCase_			= 0
																								UpperCamelCase_			= 1
																			UpperCamelCase_			= pt_model_class(snake_case__     ).eval()
																			# Flax models don't use the `use_cache` option and cache is not returned as a default.
																			# So we disable `use_cache` here for PyTorch model.
																			UpperCamelCase_			= False
																			UpperCamelCase_			= model_class(snake_case__  ,			dtype=jnp.floataa     )
																			UpperCamelCase_			= convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,			snake_case__     )
																			UpperCamelCase_			= fx_state

																			with torch.no_grad():
																								UpperCamelCase_			= pt_model(**snake_case__     ).to_tuple()

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

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

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

				@is_pt_flax_cross_test
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= 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
																			UpperCamelCase_			= self._prepare_for_class(snake_case__  ,			snake_case__     )
																			UpperCamelCase_			= {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

																			UpperCamelCase_			= pt_model_class(snake_case__     ).eval()
																			UpperCamelCase_			= False
																			UpperCamelCase_			= model_class(snake_case__  ,			dtype=jnp.floataa     )

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

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

																			with torch.no_grad():
																								UpperCamelCase_			= pt_model(**snake_case__     ).to_tuple()

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

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

																			with torch.no_grad():
																								UpperCamelCase_			= pt_model_loaded(**snake_case__     ).to_tuple()

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

				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									for model_class_name in self.all_model_classes:
														UpperCamelCase_			= model_class_name.from_pretrained("facebook/xglm-564M"     )
														UpperCamelCase_			= model(np.ones((1, 1)     )     )
														self.assertIsNotNone(snake_case__     )








"""simple docstring"""



from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


a  :						Optional[Any]					       =							'''\
@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},
}
'''

a  :						str					       =							'''\
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
'''


a  :						List[str]					       =							'''
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       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Dict:
 UpperCAmelCase__ 						=				[rgram for rgrams in rgramslist for rgram in rgrams]
 UpperCAmelCase__ 						=				Counter(_SCREAMING_SNAKE_CASE			)

 UpperCAmelCase__ 						=				Counter(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				Counter()
 for sgram, scount in sgramcounter.items():
  UpperCAmelCase__ 						=				scount * numref

 UpperCAmelCase__ 						=				Counter(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				Counter()
 for cgram, ccount in cgramcounter.items():
  UpperCAmelCase__ 						=				ccount * numref

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

 UpperCAmelCase__ 						=				0
 UpperCAmelCase__ 						=				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.
 UpperCAmelCase__ 						=				1
 UpperCAmelCase__ 						=				1
 if len(_SCREAMING_SNAKE_CASE			) > 0:
  UpperCAmelCase__ 						=				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)
  UpperCAmelCase__ 						=				keeptmpscorea / sum(keepgramcounterall_rep.values()			)
 UpperCAmelCase__ 						=				0
 if keepscore_precision > 0 or keepscore_recall > 0:
  UpperCAmelCase__ 						=				2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

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

 # ADDITION
 UpperCAmelCase__ 						=				set(_SCREAMING_SNAKE_CASE			) - set(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				set(_SCREAMING_SNAKE_CASE			) & set(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				set(_SCREAMING_SNAKE_CASE			) - set(_SCREAMING_SNAKE_CASE			)

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

 return (keepscore, delscore_precision, addscore)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Dict:
 UpperCAmelCase__ 						=				len(_SCREAMING_SNAKE_CASE			)

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

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

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




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = True       ,	_SCREAMING_SNAKE_CASE = "13a"       ,	_SCREAMING_SNAKE_CASE = True			)    ->Dict:
 # 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:
  UpperCAmelCase__ 						=				sentence.lower()

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

 if not return_str:
  UpperCAmelCase__ 						=				normalized_sent.split()

 return normalized_sent


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



    '''simple docstring'''


    def 		A__      (      self			):
     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 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     if not (len(__lowercase			) == len(__lowercase			) == len(__lowercase			)):
      raise ValueError("""Sources length must match predictions and references lengths."""			)
     UpperCAmelCase__ 						=				0
     for src, pred, refs in zip(__lowercase   ,			__lowercase   ,			__lowercase			):
      sari_score += SARIsent(normalize(__lowercase			)   ,			normalize(__lowercase			)   ,			[normalize(__lowercase			) for sent in refs]			)
     UpperCAmelCase__ 						=				sari_score / len(__lowercase			)
     return {"sari": 100 * sari_score}



