




'''simple docstring'''




from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case__          =						"""\
@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},
}
"""

snake_case__          =						"""\
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
"""


snake_case__          =						"""
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__    ( lowerCamelCase__				:   Tuple			,		lowerCamelCase__				:   str			,		lowerCamelCase__				:   Tuple			,		lowerCamelCase__				:   Any			)   ->   Union[str, Any]:
					A_ :     str =							[rgram for rgrams in rgramslist for rgram in rgrams]
					A_ :     Optional[int] =							Counter(lowerCamelCase__			)

					A_ :     Dict =							Counter(lowerCamelCase__			)
					A_ :     str =							Counter()
					for sgram, scount in sgramcounter.items():
										A_ :     Union[str, Any] =							scount * numref

					A_ :     List[str] =							Counter(lowerCamelCase__			)
					A_ :     Optional[int] =							Counter()
					for cgram, ccount in cgramcounter.items():
										A_ :     Optional[int] =							ccount * numref

					# KEEP
					A_ :     List[str] =							sgramcounter_rep & cgramcounter_rep
					A_ :     Dict =							keepgramcounter_rep & rgramcounter
					A_ :     Dict =							sgramcounter_rep & rgramcounter

					A_ :     Optional[int] =							0
					A_ :     List[str] =							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_ :     Optional[Any] =							1
					A_ :     Dict =							1
					if len(lowerCamelCase__			) > 0:
										A_ :     List[Any] =							keeptmpscorea / len(lowerCamelCase__			)
					if len(lowerCamelCase__			) > 0:
										# Fix an alleged bug [2] in the keep score computation.
										# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
										A_ :     List[str] =							keeptmpscorea / sum(keepgramcounterall_rep.values()			)
					A_ :     Optional[int] =							0
					if keepscore_precision > 0 or keepscore_recall > 0:
										A_ :     Optional[int] =							2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

					# DELETION
					A_ :     Optional[int] =							sgramcounter_rep - cgramcounter_rep
					A_ :     str =							delgramcounter_rep - rgramcounter
					A_ :     Any =							sgramcounter_rep - rgramcounter
					A_ :     Tuple =							0
					A_ :     Dict =							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_ :     Tuple =							1
					if len(lowerCamelCase__			) > 0:
										A_ :     int =							deltmpscorea / len(lowerCamelCase__			)

					# ADDITION
					A_ :     Union[str, Any] =							set(lowerCamelCase__			) - set(lowerCamelCase__			)
					A_ :     List[str] =							set(lowerCamelCase__			) & set(lowerCamelCase__			)
					A_ :     List[Any] =							set(lowerCamelCase__			) - set(lowerCamelCase__			)

					A_ :     Tuple =							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_ :     Union[str, Any] =							1
					A_ :     Dict =							1
					if len(lowerCamelCase__			) > 0:
										A_ :     List[str] =							addtmpscore / len(lowerCamelCase__			)
					if len(lowerCamelCase__			) > 0:
										A_ :     int =							addtmpscore / len(lowerCamelCase__			)
					A_ :     Optional[int] =							0
					if addscore_precision > 0 or addscore_recall > 0:
										A_ :     int =							2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

					return (keepscore, delscore_precision, addscore)




def 						snake_case__    ( lowerCamelCase__				:   Union[str, Any]			,		lowerCamelCase__				:   Optional[Any]			,		lowerCamelCase__				:   Any			)   ->   Any:
					A_ :     Optional[int] =							len(lowerCamelCase__			)

					A_ :     Dict =							ssent.split(''' '''			)
					A_ :     Union[str, Any] =							csent.split(''' '''			)
					A_ :     Optional[int] =							[]
					A_ :     Optional[int] =							[]
					A_ :     Optional[int] =							[]
					A_ :     int =							[]
					A_ :     Dict =							[]
					A_ :     int =							[]

					A_ :     str =							[]
					A_ :     Optional[Any] =							[]
					A_ :     Dict =							[]
					A_ :     str =							[]
					for rsent in rsents:
										A_ :     List[Any] =							rsent.split(''' '''			)
										A_ :     List[str] =							[]
										A_ :     Dict =							[]
										A_ :     List[str] =							[]
										ragramslist.append(lowerCamelCase__			)
										for i in range(0			,		len(lowerCamelCase__			) - 1			):
															if i < len(lowerCamelCase__			) - 1:
																				A_ :     Union[str, Any] =							ragrams[i] + ''' ''' + ragrams[i + 1]
																				ragrams.append(lowerCamelCase__			)
															if i < len(lowerCamelCase__			) - 2:
																				A_ :     List[str] =							ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
																				ragrams.append(lowerCamelCase__			)
															if i < len(lowerCamelCase__			) - 3:
																				A_ :     str =							ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
																				ragrams.append(lowerCamelCase__			)
										ragramslist.append(lowerCamelCase__			)
										ragramslist.append(lowerCamelCase__			)
										ragramslist.append(lowerCamelCase__			)

					for i in range(0			,		len(lowerCamelCase__			) - 1			):
										if i < len(lowerCamelCase__			) - 1:
															A_ :     Dict =							sagrams[i] + ''' ''' + sagrams[i + 1]
															sagrams.append(lowerCamelCase__			)
										if i < len(lowerCamelCase__			) - 2:
															A_ :     List[str] =							sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
															sagrams.append(lowerCamelCase__			)
										if i < len(lowerCamelCase__			) - 3:
															A_ :     Optional[int] =							sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
															sagrams.append(lowerCamelCase__			)

					for i in range(0			,		len(lowerCamelCase__			) - 1			):
										if i < len(lowerCamelCase__			) - 1:
															A_ :     Optional[int] =							cagrams[i] + ''' ''' + cagrams[i + 1]
															cagrams.append(lowerCamelCase__			)
										if i < len(lowerCamelCase__			) - 2:
															A_ :     Tuple =							cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
															cagrams.append(lowerCamelCase__			)
										if i < len(lowerCamelCase__			) - 3:
															A_ :     str =							cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
															cagrams.append(lowerCamelCase__			)

					((A_) ,(A_) ,(A_)) :     Any =							SARIngram(lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__			)
					((A_) ,(A_) ,(A_)) :     int =							SARIngram(lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__			)
					((A_) ,(A_) ,(A_)) :     str =							SARIngram(lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__			)
					((A_) ,(A_) ,(A_)) :     Dict =							SARIngram(lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__			)
					A_ :     Optional[Any] =							sum([keepascore, keepascore, keepascore, keepascore]			) / 4
					A_ :     str =							sum([delascore, delascore, delascore, delascore]			) / 4
					A_ :     List[str] =							sum([addascore, addascore, addascore, addascore]			) / 4
					A_ :     int =							(avgkeepscore + avgdelscore + avgaddscore) / 3
					return finalscore




def 						snake_case__    ( lowerCamelCase__				:   Union[str, Any]			,		lowerCamelCase__				:   bool = True			,		lowerCamelCase__				:   str = "13a"			,		lowerCamelCase__				:   bool = True			)   ->   Union[str, Any]:
					# 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_ :     int =							sentence.lower()

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

					if not return_str:
										A_ :     Any =							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      :       List[Any]    ):





											"""simple docstring"""







											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      :       List[Any]							, _lowerCamelCase      :       str							, _lowerCamelCase      :       Optional[int]							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







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









'''simple docstring'''




import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
							snake_case__          =						argparse.ArgumentParser()

							# Hyperparameters sent by the client are passed as command-line arguments to the script.
							parser.add_argument("""--epochs""", type=int, default=1)
							parser.add_argument("""--per_device_train_batch_size""", type=int, default=16)
							parser.add_argument("""--per_device_eval_batch_size""", type=int, default=8)
							parser.add_argument("""--model_name_or_path""", type=str)
							parser.add_argument("""--learning_rate""", type=str, default=5e-5)
							parser.add_argument("""--do_train""", type=bool, default=True)
							parser.add_argument("""--do_eval""", type=bool, default=True)
							parser.add_argument("""--output_dir""", type=str)

							snake_case__       ,						snake_case__          =						parser.parse_known_args()

							# overwrite batch size until we have tf_glue.py
							snake_case__          =						16
							snake_case__          =						16

							# Set up logging
							snake_case__          =						logging.getLogger(__name__)

							logging.basicConfig(
							    level=logging.getLevelName("""INFO"""),
							    handlers=[logging.StreamHandler(sys.stdout)],
							    format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
							)

							# Load model and tokenizer
							snake_case__          =						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							snake_case__          =						AutoTokenizer.from_pretrained(args.model_name_or_path)

							# Load dataset
							snake_case__       ,						snake_case__          =						load_dataset("""imdb""", split=["""train""", """test"""])
							snake_case__          =						train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
							snake_case__          =						test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

							# Preprocess train dataset
							snake_case__          =						train_dataset.map(
							    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
							)
							train_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

							snake_case__          =						{
							    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
							    for x in ["""input_ids""", """attention_mask"""]
							}
							snake_case__          =						tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])).batch(
							    args.per_device_train_batch_size
							)

							# Preprocess test dataset
							snake_case__          =						test_dataset.map(
							    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
							)
							test_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

							snake_case__          =						{
							    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
							    for x in ["""input_ids""", """attention_mask"""]
							}
							snake_case__          =						tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])).batch(
							    args.per_device_eval_batch_size
							)

							# fine optimizer and loss
							snake_case__          =						tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
							snake_case__          =						tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
							snake_case__          =						[tf.keras.metrics.SparseCategoricalAccuracy()]
							model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

							snake_case__          =						time.time()
							snake_case__          =						model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							snake_case__          =						time.time() - start_train_time

							logger.info("""*** Train ***""")
							logger.info(F'train_runtime = {end_train_time}')
							for key, value in train_results.history.items():
														logger.info(F'  {key} = {value}')



