






import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


lowerCamelCase_		  =						"""\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
"""

lowerCamelCase_		  =						"""\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
"""


lowerCamelCase_		  =						"""
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric(\"frugalscore\")
    >>> results = frugalscore.compute(predictions=['hello there', 'huggingface'], references=['hello world', 'hugging face'])
    >>> print([round(s, 3) for s in results[\"scores\"]])
    [0.631, 0.645]
"""




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


							'''simple docstring'''






							def   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														return datasets.MetricInfo(
														    description=_DESCRIPTION    ,    citation=_CITATION    ,    inputs_description=_KWARGS_DESCRIPTION    ,    features=datasets.Features(
														        {
														            'predictions': datasets.Value('string'     ),
														            'references': datasets.Value('string'     ),
														        }     )    ,    homepage='https://github.com/moussaKam/FrugalScore'    ,    )

							def   _lowercase (	self    ,    lowercase_     )      ->      Dict:
														'''simple docstring'''

														if self.config_name == "default":
																					lowerCAmelCase_       		=  'moussaKam/frugalscore_tiny_bert-base_bert-score'
														else:
																					lowerCAmelCase_       		=  self.config_name
														lowerCAmelCase_       		=  AutoModelForSequenceClassification.from_pretrained(lowercase_     )
														lowerCAmelCase_       		=  AutoTokenizer.from_pretrained(lowercase_     )






							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_=3_2    ,    lowercase_=1_2_8    ,    lowercase_=None    ,    )      ->      Optional[Any]:
														'''simple docstring'''

														assert len(lowercase_     ) == len(
														    lowercase_     ), "predictions and references should have the same number of sentences."
														if device is not None:
																					assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
														else:
																					lowerCAmelCase_       		=  'gpu' if torch.cuda.is_available() else 'cpu'
														lowerCAmelCase_       		=  TrainingArguments(
														    'trainer'    ,    fpaa=(device == 'gpu')    ,    per_device_eval_batch_size=lowercase_    ,    report_to='all'    ,    no_cuda=(device == 'cpu')    ,    log_level='warning'    ,    )
														lowerCAmelCase_       		=  {'sentence1': predictions, 'sentence2': references}
														lowerCAmelCase_       		=  datasets.Dataset.from_dict(lowercase_     )

														def tokenize_function(lowercase_     ):
																					return self.tokenizer(
																					    data['sentence1']    ,    data['sentence2']    ,    max_length=lowercase_    ,    truncation=lowercase_    ,    padding=lowercase_     )

														lowerCAmelCase_       		=  raw_datasets.map(lowercase_    ,    batched=lowercase_     )
														tokenized_datasets.remove_columns(['sentence1', 'sentence2']     )
														lowerCAmelCase_       		=  Trainer(self.model    ,    lowercase_    ,    tokenizer=self.tokenizer     )
														lowerCAmelCase_       		=  trainer.predict(lowercase_     )
														return {"scores": list(predictions.predictions.squeeze(-1     )     )}











import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


lowerCamelCase_		  =						get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_torch
@require_torchaudio
@require_sentencepiece
class 							a_				(		unittest.TestCase	):


							'''simple docstring'''






							def   _lowercase (	self     )      ->      Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_       		=  tempfile.mkdtemp()

														lowerCAmelCase_       		=  ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
														lowerCAmelCase_       		=  dict(zip(lowercase_    ,    range(len(lowercase_     )     )     )     )
														lowerCAmelCase_       		=  Path(self.tmpdirname     )
														save_json(lowercase_    ,    save_dir / VOCAB_FILES_NAMES['vocab_file']     )
														if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																					copyfile(lowercase_    ,    save_dir / VOCAB_FILES_NAMES['spm_file']     )

														lowerCAmelCase_       		=  SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
														tokenizer.save_pretrained(self.tmpdirname     )

														lowerCAmelCase_       		=  {
														    'feature_size': 2_4,
														    'num_mel_bins': 2_4,
														    'padding_value': 0.0,
														    'sampling_rate': 1_6_0_0_0,
														    'return_attention_mask': False,
														    'do_normalize': True,
														}
														save_json(lowercase_    ,    save_dir / FEATURE_EXTRACTOR_NAME     )

							def   _lowercase (	self    ,    **lowercase_     )      ->      List[Any]:
														'''simple docstring'''

														return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,    **lowercase_     )

							def   _lowercase (	self    ,    **lowercase_     )      ->      Optional[Any]:
														'''simple docstring'''

														return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,    **lowercase_     )

							def   _lowercase (	self     )      ->      List[Any]:
														'''simple docstring'''

														shutil.rmtree(self.tmpdirname     )

							def   _lowercase (	self     )      ->      List[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_tokenizer()
														lowerCAmelCase_       		=  self.get_feature_extractor()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														processor.save_pretrained(self.tmpdirname     )
														lowerCAmelCase_       		=  SpeechaTextProcessor.from_pretrained(self.tmpdirname     )

														self.assertEqual(processor.tokenizer.get_vocab()    ,    tokenizer.get_vocab()     )
														self.assertIsInstance(processor.tokenizer    ,    lowercase_     )

														self.assertEqual(processor.feature_extractor.to_json_string()    ,    feature_extractor.to_json_string()     )
														self.assertIsInstance(processor.feature_extractor    ,    lowercase_     )

							def   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  SpeechaTextProcessor(
														    tokenizer=self.get_tokenizer()    ,    feature_extractor=self.get_feature_extractor()     )
														processor.save_pretrained(self.tmpdirname     )

														lowerCAmelCase_       		=  self.get_tokenizer(bos_token='(BOS)'    ,    eos_token='(EOS)'     )
														lowerCAmelCase_       		=  self.get_feature_extractor(do_normalize=lowercase_    ,    padding_value=1.0     )

														lowerCAmelCase_       		=  SpeechaTextProcessor.from_pretrained(
														    self.tmpdirname    ,    bos_token='(BOS)'    ,    eos_token='(EOS)'    ,    do_normalize=lowercase_    ,    padding_value=1.0     )

														self.assertEqual(processor.tokenizer.get_vocab()    ,    tokenizer_add_kwargs.get_vocab()     )
														self.assertIsInstance(processor.tokenizer    ,    lowercase_     )

														self.assertEqual(processor.feature_extractor.to_json_string()    ,    feature_extractor_add_kwargs.to_json_string()     )
														self.assertIsInstance(processor.feature_extractor    ,    lowercase_     )

							def   _lowercase (	self     )      ->      Dict:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														lowerCAmelCase_       		=  floats_list((3, 1_0_0_0)     )

														lowerCAmelCase_       		=  feature_extractor(lowercase_    ,    return_tensors='np'     )
														lowerCAmelCase_       		=  processor(lowercase_    ,    return_tensors='np'     )

														for key in input_feat_extract.keys():
																					self.assertAlmostEqual(input_feat_extract[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2     )

							def   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														lowerCAmelCase_       		=  'This is a test string'

														lowerCAmelCase_       		=  processor(text=lowercase_     )

														lowerCAmelCase_       		=  tokenizer(lowercase_     )

														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key]    ,    encoded_processor[key]     )

							def   _lowercase (	self     )      ->      Any:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														lowerCAmelCase_       		=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														lowerCAmelCase_       		=  processor.batch_decode(lowercase_     )
														lowerCAmelCase_       		=  tokenizer.batch_decode(lowercase_     )

														self.assertListEqual(lowercase_    ,    lowercase_     )






							def   _lowercase (	self     )      ->      Optional[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														self.assertListEqual(
														    processor.model_input_names    ,    feature_extractor.model_input_names    ,    msg='`processor` and `feature_extractor` model input names do not match'    ,    )



