



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

import datasets


A__     				=	"""\
@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}
}
"""

A__     				=	"""\
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.
"""


A__     				=	"""
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  __lowerCAmelCase			(					datasets.Metric	):




				def 							snake_case       (							self		):



									"""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 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





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




				def 							snake_case       (							self						,    _snake_case						,    _snake_case						,    _snake_case=32						,    _snake_case=128						,    _snake_case=None						,    ):



									"""simple docstring"""





									assert len(_snake_case		) == len(
									    _snake_case		), "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=_snake_case						,    report_to="""all"""						,    no_cuda=(device == """cpu""")						,    log_level="""warning"""						,    )
									_lowerCAmelCase      =   {"""sentence1""": predictions, """sentence2""": references}
									_lowerCAmelCase      =   datasets.Dataset.from_dict(_snake_case		)

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

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








import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
	from PIL import Image





class  __lowerCAmelCase			(					unittest.TestCase	,						lowerCamelCase__	):




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   load_tool("""image-question-answering"""		)
									self.tool.setup()
									_lowerCAmelCase      =   load_tool("""image-question-answering"""						,    remote=_snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.tool(_snake_case						,    """How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.remote_tool(_snake_case						,    """How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.tool(image=_snake_case						,    question="""How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.remote_tool(image=_snake_case						,    question="""How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)



