


"""simple docstring"""
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


UpperCAmelCase   =	"""\
@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}
}
"""

UpperCAmelCase   =	"""\
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.
"""


UpperCAmelCase   =	"""
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   lowercase__							(				datasets.Metric							):





   def 		UpperCamelCase_     (       self)     ->     Dict:
      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 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Dict:






      if self.config_name == "default":
         _lowerCamelCase  :				List[Any]      =		"moussaKam/frugalscore_tiny_bert-base_bert-score"
      else:
         _lowerCamelCase  :				str      =		self.config_name
      _lowerCamelCase  :				Tuple      =		AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE)
      _lowerCamelCase  :				Union[str, Any]      =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=32			,      SCREAMING_SNAKE_CASE=128			,      SCREAMING_SNAKE_CASE=None			,      )     ->     int:
      assert len(SCREAMING_SNAKE_CASE) == len(
          SCREAMING_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  :				List[str]      =		"gpu" if torch.cuda.is_available() else "cpu"
      _lowerCamelCase  :				int      =		TrainingArguments(
          """trainer"""			,      fpaa=(device == """gpu""")			,      per_device_eval_batch_size=SCREAMING_SNAKE_CASE			,      report_to="""all"""			,      no_cuda=(device == """cpu""")			,      log_level="""warning"""			,      )
      _lowerCamelCase  :				Union[str, Any]      =		{"sentence1": predictions, "sentence2": references}
      _lowerCamelCase  :				List[Any]      =		datasets.Dataset.from_dict(SCREAMING_SNAKE_CASE)

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

      _lowerCamelCase  :				List[Any]      =		raw_datasets.map(SCREAMING_SNAKE_CASE			,      batched=SCREAMING_SNAKE_CASE)
      tokenized_datasets.remove_columns(["""sentence1""", """sentence2"""])
      _lowerCamelCase  :				Union[str, Any]      =		Trainer(self.model			,      SCREAMING_SNAKE_CASE			,      tokenizer=self.tokenizer)
      _lowerCamelCase  :				List[Any]      =		trainer.predict(SCREAMING_SNAKE_CASE)
      return {"scores": list(predictions.predictions.squeeze(-1))}




"""simple docstring"""
import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


UpperCAmelCase   =	datasets.utils.logging.get_logger(__name__)




@dataclass
class   lowercase__							(				datasets.BuilderConfig							):
   __UpperCAmelCase       					=				None
   __UpperCAmelCase       					=				"utf-8"
   __UpperCAmelCase       					=				None
   __UpperCAmelCase       					=				10 << 20  # 10MB
   __UpperCAmelCase       					=				False
   __UpperCAmelCase       					=				"line"







class   lowercase__							(				datasets.ArrowBasedBuilder							):
   __UpperCAmelCase       					=				TextConfig





   def 		UpperCamelCase_     (       self)     ->     int:
      return datasets.DatasetInfo(features=self.config.features)





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Optional[int]:
      if not self.config.data_files:
         raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}')
      _lowerCamelCase  :				Dict      =		dl_manager.download_and_extract(self.config.data_files)
      if isinstance(SCREAMING_SNAKE_CASE			,      (str, list, tuple)):
         _lowerCamelCase  :				Optional[Any]      =		data_files
         if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
            _lowerCamelCase  :				List[Any]      =		[files]
         _lowerCamelCase  :				List[str]      =		[dl_manager.iter_files(SCREAMING_SNAKE_CASE) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,      gen_kwargs={"""files""": files})]
      _lowerCamelCase  :				int      =		[]
      for split_name, files in data_files.items():
         if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
            _lowerCamelCase  :				Any      =		[files]
         _lowerCamelCase  :				Any      =		[dl_manager.iter_files(SCREAMING_SNAKE_CASE) for file in files]
         splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE			,      gen_kwargs={"""files""": files}))
      return splits





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     pa.Table:
      if self.config.features is not None:
         _lowerCamelCase  :				Union[str, Any]      =		self.config.features.arrow_schema
         if all(not require_storage_cast(SCREAMING_SNAKE_CASE) for feature in self.config.features.values()):
            # cheaper cast
            _lowerCamelCase  :				Any      =		pa_table.cast(SCREAMING_SNAKE_CASE)
         else:
            # more expensive cast; allows str <-> int/float or str to Audio for example
            _lowerCamelCase  :				Any      =		table_cast(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
         return pa_table
      else:
         return pa_table.cast(pa.schema({"""text""": pa.string()}))





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Any:
      _lowerCamelCase  :				Optional[Any]      =		list(self.config.features) if self.config.features is not None else ["""text"""]
      for file_idx, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE)):
         # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
         with open(SCREAMING_SNAKE_CASE			,      encoding=self.config.encoding			,      errors=self.config.errors) as f:
            if self.config.sample_by == "line":
               _lowerCamelCase  :				Union[str, Any]      =		0
               while True:
                  _lowerCamelCase  :				int      =		f.read(self.config.chunksize)
                  if not batch:
                     break
                  batch += f.readline()  # finish current line
                  # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
                  _lowerCamelCase  :				List[str]      =		StringIO(SCREAMING_SNAKE_CASE).readlines()
                  if not self.config.keep_linebreaks:
                     _lowerCamelCase  :				Optional[Any]      =		[line.rstrip("""\n""") for line in batch]
                  _lowerCamelCase  :				Any      =		pa.Table.from_arrays([pa.array(SCREAMING_SNAKE_CASE)]			,      names=SCREAMING_SNAKE_CASE)
                  # Uncomment for debugging (will print the Arrow table size and elements)
                  # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                  # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                  yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE)
                  batch_idx += 1
            elif self.config.sample_by == "paragraph":
               _lowerCamelCase  :				Optional[Any]      =		0
               _lowerCamelCase  :				Dict      =		""""""
               while True:
                  _lowerCamelCase  :				Union[str, Any]      =		f.read(self.config.chunksize)
                  if not new_batch:
                     break
                  batch += new_batch
                  batch += f.readline()  # finish current line
                  _lowerCamelCase  :				Any      =		batch.split("""\n\n""")
                  _lowerCamelCase  :				Dict      =		pa.Table.from_arrays(
                      [pa.array([example for example in batch[:-1] if example])]			,      names=SCREAMING_SNAKE_CASE)
                  # Uncomment for debugging (will print the Arrow table size and elements)
                  # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                  # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                  yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE)
                  batch_idx += 1
                  _lowerCamelCase  :				Union[str, Any]      =		batch[-1]
               if batch:
                  _lowerCamelCase  :				Optional[int]      =		pa.Table.from_arrays([pa.array([batch])]			,      names=SCREAMING_SNAKE_CASE)
                  yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE)
            elif self.config.sample_by == "document":
               _lowerCamelCase  :				Tuple      =		f.read()
               _lowerCamelCase  :				List[Any]      =		pa.Table.from_arrays([pa.array([text])]			,      names=SCREAMING_SNAKE_CASE)
               yield file_idx, self._cast_table(SCREAMING_SNAKE_CASE)

