

import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


UpperCAmelCase__	:       Optional[Any]			      =      """none"""
UpperCAmelCase__	:       List[Any]			      =      """none"""






def    __lowercase (       _A  ,      _A     )       ->  Any:
      SCREAMING_SNAKE_CASE	: List[str]					       =				value.split(_A     )
      return len(_A     ) > 1 and any((len(_A     ) == 3 for x in split)     )






def    __lowercase (       _A     )       ->  str:
      if isinstance(_A  ,      _A     ):
            return value
      if isinstance(_A  ,      _A     ):
            return float(_A     )
      if not isinstance(_A  ,      _A     ):
            raise ValueError("""Argument value is not a string. Can't parse it as float"""     )
      SCREAMING_SNAKE_CASE	: int					       =				value

      try:
            # Example: 1,000.7
            if "." in sanitized and "," in sanitized:
                  return float(sanitized.replace(""","""  ,      """"""     )     )
            # 1,000
            if "," in sanitized and _split_thousands(""","""  ,      _A     ):
                  return float(sanitized.replace(""","""  ,      """"""     )     )
            # 5,5556
            if "," in sanitized and sanitized.count(""","""     ) == 1 and not _split_thousands(""","""  ,      _A     ):
                  return float(sanitized.replace(""","""  ,      """."""     )     )
            # 0.0.0.1
            if sanitized.count("""."""     ) > 1:
                  return float(sanitized.replace("""."""  ,      """"""     )     )
            # 0,0,0,1
            if sanitized.count(""","""     ) > 1:
                  return float(sanitized.replace(""","""  ,      """"""     )     )
            return float(_A     )
      except ValueError:
            # Avoid adding the sanitized value in the error message.
            raise ValueError("""Unable to convert value to float"""     )






def    __lowercase (       _A     )       ->  Tuple:
      if answer is None:
            return None
      try:
            SCREAMING_SNAKE_CASE	: Any					       =				convert_to_float(_A     )
            if isinstance(_A  ,      _A     ) and math.isnan(_A     ):
                  return None
            return value
      except ValueError:
            return answer.lower()


UpperCAmelCase__	:       str			      =      {
    """text""": lambda x: x,
    """real""": convert_to_float,
}







class     a__    (				enum.Enum    ):







  """simple docstring"""




  UpperCAmelCase__ :	List[str]         =0
  UpperCAmelCase__ :	Tuple         =1
  UpperCAmelCase__ :	Tuple         =2
  UpperCAmelCase__ :	int         =3
  UpperCAmelCase__ :	Union[str, Any]         =4
  UpperCAmelCase__ :	int         =5







class     a__    (				enum.Enum    ):







  """simple docstring"""




  UpperCAmelCase__ :	List[Any]         =0
  UpperCAmelCase__ :	int         =1
  UpperCAmelCase__ :	List[str]         =2







@dataclasses.dataclass
class     a__    :







  """simple docstring"""




  UpperCAmelCase__ :	Text
  UpperCAmelCase__ :	_Operator
  UpperCAmelCase__ :	Any


UpperCAmelCase__	:       Dict			      =      re.compile(R"""\w+|[^\w\s]+""", re.UNICODE | re.MULTILINE | re.DOTALL)






def    __lowercase (       _A     )       ->  Any:
      return list(_TOKENIZER.findall(x.lower()     )     )






def    __lowercase (       _A  ,      _A  ,      _A     )       ->  List[Any]:
      if operator == _Operator.EQUALS:
            return src == tgt
      elif operator == _Operator.GREATER:
            return src > tgt
      elif operator == _Operator.LESSER:
            return src < tgt
      raise ValueError(F"Unknown operator: {operator}"     )






def    __lowercase (       _A  ,      _A  ,      _A     )       ->  Union[str, Any]:
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				table["""types"""]
      return _TYPE_CONVERTER[types[column]](_A     )






def    __lowercase (       _A     )       ->  Dict:
      return isinstance(_A  ,      _A     )






def    __lowercase (       _A  ,      _A  ,      _A     )       ->  Union[str, Any]:
      for cond in conditions:
            SCREAMING_SNAKE_CASE	: List[Any]					       =				row[cond.column]

            SCREAMING_SNAKE_CASE	: List[str]					       =				_parse_value(_A  ,      cond.column  ,      cond.cmp_value     )

            if _is_string(_A     ) and _is_string(_A     ):
                  SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				_normalize_for_match(_A     )
                  SCREAMING_SNAKE_CASE	: int					       =				_normalize_for_match(_A     )

            if not isinstance(_A  ,      type(_A     )     ):
                  raise ValueError("""Type difference {} != {}""".format(type(_A     )  ,      type(_A     )     )     )

            if not _compare(cond.operator  ,      _A  ,      _A     ):
                  return False
      return True






def    __lowercase (       _A  ,      _A  ,      _A     )       ->  Tuple:
      if not answer_coordinates:
            if aggregation_op == _Aggregation.COUNT:
                  return 0.0
            else:
                  return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
      if aggregation_op == _Aggregation.COUNT:
            return float(len(_A     )     )

      # If we have just one answer, if float returns it or try a conversion.
      SCREAMING_SNAKE_CASE	: List[str]					       =				[table["""rows"""][i][j] for (i, j) in answer_coordinates]
      if len(_A     ) == 1:
            try:
                  return convert_to_float(values[0]     )
            except ValueError as e:
                  if aggregation_op != _Aggregation.NONE:
                        raise e

      if aggregation_op == _Aggregation.NONE:
            return None

      # Other aggregation only support numeric values. Bail out if we have strings.
      if not all((isinstance(_A  ,      (int, float)     ) for v in values)     ):
            return None

      if aggregation_op == _Aggregation.SUM:
            return float(sum(_A     )     )
      elif aggregation_op == _Aggregation.AVERAGE:
            return sum(_A     ) / len(_A     )
      else:
            raise ValueError(F"Unknown aggregation: {aggregation_op}"     )






def    __lowercase (       _A  ,      _A     )       ->  Dict:
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				sql_query["""agg"""]
      if aggregation_op_index >= 3:
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				_Aggregation(_A     )
      else:
            SCREAMING_SNAKE_CASE	: Tuple					       =				_Aggregation.NONE

      SCREAMING_SNAKE_CASE	: Any					       =				sql_query["""sel"""]
      SCREAMING_SNAKE_CASE	: Tuple					       =				[
          _Condition(_A  ,      _Operator(_A     )  ,      _A     )
          for column, operator, cmp_value in zip(
              sql_query["""conds"""]["""column_index"""]  ,      sql_query["""conds"""]["""operator_index"""]  ,      sql_query["""conds"""]["""condition"""]     )
      ]

      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				[]
      for row in range(len(table["""rows"""]     )     ):
            if _respect_conditions(_A  ,      table["""rows"""][row]  ,      _A     ):
                  indices.append((row, target_column)     )

      if not indices:
            return [], aggregation_op

      if len(_A     ) == 1:
            return indices, aggregation_op

      # Parsing of MIN/MAX.
      if aggregation_op_index in (1, 2):
            SCREAMING_SNAKE_CASE	: Any					       =				{2: min, 1: max}
            SCREAMING_SNAKE_CASE	: int					       =				[(table["""rows"""][i][j], index) for index, (i, j) in enumerate(_A     )]
            SCREAMING_SNAKE_CASE	: List[str]					       =				functools.reduce(operators[sql_query["""agg"""]]  ,      _A     )

            SCREAMING_SNAKE_CASE	: Optional[int]					       =				[indices[reduced[1]]]
            return ret, _Aggregation.NONE

      return indices, aggregation_op






def    __lowercase (       _A  ,      _A  ,      _A     )       ->  Any:
      if float_answer is not None:
            return [str(_A     )]
      return [str(table["""real_rows"""][r][c]     ) for r, c in answer_coordinates]







def    __lowercase (       _A  ,      _A     )       ->  List:
      SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				_get_answer_coordinates(_A  ,      _A     )
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				_get_float_answer(_A  ,      _A  ,      _A     )
      SCREAMING_SNAKE_CASE	: Any					       =				_get_answer_text(_A  ,      _A  ,      _A     )
      # keep the original data the same with TaPas
      if len(_A     ) == 0:
            SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				[EMPTY_ANSWER]
      return answer_text







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

import datasets


UpperCAmelCase__	:       Tuple			      =      """\
@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__	:       Union[str, Any]			      =      """\
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__	:       str			      =      """
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      :	Any						)   ->Optional[int]:
        """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      :	Dict			,			UpperCAmelCase__      :	Tuple						)   ->Any:
        """simple docstring"""







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




  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Optional[Any]			,			UpperCAmelCase__      :	str=3_2			,			UpperCAmelCase__      :	Optional[int]=1_2_8			,			UpperCAmelCase__      :	Optional[int]=None			,			)   ->Any:
        """simple docstring"""







        assert len(UpperCAmelCase__						) == len(
            UpperCAmelCase__						), "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:
              SCREAMING_SNAKE_CASE	: List[str]					       =				"""gpu""" if torch.cuda.is_available() else """cpu"""
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				TrainingArguments(
            """trainer"""			,			fpaa=(device == """gpu""")			,			per_device_eval_batch_size=UpperCAmelCase__			,			report_to="""all"""			,			no_cuda=(device == """cpu""")			,			log_level="""warning"""			,			)
        SCREAMING_SNAKE_CASE	: int					       =				{"""sentence1""": predictions, """sentence2""": references}
        SCREAMING_SNAKE_CASE	: List[str]					       =				datasets.Dataset.from_dict(UpperCAmelCase__						)

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

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




