

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def 	snake_case_      ()			->	Union[str, Any]:
   def filter_log(__A     :						int     ):
      return False if "This IS expected if you are initializing" in record.msg else True

   __lowerCAmelCase  :			Optional[Any]					    =						datasets.utils.logging.get_logger("""transformers.modeling_utils"""     )
   logger.addFilter(__A     )
   try:
      yield
   finally:
      logger.removeFilter(__A     )


__UpperCAmelCase      = """\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
"""

__UpperCAmelCase      = """\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project's README at https://github.com/Tiiiger/bert_score#readme for more information.
"""

__UpperCAmelCase      = """
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. 'en').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bertscore = datasets.load_metric(\"bertscore\")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")
    >>> print([round(v, 2) for v in results[\"f1\"]])
    [1.0, 1.0]
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,							_KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE	(  datasets.Metric ):
  """simple docstring"""






  def        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]    )							->	int:






     """simple docstring"""







     return datasets.MetricInfo(
         description=_DESCRIPTION       ,							citation=_CITATION       ,							homepage="""https://github.com/Tiiiger/bert_score"""       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
             {
                 """predictions""": datasets.Value("""string"""       ,							id="""sequence"""    ),
                 """references""": datasets.Sequence(datasets.Value("""string"""       ,							id="""sequence"""    )       ,							id="""references"""    ),
             }    )       ,							codebase_urls=["""https://github.com/Tiiiger/bert_score"""]       ,							reference_urls=[
             """https://github.com/Tiiiger/bert_score""",
             """https://arxiv.org/abs/1904.09675""",
         ]       ,							)







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]       ,							lowerCAmelCase :    Union[str, Any]       ,							lowerCAmelCase :    int       ,							lowerCAmelCase :    List[Any]=None       ,							lowerCAmelCase :    Tuple=None       ,							lowerCAmelCase :    Tuple=None       ,							lowerCAmelCase :    Tuple=False       ,							lowerCAmelCase :    Dict=False       ,							lowerCAmelCase :    Union[str, Any]=None       ,							lowerCAmelCase :    List[str]=64       ,							lowerCAmelCase :    int=4       ,							lowerCAmelCase :    Optional[Any]=False       ,							lowerCAmelCase :    Dict=False       ,							lowerCAmelCase :    Union[str, Any]=None       ,							lowerCAmelCase :    Union[str, Any]=False       ,							)							->	Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :			int					    =						bert_score.utils.get_hash
     __lowerCAmelCase  :			Optional[int]					    =						bert_score.BERTScorer

     if version.parse(bert_score.__version__    ) >= version.parse("""0.3.10"""    ):
        __lowerCAmelCase  :			str					    =						functools.partial(lowerCAmelCase       ,							use_fast_tokenizer=lowerCAmelCase    )
        __lowerCAmelCase  :			Union[str, Any]					    =						functools.partial(lowerCAmelCase       ,							use_fast_tokenizer=lowerCAmelCase    )
     elif use_fast_tokenizer:
        raise ImportWarning(
            """To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"""
            """You can install it with `pip install \"bert-score>=0.3.10\"`."""    )

     if model_type is None:
        assert lang is not None, "either lang or model_type should be specified"
        __lowerCAmelCase  :			List[Any]					    =						bert_score.utils.langamodel[lang.lower()]

     if num_layers is None:
        __lowerCAmelCase  :			Dict					    =						bert_score.utils.modelalayers[model_type]

     __lowerCAmelCase  :			Tuple					    =						get_hash(
         model=lowerCAmelCase       ,							num_layers=lowerCAmelCase       ,							idf=lowerCAmelCase       ,							rescale_with_baseline=lowerCAmelCase       ,							use_custom_baseline=baseline_path is not None       ,							)

     with filter_logging_context():
        if not hasattr(self       ,							"""cached_bertscorer"""    ) or self.cached_bertscorer.hash != hashcode:
           __lowerCAmelCase  :			Union[str, Any]					    =						scorer(
               model_type=lowerCAmelCase       ,							num_layers=lowerCAmelCase       ,							batch_size=lowerCAmelCase       ,							nthreads=lowerCAmelCase       ,							all_layers=lowerCAmelCase       ,							idf=lowerCAmelCase       ,							device=lowerCAmelCase       ,							lang=lowerCAmelCase       ,							rescale_with_baseline=lowerCAmelCase       ,							baseline_path=lowerCAmelCase       ,							)

     ((__lowerCAmelCase)    ,(__lowerCAmelCase)    ,(__lowerCAmelCase))  :			List[str]					    =						self.cached_bertscorer.score(
         cands=lowerCAmelCase       ,							refs=lowerCAmelCase       ,							verbose=lowerCAmelCase       ,							batch_size=lowerCAmelCase       ,							)
     __lowerCAmelCase  :			List[Any]					    =						{
         """precision""": P.tolist(),
         """recall""": R.tolist(),
         """f1""": F.tolist(),
         """hashcode""": hashcode,
     }
     return output_dict







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]       ,							lowerCAmelCase :    List[Any]=None       ,							lowerCAmelCase :    int=None       ,							**lowerCAmelCase :    int    )							->	Dict:






     """simple docstring"""







     if references is not None:
        __lowerCAmelCase  :			Optional[int]					    =						[[ref] if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ) else ref for ref in references]
     super().add_batch(predictions=lowerCAmelCase       ,							references=lowerCAmelCase       ,							**lowerCAmelCase    )







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]       ,							lowerCAmelCase :    Tuple=None       ,							lowerCAmelCase :    Dict=None       ,							**lowerCAmelCase :    Any    )							->	Dict:






     """simple docstring"""







     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        __lowerCAmelCase  :			Optional[Any]					    =						[reference]
     super().add(prediction=lowerCAmelCase       ,							reference=lowerCAmelCase       ,							**lowerCAmelCase    )





"""simple docstring"""




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


__A     :    int    =		'none'
__A     :    List[Any]    =		'none'
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					value.split(lowercase__      )
					return len(lowercase__      ) > 1 and any((len(lowercase__      ) == 3 for x in split)      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					if isinstance(lowercase__			,							lowercase__      ):
										return value
					if isinstance(lowercase__			,							lowercase__      ):
										return float(lowercase__      )
					if not isinstance(lowercase__			,							lowercase__      ):
										raise ValueError("Argument value is not a string. Can't parse it as float"      )
					A			=					value

					try:
										# Example: 1,000.7
										if "." in sanitized and "," in sanitized:
															return float(sanitized.replace(","			,							""      )      )
										# 1,000
										if "," in sanitized and _split_thousands(","			,							lowercase__      ):
															return float(sanitized.replace(","			,							""      )      )
										# 5,5556
										if "," in sanitized and sanitized.count(","      ) == 1 and not _split_thousands(","			,							lowercase__      ):
															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(lowercase__      )
					except ValueError:
										# Avoid adding the sanitized value in the error message.
										raise ValueError("Unable to convert value to float"      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					if answer is None:
										return None
					try:
										A			=					convert_to_float(lowercase__      )
										if isinstance(lowercase__			,							lowercase__      ) and math.isnan(lowercase__      ):
															return None
										return value
					except ValueError:
										return answer.lower()


__A     :    List[str]    =		{
    'text': lambda x: x,
    'real': convert_to_float,
}





class 			__UpperCamelCase (   enum.Enum						):
		SCREAMING_SNAKE_CASE								=    0
		SCREAMING_SNAKE_CASE								=    1
		SCREAMING_SNAKE_CASE								=    2
		SCREAMING_SNAKE_CASE								=    3
		SCREAMING_SNAKE_CASE								=    4
		SCREAMING_SNAKE_CASE								=    5





class 			__UpperCamelCase (   enum.Enum						):
		SCREAMING_SNAKE_CASE								=    0
		SCREAMING_SNAKE_CASE								=    1
		SCREAMING_SNAKE_CASE								=    2





@dataclasses.dataclass
class 			__UpperCamelCase :
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42

__A     :    int    =		re.compile(R'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					return list(_TOKENIZER.findall(x.lower()      )      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					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    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					table["types"]
					return _TYPE_CONVERTER[types[column]](lowercase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					return isinstance(lowercase__			,							lowercase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					for cond in conditions:
										A			=					row[cond.column]

										A			=					_parse_value(lowercase__			,							cond.column			,							cond.cmp_value      )

										if _is_string(lowercase__      ) and _is_string(lowercase__      ):
															A			=					_normalize_for_match(lowercase__      )
															A			=					_normalize_for_match(lowercase__      )

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

										if not _compare(cond.operator			,							lowercase__			,							lowercase__      ):
															return False
					return True
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					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(lowercase__      )      )

					# If we have just one answer, if float returns it or try a conversion.
					A			=					[table["rows"][i][j] for (i, j) in answer_coordinates]
					if len(lowercase__      ) == 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(lowercase__			,							(int, float)      ) for v in values)      ):
										return None

					if aggregation_op == _Aggregation.SUM:
										return float(sum(lowercase__      )      )
					elif aggregation_op == _Aggregation.AVERAGE:
										return sum(lowercase__      ) / len(lowercase__      )
					else:
										raise ValueError(F"""Unknown aggregation: {aggregation_op}"""      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					sql_query["agg"]
					if aggregation_op_index >= 3:
										A			=					_Aggregation(lowercase__      )
					else:
										A			=					_Aggregation.NONE

					A			=					sql_query["sel"]
					A			=					[
					    _Condition(lowercase__			,							_Operator(lowercase__      )			,							lowercase__      )
					    for column, operator, cmp_value in zip(
					        sql_query["conds"]["column_index"]			,							sql_query["conds"]["operator_index"]			,							sql_query["conds"]["condition"]      )
					]

					A			=					[]
					for row in range(len(table["rows"]      )      ):
										if _respect_conditions(lowercase__			,							table["rows"][row]			,							lowercase__      ):
															indices.append((row, target_column)      )

					if not indices:
										return [], aggregation_op

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

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

										A			=					[indices[reduced[1]]]
										return ret, _Aggregation.NONE

					return indices, aggregation_op
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					if float_answer is not None:
										return [str(lowercase__      )]
					return [str(table["real_rows"][r][c]      ) for r, c in answer_coordinates]
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			,    A			=					_get_answer_coordinates(lowercase__			,							lowercase__      )
					A			=					_get_float_answer(lowercase__			,							lowercase__			,							lowercase__      )
					A			=					_get_answer_text(lowercase__			,							lowercase__			,							lowercase__      )
					# keep the original data the same with TaPas
					if len(lowercase__      ) == 0:
										A			=					[EMPTY_ANSWER]
					return answer_text





