





'''simple docstring'''

import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__lowerCAmelCase							:     int		     =datasets.utils.logging.get_logger(__name__)

__lowerCAmelCase							:     Tuple		     =["names", "prefix"]
__lowerCAmelCase							:     List[str]		     =["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
__lowerCAmelCase							:     Optional[int]		     =["encoding_errors", "on_bad_lines"]
__lowerCAmelCase							:     Dict		     =["date_format"]






@dataclass
class        UpperCAmelCase     (	datasets.BuilderConfig    ):
 __lowercase       		=    ","
 __lowercase       		=    None
 __lowercase       		=    "infer"
 __lowercase       		=    None
 __lowercase       		=    None
 __lowercase       		=    None
 __lowercase       		=    None
 __lowercase       		=    None
 __lowercase       		=    True
 __lowercase       		=    None
 __lowercase       		=    None
 __lowercase       		=    None
 __lowercase       		=    None
 __lowercase       		=    False
 __lowercase       		=    None
 __lowercase       		=    None
 __lowercase       		=    None
 __lowercase       		=    True
 __lowercase       		=    True
 __lowercase       		=    False
 __lowercase       		=    True
 __lowercase       		=    None
 __lowercase       		=    "."
 __lowercase       		=    None
 __lowercase       		=    '"'
 __lowercase       		=    0
 __lowercase       		=    None
 __lowercase       		=    None
 __lowercase       		=    None
 __lowercase       		=    None
 __lowercase       		=    True
 __lowercase       		=    True
 __lowercase       		=    0
 __lowercase       		=    True
 __lowercase       		=    False
 __lowercase       		=    None
 __lowercase       		=    1_0000
 __lowercase       		=    None
 __lowercase       		=    "strict"
 __lowercase       		=    "error"
 __lowercase       		=    None
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     if self.delimiter is not None:
         A__    =      self.delimiter
     if self.column_names is not None:
         A__    =      self.column_names



 @property
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     A__    =      {
         "sep": self.sep,
         "header": self.header,
         "names": self.names,
         "index_col": self.index_col,
         "usecols": self.usecols,
         "prefix": self.prefix,
         "mangle_dupe_cols": self.mangle_dupe_cols,
         "engine": self.engine,
         "converters": self.converters,
         "true_values": self.true_values,
         "false_values": self.false_values,
         "skipinitialspace": self.skipinitialspace,
         "skiprows": self.skiprows,
         "nrows": self.nrows,
         "na_values": self.na_values,
         "keep_default_na": self.keep_default_na,
         "na_filter": self.na_filter,
         "verbose": self.verbose,
         "skip_blank_lines": self.skip_blank_lines,
         "thousands": self.thousands,
         "decimal": self.decimal,
         "lineterminator": self.lineterminator,
         "quotechar": self.quotechar,
         "quoting": self.quoting,
         "escapechar": self.escapechar,
         "comment": self.comment,
         "encoding": self.encoding,
         "dialect": self.dialect,
         "error_bad_lines": self.error_bad_lines,
         "warn_bad_lines": self.warn_bad_lines,
         "skipfooter": self.skipfooter,
         "doublequote": self.doublequote,
         "memory_map": self.memory_map,
         "float_precision": self.float_precision,
         "chunksize": self.chunksize,
         "encoding_errors": self.encoding_errors,
         "on_bad_lines": self.on_bad_lines,
         "date_format": self.date_format,
     }

     # some kwargs must not be passed if they don't have a default value
     # some others are deprecated and we can also not pass them if they are the default value
     for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
         if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()						,	lowercase_							):
             del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
     if not (datasets.config.PANDAS_VERSION.major >= 2):
         for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
             del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
     if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
         for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
             del pd_read_csv_kwargs[pd_read_csv_parameter]

     return pd_read_csv_kwargs






class        UpperCAmelCase     (	datasets.ArrowBasedBuilder    ):
 __lowercase       		=    CsvConfig
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Any:
     return datasets.DatasetInfo(features=self.config.features							)
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:str							)->							Optional[Any]:
     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}"							)
     A__    =      dl_manager.download_and_extract(self.config.data_files							)
     if isinstance(lowercase_						,	(str, list, tuple)							):
         A__    =      data_files
         if isinstance(lowercase_						,	lowercase_							):
             A__    =      [files]
         A__    =      [dl_manager.iter_files(lowercase_							) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,	gen_kwargs={"files": files}							)]
     A__    =      []
     for split_name, files in data_files.items():
         if isinstance(lowercase_						,	lowercase_							):
             A__    =      [files]
         A__    =      [dl_manager.iter_files(lowercase_							) for file in files]
         splits.append(datasets.SplitGenerator(name=lowercase_						,	gen_kwargs={"files": files}							)							)
     return splits
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:pa.Table							)->							pa.Table:
     if self.config.features is not None:
         A__    =      self.config.features.arrow_schema
         if all(not require_storage_cast(lowercase_							) for feature in self.config.features.values()							):
             # cheaper cast
             A__    =      pa.Table.from_arrays([pa_table[field.name] for field in schema]						,	schema=lowercase_							)
         else:
             # more expensive cast; allows str <-> int/float or str to Audio for example
             A__    =      table_cast(lowercase_						,	lowercase_							)
     return pa_table



 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:Optional[int]							)->							Any:
     A__    =      self.config.features.arrow_schema if self.config.features else None
     # dtype allows reading an int column as str
     A__    =      (
         {
             name: dtype.to_pandas_dtype() if not require_storage_cast(lowercase_							) else object
             for name, dtype, feature in zip(schema.names						,	schema.types						,	self.config.features.values()							)
         }
         if schema is not None
         else None
     )
     for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase_							)							):
         A__    =      pd.read_csv(lowercase_						,	iterator=lowercase_						,	dtype=lowercase_						,	**self.config.pd_read_csv_kwargs							)
         try:
             for batch_idx, df in enumerate(lowercase_							):
                 A__    =      pa.Table.from_pandas(lowercase_							)
                 # 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(lowercase_							)
         except ValueError as e:
             logger.error(F"Failed to read file '{file}' with error {type(lowercase_							)}: {e}"							)
             raise










'''simple docstring'''

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCAmelCase							:     Optional[Any]		     ="\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__lowerCAmelCase							:     Union[str, Any]		     ="\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


__lowerCAmelCase							:     str		     ="\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"





def 						UpperCamelCase    (							_lowerCamelCase : List[Any]	):

    def remove_articles(_lowerCamelCase : Dict	):
        A__    =      re.compile(r"\b(a|an|the)\b"						,	re.UNICODE	)
        return re.sub(_lowerCamelCase						,	" "						,	_lowerCamelCase	)

    def white_space_fix(_lowerCamelCase : Tuple	):
        return " ".join(text.split()	)

    def remove_punc(_lowerCamelCase : int	):
        A__    =      set(string.punctuation	)
        return "".join(ch for ch in text if ch not in exclude	)

    def lower(_lowerCamelCase : Optional[int]	):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase	)	)	)	)





def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : List[str]	):
    return int(normalize_answer(_lowerCamelCase	) == normalize_answer(_lowerCamelCase	)	)





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : List[Any]	):
    A__    =      [any(compute_exact(_lowerCamelCase						,	_lowerCamelCase	) for ref in refs	) for pred, refs in zip(_lowerCamelCase						,	_lowerCamelCase	)]
    return (sum(_lowerCamelCase	) / len(_lowerCamelCase	)) * 1_00





def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : Union[str, Any]						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : str	):
    A__    =      [rgram for rgrams in rgramslist for rgram in rgrams]
    A__    =      Counter(_lowerCamelCase	)

    A__    =      Counter(_lowerCamelCase	)
    A__    =      Counter()
    for sgram, scount in sgramcounter.items():
        A__    =      scount * numref

    A__    =      Counter(_lowerCamelCase	)
    A__    =      Counter()
    for cgram, ccount in cgramcounter.items():
        A__    =      ccount * numref

    # KEEP
    A__    =      sgramcounter_rep & cgramcounter_rep
    A__    =      keepgramcounter_rep & rgramcounter
    A__    =      sgramcounter_rep & rgramcounter

    A__    =      0
    A__    =      0
    for keepgram in keepgramcountergood_rep:
        keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
        # Fix an alleged bug [2] in the keep score computation.
        # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
        keeptmpscorea += keepgramcountergood_rep[keepgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    #      a target exactly.
    A__    =      1
    A__    =      1
    if len(_lowerCamelCase	) > 0:
        A__    =      keeptmpscorea / len(_lowerCamelCase	)
    if len(_lowerCamelCase	) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        A__    =      keeptmpscorea / sum(keepgramcounterall_rep.values()	)
    A__    =      0
    if keepscore_precision > 0 or keepscore_recall > 0:
        A__    =      2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    A__    =      sgramcounter_rep - cgramcounter_rep
    A__    =      delgramcounter_rep - rgramcounter
    A__    =      sgramcounter_rep - rgramcounter
    A__    =      0
    A__    =      0
    for delgram in delgramcountergood_rep:
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    A__    =      1
    if len(_lowerCamelCase	) > 0:
        A__    =      deltmpscorea / len(_lowerCamelCase	)

    # ADDITION
    A__    =      set(_lowerCamelCase	) - set(_lowerCamelCase	)
    A__    =      set(_lowerCamelCase	) & set(_lowerCamelCase	)
    A__    =      set(_lowerCamelCase	) - set(_lowerCamelCase	)

    A__    =      0
    for addgram in addgramcountergood:
        addtmpscore += 1

    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    A__    =      1
    A__    =      1
    if len(_lowerCamelCase	) > 0:
        A__    =      addtmpscore / len(_lowerCamelCase	)
    if len(_lowerCamelCase	) > 0:
        A__    =      addtmpscore / len(_lowerCamelCase	)
    A__    =      0
    if addscore_precision > 0 or addscore_recall > 0:
        A__    =      2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)





def 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Union[str, Any]	):
    A__    =      len(_lowerCamelCase	)

    A__    =      ssent.split(" "	)
    A__    =      csent.split(" "	)
    A__    =      []
    A__    =      []
    A__    =      []
    A__    =      []
    A__    =      []
    A__    =      []

    A__    =      []
    A__    =      []
    A__    =      []
    A__    =      []
    for rsent in rsents:
        A__    =      rsent.split(" "	)
        A__    =      []
        A__    =      []
        A__    =      []
        ragramslist.append(_lowerCamelCase	)
        for i in range(0						,	len(_lowerCamelCase	) - 1	):
            if i < len(_lowerCamelCase	) - 1:
                A__    =      ragrams[i] + " " + ragrams[i + 1]
                ragrams.append(_lowerCamelCase	)
            if i < len(_lowerCamelCase	) - 2:
                A__    =      ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
                ragrams.append(_lowerCamelCase	)
            if i < len(_lowerCamelCase	) - 3:
                A__    =      ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
                ragrams.append(_lowerCamelCase	)
        ragramslist.append(_lowerCamelCase	)
        ragramslist.append(_lowerCamelCase	)
        ragramslist.append(_lowerCamelCase	)

    for i in range(0						,	len(_lowerCamelCase	) - 1	):
        if i < len(_lowerCamelCase	) - 1:
            A__    =      sagrams[i] + " " + sagrams[i + 1]
            sagrams.append(_lowerCamelCase	)
        if i < len(_lowerCamelCase	) - 2:
            A__    =      sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
            sagrams.append(_lowerCamelCase	)
        if i < len(_lowerCamelCase	) - 3:
            A__    =      sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
            sagrams.append(_lowerCamelCase	)

    for i in range(0						,	len(_lowerCamelCase	) - 1	):
        if i < len(_lowerCamelCase	) - 1:
            A__    =      cagrams[i] + " " + cagrams[i + 1]
            cagrams.append(_lowerCamelCase	)
        if i < len(_lowerCamelCase	) - 2:
            A__    =      cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
            cagrams.append(_lowerCamelCase	)
        if i < len(_lowerCamelCase	) - 3:
            A__    =      cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
            cagrams.append(_lowerCamelCase	)

    ((A__),     (A__),     (A__))    =      SARIngram(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    ((A__),     (A__),     (A__))    =      SARIngram(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    ((A__),     (A__),     (A__))    =      SARIngram(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    ((A__),     (A__),     (A__))    =      SARIngram(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    A__    =      sum([keepascore, keepascore, keepascore, keepascore]	) / 4
    A__    =      sum([delascore, delascore, delascore, delascore]	) / 4
    A__    =      sum([addascore, addascore, addascore, addascore]	) / 4
    A__    =      (avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore





def 						UpperCamelCase    (							_lowerCamelCase : Tuple						,	_lowerCamelCase : bool = True						,	_lowerCamelCase : str = "13a"						,	_lowerCamelCase : bool = True	):
    # Normalization is requried for the ASSET dataset (one of the primary
    # datasets in sentence simplification) to allow using space
    # to split the sentence. Even though Wiki-Auto and TURK datasets,
    # do not require normalization, we do it for consistency.
    # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
    # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

    if lowercase:
        A__    =      sentence.lower()

    if tokenizer in ["13a", "intl"]:
        if version.parse(sacrebleu.__version__	).major >= 2:
            A__    =      sacrebleu.metrics.bleu._get_tokenizer(_lowerCamelCase	)()(_lowerCamelCase	)
        else:
            A__    =      sacrebleu.TOKENIZERS[tokenizer]()(_lowerCamelCase	)
    elif tokenizer == "moses":
        A__    =      sacremoses.MosesTokenizer().tokenize(_lowerCamelCase						,	return_str=_lowerCamelCase						,	escape=_lowerCamelCase	)
    elif tokenizer == "penn":
        A__    =      sacremoses.MosesTokenizer().penn_tokenize(_lowerCamelCase						,	return_str=_lowerCamelCase	)
    else:
        A__    =      sentence

    if not return_str:
        A__    =      normalized_sent.split()

    return normalized_sent





def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : int						,	_lowerCamelCase : Union[str, Any]	):
    if not (len(_lowerCamelCase	) == len(_lowerCamelCase	) == len(_lowerCamelCase	)):
        raise ValueError("Sources length must match predictions and references lengths."	)
    A__    =      0
    for src, pred, refs in zip(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	):
        sari_score += SARIsent(normalize(_lowerCamelCase	)						,	normalize(_lowerCamelCase	)						,	[normalize(_lowerCamelCase	) for sent in refs]	)
    A__    =      sari_score / len(_lowerCamelCase	)
    return 1_00 * sari_score





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : Dict						,	_lowerCamelCase : List[Any]="exp"						,	_lowerCamelCase : int=None						,	_lowerCamelCase : str=False						,	_lowerCamelCase : List[str]=False						,	_lowerCamelCase : Dict=False						,	):
    A__    =      len(references[0]	)
    if any(len(_lowerCamelCase	) != references_per_prediction for refs in references	):
        raise ValueError("Sacrebleu requires the same number of references for each prediction"	)
    A__    =      [[refs[i] for refs in references] for i in range(_lowerCamelCase	)]
    A__    =      sacrebleu.corpus_bleu(
        _lowerCamelCase						,	_lowerCamelCase						,	smooth_method=_lowerCamelCase						,	smooth_value=_lowerCamelCase						,	force=_lowerCamelCase						,	lowercase=_lowerCamelCase						,	use_effective_order=_lowerCamelCase						,	)
    return output.score






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,	_KWARGS_DESCRIPTION    )
class        UpperCAmelCase     (	datasets.Metric    ):
 def 					UpperCAmelCase_							(    self				:Any							)->							Union[str, Any]:
     return datasets.MetricInfo(
         description=_DESCRIPTION						,	citation=_CITATION						,	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/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
             "https://github.com/cocoxu/simplification/blob/master/SARI.py",
             "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
             "https://github.com/mjpost/sacreBLEU",
         ]						,	reference_urls=[
             "https://www.aclweb.org/anthology/Q16-1029.pdf",
             "https://github.com/mjpost/sacreBLEU",
             "https://en.wikipedia.org/wiki/BLEU",
             "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
         ]						,	)



 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Dict						,	lowercase_				:List[Any]						,	lowercase_				:int							)->							int:
     A__    =      {}
     result.update({"sari": compute_sari(sources=lowercase_						,	predictions=lowercase_						,	references=lowercase_							)}							)
     result.update({"sacrebleu": compute_sacrebleu(predictions=lowercase_						,	references=lowercase_							)}							)
     result.update({"exact": compute_em(predictions=lowercase_						,	references=lowercase_							)}							)
     return result



