
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowercase__		:str			  =   "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\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"

lowercase__		:str			  =   "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

lowercase__		:Optional[Any]			  =   "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,  _KWARGS_DESCRIPTION		)
class     lowercase		( datasets.Metric		):




							def 			A__				(    self):
												if version.parse(scb.__version__) < version.parse('''1.4.12'''):
																	raise ImportWarning(
																	    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
																	    '''You can install it with `pip install "sacrebleu>=1.4.12"`.''')
												return datasets.MetricInfo(
												    description=_DESCRIPTION       ,citation=_CITATION       ,homepage='''https://github.com/mjpost/sacreBLEU#chrf--chrf'''       ,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/mjpost/sacreBLEU#chrf--chrf''']       ,reference_urls=[
												        '''https://github.com/m-popovic/chrF''',
												    ]       ,)





							def 			A__				(    self       ,A__       ,A__       ,A__ = CHRF.CHAR_ORDER       ,A__ = CHRF.WORD_ORDER       ,A__ = CHRF.BETA       ,A__ = False       ,A__ = False       ,A__ = False       ,):
												lowercase						     =						len(references[0])
												if any(len(A__) != references_per_prediction for refs in references):
																	raise ValueError('''Sacrebleu requires the same number of references for each prediction''')
												lowercase						     =						[[refs[i] for refs in references] for i in range(A__)]

												lowercase						     =						CHRF(A__       ,A__       ,A__       ,A__       ,A__       ,A__)
												lowercase						     =						sb_chrf.corpus_score(A__       ,A__)

												return {
												    "score": output.score,
												    "char_order": output.char_order,
												    "word_order": output.word_order,
												    "beta": output.beta,
												}






import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						np.inf

					def set_batch_size(lowerCAmelCase__		) -> None:
										nonlocal batch_size
										if isinstance(lowerCAmelCase__			,      lowerCAmelCase__		):
															lowercase						     =						min(lowerCAmelCase__			,      config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS		)
										elif isinstance(lowerCAmelCase__			,      lowerCAmelCase__		):
															lowercase						     =						min(lowerCAmelCase__			,      config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS		)
										elif isinstance(lowerCAmelCase__			,      lowerCAmelCase__		) and feature.dtype == "binary":
															lowercase						     =						min(lowerCAmelCase__			,      config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS		)

					_visit(lowerCAmelCase__			,      lowerCAmelCase__		)

					return None if batch_size is np.inf else batch_size







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = None       ,**A__       ,):
												super().__init__(
												    A__       ,split=A__       ,features=A__       ,cache_dir=A__       ,keep_in_memory=A__       ,streaming=A__       ,num_proc=A__       ,**A__       ,)
												lowercase						     =						path_or_paths if isinstance(A__       ,A__) else {self.split: path_or_paths}
												lowercase						     =						_PACKAGED_DATASETS_MODULES['''parquet'''][1]
												lowercase						     =						Parquet(
												    cache_dir=A__       ,data_files=A__       ,features=A__       ,hash=A__       ,**A__       ,)





							def 			A__				(    self):
												# Build iterable dataset
												if self.streaming:
																	lowercase						     =						self.builder.as_streaming_dataset(split=self.split)
												# Build regular (map-style) dataset
												else:
																	lowercase						     =						None
																	lowercase						     =						None
																	lowercase						     =						None
																	lowercase						     =						None

																	self.builder.download_and_prepare(
																	    download_config=A__       ,download_mode=A__       ,verification_mode=A__       ,base_path=A__       ,num_proc=self.num_proc       ,)
																	lowercase						     =						self.builder.as_dataset(
																	    split=self.split       ,verification_mode=A__       ,in_memory=self.keep_in_memory)
												return dataset







class     lowercase		:




							def __init__(    self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
												lowercase						     =						dataset
												lowercase						     =						path_or_buf
												lowercase						     =						batch_size or get_writer_batch_size(dataset.features)
												lowercase						     =						parquet_writer_kwargs




							def 			A__				(    self):
												lowercase						     =						self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

												if isinstance(self.path_or_buf       ,(str, bytes, os.PathLike)):
																	with open(self.path_or_buf       ,'''wb+''') as buffer:
																						lowercase						     =						self._write(file_obj=A__       ,batch_size=A__       ,**self.parquet_writer_kwargs)
												else:
																	lowercase						     =						self._write(file_obj=self.path_or_buf       ,batch_size=A__       ,**self.parquet_writer_kwargs)
												return written





							def 			A__				(    self       ,A__       ,A__       ,**A__):
												lowercase						     =						0
												lowercase						     =						parquet_writer_kwargs.pop('''path_or_buf'''       ,A__)
												lowercase						     =						self.dataset.features.arrow_schema

												lowercase						     =						pq.ParquetWriter(A__       ,schema=A__       ,**A__)

												for offset in logging.tqdm(
												    range(0       ,len(self.dataset)       ,A__)       ,unit='''ba'''       ,disable=not logging.is_progress_bar_enabled()       ,desc='''Creating parquet from Arrow format'''       ,):
																	lowercase						     =						query_table(
																	    table=self.dataset._data       ,key=slice(A__       ,offset + batch_size)       ,indices=self.dataset._indices if self.dataset._indices is not None else None       ,)
																	writer.write_table(A__)
																	written += batch.nbytes
												writer.close()
												return written




