



"""simple docstring"""

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

import datasets


A_						:    Any				=      '\\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'

A_						:    Optional[int]				=      '\\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'

A_						:    List[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     lowerCAmelCase__						(	datasets.Metric ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	str:





					"""simple docstring"""


					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 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       int = CHRF.CHAR_ORDER     ,       _SCREAMING_SNAKE_CASE				:       int = CHRF.WORD_ORDER     ,       _SCREAMING_SNAKE_CASE				:       int = CHRF.BETA     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Dict							=     len(references[0]    )
					if any(len(_SCREAMING_SNAKE_CASE    ) != references_per_prediction for refs in references    ):
									raise ValueError('Sacrebleu requires the same number of references for each prediction'    )
					SCREAMING_SNAKE_CASE						:							Dict							=     [[refs[i] for refs in references] for i in range(_SCREAMING_SNAKE_CASE    )]

					SCREAMING_SNAKE_CASE						:							int							=     CHRF(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     sb_chrf.corpus_score(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

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










"""simple docstring"""

from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_						:    Tuple				=      logging.get_logger(__name__)


A_						:    int				=      {
    'nielsr/canine-s': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
A_						:    List[Any]				=      111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
A_						:    Optional[Any]				=      0

A_						:    int				=      0xE000
A_						:    Any				=      0xE001
A_						:    List[Any]				=      0xE002
A_						:    Optional[Any]				=      0xE003
A_						:    int				=      0xE004

# Maps special codepoints to human-readable names.
A_						:    Dict[int, str]				=      {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
A_						:    Dict[str, int]				=      {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[Any]  						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





	def __init__(      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       Any=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       int=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Tuple=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       int=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Any=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=False     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=2_048     ,       **_SCREAMING_SNAKE_CASE				:       Tuple     ,       )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else bos_token
					SCREAMING_SNAKE_CASE						:							Tuple							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else eos_token
					SCREAMING_SNAKE_CASE						:							Any							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else sep_token
					SCREAMING_SNAKE_CASE						:							int							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else cls_token
					SCREAMING_SNAKE_CASE						:							Dict							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else pad_token

					# Mask token behave like a normal word, i.e. include the space before it
					SCREAMING_SNAKE_CASE						:							int							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else mask_token

					super().__init__(
					    bos_token=_SCREAMING_SNAKE_CASE     ,       eos_token=_SCREAMING_SNAKE_CASE     ,       sep_token=_SCREAMING_SNAKE_CASE     ,       cls_token=_SCREAMING_SNAKE_CASE     ,       pad_token=_SCREAMING_SNAKE_CASE     ,       mask_token=_SCREAMING_SNAKE_CASE     ,       add_prefix_space=_SCREAMING_SNAKE_CASE     ,       model_max_length=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE     ,       )

					# Creates a mapping for looking up the IDs of special symbols.
					SCREAMING_SNAKE_CASE						:							Dict[str, int]							=     {}
					for codepoint, name in SPECIAL_CODEPOINTS.items():
									SCREAMING_SNAKE_CASE						:							str							=     codepoint

					# Creates a mapping for looking up the string forms of special symbol IDs.
					SCREAMING_SNAKE_CASE						:							Dict[int, str]							=     {
					    codepoint: name for name, codepoint in self._special_codepoints.items()
					}

					SCREAMING_SNAKE_CASE						:							Dict							=     UNICODE_VOCAB_SIZE
					SCREAMING_SNAKE_CASE						:							List[str]							=     len(self._special_codepoints    )





	@property
	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	int:





					"""simple docstring"""


					return self._unicode_vocab_size





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	List[str]:





					"""simple docstring"""


					return list(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	int:





					"""simple docstring"""


					try:
									return ord(_SCREAMING_SNAKE_CASE    )
					except TypeError:
									raise ValueError(f"""invalid token: '{token}'"""    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	str:





					"""simple docstring"""


					try:
									if index in SPECIAL_CODEPOINTS:
													return SPECIAL_CODEPOINTS[index]
									return chr(_SCREAMING_SNAKE_CASE    )
					except TypeError:
									raise ValueError(f"""invalid id: {index}"""    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple    )						->	List[str]:





					"""simple docstring"""


					return "".join(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None    )						->	List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							Tuple							=     [self.cls_token_id]

					SCREAMING_SNAKE_CASE						:							Any							=     cls + token_ids_a + sep
					if token_ids_a is not None:
									result += token_ids_a + sep
					return result





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	List[int]:





					"""simple docstring"""


					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=_SCREAMING_SNAKE_CASE     ,       token_ids_a=_SCREAMING_SNAKE_CASE     ,       already_has_special_tokens=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							int							=     [1] + ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]
					if token_ids_a is not None:
									result += ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]
					return result





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None    )						->	List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							Dict							=     [self.cls_token_id]

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     len(cls + token_ids_a + sep    ) * [0]
					if token_ids_a is not None:
									result += len(token_ids_a + sep    ) * [1]
					return result







	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None    )						->	Any:





					"""simple docstring"""


					return ()





