






import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_ta import TaTokenizer
else:
							__lowerCamelCase	:		Optional[int]										=   None


__lowerCamelCase	:		Optional[Any]										=   logging.get_logger(__name__)

__lowerCamelCase	:		str										=   {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase	:		Any										=   {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__lowerCamelCase	:		List[Any]										=   {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}







class  _lowercase   (   __A						):
						_a							:						Tuple										=							VOCAB_FILES_NAMES
						_a							:						int										=							PRETRAINED_VOCAB_FILES_MAP
						_a							:						List[Any]										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a							:						Any										=							['input_ids', 'attention_mask']
						_a							:						List[Any]										=							TaTokenizer

						_a							:						Optional[Any]										=							[]
						def __init__(     self				,			a=None				,			a=None				,			a="</s>"				,			a="<unk>"				,			a="<pad>"				,			a=1_0_0				,			a=None				,			**a				,			):





							if extra_ids > 0 and additional_special_tokens is None:
								snake_case__  :    Tuple							       =[F"<extra_id_{i}>" for i in range(a   )]
							elif extra_ids > 0 and additional_special_tokens is not None:
								# Check that we have the right number of extra special tokens
								snake_case__  :    Dict							       =len(set(filter(lambda a   : bool("""extra_id_""" in str(a   )   )				,			a   )   )   )
								if extra_tokens != extra_ids:
									raise ValueError(
									    F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
									    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
									    """ tokens"""   )

							super().__init__(
							    a				,			tokenizer_file=a				,			eos_token=a				,			unk_token=a				,			pad_token=a				,			extra_ids=a				,			additional_special_tokens=a				,			**a				,			)

							snake_case__  :    List[str]							       =vocab_file
							snake_case__  :    List[Any]							       =False if not self.vocab_file else True
							snake_case__  :    Optional[int]							       =extra_ids
						@staticmethod
						def 						lowercase__					(     a				,			a				,			a   ):





							if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
								snake_case__  :    Optional[Any]							       =TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
								if init_max_model_length is not None and init_max_model_length != max_model_length:
									return init_max_model_length
								elif init_max_model_length is None:
									warnings.warn(
									    """This tokenizer was incorrectly instantiated with a model max length of"""
									    F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
									    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
									    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
									    F" {pretrained_model_name_or_path} automatically truncating your input to"
									    F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
									    F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
									    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
									    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""				,			a				,			)

							return max_model_length
						def 						lowercase__					(     self				,			a				,			a = None   ):





							if not self.can_save_slow_tokenizer:
								raise ValueError(
								    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
								    """tokenizer."""   )

							if not os.path.isdir(a   ):
								logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
								return
							snake_case__  :    Union[str, Any]							       =os.path.join(
							    a				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(a   ):
								copyfile(self.vocab_file				,			a   )
								logger.info(F"Copy vocab file to {out_vocab_file}"   )

							return (out_vocab_file,)
						def 						lowercase__					(     self				,			a				,			a = None   ):
							snake_case__  :    int							       =token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
								return self.prefix_tokens + token_ids_a
							else:
								snake_case__  :    Tuple							       =token_ids_a + [self.eos_token_id]
								return self.prefix_tokens + token_ids_a + token_ids_a
						def 						lowercase__					(     self				,			a				,			a = None   ):
							snake_case__  :    Dict							       =[self.eos_token_id]

							if token_ids_a is None:
								return len(token_ids_a + eos   ) * [0]
							return len(token_ids_a + eos + token_ids_a + eos   ) * [0]
						def 						lowercase__					(     self   ):
							return list(
							    set(filter(lambda a   : bool(re.search(R"""<extra_id_\d+>"""				,			a   )   ) is not None				,			self.additional_special_tokens   )   )   )



						def 						lowercase__					(     self   ):
							return [self.convert_tokens_to_ids(a   ) for token in self.get_sentinel_tokens()]













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

import datasets


__lowerCamelCase	:		Dict										=   """\
@inproceedings{popovic-2015-chrf,
    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",
    month = sep,
    year = \"2015\",
    address = \"Lisbon, Portugal\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W15-3049\",
    doi = \"10.18653/v1/W15-3049\",
    pages = \"392--395\",
}
@inproceedings{popovic-2017-chrf,
    title = \"chr{F}++: words helping character n-grams\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Second Conference on Machine Translation\",
    month = sep,
    year = \"2017\",
    address = \"Copenhagen, Denmark\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W17-4770\",
    doi = \"10.18653/v1/W17-4770\",
    pages = \"612--618\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

__lowerCamelCase	:		Optional[int]										=   """\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
"""

__lowerCamelCase	:		List[str]										=   """
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    'score' (float): The chrF (chrF++) score,
    'char_order' (int): The character n-gram order,
    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    'beta' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> 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.\"]
        >>> 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.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> 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.\"]
        >>> 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.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> 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.\"]
        >>> 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.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION						)
class  _lowercase   (   datasets.Metric						):
      def 						lowercase__					(     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 						lowercase__					(     self				,			a				,			a				,			a = CHRF.CHAR_ORDER				,			a = CHRF.WORD_ORDER				,			a = CHRF.BETA				,			a = False				,			a = False				,			a = False				,			):
       snake_case__  :    str							       =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"""   )
       snake_case__  :    List[Any]							       =[[refs[i] for refs in references] for i in range(a   )]

       snake_case__  :    int							       =CHRF(a				,			a				,			a				,			a				,			a				,			a   )
       snake_case__  :    int							       =sb_chrf.corpus_score(a				,			a   )

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





