






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


__lowerCamelCase	:		List[str]										=   logging.get_logger(__name__)


__lowerCamelCase	:		str										=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
__lowerCamelCase	:		str										=   {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase	:		Any										=   {
    """RUCAIBox/mvp""": 10_24,
}







class  _lowercase   (   _A						):
						_a							:						Optional[int]										=							VOCAB_FILES_NAMES
						_a							:						Optional[Any]										=							PRETRAINED_VOCAB_FILES_MAP
						_a							:						List[Any]										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a							:						Optional[Any]										=							['input_ids', 'attention_mask']
						_a							:						int										=							MvpTokenizer
						def __init__(     self				,			a=None				,			a=None				,			a=None				,			a="replace"				,			a="<s>"				,			a="</s>"				,			a="</s>"				,			a="<s>"				,			a="<unk>"				,			a="<pad>"				,			a="<mask>"				,			a=False				,			a=True				,			**a				,			):
							super().__init__(
							    a				,			a				,			tokenizer_file=a				,			errors=a				,			bos_token=a				,			eos_token=a				,			sep_token=a				,			cls_token=a				,			unk_token=a				,			pad_token=a				,			mask_token=a				,			add_prefix_space=a				,			trim_offsets=a				,			**a				,			)

							snake_case__  :    Dict							       =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
							if pre_tok_state.get("""add_prefix_space"""				,			a   ) != add_prefix_space:
								snake_case__  :    List[str]							       =getattr(a				,			pre_tok_state.pop("""type"""   )   )
								snake_case__  :    Optional[int]							       =add_prefix_space
								snake_case__  :    Any							       =pre_tok_class(**a   )

							snake_case__  :    Any							       =add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							snake_case__  :    str							       ="""post_processor"""
							snake_case__  :    Optional[int]							       =getattr(self.backend_tokenizer				,			a				,			a   )
							if tokenizer_component_instance:
								snake_case__  :    Any							       =json.loads(tokenizer_component_instance.__getstate__()   )

								# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
								if "sep" in state:
									snake_case__  :    Tuple							       =tuple(state["""sep"""]   )
								if "cls" in state:
									snake_case__  :    List[str]							       =tuple(state["""cls"""]   )

								snake_case__  :    str							       =False

								if state.get("""add_prefix_space"""				,			a   ) != add_prefix_space:
									snake_case__  :    str							       =add_prefix_space
									snake_case__  :    Optional[int]							       =True

								if state.get("""trim_offsets"""				,			a   ) != trim_offsets:
									snake_case__  :    Union[str, Any]							       =trim_offsets
									snake_case__  :    Union[str, Any]							       =True

								if changes_to_apply:
									snake_case__  :    Dict							       =getattr(a				,			state.pop("""type"""   )   )
									snake_case__  :    Any							       =component_class(**a   )
									setattr(self.backend_tokenizer				,			a				,			a   )
						@property
						def 						lowercase__					(     self   ):
							if self._mask_token is None:
								if self.verbose:
									logger.error("""Using mask_token, but it is not set yet."""   )
								return None
							return str(self._mask_token   )
						@mask_token.setter
						def 						lowercase__					(     self				,			a   ):
							snake_case__  :    List[str]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else value
							snake_case__  :    Dict							       =value
						def 						lowercase__					(     self				,			*a				,			**a   ):
							snake_case__  :    Dict							       =kwargs.get("""is_split_into_words"""				,			a   )

							if is_split_into_words and not self.add_prefix_space:
								raise ValueError(
								    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    """to use it with pretokenized inputs."""   )

							return super()._batch_encode_plus(*a				,			**a   )
						def 						lowercase__					(     self				,			*a				,			**a   ):
							snake_case__  :    Tuple							       =kwargs.get("""is_split_into_words"""				,			a   )

							if is_split_into_words and not self.add_prefix_space:
								raise ValueError(
								    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    """to use it with pretokenized inputs."""   )

							return super()._encode_plus(*a				,			**a   )
						def 						lowercase__					(     self				,			a				,			a = None   ):
							snake_case__  :    int							       =self._tokenizer.model.save(a				,			name=a   )
							return tuple(a   )
						def 						lowercase__					(     self				,			a				,			a=None   ):
							snake_case__  :    Dict							       =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
								return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



						def 						lowercase__					(     self				,			a				,			a = None   ):
							snake_case__  :    Any							       =[self.sep_token_id]
							snake_case__  :    Dict							       =[self.cls_token_id]

							if token_ids_a is None:
								return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]













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,
       }





