

'''simple docstring'''

import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


A_				  =							logging.get_logger(__name__)

A_				  =							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

A_				  =							{
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
A_				  =							{
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
A_				  =							{
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

A_				  =							{
    "facebook/dpr-ctx_encoder-single-nq-base": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
A_				  =							{
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
A_				  =							{
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


A_				  =							{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
A_				  =							{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
A_				  =							{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}





class 							_snake_case       (				__A  ):
   _A				:   Any			      =    VOCAB_FILES_NAMES
   _A				:   int			      =    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   _A				:   int			      =    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _A				:   Tuple			      =    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
   _A				:   Dict			      =    DPRContextEncoderTokenizer





class 							_snake_case       (				__A  ):
   _A				:   Dict			      =    VOCAB_FILES_NAMES
   _A				:   str			      =    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   _A				:   Any			      =    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _A				:   List[str]			      =    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
   _A				:   int			      =    DPRQuestionEncoderTokenizer



A_				  =							collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

A_				  =							collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


A_				  =							R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "





@add_start_docstrings(__A  )
class 							_snake_case       :
   def __call__(       self	:					Any   ,SCREAMING_SNAKE_CASE__	:					str   ,SCREAMING_SNAKE_CASE__	:					str = None   ,SCREAMING_SNAKE_CASE__	:					Any = None   ,SCREAMING_SNAKE_CASE__	:					str = False   ,SCREAMING_SNAKE_CASE__	:					List[str] = False   ,SCREAMING_SNAKE_CASE__	:					Union[str, Any] = None   ,SCREAMING_SNAKE_CASE__	:					Any = None   ,SCREAMING_SNAKE_CASE__	:					Union[str, Any] = None   ,**SCREAMING_SNAKE_CASE__	:					Any   ,):
     if titles is None and texts is None:
       return super().__call__(
           __UpperCAmelCase   ,padding=__UpperCAmelCase   ,truncation=__UpperCAmelCase   ,max_length=__UpperCAmelCase   ,return_tensors=__UpperCAmelCase   ,return_attention_mask=__UpperCAmelCase   ,**__UpperCAmelCase   ,)
     elif titles is None or texts is None:
       SCREAMING_SNAKE_CASE:List[str]  					=    titles if texts is None else texts
       return super().__call__(
           __UpperCAmelCase   ,__UpperCAmelCase   ,padding=__UpperCAmelCase   ,truncation=__UpperCAmelCase   ,max_length=__UpperCAmelCase   ,return_tensors=__UpperCAmelCase   ,return_attention_mask=__UpperCAmelCase   ,**__UpperCAmelCase   ,)
     SCREAMING_SNAKE_CASE:int  					=    titles if not isinstance(__UpperCAmelCase   ,__UpperCAmelCase							) else [titles]
     SCREAMING_SNAKE_CASE:str  					=    texts if not isinstance(__UpperCAmelCase   ,__UpperCAmelCase							) else [texts]
     SCREAMING_SNAKE_CASE:Optional[int]  					=    len(__UpperCAmelCase							)
     SCREAMING_SNAKE_CASE:Any  					=    questions if not isinstance(__UpperCAmelCase   ,__UpperCAmelCase							) else [questions] * n_passages
     assert len(__UpperCAmelCase							) == len(
         __UpperCAmelCase							), F'''There should be as many titles than texts but got {len(__UpperCAmelCase							)} titles and {len(__UpperCAmelCase							)} texts.'''
     SCREAMING_SNAKE_CASE:List[Any]  					=    super().__call__(__UpperCAmelCase   ,__UpperCAmelCase   ,padding=__UpperCAmelCase   ,truncation=__UpperCAmelCase							)["input_ids"]
     SCREAMING_SNAKE_CASE:Dict  					=    super().__call__(__UpperCAmelCase   ,add_special_tokens=__UpperCAmelCase   ,padding=__UpperCAmelCase   ,truncation=__UpperCAmelCase							)["input_ids"]
     SCREAMING_SNAKE_CASE:Optional[int]  					=    {
         "input_ids": [
             (encoded_question_and_title + encoded_text)[:max_length]
             if max_length is not None and truncation
             else encoded_question_and_title + encoded_text
             for encoded_question_and_title, encoded_text in zip(__UpperCAmelCase   ,__UpperCAmelCase							)
         ]
     }
     if return_attention_mask is not False:
       SCREAMING_SNAKE_CASE:List[Any]  					=    []
       for input_ids in encoded_inputs["input_ids"]:
         attention_mask.append([int(input_id != self.pad_token_id							) for input_id in input_ids]							)
       SCREAMING_SNAKE_CASE:Optional[int]  					=    attention_mask
     return self.pad(__UpperCAmelCase   ,padding=__UpperCAmelCase   ,max_length=__UpperCAmelCase   ,return_tensors=__UpperCAmelCase							)
   def 			__UpperCamelCase  (       self	:					List[Any]   ,SCREAMING_SNAKE_CASE__	:					Optional[Any]   ,SCREAMING_SNAKE_CASE__	:					Tuple   ,SCREAMING_SNAKE_CASE__	:					Dict = 16   ,SCREAMING_SNAKE_CASE__	:					List[str] = 64   ,SCREAMING_SNAKE_CASE__	:					str = 4   ,):
     SCREAMING_SNAKE_CASE:Any  					=    reader_input["input_ids"]
     SCREAMING_SNAKE_CASE					,			SCREAMING_SNAKE_CASE					,			SCREAMING_SNAKE_CASE:int  					=    reader_output[:3]
     SCREAMING_SNAKE_CASE:int  					=    len(__UpperCAmelCase							)
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    sorted(range(__UpperCAmelCase							)   ,reverse=__UpperCAmelCase   ,key=relevance_logits.__getitem__							)
     SCREAMING_SNAKE_CASE:Dict  					=    []
     for doc_id in sorted_docs:
       SCREAMING_SNAKE_CASE:List[Any]  					=    list(input_ids[doc_id]							)
       # assuming question & title information is at the beginning of the sequence
       SCREAMING_SNAKE_CASE:Tuple  					=    sequence_ids.index(self.sep_token_id   ,2							) + 1  # second sep id
       if sequence_ids[-1] == self.pad_token_id:
         SCREAMING_SNAKE_CASE:Union[str, Any]  					=    sequence_ids.index(self.pad_token_id							)
       else:
         SCREAMING_SNAKE_CASE:int  					=    len(__UpperCAmelCase							)

       SCREAMING_SNAKE_CASE:Union[str, Any]  					=    self._get_best_spans(
           start_logits=start_logits[doc_id][passage_offset:sequence_len]   ,end_logits=end_logits[doc_id][passage_offset:sequence_len]   ,max_answer_length=__UpperCAmelCase   ,top_spans=__UpperCAmelCase   ,)
       for start_index, end_index in best_spans:
         start_index += passage_offset
         end_index += passage_offset
         nbest_spans_predictions.append(
             DPRSpanPrediction(
                 span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]   ,relevance_score=relevance_logits[doc_id]   ,doc_id=__UpperCAmelCase   ,start_index=__UpperCAmelCase   ,end_index=__UpperCAmelCase   ,text=self.decode(sequence_ids[start_index : end_index + 1]							)   ,)							)
       if len(__UpperCAmelCase							) >= num_spans:
         break
     return nbest_spans_predictions[:num_spans]







   def 			__UpperCamelCase  (       self	:					Tuple   ,SCREAMING_SNAKE_CASE__	:					List[str]   ,SCREAMING_SNAKE_CASE__	:					List[Any]   ,SCREAMING_SNAKE_CASE__	:					Tuple   ,SCREAMING_SNAKE_CASE__	:					str   ,):
     SCREAMING_SNAKE_CASE:str  					=    []
     for start_index, start_score in enumerate(__UpperCAmelCase							):
       for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]							):
         scores.append(((start_index, start_index + answer_length), start_score + end_score)							)
     SCREAMING_SNAKE_CASE:Optional[int]  					=    sorted(__UpperCAmelCase   ,key=lambda SCREAMING_SNAKE_CASE__							: x[1]   ,reverse=__UpperCAmelCase							)
     SCREAMING_SNAKE_CASE:int  					=    []
     for (start_index, end_index), score in scores:
       assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
       SCREAMING_SNAKE_CASE:Optional[Any]  					=    end_index - start_index + 1
       assert length <= max_answer_length, F'''Span is too long: {length} > {max_answer_length}'''
       if any(
           start_index <= prev_start_index <= prev_end_index <= end_index
           or prev_start_index <= start_index <= end_index <= prev_end_index
           for (prev_start_index, prev_end_index) in chosen_span_intervals							):
         continue
       chosen_span_intervals.append((start_index, end_index)							)

       if len(__UpperCAmelCase							) == top_spans:
         break
     return chosen_span_intervals





@add_end_docstrings(__A  )
class 							_snake_case       (				__A		,     __A  ):
   _A				:   Tuple			      =    VOCAB_FILES_NAMES
   _A				:   List[Any]			      =    READER_PRETRAINED_VOCAB_FILES_MAP
   _A				:   str			      =    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _A				:   List[str]			      =    READER_PRETRAINED_INIT_CONFIGURATION
   _A				:   List[Any]			      =    ['input_ids', 'attention_mask']
   _A				:   Optional[int]			      =    DPRReaderTokenizer







"""simple docstring"""




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

import datasets


__lowerCamelCase							 =     "\\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"

__lowerCamelCase							 =     "\\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"

__lowerCamelCase							 =     "\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 			UpperCamelCase__(	datasets.Metric       ):





						def  snake_case__		(				self   )    -> Tuple:
													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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = CHRF.CHAR_ORDER						,__UpperCAmelCase = CHRF.WORD_ORDER						,__UpperCAmelCase = CHRF.BETA						,__UpperCAmelCase = False						,__UpperCAmelCase = False						,__UpperCAmelCase = False						,)    -> Union[str, Any]:
													A__		   =							len(references[0]   )
													if any(len(__UpperCAmelCase   ) != 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(__UpperCAmelCase   )]

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

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





