import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


UpperCamelCase  							=						datasets.logging.get_logger(__name__)


UpperCamelCase  							=						'''\
@InProceedings{moosavi2019minimum,
    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
    year = {2019},
    booktitle = {Proceedings of the 57th Annual Meeting of
        the Association for Computational Linguistics (Volume 1: Long Papers)},
    publisher = {Association for Computational Linguistics},
    address = {Florence, Italy},
}

@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}

@INPROCEEDINGS{Bagga98algorithmsfor,
    author = {Amit Bagga and Breck Baldwin},
    title = {Algorithms for Scoring Coreference Chains},
    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
    year = {1998},
    pages = {563--566}
}

@INPROCEEDINGS{Luo05oncoreference,
    author = {Xiaoqiang Luo},
    title = {On coreference resolution performance metrics},
    booktitle = {In Proc. of HLT/EMNLP},
    year = {2005},
    pages = {25--32},
    publisher = {URL}
}

@inproceedings{moosavi-strube-2016-coreference,
    title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",
    author = "Moosavi, Nafise Sadat  and
      Strube, Michael",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1060",
    doi = "10.18653/v1/P16-1060",
    pages = "632--642",
}

'''

UpperCamelCase  							=						'''\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].

This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column	Type	Description
1	Document ID	This is a variation on the document filename
2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3	Word number
4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5	Part-of-Speech
6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.
7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"
8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.
9	Word sense	This is the word sense of the word in Column 3.
10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11	Named Entities	These columns identifies the spans representing various named entities.
12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N	Coreference	Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html

Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md

CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
'''

UpperCamelCase  							=						'''
Calculates coreference evaluation metrics.
Args:
    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
        Each prediction is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
        Each reference is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    keep_singletons: After extracting all mentions of key or system files,
        mentions whose corresponding coreference chain is of size one,
        are considered as singletons. The default evaluation mode will include
        singletons in evaluations if they are included in the key or the system files.
        By setting \'keep_singletons=False\', all singletons in the key and system files
        will be excluded from the evaluation.
    NP_only: Most of the recent coreference resolvers only resolve NP mentions and
        leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.
    min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.
        Minimum spans are determined using the MINA algorithm.

Returns:
    \'mentions\': mentions
    \'muc\': MUC metric [Vilain et al, 1995]
    \'bcub\': B-cubed [Bagga and Baldwin, 1998]
    \'ceafe\': CEAFe [Luo et al., 2005]
    \'lea\': LEA [Moosavi and Strube, 2016]
    \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)

Examples:

    >>> coval = datasets.load_metric(\'coval\')
    >>> words = [\'bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -\',
    ... \'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)\',
    ... \'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)\',
    ... \'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -\',
    ... \'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -\',
    ... \'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -\']
    >>> references = [words]
    >>> predictions = [words]
    >>> results = coval.compute(predictions=predictions, references=references)
    >>> print(results) # doctest:+ELLIPSIS
    {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}
'''

def 			lowercase_				(			_lowerCamelCase							:       List[Any]		,	_lowerCamelCase							:       int		,	_lowerCamelCase							:       Dict=False		,	_lowerCamelCase							:       List[Any]=False		,	_lowerCamelCase							:       Optional[int]=True		,	_lowerCamelCase							:       Union[str, Any]=False		,	_lowerCamelCase							:       Optional[int]="dummy_doc"):
    lowercase__ :   Dict    					=				{doc: key_lines}
    lowercase__ :   Optional[int]    					=				{doc: sys_lines}

    lowercase__ :   Tuple    					=				{}

    lowercase__ :   Union[str, Any]    					=				0
    lowercase__ :   str    					=				0
    lowercase__ :   Tuple    					=				0
    lowercase__ :   Optional[int]    					=				0
    lowercase__ :   str    					=				0
    lowercase__ :   str    					=				0

    lowercase__					,   lowercase__ :   Union[str, Any]    					=				reader.get_doc_mentions(_lowerCamelCase		,	key_doc_lines[doc]		,	_lowerCamelCase)
    key_singletons_num += singletons_num

    if NP_only or min_span:
        lowercase__ :   int    					=				reader.set_annotated_parse_trees(_lowerCamelCase		,	key_doc_lines[doc]		,	_lowerCamelCase		,	_lowerCamelCase)

    lowercase__					,   lowercase__ :   Union[str, Any]    					=				reader.get_doc_mentions(_lowerCamelCase		,	sys_doc_lines[doc]		,	_lowerCamelCase)
    sys_singletons_num += singletons_num

    if NP_only or min_span:
        lowercase__ :   Optional[int]    					=				reader.set_annotated_parse_trees(_lowerCamelCase		,	key_doc_lines[doc]		,	_lowerCamelCase		,	_lowerCamelCase)

    if remove_nested:
        lowercase__					,   lowercase__ :   List[str]    					=				reader.remove_nested_coref_mentions(_lowerCamelCase		,	_lowerCamelCase)
        key_nested_coref_num += nested_mentions
        key_removed_nested_clusters += removed_clusters

        lowercase__					,   lowercase__ :   List[Any]    					=				reader.remove_nested_coref_mentions(_lowerCamelCase		,	_lowerCamelCase)
        sys_nested_coref_num += nested_mentions
        sys_removed_nested_clusters += removed_clusters

    lowercase__ :   List[str]    					=				reader.get_mention_assignments(_lowerCamelCase		,	_lowerCamelCase)
    lowercase__ :   Tuple    					=				reader.get_mention_assignments(_lowerCamelCase		,	_lowerCamelCase)

    lowercase__ :   str    					=				(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

    if remove_nested:
        logger.info(
            "Number of removed nested coreferring mentions in the key "
            f'''annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}''')
        logger.info(
            "Number of resulting singleton clusters in the key "
            f'''annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}''')

    if not keep_singletons:
        logger.info(
            f'''{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '''
            "files, respectively")

    return doc_coref_infos

def 			lowercase_				(			_lowerCamelCase							:       List[str]		,	_lowerCamelCase							:       str		,	_lowerCamelCase							:       List[str]		,	_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       str		,	_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       Tuple):
    lowercase__ :   Tuple    					=				get_coref_infos(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase)

    lowercase__ :   List[Any]    					=				{}
    lowercase__ :   List[str]    					=				0
    lowercase__ :   List[Any]    					=				0

    for name, metric in metrics:
        lowercase__					,   lowercase__					,   lowercase__ :   Dict    					=				evaluator.evaluate_documents(_lowerCamelCase		,	_lowerCamelCase		,	beta=1)
        if name in ["muc", "bcub", "ceafe"]:
            conll += fa
            conll_subparts_num += 1
        output_scores.update({f'''{name}/recall''': recall, f'''{name}/precision''': precision, f'''{name}/f1''': fa})

        logger.info(
            name.ljust(10)		,	f'''Recall: {recall * 100:.2f}'''		,	f''' Precision: {precision * 100:.2f}'''		,	f''' F1: {fa * 100:.2f}'''		,	)

    if conll_subparts_num == 3:
        lowercase__ :   str    					=				(conll / 3) * 100
        logger.info(f'''CoNLL score: {conll:.2f}''')
        output_scores.update({"conll_score": conll})

    return output_scores

def 			lowercase_				(			_lowerCamelCase							:       Dict):
    lowercase__ :   List[str]    					=				False
    for line in key_lines:
        if not line.startswith("#"):
            if len(line.split()) > 6:
                lowercase__ :   Union[str, Any]    					=				line.split()[5]
                if not parse_col == "-":
                    lowercase__ :   str    					=				True
                    break
                else:
                    break
    return has_gold_parse



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






   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Optional[int]:
       return datasets.MetricInfo(
           description=_DESCRIPTION	,						citation=_CITATION	,						inputs_description=_KWARGS_DESCRIPTION	,						features=datasets.Features(
               {
                   "predictions": datasets.Sequence(datasets.Value("string"  )  ),
                   "references": datasets.Sequence(datasets.Value("string"  )  ),
               }  )	,						codebase_urls=["https://github.com/ns-moosavi/coval"]	,						reference_urls=[
               "https://github.com/ns-moosavi/coval",
               "https://www.aclweb.org/anthology/P16-1060",
               "http://www.conll.cemantix.org/2012/data.html",
           ]	,						)







   def       __UpperCamelCase     (      self				:     Tuple	,						lowercase_				:     Tuple	,						lowercase_				:     Any	,						lowercase_				:     Optional[Any]=True	,						lowercase_				:     Optional[Any]=False	,						lowercase_				:     Tuple=False	,						lowercase_				:     Any=False  )					->						Dict:
       lowercase__ :   int    					=				[
           ("mentions", evaluator.mentions),
           ("muc", evaluator.muc),
           ("bcub", evaluator.b_cubed),
           ("ceafe", evaluator.ceafe),
           ("lea", evaluator.lea),
       ]

       if min_span:
           lowercase__ :   Optional[int]    					=				util.check_gold_parse_annotation(lowercase_  )
           if not has_gold_parse:
               raise NotImplementedError("References should have gold parse annotation to use 'min_span'."  )
               # util.parse_key_file(key_file)
               # key_file = key_file + ".parsed"

       lowercase__ :   Any    					=				evaluate(
           key_lines=lowercase_	,						sys_lines=lowercase_	,						metrics=lowercase_	,						NP_only=lowercase_	,						remove_nested=lowercase_	,						keep_singletons=lowercase_	,						min_span=lowercase_	,						)

       return score








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


A__  :  Union[str, Any]								=					R'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `" / "`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `" // "`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `"train"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `"compressed"`)
            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
            `"compressed"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a "dummy" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
'''



@add_start_docstrings(UpperCamelCase_						)
class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''rag'''
							_a			      = True




							def __init__(       self     :       str				,				A_     :       List[Any]=None				,				A_     :       str=True				,				A_     :       Tuple=None				,				A_     :       Union[str, Any]=None				,				A_     :       List[str]=None				,				A_     :       List[str]=None				,				A_     :       List[Any]=None				,				A_     :       Union[str, Any]=" / "				,				A_     :       Tuple=" // "				,				A_     :       Any=5				,				A_     :       Optional[Any]=3_0_0				,				A_     :       Tuple=7_6_8				,				A_     :       Union[str, Any]=8				,				A_     :       Dict="wiki_dpr"				,				A_     :       Optional[Any]="train"				,				A_     :       Dict="compressed"				,				A_     :       Optional[int]=None				,				A_     :       List[str]=None				,				A_     :       str=False				,				A_     :       Dict=False				,				A_     :       Dict=0.0				,				A_     :       List[str]=True				,				A_     :       List[str]=False				,				A_     :       List[Any]=False				,				A_     :       Any=False				,				A_     :       Optional[int]=True				,				A_     :       int=None				,				**A_     :       List[str]				,				):
									super().__init__(
									    bos_token_id=A_				,				pad_token_id=A_				,				eos_token_id=A_				,				decoder_start_token_id=A_				,				forced_eos_token_id=A_				,				is_encoder_decoder=A_				,				prefix=A_				,				vocab_size=A_				,				**A_				,				)
									assert (
									    "question_encoder" in kwargs and "generator" in kwargs
									), "Config has to be initialized with question_encoder and generator config"
									lowerCAmelCase_			:     List[str]									= kwargs.pop('''question_encoder''')
									lowerCAmelCase_			:     Tuple									= question_encoder_config.pop('''model_type''')
									lowerCAmelCase_			:     Tuple									= kwargs.pop('''generator''')
									lowerCAmelCase_			:     Dict									= decoder_config.pop('''model_type''')

									from ..auto.configuration_auto import AutoConfig

									lowerCAmelCase_			:     Union[str, Any]									= AutoConfig.for_model(A_				,				**A_)
									lowerCAmelCase_			:     int									= AutoConfig.for_model(A_				,				**A_)

									lowerCAmelCase_			:     List[Any]									= reduce_loss
									lowerCAmelCase_			:     Optional[Any]									= label_smoothing
									lowerCAmelCase_			:     Union[str, Any]									= exclude_bos_score
									lowerCAmelCase_			:     List[Any]									= do_marginalize

									lowerCAmelCase_			:     int									= title_sep
									lowerCAmelCase_			:     Optional[int]									= doc_sep
									lowerCAmelCase_			:     List[str]									= n_docs
									lowerCAmelCase_			:     int									= max_combined_length

									lowerCAmelCase_			:     Union[str, Any]									= dataset
									lowerCAmelCase_			:     int									= dataset_split
									lowerCAmelCase_			:     Dict									= index_name

									lowerCAmelCase_			:     Union[str, Any]									= retrieval_vector_size
									lowerCAmelCase_			:     Optional[Any]									= retrieval_batch_size
									lowerCAmelCase_			:     List[str]									= passages_path
									lowerCAmelCase_			:     Any									= index_path
									lowerCAmelCase_			:     int									= use_dummy_dataset

									lowerCAmelCase_			:     Tuple									= output_retrieved

									lowerCAmelCase_			:     List[Any]									= do_deduplication

									lowerCAmelCase_			:     Union[str, Any]									= use_cache

									if self.forced_eos_token_id is None:
											lowerCAmelCase_			:     List[Any]									= getattr(self.generator				,				'''forced_eos_token_id'''				,				A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       str				,				A_     :       PretrainedConfig				,				A_     :       PretrainedConfig				,				**A_     :       Any):
									return cls(question_encoder=question_encoder_config.to_dict()				,				generator=generator_config.to_dict()				,				**A_)







							def    UpperCAmelCase__			(       self     :       str):
									lowerCAmelCase_			:     str									= copy.deepcopy(self.__dict__)
									lowerCAmelCase_			:     Tuple									= self.question_encoder.to_dict()
									lowerCAmelCase_			:     Dict									= self.generator.to_dict()
									lowerCAmelCase_			:     str									= self.__class__.model_type
									return output





