


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_      : Union[str, Any]  					=       datasets.logging.get_logger(__name__)


UpperCAmelCase_      : Dict  					=       '\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",\n    author = "Moosavi, Nafise Sadat  and\n      Strube, Michael",\n    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = aug,\n    year = "2016",\n    address = "Berlin, Germany",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/P16-1060",\n    doi = "10.18653/v1/P16-1060",\n    pages = "632--642",\n}\n\n'

UpperCAmelCase_      : str  					=       '\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	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.\n5	Part-of-Speech\n6	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.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n'

UpperCAmelCase_      : int  					=       '\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting \'keep_singletons=False\', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    \'mentions\': mentions\n    \'muc\': MUC metric [Vilain et al, 1995]\n    \'bcub\': B-cubed [Bagga and Baldwin, 1998]\n    \'ceafe\': CEAFe [Luo et al., 2005]\n    \'lea\': LEA [Moosavi and Strube, 2016]\n    \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric(\'coval\')\n    >>> words = [\'bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -\',\n    ... \'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -\']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}\n'







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]  ,    __A :			List[str]  ,    __A :			List[Any]=False  ,    __A :			Tuple=False  ,    __A :			Union[str, Any]=True  ,    __A :			List[str]=False  ,    __A :			Optional[int]="dummy_doc"				)	->		Any:

       """simple docstring"""

       a_					:		Any							=  {doc: key_lines}
       a_					:		List[Any]							=  {doc: sys_lines}

       a_					:		Union[str, Any]							=  {}

       a_					:		int							=  0
       a_					:		List[Any]							=  0
       a_					:		Union[str, Any]							=  0
       a_					:		Union[str, Any]							=  0
       a_					:		int							=  0
       a_					:		Optional[int]							=  0

       a_							,		a_					:		Optional[int]							=  reader.get_doc_mentions(__A  ,    key_doc_lines[doc]  ,    __A				)
       key_singletons_num += singletons_num

       if NP_only or min_span:
              a_					:		List[Any]							=  reader.set_annotated_parse_trees(__A  ,    key_doc_lines[doc]  ,    __A  ,    __A				)

       a_							,		a_					:		List[str]							=  reader.get_doc_mentions(__A  ,    sys_doc_lines[doc]  ,    __A				)
       sys_singletons_num += singletons_num

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

       if remove_nested:
              a_							,		a_					:		Union[str, Any]							=  reader.remove_nested_coref_mentions(__A  ,    __A				)
              key_nested_coref_num += nested_mentions
              key_removed_nested_clusters += removed_clusters

              a_							,		a_					:		Union[str, Any]							=  reader.remove_nested_coref_mentions(__A  ,    __A				)
              sys_nested_coref_num += nested_mentions
              sys_removed_nested_clusters += removed_clusters

       a_					:		Any							=  reader.get_mention_assignments(__A  ,    __A				)
       a_					:		List[str]							=  reader.get_mention_assignments(__A  ,    __A				)

       a_					:		Dict							=  (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        SCREAMING_SNAKE_CASE_						(			__A :			List[Any]  ,    __A :			str  ,    __A :			str  ,    __A :			List[str]  ,    __A :			Any  ,    __A :			Optional[int]  ,    __A :			Dict				)	->		Optional[int]:

       """simple docstring"""

       a_					:		Tuple							=  get_coref_infos(__A  ,    __A  ,    __A  ,    __A  ,    __A  ,    __A				)

       a_					:		Optional[int]							=  {}
       a_					:		Union[str, Any]							=  0
       a_					:		str							=  0

       for name, metric in metrics:
              a_							,		a_							,		a_					:		Any							=  evaluator.evaluate_documents(__A  ,    __A  ,    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 * 1_00:.2f}"""  ,    F""" Precision: {precision * 1_00:.2f}"""  ,    F""" F1: {fa * 1_00:.2f}"""  ,    )

       if conll_subparts_num == 3:
              a_					:		List[Any]							=  (conll / 3) * 1_00
              logger.info(F"""CoNLL score: {conll:.2f}"""				)
              output_scores.update({'conll_score': conll}				)

       return output_scores







def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple				)	->		Union[str, Any]:

       """simple docstring"""

       a_					:		Union[str, Any]							=  False
       for line in key_lines:
              if not line.startswith('#'				):
                     if len(line.split()				) > 6:
                            a_					:		str							=  line.split()[5]
                            if not parse_col == "-":
                                   a_					:		Optional[int]							=  True
                                   break
                            else:
                                   break
       return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,    _KWARGS_DESCRIPTION			)
class  SCREAMING_SNAKE_CASE__  (				datasets.Metric			):
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			List[Any]:
             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        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Dict=True     ,							SCREAMING_SNAKE_CASE__				:       str=False     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=False     ,							SCREAMING_SNAKE_CASE__				:       List[str]=False     )		->			List[str]:
             a_					:		str							=  [
                 ('mentions', evaluator.mentions),
                 ('muc', evaluator.muc),
                 ('bcub', evaluator.b_cubed),
                 ('ceafe', evaluator.ceafe),
                 ('lea', evaluator.lea),
             ]

             if min_span:
                    a_					:		Tuple							=  util.check_gold_parse_annotation(SCREAMING_SNAKE_CASE__     )
                    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"

             a_					:		List[str]							=  evaluate(
                 key_lines=SCREAMING_SNAKE_CASE__     ,							sys_lines=SCREAMING_SNAKE_CASE__     ,							metrics=SCREAMING_SNAKE_CASE__     ,							NP_only=SCREAMING_SNAKE_CASE__     ,							remove_nested=SCREAMING_SNAKE_CASE__     ,							keep_singletons=SCREAMING_SNAKE_CASE__     ,							min_span=SCREAMING_SNAKE_CASE__     ,							)

             return score






'''simple docstring'''






from sklearn.metrics import recall_score

import datasets


__A					:  Dict											=      "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"


__A					:  List[Any]											=      "\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {'recall': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {'recall': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {'recall': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'recall': array([1., 0., 0.])}\n"


__A					:  str											=      "\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"






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







    """simple docstring"""



    def __lowercase   (   self    :  str		)   ->  Dict:
      return datasets.MetricInfo(
          description=_DESCRIPTION    ,						citation=_CITATION    ,						inputs_description=_KWARGS_DESCRIPTION    ,						features=datasets.Features(
              {
                  """predictions""": datasets.Sequence(datasets.Value("""int32"""		)		),
                  """references""": datasets.Sequence(datasets.Value("""int32"""		)		),
              }
              if self.config_name == """multilabel"""
              else {
                  """predictions""": datasets.Value("""int32"""		),
                  """references""": datasets.Value("""int32"""		),
              }		)    ,						reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""]    ,						)





    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  Optional[Any]    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Optional[Any]=None    ,						lowerCamelCase    :  Optional[int]=1    ,						lowerCamelCase    :  Union[str, Any]="binary"    ,						lowerCamelCase    :  Any=None    ,						lowerCamelCase    :  str="warn"    ,						)   ->  List[Any]:
      lowerCAmelCase_			:      Optional[int]			=   recall_score(
          lowerCamelCase    ,						lowerCamelCase    ,						labels=lowerCamelCase    ,						pos_label=lowerCamelCase    ,						average=lowerCamelCase    ,						sample_weight=lowerCamelCase    ,						zero_division=lowerCamelCase    ,						)
      return {"recall": float(lowerCamelCase		) if score.size == 1 else score}



