


from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCAmelCase_   :				List[str]						      = TypeVar('T')
UpperCAmelCase_   :				int						      = TypeVar('U')
class 				SCREAMING_SNAKE_CASE__						(       Generic[T, U]    ):
      def __init__( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : T | None  ,							SCREAMING_SNAKE_CASE__       : U | None  )	->    List[Any]:
             a_      :	Optional[Any]							=						key
             a_      :	str							=						val
             a_      :	DoubleLinkedListNode[T, U] | None							=						None
             a_      :	DoubleLinkedListNode[T, U] | None							=						None

      def __repr__( self       : Dict  )	->    str:
             return (
                 F"""Node: key: {self.key}, val: {self.val}, """
                 F"""has next: {bool(self.next  )}, has prev: {bool(self.prev  )}"""
             )
class 				SCREAMING_SNAKE_CASE__						(       Generic[T, U]    ):
      def __init__( self       : Optional[int]  )	->    None:
             a_      :	DoubleLinkedListNode[T, U]							=						DoubleLinkedListNode(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             a_      :	DoubleLinkedListNode[T, U]							=						DoubleLinkedListNode(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             a_							,	a_      :	Optional[int]							=						self.rear, self.head
      def __repr__( self       : int  )	->    str:
             a_      :	Dict							=						['DoubleLinkedList']
             a_      :	int							=						self.head
             while node.next is not None:
                    rep.append(str(SCREAMING_SNAKE_CASE__  )  )
                    a_      :	List[Any]							=						node.next
             rep.append(str(self.rear  )  )
             return ",\n    ".join(SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : DoubleLinkedListNode[T, U]  )	->    None:
             a_      :	Union[str, Any]							=						self.rear.prev

             # All nodes other than self.head are guaranteed to have non-None previous
             assert previous is not None

             a_      :	Union[str, Any]							=						node
             a_      :	List[str]							=						previous
             a_      :	Optional[int]							=						node
             a_      :	Tuple							=						self.rear

      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : DoubleLinkedListNode[T, U]  )	->    DoubleLinkedListNode[T, U] | None:

             if node.prev is None or node.next is None:
                    return None

             a_      :	Union[str, Any]							=						node.next
             a_      :	List[str]							=						node.prev
             a_      :	Union[str, Any]							=						None
             a_      :	str							=						None
             return node
class 				SCREAMING_SNAKE_CASE__						(       Generic[T, U]    ):
      snake_case__       :  dict[Callable[[T], U], LRUCache[T, U]]       =		{}
      def __init__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : int  )	->    Optional[int]:
             a_      :	DoubleLinkedList[T, U]							=						DoubleLinkedList()
             a_      :	Optional[int]							=						capacity
             a_      :	Tuple							=						0
             a_      :	Optional[int]							=						0
             a_      :	List[str]							=						0
             a_      :	dict[T, DoubleLinkedListNode[T, U]]							=						{}
      def __repr__( self       : str  )	->    str:
             return (
                 F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
                 F"""capacity={self.capacity}, current size={self.num_keys})"""
             )
      def __contains__( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : T  )	->    bool:
             return key in self.cache
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : T  )	->    U | None:
             # Note: pythonic interface would throw KeyError rather than return None

             if key in self.cache:
                    self.hits += 1
                    a_      :	DoubleLinkedListNode[T, U]							=						self.cache[key]
                    a_      :	str							=						self.list.remove(self.cache[key]  )
                    assert node == value_node

                    # node is guaranteed not None because it is in self.cache
                    assert node is not None
                    self.list.add(SCREAMING_SNAKE_CASE__  )
                    return node.val
             self.miss += 1
             return None
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							SCREAMING_SNAKE_CASE__       : T  ,							SCREAMING_SNAKE_CASE__       : U  )	->    None:

             if key not in self.cache:
                    if self.num_keys >= self.capacity:
                           # delete first node (oldest) when over capacity
                           a_      :	Optional[Any]							=						self.list.head.next

                           # guaranteed to have a non-None first node when num_keys > 0
                           # explain to type checker via assertions
                           assert first_node is not None
                           assert first_node.key is not None
                           assert (
                               self.list.remove(SCREAMING_SNAKE_CASE__  ) is not None
                           )  # node guaranteed to be in list assert node.key is not None

                           del self.cache[first_node.key]
                           self.num_keys -= 1
                    a_      :	Union[str, Any]							=						DoubleLinkedListNode(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    self.list.add(self.cache[key]  )
                    self.num_keys += 1

             else:
                    # bump node to the end of the list, update value
                    a_      :	Dict							=						self.list.remove(self.cache[key]  )
                    assert node is not None  # node guaranteed to be in list
                    a_      :	Dict							=						value
                    self.list.add(SCREAMING_SNAKE_CASE__  )

      @classmethod
      def  SCREAMING_SNAKE_CASE							( cls       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : int = 1_2_8  )	->    Callable[[Callable[[T], U]], Callable[..., U]]:

             def cache_decorator_inner(SCREAMING_SNAKE_CASE__       : Callable[[T], U]  ) -> Callable[..., U]:
                    def cache_decorator_wrapper(*SCREAMING_SNAKE_CASE__       : T  ) -> U:
                           if func not in cls.decorator_function_to_instance_map:
                                  a_      :	Any							=						LRUCache(SCREAMING_SNAKE_CASE__  )

                           a_      :	List[Any]							=						cls.decorator_function_to_instance_map[func].get(args[0]  )
                           if result is None:
                                  a_      :	str							=						func(*SCREAMING_SNAKE_CASE__  )
                                  cls.decorator_function_to_instance_map[func].put(args[0]  ,							SCREAMING_SNAKE_CASE__  )
                           return result

                    def cache_info() -> LRUCache[T, U]:
                           return cls.decorator_function_to_instance_map[func]

                    setattr(SCREAMING_SNAKE_CASE__  ,							'cache_info'  ,							SCREAMING_SNAKE_CASE__  )  # noqa: B010

                    return cache_decorator_wrapper

             return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.testmod()






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_   :				int						      = 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_   :				Tuple						      = '\\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_   :				Optional[Any]						      = '\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				:    List[str]						,					__A				:    Any						,					__A				:    List[Any]=False						,					__A				:    Tuple=False						,					__A				:    Any=True						,					__A				:    Any=False						,					__A				:    Any="dummy_doc"       )			->	List[str]:

       """simple docstring"""

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

       a_      :	List[Any]							=						{}

       a_      :	Tuple							=						0
       a_      :	List[str]							=						0
       a_      :	Union[str, Any]							=						0
       a_      :	List[Any]							=						0
       a_      :	List[str]							=						0
       a_      :	Union[str, Any]							=						0

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

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

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

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

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

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

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

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

       """simple docstring"""

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

       a_      :	List[Any]							=						{}
       a_      :	int							=						0
       a_      :	Optional[int]							=						0

       for name, metric in metrics:
              a_							,	a_							,	a_      :	Tuple							=						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[str]							=						(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				:    int       )			->	Dict:

       """simple docstring"""

       a_      :	List[Any]							=						False
       for line in key_lines:
              if not line.startswith('#'       ):
                     if len(line.split()       ) > 6:
                            a_      :	List[Any]							=						line.split()[5]
                            if not parse_col == "-":
                                   a_      :	List[Any]							=						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       : Union[str, Any]  )	->    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  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							SCREAMING_SNAKE_CASE__       : Tuple=False  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=False  ,							SCREAMING_SNAKE_CASE__       : int=False  )	->    Tuple:
             a_      :	List[str]							=						[
                 ('mentions', evaluator.mentions),
                 ('muc', evaluator.muc),
                 ('bcub', evaluator.b_cubed),
                 ('ceafe', evaluator.ceafe),
                 ('lea', evaluator.lea),
             ]

             if min_span:
                    a_      :	Union[str, Any]							=						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[Any]							=						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


