


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 	UpperCamelCase__ (     UpperCAmelCase_     ,  UpperCAmelCase_     ,  UpperCAmelCase_=False     ,  UpperCAmelCase_=False     ,  UpperCAmelCase_=True     ,  UpperCAmelCase_=False     ,  UpperCAmelCase_="dummy_doc"      )    ->				Optional[int]:




       '''simple docstring'''






       _lowercase       :					List[Any]     							=      {doc: key_lines}
       _lowercase       :					int     							=      {doc: sys_lines}

       _lowercase       :					Optional[Any]     							=      {}

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

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

       if NP_only or min_span:
              _lowercase       :					Any     							=      reader.set_annotated_parse_trees(lowercase_     ,  key_doc_lines[doc]     ,  lowercase_     ,  lowercase_      )

       _lowercase							,						_lowercase       :					str     							=      reader.get_doc_mentions(lowercase_     ,  sys_doc_lines[doc]     ,  lowercase_      )
       sys_singletons_num += singletons_num

       if NP_only or min_span:
              _lowercase       :					str     							=      reader.set_annotated_parse_trees(lowercase_     ,  key_doc_lines[doc]     ,  lowercase_     ,  lowercase_      )

       if remove_nested:
              _lowercase							,						_lowercase       :					Any     							=      reader.remove_nested_coref_mentions(lowercase_     ,  lowercase_      )
              key_nested_coref_num += nested_mentions
              key_removed_nested_clusters += removed_clusters

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

       _lowercase       :					Union[str, Any]     							=      reader.get_mention_assignments(lowercase_     ,  lowercase_      )
       _lowercase       :					int     							=      reader.get_mention_assignments(lowercase_     ,  lowercase_      )

       _lowercase       :					List[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 	UpperCamelCase__ (     UpperCAmelCase_     ,  UpperCAmelCase_     ,  UpperCAmelCase_     ,  UpperCAmelCase_     ,  UpperCAmelCase_     ,  UpperCAmelCase_     ,  UpperCAmelCase_      )    ->				List[Any]:




       '''simple docstring'''






       _lowercase       :					Union[str, Any]     							=      get_coref_infos(lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_      )

       _lowercase       :					Any     							=      {}
       _lowercase       :					Any     							=      0
       _lowercase       :					Any     							=      0

       for name, metric in metrics:
              _lowercase							,						_lowercase							,						_lowercase       :					Tuple     							=      evaluator.evaluate_documents(lowercase_     ,  lowercase_     ,  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       :					Optional[Any]     							=      (conll / 3) * 100
              logger.info(F'CoNLL score: {conll:.2f}'      )
              output_scores.update({'''conll_score''': conll}      )

       return output_scores

def 	UpperCamelCase__ (     UpperCAmelCase_      )    ->				Dict:




       '''simple docstring'''






       _lowercase       :					int     							=      False
       for line in key_lines:
              if not line.startswith('''#'''      ):
                     if len(line.split()      ) > 6:
                            _lowercase       :					List[Any]     							=      line.split()[5]
                            if not parse_col == "-":
                                   _lowercase       :					Tuple     							=      True
                                   break
                            else:
                                   break
       return has_gold_parse







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



     '''simple docstring'''




     def 	lowerCAmelCase_ (			self							:							List[str]		):





            """simple docstring"""



            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 	lowerCAmelCase_ (			self							:							Optional[Any]						,   UpperCamelCase							:							Union[str, Any]						,   UpperCamelCase							:							Dict						,   UpperCamelCase							:							Any=True						,   UpperCamelCase							:							Any=False						,   UpperCamelCase							:							int=False						,   UpperCamelCase							:							Optional[Any]=False		):





            """simple docstring"""



            _lowercase       :					Optional[int]     							=      [
                ('''mentions''', evaluator.mentions),
                ('''muc''', evaluator.muc),
                ('''bcub''', evaluator.b_cubed),
                ('''ceafe''', evaluator.ceafe),
                ('''lea''', evaluator.lea),
            ]

            if min_span:
                   _lowercase       :					List[Any]     							=      util.check_gold_parse_annotation(UpperCAmelCase__		)
                   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       :					Union[str, Any]     							=      evaluate(
                key_lines=UpperCAmelCase__						,   sys_lines=UpperCAmelCase__						,   metrics=UpperCAmelCase__						,   NP_only=UpperCAmelCase__						,   remove_nested=UpperCAmelCase__						,   keep_singletons=UpperCAmelCase__						,   min_span=UpperCAmelCase__						,   )

            return score





import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			int:



       """simple docstring"""

       return 1.0 / (1.0 + np.exp(-_outputs		))







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Union[str, Any]:



       """simple docstring"""

       A__ 					=       np.max(_outputs					,							axis=-1					,							keepdims=lowercase_		)
       A__ 					=       np.exp(_outputs - maxes		)
       return shifted_exp / shifted_exp.sum(axis=-1					,							keepdims=lowercase_		)


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''sigmoid'''
    UpperCAmelCase__				    = '''softmax'''
    UpperCAmelCase__				    = '''none'''


@add_end_docstrings(
    UpperCAmelCase__						,   R'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    '''						,   )
class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = ClassificationFunction.NONE


    def __init__(     self :      Any   ,					**UpperCAmelCase__ :      Optional[Any])		->str:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           self.check_model_type(
               TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
               if self.framework == '''tf'''
               else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      Tuple=None   ,					UpperCAmelCase__ :      Any=None   ,					UpperCAmelCase__ :      int=""   ,					**UpperCAmelCase__ :      Any)		->int:





           '''simple docstring'''

           A__ 					=       tokenizer_kwargs

           A__ 					=       {}
           if hasattr(self.model.config   ,					'''return_all_scores''') and return_all_scores is None:
                  A__ 					=       self.model.config.return_all_scores

           if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__) or top_k is None:
                  A__ 					=       top_k
                  A__ 					=       False
           elif return_all_scores is not None:
                  warnings.warn(
                      '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
                      ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''   ,					UpperCAmelCase__   ,					)
                  if return_all_scores:
                         A__ 					=       None
                  else:
                         A__ 					=       1

           if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__):
                  A__ 					=       ClassificationFunction[function_to_apply.upper()]

           if function_to_apply is not None:
                  A__ 					=       function_to_apply
           return preprocess_params, {}, postprocess_params


    def __call__(     self :      str   ,					*UpperCAmelCase__ :      List[Any]   ,					**UpperCAmelCase__ :      Optional[int])		->Union[str, Any]:





           '''simple docstring'''

           A__ 					=       super().__call__(*UpperCAmelCase__   ,					**UpperCAmelCase__)
           # TODO try and retrieve it in a nicer way from _sanitize_parameters.
           A__ 					=       '''top_k''' not in kwargs
           if isinstance(args[0]   ,					UpperCAmelCase__) and _legacy:
                  # This pipeline is odd, and return a list when single item is run
                  return [result]
           else:
                  return result


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					UpperCAmelCase__ :      Any   ,					**UpperCAmelCase__ :      str)		->Dict[str, GenericTensor]:





           '''simple docstring'''

           A__ 					=       self.framework
           if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__):
                  return self.tokenizer(**UpperCAmelCase__   ,					return_tensors=UpperCAmelCase__   ,					**UpperCAmelCase__)
           elif isinstance(UpperCAmelCase__   ,					UpperCAmelCase__) and len(UpperCAmelCase__) == 1 and isinstance(inputs[0]   ,					UpperCAmelCase__) and len(inputs[0]) == 2:
                  # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
                  return self.tokenizer(
                      text=inputs[0][0]   ,					text_pair=inputs[0][1]   ,					return_tensors=UpperCAmelCase__   ,					**UpperCAmelCase__)
           elif isinstance(UpperCAmelCase__   ,					UpperCAmelCase__):
                  # This is likely an invalid usage of the pipeline attempting to pass text pairs.
                  raise ValueError(
                      '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
                      ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.''')
           return self.tokenizer(UpperCAmelCase__   ,					return_tensors=UpperCAmelCase__   ,					**UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      Tuple)		->Tuple:





           '''simple docstring'''

           return self.model(**UpperCAmelCase__)





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      Optional[int]=None   ,					UpperCAmelCase__ :      List[Any]=1   ,					UpperCAmelCase__ :      str=True)		->Dict:





           '''simple docstring'''

           if function_to_apply is None:
                  if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                         A__ 					=       ClassificationFunction.SIGMOID
                  elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                         A__ 					=       ClassificationFunction.SOFTMAX
                  elif hasattr(self.model.config   ,					'''function_to_apply''') and function_to_apply is None:
                         A__ 					=       self.model.config.function_to_apply
                  else:
                         A__ 					=       ClassificationFunction.NONE

           A__ 					=       model_outputs['''logits'''][0]
           A__ 					=       outputs.numpy()

           if function_to_apply == ClassificationFunction.SIGMOID:
                  A__ 					=       sigmoid(UpperCAmelCase__)
           elif function_to_apply == ClassificationFunction.SOFTMAX:
                  A__ 					=       softmax(UpperCAmelCase__)
           elif function_to_apply == ClassificationFunction.NONE:
                  A__ 					=       outputs
           else:
                  raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}""")

           if top_k == 1 and _legacy:
                  return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

           A__ 					=       [
               {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(UpperCAmelCase__)
           ]
           if not _legacy:
                  dict_scores.sort(key=lambda UpperCAmelCase__: x["score"]   ,					reverse=UpperCAmelCase__)
                  if top_k is not None:
                         A__ 					=       dict_scores[:top_k]
           return dict_scores


