

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


lowerCAmelCase     :   Tuple						     =  datasets.logging.get_logger(__name__)


lowerCAmelCase     :   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'

lowerCAmelCase     :   List[Any]						     =  '\\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'

lowerCAmelCase     :   List[str]						     =  '\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    A_  (							a					,   a					,   a=False					,   a=False					,   a=True					,   a=False					,   a="dummy_doc"  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Any           =     {doc: key_lines}
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {doc: sys_lines}

      SCREAMING_SNAKE_CASE_				:  List[str]           =     {}

      SCREAMING_SNAKE_CASE_				:  str           =     0
      SCREAMING_SNAKE_CASE_				:  Optional[int]           =     0
      SCREAMING_SNAKE_CASE_				:  Dict           =     0
      SCREAMING_SNAKE_CASE_				:  Tuple           =     0
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     0
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     0

      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     reader.get_doc_mentions(a					,   key_doc_lines[doc]					,   a  )
      key_singletons_num += singletons_num

      if NP_only or min_span:
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     reader.set_annotated_parse_trees(a					,   key_doc_lines[doc]					,   a					,   a  )

      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Dict           =     reader.get_doc_mentions(a					,   sys_doc_lines[doc]					,   a  )
      sys_singletons_num += singletons_num

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

      if remove_nested:
            SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Dict           =     reader.remove_nested_coref_mentions(a					,   a  )
            key_nested_coref_num += nested_mentions
            key_removed_nested_clusters += removed_clusters

            SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  int           =     reader.remove_nested_coref_mentions(a					,   a  )
            sys_nested_coref_num += nested_mentions
            sys_removed_nested_clusters += removed_clusters

      SCREAMING_SNAKE_CASE_				:  str           =     reader.get_mention_assignments(a					,   a  )
      SCREAMING_SNAKE_CASE_				:  Dict           =     reader.get_mention_assignments(a					,   a  )

      SCREAMING_SNAKE_CASE_				:  Union[str, 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    A_  (							a					,   a					,   a					,   a					,   a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Any           =     get_coref_infos(a					,   a					,   a					,   a					,   a					,   a  )

      SCREAMING_SNAKE_CASE_				:  Dict           =     {}
      SCREAMING_SNAKE_CASE_				:  int           =     0
      SCREAMING_SNAKE_CASE_				:  str           =     0

      for name, metric in metrics:
            SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Union[str, 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(1_0  )					,   f"Recall: {recall * 1_0_0:.2f}"					,   f" Precision: {precision * 1_0_0:.2f}"					,   f" F1: {fa * 1_0_0:.2f}"					,   )

      if conll_subparts_num == 3:
            SCREAMING_SNAKE_CASE_				:  str           =     (conll / 3) * 1_0_0
            logger.info(f"CoNLL score: {conll:.2f}"  )
            output_scores.update({'conll_score': conll}  )

      return output_scores

def    A_  (							a  ):

      """simple docstring"""



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



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

  def  UpperCAmelCase    (				self					):





        """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  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=False					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [
            ('mentions', evaluator.mentions),
            ('muc', evaluator.muc),
            ('bcub', evaluator.b_cubed),
            ('ceafe', evaluator.ceafe),
            ('lea', evaluator.lea),
        ]

        if min_span:
              SCREAMING_SNAKE_CASE_				:  Dict           =     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"

        SCREAMING_SNAKE_CASE_				:  Tuple           =     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









# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
    import torch


lowerCAmelCase     :   Optional[int]						     =  logging.get_logger(__name__)
@dataclass
class  _A		:

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=6.0 ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE="fp4" ,					_SCREAMING_SNAKE_CASE=False ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     load_in_abit
        SCREAMING_SNAKE_CASE_				:  Tuple           =     load_in_abit
        SCREAMING_SNAKE_CASE_				:  Dict           =     llm_inta_threshold
        SCREAMING_SNAKE_CASE_				:  Tuple           =     llm_inta_skip_modules
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     llm_inta_enable_fpaa_cpu_offload
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     llm_inta_has_fpaa_weight
        SCREAMING_SNAKE_CASE_				:  List[str]           =     bnb_abit_quant_type
        SCREAMING_SNAKE_CASE_				:  int           =     bnb_abit_use_double_quant

        if bnb_abit_compute_dtype is None:
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.floataa
        elif isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     getattr(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        elif isinstance(_SCREAMING_SNAKE_CASE ,					torch.dtype					):
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     bnb_abit_compute_dtype
        else:
              raise ValueError('bnb_4bit_compute_dtype must be a string or a torch.dtype'					)

        self.post_init()

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        if not isinstance(self.llm_inta_threshold ,					_SCREAMING_SNAKE_CASE					):
              raise ValueError('llm_int8_threshold must be a float'					)

        if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules ,					_SCREAMING_SNAKE_CASE					):
              raise ValueError('llm_int8_skip_modules must be a list of strings'					)
        if not isinstance(self.llm_inta_enable_fpaa_cpu_offload ,					_SCREAMING_SNAKE_CASE					):
              raise ValueError('llm_int8_enable_fp32_cpu_offload must be a boolean'					)

        if not isinstance(self.llm_inta_has_fpaa_weight ,					_SCREAMING_SNAKE_CASE					):
              raise ValueError('llm_int8_has_fp16_weight must be a boolean'					)

        if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype ,					torch.dtype					):
              raise ValueError('bnb_4bit_compute_dtype must be torch.dtype'					)

        if not isinstance(self.bnb_abit_quant_type ,					_SCREAMING_SNAKE_CASE					):
              raise ValueError('bnb_4bit_quant_type must be a string'					)

        if not isinstance(self.bnb_abit_use_double_quant ,					_SCREAMING_SNAKE_CASE					):
              raise ValueError('bnb_4bit_use_double_quant must be a boolean'					)

        if self.load_in_abit and not version.parse(importlib.metadata.version('bitsandbytes'					)					) >= version.parse(
            '0.39.0'					):
              raise ValueError(
                  '4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version'					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.load_in_abit or self.load_in_abit

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        if self.load_in_abit:
              return "llm_int8"
        elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
              return "fp4"
        elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
              return "nf4"
        else:
              return None

  @classmethod
  def  UpperCAmelCase    (				cls ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     cls(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     []
        for key, value in kwargs.items():
              if hasattr(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
                    setattr(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
                    to_remove.append(_SCREAMING_SNAKE_CASE					)
        for key in to_remove:
              kwargs.pop(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        if return_unused_kwargs:
              return config, kwargs
        else:
              return config

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        with open(_SCREAMING_SNAKE_CASE ,					'w' ,					encoding='utf-8'					) as writer:
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.to_dict()
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     json.dumps(_SCREAMING_SNAKE_CASE ,					indent=2 ,					sort_keys=_SCREAMING_SNAKE_CASE					) + '\n'

              writer.write(_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     copy.deepcopy(self.__dict__					)
        SCREAMING_SNAKE_CASE_				:  str           =     str(output['bnb_4bit_compute_dtype']					).split('.'					)[1]

        return output

  def __repr__(				self					):





        """simple docstring"""







        return f"{self.__class__.__name__} {self.to_json_string()}"

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE = True					):





        """simple docstring"""







        if use_diff is True:
              SCREAMING_SNAKE_CASE_				:  int           =     self.to_diff_dict()
        else:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     self.to_dict()
        return json.dumps(_SCREAMING_SNAKE_CASE ,					indent=2 ,					sort_keys=_SCREAMING_SNAKE_CASE					) + "\n"



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.to_dict()

        # get the default config dict
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     BitsAndBytesConfig().to_dict()

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {}

        # only serialize values that differ from the default config
        for key, value in config_dict.items():
              if value != default_config_dict[key]:
                    SCREAMING_SNAKE_CASE_				:  int           =     value

        return serializable_config_dict






