
'''simple docstring'''



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_lowercase    =		2




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       *,  # begin keyword-only arguments
						    _lowercase="<s>"   ,       _lowercase="<pad>"   ,       _lowercase="</s>"   ,       _lowercase="<unk>"   ,       _lowercase=None   ,       ):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			bos, unk, pad, eos
												_lowerCAmelCase  						=			[]
												_lowerCAmelCase  						=			[]
												_lowerCAmelCase  						=			{}
												_lowerCAmelCase  						=			self.add_symbol(_lowercase				)
												_lowerCAmelCase  						=			self.add_symbol(_lowercase				)
												_lowerCAmelCase  						=			self.add_symbol(_lowercase				)
												_lowerCAmelCase  						=			self.add_symbol(_lowercase				)
												if extra_special_symbols:
																		for s in extra_special_symbols:
																								self.add_symbol(_lowercase				)
												_lowerCAmelCase  						=			len(self.symbols				)




						def __eq__( self   ,       _lowercase				):

												"""simple docstring"""



												return self.indices == other.indices




						def __getitem__( self   ,       _lowercase				):

												"""simple docstring"""



												if idx < len(self.symbols				):
																		return self.symbols[idx]
												return self.unk_word




						def __len__( self				):

												"""simple docstring"""



												return len(self.symbols				)




						def __contains__( self   ,       _lowercase				):

												"""simple docstring"""



												return sym in self.indices




						@classmethod
						def  _lowercase     ( cls   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			cls()
												d.add_from_file(_lowercase				)
												return d




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase=1   ,       _lowercase=False				):

												"""simple docstring"""



												if word in self.indices and not overwrite:
																		_lowerCAmelCase  						=			self.indices[word]
																		_lowerCAmelCase  						=			self.count[idx] + n
																		return idx
												else:
																		_lowerCAmelCase  						=			len(self.symbols				)
																		_lowerCAmelCase  						=			idx
																		self.symbols.append(_lowercase				)
																		self.count.append(_lowercase				)
																		return idx




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												return 0




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												if isinstance(_lowercase   ,       _lowercase				):
																		try:
																								with open(_lowercase   ,       """r"""   ,       encoding="""utf-8"""				) as fd:
																														self.add_from_file(_lowercase				)
																		except FileNotFoundError as fnfe:
																								raise fnfe
																		except UnicodeError:
																								raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(_lowercase				)				)
																		return

												_lowerCAmelCase  						=			f.readlines()
												_lowerCAmelCase  						=			self._load_meta(_lowercase				)

												for line in lines[indices_start_line:]:
																		try:
																								_lowerCAmelCase						,   _lowerCAmelCase  						=			line.rstrip().rsplit(""" """   ,       1				)
																								if field == "#fairseq:overwrite":
																														_lowerCAmelCase  						=			True
																														_lowerCAmelCase						,   _lowerCAmelCase  						=			line.rsplit(""" """   ,       1				)
																								else:
																														_lowerCAmelCase  						=			False
																								_lowerCAmelCase  						=			int(_lowercase				)
																								_lowerCAmelCase  						=			line
																								if word in self and not overwrite:
																														raise RuntimeError(
																														    """Duplicate word found when loading Dictionary: '{}'. """
																														    """Duplicate words can overwrite earlier ones by adding the """
																														    """#fairseq:overwrite flag at the end of the corresponding row """
																														    """in the dictionary file. If using the Camembert model, please """
																														    """download an updated copy of the model file.""".format(_lowercase				)				)
																								self.add_symbol(_lowercase   ,       n=_lowercase   ,       overwrite=_lowercase				)
																		except ValueError:
																								raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""				)






def A				(__lowerCamelCase				:str		):
						# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
						# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
						_lowerCAmelCase  						=			dict((re.sub(r"""@@$"""			,     """"""			,     __lowerCamelCase		), v) if k.endswith("""@@"""		) else (re.sub(r"""$"""			,     """</w>"""			,     __lowerCamelCase		), v) for k, v in d.items()		)
						_lowerCAmelCase  						=			"""<s> <pad> </s> <unk>""".split()
						# restore the special tokens
						for k in keep_keys:
												del da[f'{k}</w>']
												_lowerCAmelCase  						=			d[k]  # restore
						return da


def A				(__lowerCamelCase				:Any			,     __lowerCamelCase				:int		):
						# prep
						if not os.path.exists(__lowerCamelCase		):
												raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'		)
						os.makedirs(__lowerCamelCase			,     exist_ok=__lowerCamelCase		)
						print(f'Writing results to {pytorch_dump_folder_path}'		)

						# handle various types of models

						_lowerCAmelCase  						=			os.path.join(__lowerCamelCase			,     """checkpoint.pt"""		)
						if not os.path.isfile(__lowerCamelCase		):
												raise ValueError(f'path to the file {checkpoint_file} does not exist!'		)
						_lowerCAmelCase  						=			torch.load(__lowerCamelCase			,     map_location="""cpu"""		)

						_lowerCAmelCase  						=			chkpt["""cfg"""]["""model"""]

						# dicts
						_lowerCAmelCase  						=			os.path.join(__lowerCamelCase			,     """dict.txt"""		)
						if not os.path.isfile(__lowerCamelCase		):
												raise ValueError(f'path to the file {dict_file} does not exist!'		)
						_lowerCAmelCase  						=			Dictionary.load(__lowerCamelCase		)
						_lowerCAmelCase  						=			rewrite_dict_keys(src_dict.indices		)
						_lowerCAmelCase  						=			len(__lowerCamelCase		)
						_lowerCAmelCase  						=			os.path.join(__lowerCamelCase			,     VOCAB_FILES_NAMES["""vocab_file"""]		)
						print(f'Generating {src_vocab_file} of {src_vocab_size} records'		)
						with open(__lowerCamelCase			,     """w"""			,     encoding="""utf-8"""		) as f:
												f.write(json.dumps(__lowerCamelCase			,     ensure_ascii=__lowerCamelCase			,     indent=__lowerCamelCase		)		)

						# merges_file (bpecodes)
						_lowerCAmelCase  						=			os.path.join(__lowerCamelCase			,     """bpecodes"""		)
						if not os.path.isfile(__lowerCamelCase		):
												raise ValueError(f'path to the file {bpecodes_file} does not exist!'		)

						_lowerCAmelCase  						=			os.path.join(__lowerCamelCase			,     VOCAB_FILES_NAMES["""merges_file"""]		)
						shutil.copyfile(__lowerCamelCase			,     __lowerCamelCase		)

						# model config
						_lowerCAmelCase  						=			os.path.join(__lowerCamelCase			,     """config.json"""		)

						_lowerCAmelCase  						=			{
						    """activation_dropout""": args["""activation_dropout"""],
						    """architectures""": ["""BioGptForCausalLM"""],
						    """attention_probs_dropout_prob""": args["""attention_dropout"""],
						    """bos_token_id""": 0,
						    """eos_token_id""": 2,
						    """hidden_act""": args["""activation_fn"""],
						    """hidden_dropout_prob""": args["""dropout"""],
						    """hidden_size""": args["""decoder_embed_dim"""],
						    """initializer_range""": 0.02,
						    """intermediate_size""": args["""decoder_ffn_embed_dim"""],
						    """layer_norm_eps""": 1e-12,
						    """layerdrop""": args["""decoder_layerdrop"""],
						    """max_position_embeddings""": args["""max_target_positions"""],
						    """model_type""": """biogpt""",
						    """num_attention_heads""": args["""decoder_attention_heads"""],
						    """num_hidden_layers""": args["""decoder_layers"""],
						    """pad_token_id""": 1,
						    """scale_embedding""": not args["""no_scale_embedding"""],
						    """tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
						    """vocab_size""": src_vocab_size,
						}

						# good hparam defaults to start with

						print(f'Generating {biogpt_model_config_file}'		)
						with open(__lowerCamelCase			,     """w"""			,     encoding="""utf-8"""		) as f:
												f.write(json.dumps(__lowerCamelCase			,     ensure_ascii=__lowerCamelCase			,     indent=__lowerCamelCase		)		)

						# tokenizer config
						_lowerCAmelCase  						=			os.path.join(__lowerCamelCase			,     __lowerCamelCase		)

						_lowerCAmelCase  						=			{
						    """bos_token""": """<s>""",
						    """eos_token""": """</s>""",
						    """model_max_length""": 1024,
						    """pad_token""": """<pad>""",
						    """special_tokens_map_file""": None,
						    """tokenizer_class""": """BioGptTokenizer""",
						    """unk_token""": """<unk>""",
						}

						print(f'Generating {biogpt_tokenizer_config_file}'		)
						with open(__lowerCamelCase			,     """w"""			,     encoding="""utf-8"""		) as f:
												f.write(json.dumps(__lowerCamelCase			,     ensure_ascii=__lowerCamelCase			,     indent=__lowerCamelCase		)		)

						# model
						_lowerCAmelCase  						=			chkpt["""model"""]

						# remove unneeded keys
						_lowerCAmelCase  						=			[
						    """decoder.version""",
						]
						for k in ignore_keys:
												model_state_dict.pop(__lowerCamelCase			,     __lowerCamelCase		)

						_lowerCAmelCase  						=			list(model_state_dict.keys()		)
						for layer_name in layer_names:
												if layer_name.endswith("""output_projection.weight"""		):
																		_lowerCAmelCase  						=			model_state_dict.pop(__lowerCamelCase		)
												else:
																		_lowerCAmelCase  						=			model_state_dict.pop(__lowerCamelCase		)

						_lowerCAmelCase  						=			BioGptConfig.from_pretrained(__lowerCamelCase		)
						_lowerCAmelCase  						=			BioGptForCausalLM(__lowerCamelCase		)

						# check that it loads ok
						model_new.load_state_dict(__lowerCamelCase		)

						# save
						_lowerCAmelCase  						=			os.path.join(__lowerCamelCase			,     __lowerCamelCase		)
						print(f'Generating {pytorch_weights_dump_path}'		)
						torch.save(__lowerCamelCase			,     __lowerCamelCase		)

						print("""Conversion is done!"""		)


if __name__ == "__main__":
						_lowercase    =		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--biogpt_checkpoint_path""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
						        """ bpecodes, etc."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_lowercase    =		parser.parse_args()
						convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)


'''simple docstring'''



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


_lowercase    =		datasets.logging.get_logger(__name__)


_lowercase    =		"""\
@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\",
}

"""

_lowercase    =		"""\
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.
"""

_lowercase    =		"""
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 A				(__lowerCamelCase				:str			,     __lowerCamelCase				:Optional[Any]			,     __lowerCamelCase				:Union[str, Any]=False			,     __lowerCamelCase				:List[Any]=False			,     __lowerCamelCase				:str=True			,     __lowerCamelCase				:str=False			,     __lowerCamelCase				:str="dummy_doc"		):
						_lowerCAmelCase  						=			{doc: key_lines}
						_lowerCAmelCase  						=			{doc: sys_lines}

						_lowerCAmelCase  						=			{}

						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			0

						_lowerCAmelCase						,   _lowerCAmelCase  						=			reader.get_doc_mentions(__lowerCamelCase			,     key_doc_lines[doc]			,     __lowerCamelCase		)
						key_singletons_num += singletons_num

						if NP_only or min_span:
												_lowerCAmelCase  						=			reader.set_annotated_parse_trees(__lowerCamelCase			,     key_doc_lines[doc]			,     __lowerCamelCase			,     __lowerCamelCase		)

						_lowerCAmelCase						,   _lowerCAmelCase  						=			reader.get_doc_mentions(__lowerCamelCase			,     sys_doc_lines[doc]			,     __lowerCamelCase		)
						sys_singletons_num += singletons_num

						if NP_only or min_span:
												_lowerCAmelCase  						=			reader.set_annotated_parse_trees(__lowerCamelCase			,     key_doc_lines[doc]			,     __lowerCamelCase			,     __lowerCamelCase		)

						if remove_nested:
												_lowerCAmelCase						,   _lowerCAmelCase  						=			reader.remove_nested_coref_mentions(__lowerCamelCase			,     __lowerCamelCase		)
												key_nested_coref_num += nested_mentions
												key_removed_nested_clusters += removed_clusters

												_lowerCAmelCase						,   _lowerCAmelCase  						=			reader.remove_nested_coref_mentions(__lowerCamelCase			,     __lowerCamelCase		)
												sys_nested_coref_num += nested_mentions
												sys_removed_nested_clusters += removed_clusters

						_lowerCAmelCase  						=			reader.get_mention_assignments(__lowerCamelCase			,     __lowerCamelCase		)
						_lowerCAmelCase  						=			reader.get_mention_assignments(__lowerCamelCase			,     __lowerCamelCase		)

						_lowerCAmelCase  						=			(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				(__lowerCamelCase				:List[str]			,     __lowerCamelCase				:str			,     __lowerCamelCase				:str			,     __lowerCamelCase				:int			,     __lowerCamelCase				:int			,     __lowerCamelCase				:Optional[Any]			,     __lowerCamelCase				:Optional[Any]		):
						_lowerCAmelCase  						=			get_coref_infos(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)

						_lowerCAmelCase  						=			{}
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			0

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

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

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

						return output_scores


def A				(__lowerCamelCase				:List[str]		):
						_lowerCAmelCase  						=			False
						for line in key_lines:
												if not line.startswith("""#"""		):
																		if len(line.split()		) > 6:
																								_lowerCAmelCase  						=			line.split()[5]
																								if not parse_col == "-":
																														_lowerCAmelCase  						=			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  _lowercase     ( 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  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase=True   ,       _lowercase=False   ,       _lowercase=False   ,       _lowercase=False				):

												"""simple docstring"""



												_lowerCAmelCase  						=			[
												    ("""mentions""", evaluator.mentions),
												    ("""muc""", evaluator.muc),
												    ("""bcub""", evaluator.b_cubed),
												    ("""ceafe""", evaluator.ceafe),
												    ("""lea""", evaluator.lea),
												]

												if min_span:
																		_lowerCAmelCase  						=			util.check_gold_parse_annotation(_lowercase				)
																		if not has_gold_parse:
																								raise NotImplementedError("""References should have gold parse annotation to use 'min_span'."""				)
																								# util.parse_key_file(key_file)
																								# key_file = key_file + ".parsed"

												_lowerCAmelCase  						=			evaluate(
												    key_lines=_lowercase   ,       sys_lines=_lowercase   ,       metrics=_lowercase   ,       NP_only=_lowercase   ,       remove_nested=_lowercase   ,       keep_singletons=_lowercase   ,       min_span=_lowercase   ,       )

												return score
