


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase__  :Any             =							logging.get_logger(__name__)

lowerCAmelCase__  :Any             =							{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}

def 	lowerCAmelCase__			(      a__:		Dict      ,  a__:		Any      ,  a__:		Tuple      ,  a__:		int      ,  a__:		Optional[Any]	)  ->		List[Any]:


							'''simple docstring'''





							for attribute in key.split('.'	):
														_UpperCAmelCase					=      getattr(a__      ,  a__	)

							if weight_type is not None:
														_UpperCAmelCase					=      getattr(a__      ,  a__	).shape
							else:
														_UpperCAmelCase					=      hf_pointer.shape

							assert hf_shape == value.shape, (
							    F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
							    F''' {value.shape} for {full_name}'''
							)

							if weight_type == "weight":
														_UpperCAmelCase					=      value
							elif weight_type == "weight_g":
														_UpperCAmelCase					=      value
							elif weight_type == "weight_v":
														_UpperCAmelCase					=      value
							elif weight_type == "bias":
														_UpperCAmelCase					=      value
							else:
														_UpperCAmelCase					=      value

							logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''	)

def 	lowerCAmelCase__			(      a__:		int      ,  a__:		Optional[Any]      ,  a__:		Dict	)  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							_UpperCAmelCase					=      fairseq_model.state_dict()

							_UpperCAmelCase					=      hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														_UpperCAmelCase					=      False
														if "conv_layers" in name:
																					load_conv_layer(
																					    a__      ,  a__      ,  a__      ,  a__      ,  hf_model.config.feat_extract_norm == 'group'      ,  )
																					_UpperCAmelCase					=      True
														else:
																					for key, mapped_key in MAPPING.items():
																												_UpperCAmelCase					=      'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

																												if key in name or key.split('w2v_model.'	)[-1] == name.split('.'	)[0]:
																																			_UpperCAmelCase					=      True
																																			if "*" in mapped_key:
																																										_UpperCAmelCase					=      name.split(a__	)[0].split('.'	)[-2]
																																										_UpperCAmelCase					=      mapped_key.replace('*'      ,  a__	)
																																			if "weight_g" in name:
																																										_UpperCAmelCase					=      'weight_g'
																																			elif "weight_v" in name:
																																										_UpperCAmelCase					=      'weight_v'
																																			elif "weight" in name:
																																										_UpperCAmelCase					=      'weight'
																																			elif "bias" in name:
																																										_UpperCAmelCase					=      'bias'
																																			else:
																																										_UpperCAmelCase					=      None
																																			set_recursively(a__      ,  a__      ,  a__      ,  a__      ,  a__	)
																												continue
														if not is_used:
																					unused_weights.append(a__	)

							logger.warning(F'''Unused weights: {unused_weights}'''	)

def 	lowerCAmelCase__			(      a__:		Optional[int]      ,  a__:		Union[str, Any]      ,  a__:		str      ,  a__:		Dict      ,  a__:		Optional[int]	)  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=      full_name.split('conv_layers.'	)[-1]
							_UpperCAmelCase					=      name.split('.'	)
							_UpperCAmelCase					=      int(items[0]	)
							_UpperCAmelCase					=      int(items[1]	)

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F'''{full_name} has size {value.shape}, but'''
																					    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
																					)
																					_UpperCAmelCase					=      value
																					logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F'''{full_name} has size {value.shape}, but'''
																					    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
																					)
																					_UpperCAmelCase					=      value
																					logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
																					    " found."
																					)
																					_UpperCAmelCase					=      value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F'''{full_name} has size {value.shape}, but'''
																					    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
																					)
																					_UpperCAmelCase					=      value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
							else:
														unused_weights.append(a__	)

def 	lowerCAmelCase__			(      a__:		List[Any]      ,  a__:		Any	)  ->		int:


							'''simple docstring'''





							_UpperCAmelCase					=      SEWConfig()
							if is_finetuned:
														_UpperCAmelCase					=      model.wav_encoder.wav_model.cfg
							else:
														_UpperCAmelCase					=      model.cfg

							_UpperCAmelCase					=      fs_config.conv_bias
							_UpperCAmelCase					=      eval(fs_config.conv_feature_layers	)
							_UpperCAmelCase					=      [x[0] for x in conv_layers]
							_UpperCAmelCase					=      [x[1] for x in conv_layers]
							_UpperCAmelCase					=      [x[2] for x in conv_layers]
							_UpperCAmelCase					=      'gelu'
							_UpperCAmelCase					=      'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
							_UpperCAmelCase					=      0.0
							_UpperCAmelCase					=      fs_config.activation_fn.name
							_UpperCAmelCase					=      fs_config.encoder_embed_dim
							_UpperCAmelCase					=      0.02
							_UpperCAmelCase					=      fs_config.encoder_ffn_embed_dim
							_UpperCAmelCase					=      1e-5
							_UpperCAmelCase					=      fs_config.encoder_layerdrop
							_UpperCAmelCase					=      fs_config.encoder_attention_heads
							_UpperCAmelCase					=      fs_config.conv_pos_groups
							_UpperCAmelCase					=      fs_config.conv_pos
							_UpperCAmelCase					=      len(a__	)
							_UpperCAmelCase					=      fs_config.encoder_layers
							_UpperCAmelCase					=      fs_config.squeeze_factor

							# take care of any params that are overridden by the Wav2VecCtc model
							if is_finetuned:
														_UpperCAmelCase					=      model.cfg
														_UpperCAmelCase					=      fs_config.final_dropout
														_UpperCAmelCase					=      fs_config.layerdrop
							_UpperCAmelCase					=      fs_config.activation_dropout
							_UpperCAmelCase					=      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
							_UpperCAmelCase					=      fs_config.attention_dropout
							_UpperCAmelCase					=      fs_config.dropout_input
							_UpperCAmelCase					=      fs_config.dropout
							_UpperCAmelCase					=      fs_config.mask_channel_length
							_UpperCAmelCase					=      fs_config.mask_channel_prob
							_UpperCAmelCase					=      fs_config.mask_length
							_UpperCAmelCase					=      fs_config.mask_prob

							_UpperCAmelCase					=      'Wav2Vec2FeatureExtractor'
							_UpperCAmelCase					=      'Wav2Vec2CTCTokenizer'

							return config

@torch.no_grad()
def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		List[str]      ,  a__:		Union[str, Any]=None      ,  a__:		Tuple=None      ,  a__:		List[str]=True	)  ->		int:


							'''simple docstring'''





							if is_finetuned:
														_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]      ,  arg_overrides={'data': '/'.join(dict_path.split('/'	)[:-1]	)}	)
							else:
														_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

							if config_path is not None:
														_UpperCAmelCase					=      SEWConfig.from_pretrained(a__	)
							else:
														_UpperCAmelCase					=      convert_config(model[0]      ,  a__	)
							_UpperCAmelCase					=      model[0].eval()

							_UpperCAmelCase					=      True if config.feat_extract_norm == 'layer' else False
							_UpperCAmelCase					=      WavaVecaFeatureExtractor(
							    feature_size=1      ,  sampling_rate=1_6_0_0_0      ,  padding_value=0      ,  do_normalize=a__      ,  return_attention_mask=a__      ,  )

							if is_finetuned:
														if dict_path:
																					_UpperCAmelCase					=      Dictionary.load(a__	)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					_UpperCAmelCase					=      target_dict.pad_index
																					_UpperCAmelCase					=      target_dict.bos_index
																					_UpperCAmelCase					=      target_dict.pad_index
																					_UpperCAmelCase					=      target_dict.bos_index
																					_UpperCAmelCase					=      target_dict.eos_index
																					_UpperCAmelCase					=      len(target_dict.symbols	)
																					_UpperCAmelCase					=      os.path.join(a__      ,  'vocab.json'	)
																					if not os.path.isdir(a__	):
																												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(a__	)	)
																												return
																					os.makedirs(a__      ,  exist_ok=a__	)
																					with open(a__      ,  'w'      ,  encoding='utf-8'	) as vocab_handle:
																												json.dump(target_dict.indices      ,  a__	)
																					_UpperCAmelCase					=      WavaVecaCTCTokenizer(
																					    a__      ,  unk_token=target_dict.unk_word      ,  pad_token=target_dict.pad_word      ,  bos_token=target_dict.bos_word      ,  eos_token=target_dict.eos_word      ,  word_delimiter_token='|'      ,  do_lower_case=a__      ,  )
																					_UpperCAmelCase					=      WavaVecaProcessor(feature_extractor=a__      ,  tokenizer=a__	)
																					processor.save_pretrained(a__	)

														_UpperCAmelCase					=      SEWForCTC(a__	)
							else:
														_UpperCAmelCase					=      SEWModel(a__	)
														feature_extractor.save_pretrained(a__	)

							recursively_load_weights(a__      ,  a__      ,  a__	)

							hf_model.save_pretrained(a__	)


if __name__ == "__main__":
		lowerCAmelCase__  :Optional[int]             =							argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument(
		    '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
		)
		lowerCAmelCase__  :List[str]             =							parser.parse_args()
		convert_sew_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
		)




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__  :Optional[Any]             =							datasets.logging.get_logger(__name__)


lowerCAmelCase__  :str             =							'''\
@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",
}

'''

lowerCAmelCase__  :List[Any]             =							'''\
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.
'''

lowerCAmelCase__  :Optional[int]             =							'''
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 	lowerCAmelCase__			(      a__:		int      ,  a__:		int      ,  a__:		Dict=False      ,  a__:		str=False      ,  a__:		Optional[int]=True      ,  a__:		Any=False      ,  a__:		str="dummy_doc"	)  ->		Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      {doc: key_lines}
							_UpperCAmelCase					=      {doc: sys_lines}

							_UpperCAmelCase					=      {}

							_UpperCAmelCase					=      0
							_UpperCAmelCase					=      0
							_UpperCAmelCase					=      0
							_UpperCAmelCase					=      0
							_UpperCAmelCase					=      0
							_UpperCAmelCase					=      0

							_UpperCAmelCase		,	_UpperCAmelCase					=      reader.get_doc_mentions(a__      ,  key_doc_lines[doc]      ,  a__	)
							key_singletons_num += singletons_num

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

							_UpperCAmelCase		,	_UpperCAmelCase					=      reader.get_doc_mentions(a__      ,  sys_doc_lines[doc]      ,  a__	)
							sys_singletons_num += singletons_num

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

							if remove_nested:
														_UpperCAmelCase		,	_UpperCAmelCase					=      reader.remove_nested_coref_mentions(a__      ,  a__	)
														key_nested_coref_num += nested_mentions
														key_removed_nested_clusters += removed_clusters

														_UpperCAmelCase		,	_UpperCAmelCase					=      reader.remove_nested_coref_mentions(a__      ,  a__	)
														sys_nested_coref_num += nested_mentions
														sys_removed_nested_clusters += removed_clusters

							_UpperCAmelCase					=      reader.get_mention_assignments(a__      ,  a__	)
							_UpperCAmelCase					=      reader.get_mention_assignments(a__      ,  a__	)

							_UpperCAmelCase					=      (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 	lowerCAmelCase__			(      a__:		Any      ,  a__:		List[str]      ,  a__:		List[str]      ,  a__:		Optional[int]      ,  a__:		Optional[Any]      ,  a__:		Any      ,  a__:		Any	)  ->		Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      get_coref_infos(a__      ,  a__      ,  a__      ,  a__      ,  a__      ,  a__	)

							_UpperCAmelCase					=      {}
							_UpperCAmelCase					=      0
							_UpperCAmelCase					=      0

							for name, metric in metrics:
														_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      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:
														_UpperCAmelCase					=      (conll / 3) * 1_0_0
														logger.info(F'''CoNLL score: {conll:.2f}'''	)
														output_scores.update({'conll_score': conll}	)

							return output_scores

def 	lowerCAmelCase__			(      a__:		Union[str, Any]	)  ->		Dict:


							'''simple docstring'''





							_UpperCAmelCase					=      False
							for line in key_lines:
														if not line.startswith('#'	):
																					if len(line.split()	) > 6:
																												_UpperCAmelCase					=      line.split()[5]
																												if not parse_col == "-":
																																			_UpperCAmelCase					=      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						)			->	List[Any]:




									"""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						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      [
									    ('mentions', evaluator.mentions),
									    ('muc', evaluator.muc),
									    ('bcub', evaluator.b_cubed),
									    ('ceafe', evaluator.ceafe),
									    ('lea', evaluator.lea),
									]

									if min_span:
																_UpperCAmelCase					=      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"

									_UpperCAmelCase					=      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
