
"""simple docstring"""


import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  torch.exp(lowerCAmelCase__						)
			lowerCamelCase_					=  torch.sum(lowerCAmelCase__	,dim=1						)  # sum of exp(x_i)
			lowerCamelCase_					=  torch.sum(x * exp_x	,dim=1						)  # sum of x_i * exp(x_i)
			return torch.log(lowerCAmelCase__						) - B / A




class  __lowerCamelCase  ( nn.Module			):




				def __init__(				self							,						UpperCAmelCase   ):
							super().__init__()
							lowerCamelCase_					=  config.output_attentions
							lowerCamelCase_					=  config.output_hidden_states
							lowerCamelCase_					=  nn.ModuleList([BertLayer(UpperCAmelCase   ) for _ in range(config.num_hidden_layers   )]   )
							lowerCamelCase_					=  nn.ModuleList([BertHighway(UpperCAmelCase   ) for _ in range(config.num_hidden_layers   )]   )

							lowerCamelCase_					=  [-1 for _ in range(config.num_hidden_layers   )]




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							if (type(UpperCAmelCase   ) is float) or (type(UpperCAmelCase   ) is int):
										for i in range(len(self.early_exit_entropy   )   ):
													lowerCamelCase_					=  x
							else:
										lowerCamelCase_					=  x




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  pooler.state_dict()
							for highway in self.highway:
										for name, param in highway.pooler.state_dict().items():
													param.copy_(loaded_model[name]   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						):
							lowerCamelCase_					=  ()
							lowerCamelCase_					=  ()
							lowerCamelCase_					=  ()
							for i, layer_module in enumerate(self.layer   ):
										if self.output_hidden_states:
													lowerCamelCase_					=  all_hidden_states + (hidden_states,)

										lowerCamelCase_					=  layer_module(
										    UpperCAmelCase							,						UpperCAmelCase							,						head_mask[i]							,						UpperCAmelCase							,						UpperCAmelCase   )
										lowerCamelCase_					=  layer_outputs[0]

										if self.output_attentions:
													lowerCamelCase_					=  all_attentions + (layer_outputs[1],)

										lowerCamelCase_					=  (hidden_states,)
										if self.output_hidden_states:
													lowerCamelCase_					=  current_outputs + (all_hidden_states,)
										if self.output_attentions:
													lowerCamelCase_					=  current_outputs + (all_attentions,)

										lowerCamelCase_					=  self.highway[i](UpperCAmelCase   )
										# logits, pooled_output

										if not self.training:
													lowerCamelCase_					=  highway_exit[0]
													lowerCamelCase_					=  entropy(UpperCAmelCase   )
													lowerCamelCase_					=  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
													lowerCamelCase_					=  all_highway_exits + (highway_exit,)

													if highway_entropy < self.early_exit_entropy[i]:
																lowerCamelCase_					=  (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																raise HighwayException(UpperCAmelCase							,						i + 1   )
										else:
													lowerCamelCase_					=  all_highway_exits + (highway_exit,)

        # Add last layer
							if self.output_hidden_states:
										lowerCamelCase_					=  all_hidden_states + (hidden_states,)

							lowerCamelCase_					=  (hidden_states,)
							if self.output_hidden_states:
										lowerCamelCase_					=  outputs + (all_hidden_states,)
							if self.output_attentions:
										lowerCamelCase_					=  outputs + (all_attentions,)

							lowerCamelCase_					=  outputs + (all_highway_exits,)
							return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits




@add_start_docstrings(
    'The Bert Model transformer with early exiting (DeeBERT). '						,	lowerCAmelCase						,	)
class  __lowerCamelCase  ( lowerCAmelCase			):




				def __init__(				self							,						UpperCAmelCase   ):
							super().__init__(UpperCAmelCase   )
							lowerCamelCase_					=  config

							lowerCamelCase_					=  BertEmbeddings(UpperCAmelCase   )
							lowerCamelCase_					=  DeeBertEncoder(UpperCAmelCase   )
							lowerCamelCase_					=  BertPooler(UpperCAmelCase   )

							self.init_weights()




				def        UpperCAmelCase__						(				self   ):
							self.encoder.init_highway_pooler(self.pooler   )




				def        UpperCAmelCase__						(				self   ):
							return self.embeddings.word_embeddings




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  value




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							for layer, heads in heads_to_prune.items():
										self.encoder.layer[layer].attention.prune_heads(UpperCAmelCase   )





				@add_start_docstrings_to_model_forward(UpperCAmelCase   )
				def        UpperCAmelCase__						(				self							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						):
							if input_ids is not None and inputs_embeds is not None:
										raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''   )
							elif input_ids is not None:
										lowerCamelCase_					=  input_ids.size()
							elif inputs_embeds is not None:
										lowerCamelCase_					=  inputs_embeds.size()[:-1]
							else:
										raise ValueError('''You have to specify either input_ids or inputs_embeds'''   )

							lowerCamelCase_					=  input_ids.device if input_ids is not None else inputs_embeds.device

							if attention_mask is None:
										lowerCamelCase_					=  torch.ones(UpperCAmelCase							,						device=UpperCAmelCase   )
							if encoder_attention_mask is None:
										lowerCamelCase_					=  torch.ones(UpperCAmelCase							,						device=UpperCAmelCase   )
							if token_type_ids is None:
										lowerCamelCase_					=  torch.zeros(UpperCAmelCase							,						dtype=torch.long							,						device=UpperCAmelCase   )

							# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
							# ourselves in which case we just need to make it broadcastable to all heads.
							lowerCamelCase_					=  self.get_extended_attention_mask(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )

							# If a 2D ou 3D attention mask is provided for the cross-attention
							# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
							if encoder_attention_mask.dim() == 3:
										lowerCamelCase_					=  encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
										lowerCamelCase_					=  encoder_attention_mask[:, None, None, :]

							lowerCamelCase_					=  encoder_extended_attention_mask.to(
							    dtype=next(self.parameters()   ).dtype   )  # fp16 compatibility
							lowerCamelCase_					=  (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

							# Prepare head mask if needed
							# 1.0 in head_mask indicate we keep the head
							# attention_probs has shape bsz x n_heads x N x N
							# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
							# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
							lowerCamelCase_					=  self.get_head_mask(UpperCAmelCase							,						self.config.num_hidden_layers   )

							lowerCamelCase_					=  self.embeddings(
							    input_ids=UpperCAmelCase							,						position_ids=UpperCAmelCase							,						token_type_ids=UpperCAmelCase							,						inputs_embeds=UpperCAmelCase   )
							lowerCamelCase_					=  self.encoder(
							    UpperCAmelCase							,						attention_mask=UpperCAmelCase							,						head_mask=UpperCAmelCase							,						encoder_hidden_states=UpperCAmelCase							,						encoder_attention_mask=UpperCAmelCase							,						)
							lowerCamelCase_					=  encoder_outputs[0]
							lowerCamelCase_					=  self.pooler(UpperCAmelCase   )

							lowerCamelCase_					=  (
							    sequence_output,
							    pooled_output,
							) + encoder_outputs[
							    1:
							]  # add hidden_states and attentions if they are here
							return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits




class  __lowerCamelCase  ( lowerCAmelCase			):




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  message
							lowerCamelCase_					=  exit_layer  # start from 1!




class  __lowerCamelCase  ( nn.Module			):




				def __init__(				self							,						UpperCAmelCase   ):
							super().__init__()
							lowerCamelCase_					=  BertPooler(UpperCAmelCase   )
							lowerCamelCase_					=  nn.Dropout(config.hidden_dropout_prob   )
							lowerCamelCase_					=  nn.Linear(config.hidden_size							,						config.num_labels   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							# Pooler
							lowerCamelCase_					=  encoder_outputs[0]
							lowerCamelCase_					=  self.pooler(UpperCAmelCase   )
							# "return" pooler_output

							# BertModel
							lowerCamelCase_					=  (pooler_input, pooler_output) + encoder_outputs[1:]
							# "return" bmodel_output

							# Dropout and classification
							lowerCamelCase_					=  bmodel_output[1]

							lowerCamelCase_					=  self.dropout(UpperCAmelCase   )
							lowerCamelCase_					=  self.classifier(UpperCAmelCase   )

							return logits, pooled_output




@add_start_docstrings(
    'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. '						,	lowerCAmelCase						,	)
class  __lowerCamelCase  ( lowerCAmelCase			):




				def __init__(				self							,						UpperCAmelCase   ):
							super().__init__(UpperCAmelCase   )
							lowerCamelCase_					=  config.num_labels
							lowerCamelCase_					=  config.num_hidden_layers

							lowerCamelCase_					=  DeeBertModel(UpperCAmelCase   )
							lowerCamelCase_					=  nn.Dropout(config.hidden_dropout_prob   )
							lowerCamelCase_					=  nn.Linear(config.hidden_size							,						self.config.num_labels   )

							self.init_weights()





				@add_start_docstrings_to_model_forward(UpperCAmelCase   )
				def        UpperCAmelCase__						(				self							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=-1							,						UpperCAmelCase=False							,						):
							lowerCamelCase_					=  self.num_layers
							try:
										lowerCamelCase_					=  self.bert(
										    UpperCAmelCase							,						attention_mask=UpperCAmelCase							,						token_type_ids=UpperCAmelCase							,						position_ids=UpperCAmelCase							,						head_mask=UpperCAmelCase							,						inputs_embeds=UpperCAmelCase							,						)
										# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

										lowerCamelCase_					=  outputs[1]

										lowerCamelCase_					=  self.dropout(UpperCAmelCase   )
										lowerCamelCase_					=  self.classifier(UpperCAmelCase   )
										lowerCamelCase_					=  (logits,) + outputs[2:]  # add hidden states and attention if they are here
							except HighwayException as e:
										lowerCamelCase_					=  e.message
										lowerCamelCase_					=  e.exit_layer
										lowerCamelCase_					=  outputs[0]

							if not self.training:
										lowerCamelCase_					=  entropy(UpperCAmelCase   )
										lowerCamelCase_					=  []
										lowerCamelCase_					=  []
							if labels is not None:
										if self.num_labels == 1:
													#  We are doing regression
													lowerCamelCase_					=  MSELoss()
													lowerCamelCase_					=  loss_fct(logits.view(-1   )							,						labels.view(-1   )   )
										else:
													lowerCamelCase_					=  CrossEntropyLoss()
													lowerCamelCase_					=  loss_fct(logits.view(-1							,						self.num_labels   )							,						labels.view(-1   )   )

										# work with highway exits
										lowerCamelCase_					=  []
										for highway_exit in outputs[-1]:
													lowerCamelCase_					=  highway_exit[0]
													if not self.training:
																highway_logits_all.append(UpperCAmelCase   )
																highway_entropy.append(highway_exit[2]   )
													if self.num_labels == 1:
																#  We are doing regression
																lowerCamelCase_					=  MSELoss()
																lowerCamelCase_					=  loss_fct(highway_logits.view(-1   )							,						labels.view(-1   )   )
													else:
																lowerCamelCase_					=  CrossEntropyLoss()
																lowerCamelCase_					=  loss_fct(highway_logits.view(-1							,						self.num_labels   )							,						labels.view(-1   )   )
													highway_losses.append(UpperCAmelCase   )

										if train_highway:
													lowerCamelCase_					=  (sum(highway_losses[:-1]   ),) + outputs
													# exclude the final highway, of course
										else:
													lowerCamelCase_					=  (loss,) + outputs
							if not self.training:
										lowerCamelCase_					=  outputs + ((original_entropy, highway_entropy), exit_layer)
										if output_layer >= 0:
													lowerCamelCase_					=  (
													    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
													)  # use the highway of the last layer

							return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)





"""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


A_    							=					datasets.logging.get_logger(__name__)


A_    							=					"""\
@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\",
}

"""

A_    							=					"""\
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.
"""

A_    							=					"""
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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=False	,lowerCAmelCase__=False	,lowerCAmelCase__=True	,lowerCAmelCase__=False	,lowerCAmelCase__="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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			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  lowercase						(	lowerCAmelCase__						):
			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  __lowerCamelCase  ( datasets.Metric			):




				def        UpperCAmelCase__						(				self   ):
							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							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase=True							,						UpperCAmelCase=False							,						UpperCAmelCase=False							,						UpperCAmelCase=False   ):
							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(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"

							lowerCamelCase_					=  evaluate(
							    key_lines=UpperCAmelCase							,						sys_lines=UpperCAmelCase							,						metrics=UpperCAmelCase							,						NP_only=UpperCAmelCase							,						remove_nested=UpperCAmelCase							,						keep_singletons=UpperCAmelCase							,						min_span=UpperCAmelCase							,						)

							return score



