


import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
						import tensorflow as tf
						from tensorflow.python.framework.errors_impl import ResourceExhaustedError

						from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
						import pyanvml.pyanvml as nvml

snake_case							:       Tuple     			=      logging.get_logger(__name__)






def 							__lowercase   ( __lowerCAmelCase							:				bool					,    __lowerCAmelCase							:				bool   ):
							def run_func(__lowerCAmelCase							:				Tuple   ):
														@wraps(__lowerCAmelCase   )
														def run_in_eager_mode(*__lowerCAmelCase							:				Tuple					,    **__lowerCAmelCase							:				Optional[Any]   ):
																					return func(*__lowerCAmelCase					,    **__lowerCAmelCase   )

														@wraps(__lowerCAmelCase   )
														@tf.function(experimental_compile=__lowerCAmelCase   )
														def run_in_graph_mode(*__lowerCAmelCase							:				Union[str, Any]					,    **__lowerCAmelCase							:				Union[str, Any]   ):
																					return func(*__lowerCAmelCase					,    **__lowerCAmelCase   )

														if do_eager_mode is True:
																					if use_xla is not False:
																												raise ValueError(
																												    'Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'   )
																					return run_in_eager_mode
														else:
																					return run_in_graph_mode

							return run_func






def 							__lowercase   ( __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				int   ):
							a__             =     random.Random()
							a__             =     [rng.randint(0					,    vocab_size - 1   ) for i in range(batch_size * sequence_length   )]
							return tf.constant(__lowerCAmelCase					,    shape=(batch_size, sequence_length)					,    dtype=tf.intaa   )







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    TensorFlowBenchmarkArguments
					UpperCAmelCase__					:    PretrainedConfig
					UpperCAmelCase__					:    str          =			"TensorFlow"
					@property
					def  lowerCamelCase__(      self   :List[str]      )					->    Union[str, Any]:
												return tf.__version__
					def  lowerCamelCase__(      self   :int			,__snake_case   :str			,__snake_case   :int			,__snake_case   :int      )					->    float:
												# initialize GPU on separate process
												a__             =     self.args.strategy
												if strategy is None:
																			raise ValueError('A device strategy has to be initialized before using TensorFlow.'      )
												a__             =     self._prepare_inference_func(__snake_case			,__snake_case			,__snake_case      )
												return self._measure_speed(_inference      )
					def  lowerCamelCase__(      self   :Optional[Any]			,__snake_case   :str			,__snake_case   :int			,__snake_case   :int      )					->    float:
												a__             =     self.args.strategy
												if strategy is None:
																			raise ValueError('A device strategy has to be initialized before using TensorFlow.'      )
												a__             =     self._prepare_train_func(__snake_case			,__snake_case			,__snake_case      )
												return self._measure_speed(_train      )
					def  lowerCamelCase__(      self   :Optional[int]			,__snake_case   :str			,__snake_case   :int			,__snake_case   :int      )					->    [Memory, Optional[MemorySummary]]:
												# initialize GPU on separate process
												if self.args.is_gpu:
																			tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]			,__snake_case      )
												a__             =     self.args.strategy
												if strategy is None:
																			raise ValueError('A device strategy has to be initialized before using TensorFlow.'      )
												a__             =     self._prepare_inference_func(__snake_case			,__snake_case			,__snake_case      )
												return self._measure_memory(_inference      )
					def  lowerCamelCase__(      self   :Optional[Any]			,__snake_case   :str			,__snake_case   :int			,__snake_case   :int      )					->    [Memory, Optional[MemorySummary]]:
												if self.args.is_gpu:
																			tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]			,__snake_case      )
												a__             =     self.args.strategy
												if strategy is None:
																			raise ValueError('A device strategy has to be initialized before using TensorFlow.'      )

												a__             =     self._prepare_train_func(__snake_case			,__snake_case			,__snake_case      )
												return self._measure_memory(_train      )
					def  lowerCamelCase__(      self   :str			,__snake_case   :str			,__snake_case   :int			,__snake_case   :int      )					->    Callable[[], None]:
												a__             =     self.config_dict[model_name]

												if self.args.fpaa:
																			raise NotImplementedError('Mixed precision is currently not supported.'      )

												a__             =     (
												    hasattr(__snake_case			,'architectures'      )
												    and isinstance(config.architectures			,__snake_case      )
												    and len(config.architectures      ) > 0
												)
												if not self.args.only_pretrain_model and has_model_class_in_config:
																			try:
																										a__             =     'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
																										a__             =     __import__('transformers'			,fromlist=[model_class]      )
																										a__             =     getattr(__snake_case			,__snake_case      )
																										a__             =     model_cls(__snake_case      )
																			except ImportError:
																										raise ImportError(
																										    F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
																										    ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'      )
												else:
																			a__             =     TF_MODEL_MAPPING[config.__class__](__snake_case      )

												# encoder-decoder has vocab size saved differently
												a__             =     config.vocab_size if hasattr(__snake_case			,'vocab_size'      ) else config.encoder.vocab_size
												a__             =     random_input_ids(__snake_case			,__snake_case			,__snake_case      )

												@run_with_tf_optimizations(self.args.eager_mode			,self.args.use_xla      )
												def encoder_decoder_forward():
																			return model(__snake_case			,decoder_input_ids=__snake_case			,training=__snake_case      )

												@run_with_tf_optimizations(self.args.eager_mode			,self.args.use_xla      )
												def encoder_forward():
																			return model(__snake_case			,training=__snake_case      )

												a__             =     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

												return _inference
					def  lowerCamelCase__(      self   :Union[str, Any]			,__snake_case   :str			,__snake_case   :int			,__snake_case   :int      )					->    Callable[[], None]:
												a__             =     self.config_dict[model_name]

												if self.args.eager_mode is not False:
																			raise ValueError('Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'      )

												if self.args.fpaa:
																			raise NotImplementedError('Mixed precision is currently not supported.'      )

												a__             =     (
												    hasattr(__snake_case			,'architectures'      )
												    and isinstance(config.architectures			,__snake_case      )
												    and len(config.architectures      ) > 0
												)
												if not self.args.only_pretrain_model and has_model_class_in_config:
																			try:
																										a__             =     'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
																										a__             =     __import__('transformers'			,fromlist=[model_class]      )
																										a__             =     getattr(__snake_case			,__snake_case      )
																										a__             =     model_cls(__snake_case      )
																			except ImportError:
																										raise ImportError(
																										    F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
																										    ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'      )
												else:
																			a__             =     TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__snake_case      )

												# encoder-decoder has vocab size saved differently
												a__             =     config.vocab_size if hasattr(__snake_case			,'vocab_size'      ) else config.encoder.vocab_size
												a__             =     random_input_ids(__snake_case			,__snake_case			,__snake_case      )

												@run_with_tf_optimizations(self.args.eager_mode			,self.args.use_xla      )
												def encoder_decoder_train():
																			a__             =     model(__snake_case			,decoder_input_ids=__snake_case			,labels=__snake_case			,training=__snake_case      )[0]
																			a__             =     tf.gradients(__snake_case			,model.trainable_variables      )
																			return gradients

												@run_with_tf_optimizations(self.args.eager_mode			,self.args.use_xla      )
												def encoder_train():
																			a__             =     model(__snake_case			,labels=__snake_case			,training=__snake_case      )[0]
																			a__             =     tf.gradients(__snake_case			,model.trainable_variables      )
																			return gradients

												a__             =     encoder_decoder_train if config.is_encoder_decoder else encoder_train

												return _train
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :str      )					->    float:
												with self.args.strategy.scope():
																			try:
																										if self.args.is_tpu or self.args.use_xla:
																																	# run additional 10 times to stabilize compilation for tpu
																																	logger.info('Do inference on TPU. Running model 5 times to stabilize compilation'      )
																																	timeit.repeat(__snake_case			,repeat=1			,number=5      )

																										# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
																										a__             =     timeit.repeat(
																										    __snake_case			,repeat=self.args.repeat			,number=10			,)

																										return min(__snake_case      ) / 10.0
																			except ResourceExhaustedError as e:
																										self.print_fn(F'Doesn\'t fit on GPU. {e}'      )



					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :Callable[[], None]      )					->    [Memory, MemorySummary]:
												logger.info(
												    'Note that TensorFlow allocates more memory than '
												    'it might need to speed up computation. '
												    'The memory reported here corresponds to the memory '
												    'reported by `nvidia-smi`, which can vary depending '
												    'on total available memory on the GPU that is used.'      )
												with self.args.strategy.scope():
																			try:
																										if self.args.trace_memory_line_by_line:
																																	if not self.args.eager_mode:
																																								raise ValueError(
																																								    '`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'
																																								    ' consumption line by line.'      )
																																	a__             =     start_memory_tracing('transformers'      )

																										if self.args.is_tpu:
																																	# tpu
																																	raise NotImplementedError(
																																	    'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'
																																	    ' with `args.memory=False`'      )
																										elif self.args.is_gpu:
																																	# gpu
																																	if not is_pyanvml_available():
																																								logger.warning(
																																								    'py3nvml not installed, we won\'t log GPU memory usage. '
																																								    'Install py3nvml (pip install py3nvml) to log information about GPU.'      )
																																								a__             =     'N/A'
																																	else:
																																								logger.info(
																																								    'Measuring total GPU usage on GPU device. Make sure to not have additional processes'
																																								    ' running on the same GPU.'      )
																																								# init nvml
																																								nvml.nvmlInit()
																																								func()
																																								a__             =     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
																																								a__             =     nvml.nvmlDeviceGetMemoryInfo(__snake_case      )
																																								a__             =     meminfo.used
																																								a__             =     Memory(__snake_case      )
																																								# shutdown nvml
																																								nvml.nvmlShutdown()
																										else:
																																	# cpu
																																	if self.args.trace_memory_line_by_line:
																																								logger.info(
																																								    'When enabling line by line tracing, the max peak memory for CPU is inaccurate in'
																																								    ' TensorFlow.'      )
																																								a__             =     None
																																	else:
																																								a__             =     measure_peak_memory_cpu(__snake_case      )
																																								a__             =     Memory(__snake_case      ) if isinstance(__snake_case			,__snake_case      ) else memory_bytes
																										if self.args.trace_memory_line_by_line:
																																	a__             =     stop_memory_tracing(__snake_case      )
																																	if memory is None:
																																								a__             =     summary.total
																										else:
																																	a__             =     None

																										return memory, summary
																			except ResourceExhaustedError as e:
																										self.print_fn(F'Doesn\'t fit on GPU. {e}'      )
																										return "N/A", None








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


snake_case							:       List[Any]     			=      datasets.logging.get_logger(__name__)


snake_case							:       List[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",
}

'''

snake_case							:       Tuple     			=      '''\
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.
'''

snake_case							:       str     			=      '''
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							:				List[Any]					,    __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				Dict=False					,    __lowerCAmelCase							:				List[Any]=False					,    __lowerCAmelCase							:				Optional[Any]=True					,    __lowerCAmelCase							:				Dict=False					,    __lowerCAmelCase							:				Any="dummy_doc"   ):
							a__             =     {doc: key_lines}
							a__             =     {doc: sys_lines}

							a__             =     {}

							a__             =     0
							a__             =     0
							a__             =     0
							a__             =     0
							a__             =     0
							a__             =     0

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

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

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

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

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

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

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

							a__             =     (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							:				Any					,    __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				List[str]					,    __lowerCAmelCase							:				Any   ):
							a__             =     get_coref_infos(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )

							a__             =     {}
							a__             =     0
							a__             =     0

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

							return output_scores






def 							__lowercase   ( __lowerCAmelCase							:				str   ):
							a__             =     False
							for line in key_lines:
														if not line.startswith('#'   ):
																					if len(line.split()   ) > 6:
																												a__             =     line.split()[5]
																												if not parse_col == "-":
																																			a__             =     True
																																			break
																												else:
																																			break
							return has_gold_parse







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,						_KWARGS_DESCRIPTION      )
class        snake_case_   (datasets.Metric      ):
					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[str]:
												return datasets.MetricInfo(
												    description=_DESCRIPTION			,citation=_CITATION			,inputs_description=_KWARGS_DESCRIPTION			,features=datasets.Features(
												        {
												            'predictions': datasets.Sequence(datasets.Value('string'      )      ),
												            'references': datasets.Sequence(datasets.Value('string'      )      ),
												        }      )			,codebase_urls=['https://github.com/ns-moosavi/coval']			,reference_urls=[
												        'https://github.com/ns-moosavi/coval',
												        'https://www.aclweb.org/anthology/P16-1060',
												        'http://www.conll.cemantix.org/2012/data.html',
												    ]			,)



					def  lowerCamelCase__(      self   :List[str]			,__snake_case   :Optional[int]			,__snake_case   :int			,__snake_case   :Dict=True			,__snake_case   :Union[str, Any]=False			,__snake_case   :Dict=False			,__snake_case   :Union[str, Any]=False      )					->    Tuple:
												a__             =     [
												    ('mentions', evaluator.mentions),
												    ('muc', evaluator.muc),
												    ('bcub', evaluator.b_cubed),
												    ('ceafe', evaluator.ceafe),
												    ('lea', evaluator.lea),
												]

												if min_span:
																			a__             =     util.check_gold_parse_annotation(__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"

												a__             =     evaluate(
												    key_lines=__snake_case			,sys_lines=__snake_case			,metrics=__snake_case			,NP_only=__snake_case			,remove_nested=__snake_case			,keep_singletons=__snake_case			,min_span=__snake_case			,)

												return score




