
"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				**snake_case_				)       ->  Union[str, Any]:
													__lowerCAmelCase     =  feature_size
													__lowerCAmelCase     =  sampling_rate
													__lowerCAmelCase     =  padding_value

													__lowerCAmelCase     =  kwargs.pop("""padding_side"""    ,				"""right"""				)
													__lowerCAmelCase     =  kwargs.pop("""return_attention_mask"""    ,				snake_case_				)

													super().__init__(**snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_ = True    ,				snake_case_ = None    ,				snake_case_ = False    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				)       ->  BatchFeature:
													# If we have a list of dicts, let's convert it in a dict of lists
													# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
													if isinstance(snake_case_    ,				(list, tuple)				) and isinstance(processed_features[0]    ,				(dict, BatchFeature)				):
																			__lowerCAmelCase     =  {
																			    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
																			}

													# The model's main input name, usually `input_values`, has be passed for padding
													if self.model_input_names[0] not in processed_features:
																			raise ValueError(
																			    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
																			    f""" to this method that includes {self.model_input_names[0]}, but you provided"""
																			    f""" {list(processed_features.keys()				)}"""				)

													__lowerCAmelCase     =  processed_features[self.model_input_names[0]]
													__lowerCAmelCase     =  (
													    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
													)

													if len(snake_case_				) == 0:
																			if return_attention_mask:
																									__lowerCAmelCase     =  []
																			return processed_features

													# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
													# and rebuild them afterwards if no return_tensors is specified
													# Note that we lose the specific device the tensor may be on for PyTorch

													__lowerCAmelCase     =  required_input[0]
													if isinstance(snake_case_    ,				(list, tuple)				):
																			# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
																			__lowerCAmelCase     =  0
																			while len(required_input[index]				) == 0:
																									index += 1
																			if index < len(snake_case_				):
																									__lowerCAmelCase     =  required_input[index][0]

													if return_tensors is None:
																			if is_tf_tensor(snake_case_				):
																									__lowerCAmelCase     =  """tf"""
																			elif is_torch_tensor(snake_case_				):
																									__lowerCAmelCase     =  """pt"""
																			elif isinstance(snake_case_    ,				(int, float, list, tuple, np.ndarray)				):
																									__lowerCAmelCase     =  """np"""
																			else:
																									raise ValueError(
																									    f"""type of {first_element} unknown: {type(snake_case_				)}. """
																									    """Should be one of a python, numpy, pytorch or tensorflow object."""				)

													for key, value in processed_features.items():
																			if isinstance(value[0]    ,				(int, float)				):
																									__lowerCAmelCase     =  to_numpy(snake_case_				)
																			else:
																									__lowerCAmelCase     =  [to_numpy(snake_case_				) for v in value]

        # Convert padding_strategy in PaddingStrategy
													__lowerCAmelCase     =  self._get_padding_strategies(padding=snake_case_    ,				max_length=snake_case_				)

													__lowerCAmelCase     =  processed_features[self.model_input_names[0]]

													__lowerCAmelCase     =  len(snake_case_				)
													if not all(len(snake_case_				) == batch_size for v in processed_features.values()				):
																			raise ValueError("""Some items in the output dictionary have a different batch size than others."""				)

													__lowerCAmelCase     =  []
													for i in range(snake_case_				):
																			__lowerCAmelCase     =  {k: v[i] for k, v in processed_features.items()}
																			# truncation
																			__lowerCAmelCase     =  self._truncate(
																			    snake_case_    ,				max_length=snake_case_    ,				pad_to_multiple_of=snake_case_    ,				truncation=snake_case_    ,				)
																			truncated_inputs.append(snake_case_				)

													if padding_strategy == PaddingStrategy.LONGEST:
																			# make sure that `max_length` cannot be longer than the longest truncated length
																			__lowerCAmelCase     =  max(len(input_slice[self.model_input_names[0]]				) for input_slice in truncated_inputs				)
																			__lowerCAmelCase     =  PaddingStrategy.MAX_LENGTH

													__lowerCAmelCase     =  {}
													for i in range(snake_case_				):
																			# padding
																			__lowerCAmelCase     =  self._pad(
																			    truncated_inputs[i]    ,				max_length=snake_case_    ,				padding_strategy=snake_case_    ,				pad_to_multiple_of=snake_case_    ,				return_attention_mask=snake_case_    ,				)

																			for key, value in outputs.items():
																									if key not in batch_outputs:
																															__lowerCAmelCase     =  []
																									if value.dtype is np.dtype(np.floataa				):
																															__lowerCAmelCase     =  value.astype(np.floataa				)
																									batch_outputs[key].append(snake_case_				)

													return BatchFeature(snake_case_    ,				tensor_type=snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None    ,				snake_case_ = PaddingStrategy.DO_NOT_PAD    ,				snake_case_ = None    ,				snake_case_ = None    ,				)       ->  dict:
													__lowerCAmelCase     =  processed_features[self.model_input_names[0]]

													if padding_strategy == PaddingStrategy.LONGEST:
																			__lowerCAmelCase     =  len(snake_case_				)

													if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																			__lowerCAmelCase     =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

													__lowerCAmelCase     =  padding_strategy != PaddingStrategy.DO_NOT_PAD and len(snake_case_				) < max_length

													if return_attention_mask and "attention_mask" not in processed_features:
																			__lowerCAmelCase     =  np.ones(len(snake_case_				)    ,				dtype=np.intaa				)

													if needs_to_be_padded:
																			__lowerCAmelCase     =  max_length - len(snake_case_				)
																			if self.padding_side == "right":
																									if return_attention_mask:
																															__lowerCAmelCase     =  np.pad(
																															    processed_features["""attention_mask"""]    ,				(0, difference)				)
																									__lowerCAmelCase     =  ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																									__lowerCAmelCase     =  np.pad(
																									    snake_case_    ,				snake_case_    ,				"""constant"""    ,				constant_values=self.padding_value				)
																			elif self.padding_side == "left":
																									if return_attention_mask:
																															__lowerCAmelCase     =  np.pad(
																															    processed_features["""attention_mask"""]    ,				(difference, 0)				)
																									__lowerCAmelCase     =  ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																									__lowerCAmelCase     =  np.pad(
																									    snake_case_    ,				snake_case_    ,				"""constant"""    ,				constant_values=self.padding_value				)
																			else:
																									raise ValueError("""Invalid padding strategy:""" + str(self.padding_side				)				)

													return processed_features




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				)       ->  Optional[Any]:
													if not truncation:
																			return processed_features
													elif truncation and max_length is None:
																			raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""				)

													__lowerCAmelCase     =  processed_features[self.model_input_names[0]]

													# find `max_length` that fits `pad_to_multiple_of`
													if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																			__lowerCAmelCase     =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

													__lowerCAmelCase     =  len(snake_case_				) > max_length

													if needs_to_be_truncated:
																			__lowerCAmelCase     =  processed_features[self.model_input_names[0]][:max_length]
																			if "attention_mask" in processed_features:
																									__lowerCAmelCase     =  processed_features["""attention_mask"""][:max_length]

													return processed_features




							def    A__						( self    ,				snake_case_=False    ,				snake_case_=None				)       ->  str:

													# Get padding strategy
													if padding is not False:
																			if padding is True:
																									__lowerCAmelCase     =  PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																			elif not isinstance(snake_case_    ,				snake_case_				):
																									__lowerCAmelCase     =  PaddingStrategy(snake_case_				)
																			elif isinstance(snake_case_    ,				snake_case_				):
																									__lowerCAmelCase     =  padding
													else:
																			__lowerCAmelCase     =  PaddingStrategy.DO_NOT_PAD

													# Set max length if needed
													if max_length is None:
																			if padding_strategy == PaddingStrategy.MAX_LENGTH:
																									raise ValueError(
																									    f"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""				)

        # Test if we have a padding value
													if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
																			raise ValueError(
																			    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
																			    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""				)

													return padding_strategy


"""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


SCREAMING_SNAKE_CASE_					     =	datasets.logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_					     =	'''\
@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",
}

'''

SCREAMING_SNAKE_CASE_					     =	'''\
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.
'''

SCREAMING_SNAKE_CASE_					     =	'''
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 ):



							'''simple docstring'''





							def    A__						( self				)       ->  Any:
													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    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_=True    ,				snake_case_=False    ,				snake_case_=False    ,				snake_case_=False				)       ->  str:
													__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(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"

													__lowerCAmelCase     =  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
