

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowerCamelCase    :       Union[str, Any]        =							1_6
_lowerCamelCase    :       int        =							3_2

def       a__					(		UpperCAmelCase							:      Accelerator    ,			UpperCAmelCase							:      int = 16      )  ->						str:
  UpperCAmelCase	:							int						=			AutoTokenizer.from_pretrained('''bert-base-cased'''      )
  UpperCAmelCase	:							List[str]						=			load_dataset('''glue'''    ,			'''mrpc'''      )

  def tokenize_function(UpperCAmelCase							:      Any      ):
    # max_length=None => use the model max length (it's actually the default)
    UpperCAmelCase	:							Optional[Any]						=			tokenizer(examples['''sentence1''']    ,			examples['''sentence2''']    ,			truncation=UpperCAmelCase    ,			max_length=UpperCAmelCase      )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    UpperCAmelCase	:							Dict						=			datasets.map(
        UpperCAmelCase    ,			batched=UpperCAmelCase    ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']    ,			)

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  UpperCAmelCase	:							Dict						=			tokenized_datasets.rename_column('''label'''    ,			'''labels'''      )

  def collate_fn(UpperCAmelCase							:      Optional[int]      ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    UpperCAmelCase	:							Any						=			128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      UpperCAmelCase	:							str						=			16
    elif accelerator.mixed_precision != "no":
      UpperCAmelCase	:							str						=			8
    else:
      UpperCAmelCase	:							List[str]						=			None

    return tokenizer.pad(
        UpperCAmelCase    ,			padding='''longest'''    ,			max_length=UpperCAmelCase    ,			pad_to_multiple_of=UpperCAmelCase    ,			return_tensors='''pt'''    ,			)

  # Instantiate dataloaders.
  UpperCAmelCase	:							List[Any]						=			DataLoader(
      tokenized_datasets['''train''']    ,			shuffle=UpperCAmelCase    ,			collate_fn=UpperCAmelCase    ,			batch_size=UpperCAmelCase      )
  UpperCAmelCase	:							List[Any]						=			DataLoader(
      tokenized_datasets['''validation''']    ,			shuffle=UpperCAmelCase    ,			collate_fn=UpperCAmelCase    ,			batch_size=UpperCAmelCase      )

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     _lowerCamelCase    :       Any        =							mocked_dataloaders  # noqa: F811

def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      Optional[int]      )  ->						Tuple:
  # For testing only
  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,			UpperCAmelCase      ) == "1":
    UpperCAmelCase	:							int						=			2
  # Initialize accelerator
  UpperCAmelCase	:							Optional[Any]						=			Accelerator(cpu=args.cpu    ,			mixed_precision=args.mixed_precision      )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  UpperCAmelCase	:							List[str]						=			config['''lr''']
  UpperCAmelCase	:							Union[str, Any]						=			int(config['''num_epochs''']      )
  UpperCAmelCase	:							Tuple						=			int(config['''seed''']      )
  UpperCAmelCase	:							Tuple						=			int(config['''batch_size''']      )

  UpperCAmelCase	:							int						=			evaluate.load('''glue'''    ,			'''mrpc'''      )

  # If the batch size is too big we use gradient accumulation
  UpperCAmelCase	:							List[str]						=			1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    UpperCAmelCase	:							Any						=			batch_size // MAX_GPU_BATCH_SIZE
    UpperCAmelCase	:							Dict						=			MAX_GPU_BATCH_SIZE

  set_seed(UpperCAmelCase      )
  UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			get_dataloaders(UpperCAmelCase    ,			UpperCAmelCase      )
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  UpperCAmelCase	:							Any						=			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''    ,			return_dict=UpperCAmelCase      )

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  UpperCAmelCase	:							Dict						=			model.to(accelerator.device      )

  # Instantiate optimizer
  UpperCAmelCase	:							List[str]						=			AdamW(params=model.parameters()    ,			lr=UpperCAmelCase      )

  # Instantiate scheduler
  UpperCAmelCase	:							int						=			get_linear_schedule_with_warmup(
      optimizer=UpperCAmelCase    ,			num_warmup_steps=100    ,			num_training_steps=(len(UpperCAmelCase      ) * num_epochs) // gradient_accumulation_steps    ,			)

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			accelerator.prepare(
      UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )

  # Now we train the model
  for epoch in range(UpperCAmelCase      ):
    model.train()
    for step, batch in enumerate(UpperCAmelCase      ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device      )
      UpperCAmelCase	:							int						=			model(**UpperCAmelCase      )
      UpperCAmelCase	:							List[Any]						=			outputs.loss
      UpperCAmelCase	:							Any						=			loss / gradient_accumulation_steps
      accelerator.backward(UpperCAmelCase      )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    UpperCAmelCase	:							List[str]						=			0
    for step, batch in enumerate(UpperCAmelCase      ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device      )
      with torch.no_grad():
        UpperCAmelCase	:							List[str]						=			model(**UpperCAmelCase      )
      UpperCAmelCase	:							Union[str, Any]						=			outputs.logits.argmax(dim=-1      )
      UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			accelerator.gather((predictions, batch['''labels'''])      )
      # New Code #
      # First we check if it's a distributed system
      if accelerator.use_distributed:
        # Then see if we're on the last batch of our eval dataloader
        if step == len(UpperCAmelCase      ) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          UpperCAmelCase	:							str						=			predictions[: len(eval_dataloader.dataset      ) - samples_seen]
          UpperCAmelCase	:							Union[str, Any]						=			references[: len(eval_dataloader.dataset      ) - samples_seen]
        else:
          # Otherwise we add the number of samples seen
          samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
      metric.add_batch(
          predictions=UpperCAmelCase    ,			references=UpperCAmelCase    ,			)

    UpperCAmelCase	:							List[Any]						=			metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''    ,			UpperCAmelCase      )

def       a__					(		)  ->						List[Any]:
  UpperCAmelCase	:							str						=			argparse.ArgumentParser(description='''Simple example of training script.'''      )
  parser.add_argument(
      '''--mixed_precision'''    ,			type=UpperCAmelCase    ,			default=UpperCAmelCase    ,			choices=['''no''', '''fp16''', '''bf16''', '''fp8''']    ,			help='''Whether to use mixed precision. Choose'''
      '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
      '''and an Nvidia Ampere GPU.'''    ,			)
  parser.add_argument('''--cpu'''    ,			action='''store_true'''    ,			help='''If passed, will train on the CPU.'''      )
  UpperCAmelCase	:							str						=			parser.parse_args()
  UpperCAmelCase	:							Optional[int]						=			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(UpperCAmelCase    ,			UpperCAmelCase      )


if __name__ == "__main__":
     main()








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    :       int        =							datasets.logging.get_logger(__name__)


_lowerCamelCase    :       Any        =							"\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",\n    author = \"Moosavi, Nafise Sadat  and\n      Strube, Michael\",\n    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = aug,\n    year = \"2016\",\n    address = \"Berlin, Germany\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P16-1060\",\n    doi = \"10.18653/v1/P16-1060\",\n    pages = \"632--642\",\n}\n\n"

_lowerCamelCase    :       Tuple        =							"\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	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.\n5	Part-of-Speech\n6	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.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n"

_lowerCamelCase    :       List[str]        =							"\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting 'keep_singletons=False', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    'mentions': mentions\n    'muc': MUC metric [Vilain et al, 1995]\n    'bcub': B-cubed [Bagga and Baldwin, 1998]\n    'ceafe': CEAFe [Luo et al., 2005]\n    'lea': LEA [Moosavi and Strube, 2016]\n    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric('coval')\n    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',\n    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',\n    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',\n    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',\n    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',\n    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}\n"

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      str    ,			UpperCAmelCase							:      Union[str, Any]=False    ,			UpperCAmelCase							:      Optional[Any]=False    ,			UpperCAmelCase							:      Optional[Any]=True    ,			UpperCAmelCase							:      List[str]=False    ,			UpperCAmelCase							:      List[str]="dummy_doc"      )  ->						Any:
  UpperCAmelCase	:							Dict						=			{doc: key_lines}
  UpperCAmelCase	:							Any						=			{doc: sys_lines}

  UpperCAmelCase	:							int						=			{}

  UpperCAmelCase	:							List[Any]						=			0
  UpperCAmelCase	:							Any						=			0
  UpperCAmelCase	:							Optional[int]						=			0
  UpperCAmelCase	:							Optional[Any]						=			0
  UpperCAmelCase	:							int						=			0
  UpperCAmelCase	:							List[str]						=			0

  UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			reader.get_doc_mentions(UpperCAmelCase    ,			key_doc_lines[doc]    ,			UpperCAmelCase      )
  key_singletons_num += singletons_num

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

  UpperCAmelCase			,						UpperCAmelCase	:							str						=			reader.get_doc_mentions(UpperCAmelCase    ,			sys_doc_lines[doc]    ,			UpperCAmelCase      )
  sys_singletons_num += singletons_num

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

  if remove_nested:
    UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			reader.remove_nested_coref_mentions(UpperCAmelCase    ,			UpperCAmelCase      )
    key_nested_coref_num += nested_mentions
    key_removed_nested_clusters += removed_clusters

    UpperCAmelCase			,						UpperCAmelCase	:							List[str]						=			reader.remove_nested_coref_mentions(UpperCAmelCase    ,			UpperCAmelCase      )
    sys_nested_coref_num += nested_mentions
    sys_removed_nested_clusters += removed_clusters

  UpperCAmelCase	:							Union[str, Any]						=			reader.get_mention_assignments(UpperCAmelCase    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Optional[int]						=			reader.get_mention_assignments(UpperCAmelCase    ,			UpperCAmelCase      )

  UpperCAmelCase	:							str						=			(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

  if remove_nested:
    logger.info(
        '''Number of removed nested coreferring mentions in the key '''
        f'''annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}'''      )
    logger.info(
        '''Number of resulting singleton clusters in the key '''
        f'''annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}'''      )

  if not keep_singletons:
    logger.info(
        f'''{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '''
        '''files, respectively'''      )

  return doc_coref_infos

def       a__					(		UpperCAmelCase							:      Tuple    ,			UpperCAmelCase							:      Optional[Any]    ,			UpperCAmelCase							:      Dict    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      Tuple    ,			UpperCAmelCase							:      int    ,			UpperCAmelCase							:      Optional[Any]      )  ->						Union[str, Any]:
  UpperCAmelCase	:							Optional[int]						=			get_coref_infos(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )

  UpperCAmelCase	:							Dict						=			{}
  UpperCAmelCase	:							Dict						=			0
  UpperCAmelCase	:							Tuple						=			0

  for name, metric in metrics:
    UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							List[str]						=			evaluator.evaluate_documents(UpperCAmelCase    ,			UpperCAmelCase    ,			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:
    UpperCAmelCase	:							Optional[int]						=			(conll / 3) * 100
    logger.info(f'''CoNLL score: {conll:.2f}'''      )
    output_scores.update({'''conll_score''': conll}      )

  return output_scores

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






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,  _KWARGS_DESCRIPTION						)
class  __UpperCAmelCase					(	datasets.Metric						):



   def 	__magic_name__   (							self							:					Union[str, 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 	__magic_name__   (							self							:					str,		__A							:					Any,		__A							:					Dict,		__A							:					List[str]=True,		__A							:					Optional[Any]=False,		__A							:					List[Any]=False,		__A							:					Tuple=False						):
     UpperCAmelCase	:							Optional[Any]						=			[
         ('''mentions''', evaluator.mentions),
         ('''muc''', evaluator.muc),
         ('''bcub''', evaluator.b_cubed),
         ('''ceafe''', evaluator.ceafe),
         ('''lea''', evaluator.lea),
     ]

     if min_span:
       UpperCAmelCase	:							List[Any]						=			util.check_gold_parse_annotation(__A						)
       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	:							Union[str, Any]						=			evaluate(
         key_lines=__A,		sys_lines=__A,		metrics=__A,		NP_only=__A,		remove_nested=__A,		keep_singletons=__A,		min_span=__A,		)

     return score





