
'''simple docstring'''

import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator







def 	A  (   UpperCamelCase_       :  Dict   )       ->							Tuple:







			'''simple docstring'''



			lowerCAmelCase__										=				fname.split(os.path.sep   )[-1]
			return re.search(r"^(.*)_\d+\.jpg$"    ,						UpperCamelCase_   ).groups()[0]






class  A  (						SCREAMING_SNAKE_CASE__     ):





	def __init__(       self      :							List[Any]   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Any=None   ,				__magic_name__      :							List[Any]=None				):


				"""simple docstring"""

				lowerCAmelCase__										=				file_names
				lowerCAmelCase__										=				image_transform
				lowerCAmelCase__										=				label_to_id





	def __len__(       self      :							Any				):


				"""simple docstring"""

				return len(self.file_names				)







	def __getitem__(       self      :							List[Any]   ,				__magic_name__      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.file_names[idx]
				lowerCAmelCase__										=				PIL.Image.open(__magic_name__				)
				lowerCAmelCase__										=				raw_image.convert("RGB"				)
				if self.image_transform is not None:
							lowerCAmelCase__										=				self.image_transform(__magic_name__				)
				lowerCAmelCase__										=				extract_label(__magic_name__				)
				if self.label_to_id is not None:
							lowerCAmelCase__										=				self.label_to_id[label]
				return {"image": image, "label": label}







def 	A  (   UpperCamelCase_       :  Tuple    ,						UpperCamelCase_       :  List[str]   )       ->							List[str]:







			'''simple docstring'''



			if args.with_tracking:
						lowerCAmelCase__										=				Accelerator(
						    cpu=args.cpu    ,						mixed_precision=args.mixed_precision    ,						log_with="all"    ,						project_dir=args.project_dir   )
			else:
						lowerCAmelCase__										=				Accelerator(cpu=args.cpu    ,						mixed_precision=args.mixed_precision   )

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCAmelCase__										=				config["lr"]
			lowerCAmelCase__										=				int(config["num_epochs"]   )
			lowerCAmelCase__										=				int(config["seed"]   )
			lowerCAmelCase__										=				int(config["batch_size"]   )
			lowerCAmelCase__										=				config["image_size"]
			if not isinstance(UpperCamelCase_    ,						(list, tuple)   ):
						lowerCAmelCase__										=				(image_size, image_size)

			# Parse out whether we are saving every epoch or after a certain number of batches
			if hasattr(args.checkpointing_steps    ,						"isdigit"   ):
						if args.checkpointing_steps == "epoch":
									lowerCAmelCase__										=				args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									lowerCAmelCase__										=				int(args.checkpointing_steps   )
						else:
									raise ValueError(
									    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
			else:
						lowerCAmelCase__										=				None

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						lowerCAmelCase__										=				os.path.split(UpperCamelCase_   )[-1].split("."   )[0]
						accelerator.init_trackers(UpperCamelCase_    ,						UpperCamelCase_   )

			# Grab all the image filenames
			lowerCAmelCase__										=				[os.path.join(args.data_dir    ,						UpperCamelCase_   ) for fname in os.listdir(args.data_dir   ) if fname.endswith(".jpg"   )]

			# Build the label correspondences
			lowerCAmelCase__										=				[extract_label(UpperCamelCase_   ) for fname in file_names]
			lowerCAmelCase__										=				list(set(UpperCamelCase_   )   )
			id_to_label.sort()
			lowerCAmelCase__										=				{lbl: i for i, lbl in enumerate(UpperCamelCase_   )}

			# Set the seed before splitting the data.
			np.random.seed(UpperCamelCase_   )
			torch.manual_seed(UpperCamelCase_   )
			torch.cuda.manual_seed_all(UpperCamelCase_   )

			# Split our filenames between train and validation
			lowerCAmelCase__										=				np.random.permutation(len(UpperCamelCase_   )   )
			lowerCAmelCase__										=				int(0.8 * len(UpperCamelCase_   )   )
			lowerCAmelCase__										=				random_perm[:cut]
			lowerCAmelCase__										=				random_perm[cut:]

			# For training we use a simple RandomResizedCrop
			lowerCAmelCase__										=				Compose([RandomResizedCrop(UpperCamelCase_    ,						scale=(0.5, 1.0)   ), ToTensor()]   )
			lowerCAmelCase__										=				PetsDataset(
			    [file_names[i] for i in train_split]    ,						image_transform=UpperCamelCase_    ,						label_to_id=UpperCamelCase_   )

			# For evaluation, we use a deterministic Resize
			lowerCAmelCase__										=				Compose([Resize(UpperCamelCase_   ), ToTensor()]   )
			lowerCAmelCase__										=				PetsDataset([file_names[i] for i in eval_split]    ,						image_transform=UpperCamelCase_    ,						label_to_id=UpperCamelCase_   )

			# Instantiate dataloaders.
			lowerCAmelCase__										=				DataLoader(UpperCamelCase_    ,						shuffle=UpperCamelCase_    ,						batch_size=UpperCamelCase_    ,						num_workers=4   )
			lowerCAmelCase__										=				DataLoader(UpperCamelCase_    ,						shuffle=UpperCamelCase_    ,						batch_size=UpperCamelCase_    ,						num_workers=4   )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCAmelCase__										=				create_model("resnet50d"    ,						pretrained=UpperCamelCase_    ,						num_classes=len(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).
			lowerCAmelCase__										=				model.to(accelerator.device   )

			# Freezing the base model
			for param in model.parameters():
						lowerCAmelCase__										=				False
			for param in model.get_classifier().parameters():
						lowerCAmelCase__										=				True

			# We normalize the batches of images to be a bit faster.
			lowerCAmelCase__										=				torch.tensor(model.default_cfg["mean"]   )[None, :, None, None].to(accelerator.device   )
			lowerCAmelCase__										=				torch.tensor(model.default_cfg["std"]   )[None, :, None, None].to(accelerator.device   )

			# Instantiate optimizer
			lowerCAmelCase__										=				torch.optim.Adam(params=model.parameters()    ,						lr=lr / 25   )

			# Instantiate learning rate scheduler
			lowerCAmelCase__										=				OneCycleLR(optimizer=UpperCamelCase_    ,						max_lr=UpperCamelCase_    ,						epochs=UpperCamelCase_    ,						steps_per_epoch=len(UpperCamelCase_   )   )

			# 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.
			lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__										=				accelerator.prepare(
			    UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_   )
			# We need to keep track of how many total steps we have iterated over
			lowerCAmelCase__										=				0
			# We also need to keep track of the starting epoch so files are named properly
			lowerCAmelCase__										=				0

			# Potentially load in the weights and states from a previous save
			if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
									accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""   )
									accelerator.load_state(args.resume_from_checkpoint   )
									lowerCAmelCase__										=				os.path.basename(args.resume_from_checkpoint   )
						else:
									# Get the most recent checkpoint
									lowerCAmelCase__										=				[f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
									dirs.sort(key=os.path.getctime   )
									lowerCAmelCase__										=				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						lowerCAmelCase__										=				os.path.splitext(UpperCamelCase_   )[0]

						if "epoch" in training_difference:
									lowerCAmelCase__										=				int(training_difference.replace("epoch_"    ,						""   )   ) + 1
									lowerCAmelCase__										=				None
						else:
									lowerCAmelCase__										=				int(training_difference.replace("step_"    ,						""   )   )
									lowerCAmelCase__										=				resume_step // len(UpperCamelCase_   )
									resume_step -= starting_epoch * len(UpperCamelCase_   )

    # Now we train the model
			for epoch in range(UpperCamelCase_    ,						UpperCamelCase_   ):
						model.train()
						if args.with_tracking:
									lowerCAmelCase__										=				0
						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
									# We need to skip steps until we reach the resumed step
									lowerCAmelCase__										=				accelerator.skip_first_batches(UpperCamelCase_    ,						UpperCamelCase_   )
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									lowerCAmelCase__										=				train_dataloader
						for batch in active_dataloader:
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									lowerCAmelCase__										=				{k: v.to(accelerator.device   ) for k, v in batch.items()}
									lowerCAmelCase__										=				(batch["image"] - mean) / std
									lowerCAmelCase__										=				model(UpperCamelCase_   )
									lowerCAmelCase__										=				torch.nn.functional.cross_entropy(UpperCamelCase_    ,						batch["label"]   )
									# We keep track of the loss at each epoch
									if args.with_tracking:
												total_loss += loss.detach().float()
									accelerator.backward(UpperCamelCase_   )
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()
									overall_step += 1
									if isinstance(UpperCamelCase_    ,						UpperCamelCase_   ):
												lowerCAmelCase__										=				F"""step_{overall_step}"""
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		lowerCAmelCase__										=				os.path.join(args.output_dir    ,						UpperCamelCase_   )
															accelerator.save_state(UpperCamelCase_   )
						model.eval()
						lowerCAmelCase__										=				0
						lowerCAmelCase__										=				0
						for step, batch in enumerate(UpperCamelCase_   ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									lowerCAmelCase__										=				{k: v.to(accelerator.device   ) for k, v in batch.items()}
									lowerCAmelCase__										=				(batch["image"] - mean) / std
									with torch.no_grad():
												lowerCAmelCase__										=				model(UpperCamelCase_   )
									lowerCAmelCase__										=				outputs.argmax(dim=-1   )
									lowerCAmelCase__						,lowerCAmelCase__										=				accelerator.gather_for_metrics((predictions, batch["label"])   )
									lowerCAmelCase__										=				predictions == references
									num_elems += accurate_preds.shape[0]
									accurate += accurate_preds.long().sum()

						lowerCAmelCase__										=				accurate.item() / num_elems
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"""epoch {epoch}: {1_00 * eval_metric:.2f}"""   )
						if args.with_tracking:
									accelerator.log(
									    {
									        "accuracy": 1_00 * eval_metric,
									        "train_loss": total_loss.item() / len(UpperCamelCase_   ),
									        "epoch": epoch,
									    }    ,						step=UpperCamelCase_    ,						)
						if checkpointing_steps == "epoch":
									lowerCAmelCase__										=				F"""epoch_{epoch}"""
									if args.output_dir is not None:
												lowerCAmelCase__										=				os.path.join(args.output_dir    ,						UpperCamelCase_   )
									accelerator.save_state(UpperCamelCase_   )

			if args.with_tracking:
						accelerator.end_training()







def 	A  (   )       ->							Optional[int]:







			'''simple docstring'''



			lowerCAmelCase__										=				argparse.ArgumentParser(description="Simple example of training script."   )
			parser.add_argument("--data_dir"    ,						required=UpperCamelCase_    ,						help="The data folder on disk."   )
			parser.add_argument("--fp16"    ,						action="store_true"    ,						help="If passed, will use FP16 training."   )
			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."   )
			parser.add_argument(
			    "--checkpointing_steps"    ,						type=UpperCamelCase_    ,						default=UpperCamelCase_    ,						help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."    ,						)
			parser.add_argument(
			    "--output_dir"    ,						type=UpperCamelCase_    ,						default="."    ,						help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,						)
			parser.add_argument(
			    "--resume_from_checkpoint"    ,						type=UpperCamelCase_    ,						default=UpperCamelCase_    ,						help="If the training should continue from a checkpoint folder."    ,						)
			parser.add_argument(
			    "--with_tracking"    ,						action="store_true"    ,						help="Whether to load in all available experiment trackers from the environment and use them for logging."    ,						)
			parser.add_argument(
			    "--project_dir"    ,						type=UpperCamelCase_    ,						default="logs"    ,						help="Location on where to store experiment tracking logs` and relevent project information"    ,						)
			lowerCAmelCase__										=				parser.parse_args()
			lowerCAmelCase__										=				{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 2_24}
			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


__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 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_=False	,    lowerCAmelCase_=False	,    lowerCAmelCase_=True	,    lowerCAmelCase_=False	,    lowerCAmelCase_="dummy_doc" )    ->Optional[Any]:
   UpperCAmelCase      					=  {doc: key_lines}
   UpperCAmelCase      					=  {doc: sys_lines}

   UpperCAmelCase      					=  {}

   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0

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

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

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

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

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

      UpperCAmelCase					,	UpperCAmelCase      					=  reader.remove_nested_coref_mentions(lowerCAmelCase_	,    lowerCAmelCase_ )
      sys_nested_coref_num += nested_mentions
      sys_removed_nested_clusters += removed_clusters

   UpperCAmelCase      					=  reader.get_mention_assignments(lowerCAmelCase_	,    lowerCAmelCase_ )
   UpperCAmelCase      					=  reader.get_mention_assignments(lowerCAmelCase_	,    lowerCAmelCase_ )

   UpperCAmelCase      					=  (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

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

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

   return doc_coref_infos
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  get_coref_infos(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )

   UpperCAmelCase      					=  {}
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0

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

   return output_scores
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->List[Any]:
   UpperCAmelCase      					=  False
   for line in key_lines:
      if not line.startswith("""#""" ):
         if len(line.split() ) > 6:
            UpperCAmelCase      					=  line.split()[5]
            if not parse_col == "-":
               UpperCAmelCase      					=  True
               break
            else:
               break
   return has_gold_parse







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





      def 							_lowercase					(		self    :       int							)     -> str:






         """simple docstring"""







         return datasets.MetricInfo(
             description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
                 {
                     """predictions""": datasets.Sequence(datasets.Value("""string"""							)							),
                     """references""": datasets.Sequence(datasets.Value("""string"""							)							),
                 }							)       ,							codebase_urls=["""https://github.com/ns-moosavi/coval"""]       ,							reference_urls=[
                 """https://github.com/ns-moosavi/coval""",
                 """https://www.aclweb.org/anthology/P16-1060""",
                 """http://www.conll.cemantix.org/2012/data.html""",
             ]       ,							)





      def 							_lowercase					(		self    :       Union[str, Any]       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       Optional[int]=True       ,							__lowerCamelCase    :       Any=False       ,							__lowerCamelCase    :       List[Any]=False       ,							__lowerCamelCase    :       List[Any]=False							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  [
             ("""mentions""", evaluator.mentions),
             ("""muc""", evaluator.muc),
             ("""bcub""", evaluator.b_cubed),
             ("""ceafe""", evaluator.ceafe),
             ("""lea""", evaluator.lea),
         ]

         if min_span:
            UpperCAmelCase      					=  util.check_gold_parse_annotation(__lowerCamelCase							)
            if not has_gold_parse:
               raise NotImplementedError("""References should have gold parse annotation to use 'min_span'."""							)
               # util.parse_key_file(key_file)
               # key_file = key_file + ".parsed"

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

         return score



