


'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCAmelCase						=					logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-classification/requirements.txt""")

lowerCAmelCase						=					list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCAmelCase						=					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def 					lowerCamelCase_ ( __UpperCamelCase					:       str		)					->	Optional[int]:







     """simple docstring"""
     with open(__UpperCamelCase							,    'rb'		) as f:
          _A				=							Image.open(__UpperCamelCase		)
          return im.convert('RGB'		)






@dataclass
class        lowerCAmelCase_  :
       __UpperCAmelCase					=field(
           default=UpperCAmelCase      ,							metadata={
               'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
           }      ,							)
       __UpperCAmelCase					=field(
           default=UpperCAmelCase      ,							metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}	)
       __UpperCAmelCase					=field(default=UpperCAmelCase      ,							metadata={'help': 'A folder containing the training data.'}	)
       __UpperCAmelCase					=field(default=UpperCAmelCase      ,							metadata={'help': 'A folder containing the validation data.'}	)
       __UpperCAmelCase					=field(
           default=0.15      ,							metadata={'help': 'Percent to split off of train for validation.'}	)
       __UpperCAmelCase					=field(
           default=UpperCAmelCase      ,							metadata={
               'help': (
                   'For debugging purposes or quicker training, truncate the number of training examples to this '
                   'value if set.'
               )
           }      ,							)
       __UpperCAmelCase					=field(
           default=UpperCAmelCase      ,							metadata={
               'help': (
                   'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
                   'value if set.'
               )
           }      ,							)






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
                 raise ValueError(
                     'You must specify either a dataset name from the hub or a train and/or validation directory.'   )






@dataclass
class        lowerCAmelCase_  :
       __UpperCAmelCase					=field(
           default='google/vit-base-patch16-224-in21k'      ,							metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      ,							)
       __UpperCAmelCase					=field(
           default=UpperCAmelCase      ,							metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(UpperCAmelCase	)}      ,							)
       __UpperCAmelCase					=field(
           default=UpperCAmelCase      ,							metadata={'help': 'Pretrained config name or path if not the same as model_name'}	)
       __UpperCAmelCase					=field(
           default=UpperCAmelCase      ,							metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}	)
       __UpperCAmelCase					=field(
           default='main'      ,							metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}      ,							)
       __UpperCAmelCase					=field(default=UpperCAmelCase      ,							metadata={'help': 'Name or path of preprocessor config.'}	)
       __UpperCAmelCase					=field(
           default=UpperCAmelCase      ,							metadata={
               'help': (
                   'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                   'with private models).'
               )
           }      ,							)
       __UpperCAmelCase					=field(
           default=UpperCAmelCase      ,							metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}      ,							)






def 					lowerCamelCase_ ( __UpperCamelCase					:       List[str]		)					->	str:







     """simple docstring"""
     _A				=							torch.stack([example['pixel_values'] for example in examples]		)
     _A				=							torch.tensor([example['labels'] for example in examples]		)
     return {"pixel_values": pixel_values, "labels": labels}

def 					lowerCamelCase_ ( )					->	str:







     """simple docstring"""
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.

     _A				=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
     if len(sys.argv		) == 2 and sys.argv[1].endswith('.json'		):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          _A		,				_A		,				_A				=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
     else:
          _A		,				_A		,				_A				=							parser.parse_args_into_dataclasses()

     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
     send_example_telemetry('run_image_classification'							,    __UpperCamelCase							,    __UpperCamelCase		)

     # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,    datefmt='%m/%d/%Y %H:%M:%S'							,    handlers=[logging.StreamHandler(sys.stdout		)]							,    )

     if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()

     _A				=							training_args.get_process_log_level()
     logger.setLevel(__UpperCamelCase		)
     transformers.utils.logging.set_verbosity(__UpperCamelCase		)
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()

     # Log on each process the small summary:
     logger.warning(
         F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
         + F'distributed training: {bool(training_args.local_rank != -1		)}, 16-bits training: {training_args.fpaa}'		)
     logger.info(F'Training/evaluation parameters {training_args}'		)

     # Detecting last checkpoint.
     _A				=							None
     if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
          _A				=							get_last_checkpoint(training_args.output_dir		)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir		)		) > 0:
               raise ValueError(
                   F'Output directory ({training_args.output_dir}) already exists and is not empty. '
                   'Use --overwrite_output_dir to overcome.'		)
          elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
               logger.info(
                   F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                   'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'		)

    # Set seed before initializing model.
     set_seed(training_args.seed		)

     # Initialize our dataset and prepare it for the 'image-classification' task.
     if data_args.dataset_name is not None:
          _A				=							load_dataset(
              data_args.dataset_name							,    data_args.dataset_config_name							,    cache_dir=model_args.cache_dir							,    task='image-classification'							,    use_auth_token=True if model_args.use_auth_token else None							,    )
     else:
          _A				=							{}
          if data_args.train_dir is not None:
               _A				=							os.path.join(data_args.train_dir							,    '**'		)
          if data_args.validation_dir is not None:
               _A				=							os.path.join(data_args.validation_dir							,    '**'		)
          _A				=							load_dataset(
              'imagefolder'							,    data_files=__UpperCamelCase							,    cache_dir=model_args.cache_dir							,    task='image-classification'							,    )

     # If we don't have a validation split, split off a percentage of train as validation.
     _A				=							None if 'validation' in dataset.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split							,    __UpperCamelCase		) and data_args.train_val_split > 0.0:
          _A				=							dataset['train'].train_test_split(data_args.train_val_split		)
          _A				=							split['train']
          _A				=							split['test']

     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
     _A				=							dataset['train'].features['labels'].names
     _A		,				_A				=							{}, {}
     for i, label in enumerate(__UpperCamelCase		):
          _A				=							str(__UpperCamelCase		)
          _A				=							label

     # Load the accuracy metric from the datasets package
     _A				=							evaluate.load('accuracy'		)

     # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.
     def compute_metrics(__UpperCamelCase					:       List[Any]		):
          return metric.compute(predictions=np.argmax(p.predictions							,    axis=1		)							,    references=p.label_ids		)

     _A				=							AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path							,    num_labels=len(__UpperCamelCase		)							,    labelaid=__UpperCamelCase							,    idalabel=__UpperCamelCase							,    finetuning_task='image-classification'							,    cache_dir=model_args.cache_dir							,    revision=model_args.model_revision							,    use_auth_token=True if model_args.use_auth_token else None							,    )
     _A				=							AutoModelForImageClassification.from_pretrained(
         model_args.model_name_or_path							,    from_tf=bool('.ckpt' in model_args.model_name_or_path		)							,    config=__UpperCamelCase							,    cache_dir=model_args.cache_dir							,    revision=model_args.model_revision							,    use_auth_token=True if model_args.use_auth_token else None							,    ignore_mismatched_sizes=model_args.ignore_mismatched_sizes							,    )
     _A				=							AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path							,    cache_dir=model_args.cache_dir							,    revision=model_args.model_revision							,    use_auth_token=True if model_args.use_auth_token else None							,    )

     # Define torchvision transforms to be applied to each image.
     if "shortest_edge" in image_processor.size:
          _A				=							image_processor.size['shortest_edge']
     else:
          _A				=							(image_processor.size['height'], image_processor.size['width'])
     _A				=							Normalize(mean=image_processor.image_mean							,    std=image_processor.image_std		)
     _A				=							Compose(
         [
             RandomResizedCrop(__UpperCamelCase		),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]		)
     _A				=							Compose(
         [
             Resize(__UpperCamelCase		),
             CenterCrop(__UpperCamelCase		),
             ToTensor(),
             normalize,
         ]		)

     def train_transforms(__UpperCamelCase					:       Optional[Any]		):
          _A				=							[
              _train_transforms(pil_img.convert('RGB'		)		) for pil_img in example_batch['image']
          ]
          return example_batch

     def val_transforms(__UpperCamelCase					:       List[str]		):
          _A				=							[_val_transforms(pil_img.convert('RGB'		)		) for pil_img in example_batch['image']]
          return example_batch

     if training_args.do_train:
          if "train" not in dataset:
               raise ValueError('--do_train requires a train dataset'		)
          if data_args.max_train_samples is not None:
               _A				=							(
                   dataset['train'].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
               )
          # Set the training transforms
          dataset["train"].set_transform(__UpperCamelCase		)

     if training_args.do_eval:
          if "validation" not in dataset:
               raise ValueError('--do_eval requires a validation dataset'		)
          if data_args.max_eval_samples is not None:
               _A				=							(
                   dataset['validation'].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
               )
          # Set the validation transforms
          dataset["validation"].set_transform(__UpperCamelCase		)

     # Initalize our trainer
     _A				=							Trainer(
         model=__UpperCamelCase							,    args=__UpperCamelCase							,    train_dataset=dataset['train'] if training_args.do_train else None							,    eval_dataset=dataset['validation'] if training_args.do_eval else None							,    compute_metrics=__UpperCamelCase							,    tokenizer=__UpperCamelCase							,    data_collator=__UpperCamelCase							,    )

     # Training
     if training_args.do_train:
          _A				=							None
          if training_args.resume_from_checkpoint is not None:
               _A				=							training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               _A				=							last_checkpoint
          _A				=							trainer.train(resume_from_checkpoint=__UpperCamelCase		)
          trainer.save_model()
          trainer.log_metrics('train'							,    train_result.metrics		)
          trainer.save_metrics('train'							,    train_result.metrics		)
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          _A				=							trainer.evaluate()
          trainer.log_metrics('eval'							,    __UpperCamelCase		)
          trainer.save_metrics('eval'							,    __UpperCamelCase		)

     # Write model card and (optionally) push to hub
     _A				=							{
         'finetuned_from': model_args.model_name_or_path,
         'tasks': 'image-classification',
         'dataset': data_args.dataset_name,
         'tags': ['image-classification', 'vision'],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**__UpperCamelCase		)
     else:
          trainer.create_model_card(**__UpperCamelCase		)


if __name__ == "__main__":
    main()









'''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


lowerCAmelCase						=					datasets.logging.get_logger(__name__)


lowerCAmelCase						=					"""\
@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\",
}

"""

lowerCAmelCase						=					"""\
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.
"""

lowerCAmelCase						=					"""
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 					lowerCamelCase_ ( __UpperCamelCase					:       List[str]							,    __UpperCamelCase					:       Any							,    __UpperCamelCase					:       int=False							,    __UpperCamelCase					:       Optional[int]=False							,    __UpperCamelCase					:       List[str]=True							,    __UpperCamelCase					:       int=False							,    __UpperCamelCase					:       Union[str, Any]="dummy_doc"		)					->	Optional[Any]:







     """simple docstring"""
     _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(__UpperCamelCase							,    key_doc_lines[doc]							,    __UpperCamelCase		)
     key_singletons_num += singletons_num

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

     _A		,				_A				=							reader.get_doc_mentions(__UpperCamelCase							,    sys_doc_lines[doc]							,    __UpperCamelCase		)
     sys_singletons_num += singletons_num

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

     if remove_nested:
          _A		,				_A				=							reader.remove_nested_coref_mentions(__UpperCamelCase							,    __UpperCamelCase		)
          key_nested_coref_num += nested_mentions
          key_removed_nested_clusters += removed_clusters

          _A		,				_A				=							reader.remove_nested_coref_mentions(__UpperCamelCase							,    __UpperCamelCase		)
          sys_nested_coref_num += nested_mentions
          sys_removed_nested_clusters += removed_clusters

     _A				=							reader.get_mention_assignments(__UpperCamelCase							,    __UpperCamelCase		)
     _A				=							reader.get_mention_assignments(__UpperCamelCase							,    __UpperCamelCase		)

     _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 					lowerCamelCase_ ( __UpperCamelCase					:       List[str]							,    __UpperCamelCase					:       Dict							,    __UpperCamelCase					:       int							,    __UpperCamelCase					:       Dict							,    __UpperCamelCase					:       Any							,    __UpperCamelCase					:       str							,    __UpperCamelCase					:       List[str]		)					->	Optional[int]:







     """simple docstring"""
     _A				=							get_coref_infos(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)

     _A				=							{}
     _A				=							0
     _A				=							0

     for name, metric in metrics:
          _A		,				_A		,				_A				=							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(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 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]		)					->	Tuple:







     """simple docstring"""
     _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        lowerCAmelCase_  (     datasets.Metric	):






       def   UpperCamelCase						( 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   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True			,     _UpperCamelCase=False			,     _UpperCamelCase=False			,     _UpperCamelCase=False   )->      Any:
            _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(_UpperCamelCase   )
                 if not has_gold_parse:
                      raise NotImplementedError('References should have gold parse annotation to use \'min_span\'.'   )
                      # util.parse_key_file(key_file)
                      # key_file = key_file + ".parsed"

            _A				=							evaluate(
                key_lines=_UpperCamelCase			,     sys_lines=_UpperCamelCase			,     metrics=_UpperCamelCase			,     NP_only=_UpperCamelCase			,     remove_nested=_UpperCamelCase			,     keep_singletons=_UpperCamelCase			,     min_span=_UpperCamelCase			,     )

            return score





