


'''simple docstring'''
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFDeiTForImageClassification,
      TFDeiTForImageClassificationWithTeacher,
      TFDeiTForMaskedImageModeling,
      TFDeiTModel,
  )
  from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import DeiTImageProcessor




class 			lowerCamelCase_   :






      def __init__(					self				:    int						,						_A				:    List[Any]						,						_A				:    Union[str, Any]=13						,						_A				:    List[str]=30						,						_A				:    Union[str, Any]=2						,						_A				:    Optional[Any]=3						,						_A				:    Tuple=True						,						_A				:    str=True						,						_A				:    int=32						,						_A				:    List[Any]=2						,						_A				:    Union[str, Any]=4						,						_A				:    List[str]=37						,						_A				:    str="gelu"						,						_A				:    Optional[int]=0.1						,						_A				:    Tuple=0.1						,						_A				:    int=10						,						_A				:    Tuple=0.0_2						,						_A				:    List[str]=3						,						_A				:    Optional[int]=None						,						_A				:    Dict=2						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				parent
          UpperCAmelCase__			:		List[str]													=				batch_size
          UpperCAmelCase__			:		str													=				image_size
          UpperCAmelCase__			:		Optional[int]													=				patch_size
          UpperCAmelCase__			:		Tuple													=				num_channels
          UpperCAmelCase__			:		Any													=				is_training
          UpperCAmelCase__			:		List[str]													=				use_labels
          UpperCAmelCase__			:		Union[str, Any]													=				hidden_size
          UpperCAmelCase__			:		int													=				num_hidden_layers
          UpperCAmelCase__			:		Dict													=				num_attention_heads
          UpperCAmelCase__			:		Optional[Any]													=				intermediate_size
          UpperCAmelCase__			:		Union[str, Any]													=				hidden_act
          UpperCAmelCase__			:		Dict													=				hidden_dropout_prob
          UpperCAmelCase__			:		Tuple													=				attention_probs_dropout_prob
          UpperCAmelCase__			:		List[str]													=				type_sequence_label_size
          UpperCAmelCase__			:		List[str]													=				initializer_range
          UpperCAmelCase__			:		str													=				scope
          UpperCAmelCase__			:		Union[str, Any]													=				encoder_stride

          # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
          UpperCAmelCase__			:		List[str]													=				(image_size // patch_size) ** 2
          UpperCAmelCase__			:		Union[str, Any]													=				num_patches + 2






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

          UpperCAmelCase__			:		List[Any]													=				None
          if self.use_labels:
              UpperCAmelCase__			:		int													=				ids_tensor([self.batch_size]						,						self.type_sequence_label_size )

          UpperCAmelCase__			:		str													=				self.get_config()

          return config, pixel_values, labels






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          return DeiTConfig(
              image_size=self.image_size						,						patch_size=self.patch_size						,						num_channels=self.num_channels						,						hidden_size=self.hidden_size						,						num_hidden_layers=self.num_hidden_layers						,						num_attention_heads=self.num_attention_heads						,						intermediate_size=self.intermediate_size						,						hidden_act=self.hidden_act						,						hidden_dropout_prob=self.hidden_dropout_prob						,						attention_probs_dropout_prob=self.attention_probs_dropout_prob						,						is_decoder=_A						,						initializer_range=self.initializer_range						,						encoder_stride=self.encoder_stride						,						)






      def lowercase_							(					self				:    int						,						_A				:    int						,						_A				:    Dict						,						_A				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				TFDeiTModel(config=_A )
          UpperCAmelCase__			:		Optional[int]													=				model(_A )
          self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size) )






      def lowercase_							(					self				:    Optional[int]						,						_A				:    Dict						,						_A				:    List[str]						,						_A				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				TFDeiTForMaskedImageModeling(config=_A )
          UpperCAmelCase__			:		Tuple													=				model(_A )
          self.parent.assertEqual(
              result.reconstruction.shape						,						(self.batch_size, self.num_channels, self.image_size, self.image_size) )

          # test greyscale images
          UpperCAmelCase__			:		Any													=				1
          UpperCAmelCase__			:		Union[str, Any]													=				TFDeiTForMaskedImageModeling(_A )

          UpperCAmelCase__			:		Optional[int]													=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
          UpperCAmelCase__			:		List[str]													=				model(_A )
          self.parent.assertEqual(result.reconstruction.shape						,						(self.batch_size, 1, self.image_size, self.image_size) )






      def lowercase_							(					self				:    Dict						,						_A				:    str						,						_A				:    Optional[Any]						,						_A				:    Union[str, Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				self.type_sequence_label_size
          UpperCAmelCase__			:		List[Any]													=				TFDeiTForImageClassification(_A )
          UpperCAmelCase__			:		Any													=				model(_A						,						labels=_A )
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size) )

          # test greyscale images
          UpperCAmelCase__			:		Optional[int]													=				1
          UpperCAmelCase__			:		Tuple													=				TFDeiTForImageClassification(_A )

          UpperCAmelCase__			:		Tuple													=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
          UpperCAmelCase__			:		List[Any]													=				model(_A						,						labels=_A )
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size) )






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				self.prepare_config_and_inputs()
          UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[Any]													=				config_and_inputs
          UpperCAmelCase__			:		int													=				{'''pixel_values''': pixel_values}
          return config, inputs_dict




@require_tf
class 			lowerCamelCase_   (	__a     ,       __a     ,       unittest.TestCase      ):
      lowerCAmelCase__		     =     (
          (
              TFDeiTModel,
              TFDeiTForImageClassification,
              TFDeiTForImageClassificationWithTeacher,
              TFDeiTForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      lowerCAmelCase__		     =     (
          {
              'feature-extraction': TFDeiTModel,
              'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
          }
          if is_tf_available()
          else {}
      )

      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     False






      def lowercase_							(					self				:    Union[str, Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				TFDeiTModelTester(self )
          UpperCAmelCase__			:		Optional[int]													=				ConfigTester(self						,						config_class=_A						,						has_text_modality=_A						,						hidden_size=37 )






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          self.config_tester.run_common_tests()






      @unittest.skip(reason='''DeiT does not use inputs_embeds''' )
      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          pass






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase__			:		Optional[Any]													=				model_class(_A )
              self.assertIsInstance(model.get_input_embeddings()						,						(tf.keras.layers.Layer) )
              UpperCAmelCase__			:		Dict													=				model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(_A						,						tf.keras.layers.Dense ) )






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase__			:		str													=				model_class(_A )
              UpperCAmelCase__			:		Any													=				inspect.signature(model.call )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase__			:		List[Any]													=				[*signature.parameters.keys()]

              UpperCAmelCase__			:		Optional[Any]													=				['''pixel_values''']
              self.assertListEqual(arg_names[:1]						,						_A )






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_A )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*_A )






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_A )






      def lowercase_							(					self				:    Optional[int]						,						_A				:    Union[str, Any]						,						_A				:    Tuple						,						_A				:    str=False ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				super()._prepare_for_class(_A						,						_A						,						return_labels=_A )

          if return_labels:
              if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
                  del inputs_dict["labels"]

          return inputs_dict






      @slow
      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase__			:		Dict													=				TFDeiTModel.from_pretrained(_A )
              self.assertIsNotNone(_A )





def 					a__       (					)    ->	Union[str, Any]:
    UpperCAmelCase__			:		int													=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
    return image







@require_tf
@require_vision
class 			lowerCamelCase_   (	unittest.TestCase      ):






      @cached_property
      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          return (
              DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224''' )
              if is_vision_available()
              else None
          )






      @slow
      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224''' )

          UpperCAmelCase__			:		int													=				self.default_image_processor
          UpperCAmelCase__			:		Union[str, Any]													=				prepare_img()
          UpperCAmelCase__			:		int													=				image_processor(images=_A						,						return_tensors='''tf''' )

          # forward pass
          UpperCAmelCase__			:		Optional[int]													=				model(**_A )

          # verify the logits
          UpperCAmelCase__			:		str													=				tf.TensorShape((1, 1_000) )
          self.assertEqual(outputs.logits.shape						,						_A )

          UpperCAmelCase__			:		str													=				tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1] )

          self.assertTrue(np.allclose(outputs.logits[0, :3]						,						_A						,						atol=1e-4 ) )




'''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


UpperCamelCase__    					=      datasets.logging.get_logger(__name__)


UpperCamelCase__    					=      '''\
@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",
}

'''

UpperCamelCase__    					=      '''\
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.
'''

UpperCamelCase__    					=      '''
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 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=False  ,					lowerCAmelCase__=False  ,					lowerCAmelCase__=True  ,					lowerCAmelCase__=False  ,					lowerCAmelCase__="dummy_doc"						)    ->	Dict:
    UpperCAmelCase__			:		List[str]													=				{doc: key_lines}
    UpperCAmelCase__			:		int													=				{doc: sys_lines}

    UpperCAmelCase__			:		Optional[Any]													=				{}

    UpperCAmelCase__			:		Optional[Any]													=				0
    UpperCAmelCase__			:		Optional[Any]													=				0
    UpperCAmelCase__			:		Union[str, Any]													=				0
    UpperCAmelCase__			:		Optional[int]													=				0
    UpperCAmelCase__			:		List[Any]													=				0
    UpperCAmelCase__			:		Union[str, Any]													=				0

    UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[Any]													=				reader.get_doc_mentions(lowerCAmelCase__  ,					key_doc_lines[doc]  ,					lowerCAmelCase__						)
    key_singletons_num += singletons_num

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

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

    if NP_only or min_span:
        UpperCAmelCase__			:		str													=				reader.set_annotated_parse_trees(lowerCAmelCase__  ,					key_doc_lines[doc]  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

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

        UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[Any]													=				reader.remove_nested_coref_mentions(lowerCAmelCase__  ,					lowerCAmelCase__						)
        sys_nested_coref_num += nested_mentions
        sys_removed_nested_clusters += removed_clusters

    UpperCAmelCase__			:		Dict													=				reader.get_mention_assignments(lowerCAmelCase__  ,					lowerCAmelCase__						)
    UpperCAmelCase__			:		str													=				reader.get_mention_assignments(lowerCAmelCase__  ,					lowerCAmelCase__						)

    UpperCAmelCase__			:		Optional[Any]													=				(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__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Tuple:
    UpperCAmelCase__			:		str													=				get_coref_infos(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

    UpperCAmelCase__			:		Optional[Any]													=				{}
    UpperCAmelCase__			:		str													=				0
    UpperCAmelCase__			:		Optional[int]													=				0

    for name, metric in metrics:
        UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				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 * 1_00:.2f}"""  ,					F""" Precision: {precision * 1_00:.2f}"""  ,					F""" F1: {fa * 1_00:.2f}"""  ,					)

    if conll_subparts_num == 3:
        UpperCAmelCase__			:		Any													=				(conll / 3) * 1_00
        logger.info(F"""CoNLL score: {conll:.2f}"""						)
        output_scores.update({'''conll_score''': conll}						)

    return output_scores





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







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






      def lowercase_							(					self				:    Any ):


          '''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				:    Tuple						,						_A				:    Union[str, Any]						,						_A				:    Tuple						,						_A				:    Dict=True						,						_A				:    Optional[int]=False						,						_A				:    str=False						,						_A				:    List[str]=False ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				[
              ('''mentions''', evaluator.mentions),
              ('''muc''', evaluator.muc),
              ('''bcub''', evaluator.b_cubed),
              ('''ceafe''', evaluator.ceafe),
              ('''lea''', evaluator.lea),
          ]

          if min_span:
              UpperCAmelCase__			:		int													=				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__			:		List[str]													=				evaluate(
              key_lines=_A						,						sys_lines=_A						,						metrics=_A						,						NP_only=_A						,						remove_nested=_A						,						keep_singletons=_A						,						min_span=_A						,						)

          return score
