


from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class 				SCREAMING_SNAKE_CASE__						:
      snake_case__       :  Union[str, Any]       =		PegasusConfig
      snake_case__       :  Optional[int]       =		{}
      snake_case__       :  Tuple       =		'''gelu'''
      def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : List[str]=1_3  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=7  ,							SCREAMING_SNAKE_CASE__       : Dict=True  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=False  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=9_9  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=3_2  ,							SCREAMING_SNAKE_CASE__       : List[Any]=2  ,							SCREAMING_SNAKE_CASE__       : List[Any]=4  ,							SCREAMING_SNAKE_CASE__       : Any=3_7  ,							SCREAMING_SNAKE_CASE__       : Any=0.1  ,							SCREAMING_SNAKE_CASE__       : Dict=0.1  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=4_0  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=2  ,							SCREAMING_SNAKE_CASE__       : Any=1  ,							SCREAMING_SNAKE_CASE__       : List[str]=0  ,							)	->    Tuple:
             a_      :	List[str]							=						parent
             a_      :	Tuple							=						batch_size
             a_      :	List[Any]							=						seq_length
             a_      :	Optional[int]							=						is_training
             a_      :	Any							=						use_labels
             a_      :	Dict							=						vocab_size
             a_      :	Tuple							=						hidden_size
             a_      :	List[str]							=						num_hidden_layers
             a_      :	Dict							=						num_attention_heads
             a_      :	int							=						intermediate_size

             a_      :	Tuple							=						hidden_dropout_prob
             a_      :	List[Any]							=						attention_probs_dropout_prob
             a_      :	Dict							=						max_position_embeddings
             a_      :	Union[str, Any]							=						eos_token_id
             a_      :	Union[str, Any]							=						pad_token_id
             a_      :	Dict							=						bos_token_id
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    str:
             a_      :	Optional[Any]							=						ids_tensor([self.batch_size, self.seq_length - 1]  ,							self.vocab_size  )
             a_      :	Optional[int]							=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )  ,							1  )
             a_      :	str							=						tf.concat([input_ids, eos_tensor]  ,							axis=1  )

             a_      :	List[str]							=						ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size  )

             a_      :	List[str]							=						self.config_cls(
                 vocab_size=self.vocab_size  ,							d_model=self.hidden_size  ,							encoder_layers=self.num_hidden_layers  ,							decoder_layers=self.num_hidden_layers  ,							encoder_attention_heads=self.num_attention_heads  ,							decoder_attention_heads=self.num_attention_heads  ,							encoder_ffn_dim=self.intermediate_size  ,							decoder_ffn_dim=self.intermediate_size  ,							dropout=self.hidden_dropout_prob  ,							attention_dropout=self.attention_probs_dropout_prob  ,							max_position_embeddings=self.max_position_embeddings  ,							eos_token_ids=[2]  ,							bos_token_id=self.bos_token_id  ,							pad_token_id=self.pad_token_id  ,							decoder_start_token_id=self.pad_token_id  ,							**self.config_updates  ,							)
             a_      :	List[str]							=						prepare_pegasus_inputs_dict(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             return config, inputs_dict

      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Any  )	->    List[str]:
             a_      :	Any							=						TFPegasusModel(config=SCREAMING_SNAKE_CASE__  ).get_decoder()
             a_      :	str							=						inputs_dict['input_ids']

             a_      :	Any							=						input_ids[:1, :]
             a_      :	List[Any]							=						inputs_dict['attention_mask'][:1, :]
             a_      :	Dict							=						inputs_dict['head_mask']
             a_      :	str							=						1

             # first forward pass
             a_      :	Dict							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							head_mask=SCREAMING_SNAKE_CASE__  ,							use_cache=SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	Optional[int]							=						outputs.to_tuple()

             # create hypothetical next token and extent to next_input_ids
             a_      :	Dict							=						ids_tensor((self.batch_size, 3)  ,							config.vocab_size  )
             a_      :	Tuple							=						tf.cast(ids_tensor((self.batch_size, 3)  ,							2  )  ,							tf.inta  )

             # append to next input_ids and
             a_      :	Optional[Any]							=						tf.concat([input_ids, next_tokens]  ,							axis=-1  )
             a_      :	Optional[int]							=						tf.concat([attention_mask, next_attn_mask]  ,							axis=-1  )

             a_      :	List[str]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  )[0]
             a_      :	Optional[int]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							past_key_values=SCREAMING_SNAKE_CASE__  )[0]

             self.parent.assertEqual(next_tokens.shape[1]  ,							output_from_past.shape[1]  )

             # select random slice
             a_      :	int							=						int(ids_tensor((1,)  ,							output_from_past.shape[-1]  )  )
             a_      :	Any							=						output_from_no_past[:, -3:, random_slice_idx]
             a_      :	Any							=						output_from_past[:, :, random_slice_idx]

             # test that outputs are equal for slice
             tf.debugging.assert_near(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							rtol=1E-3  )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int						,					__A				:    Optional[int]						,					__A				:    Optional[int]=None						,					__A				:    Optional[int]=None						,					__A				:    Dict=None						,					__A				:    Dict=None						,					__A				:    int=None						,					)			->	Any:

       """simple docstring"""

       if attention_mask is None:
              a_      :	Optional[Any]							=						tf.cast(tf.math.not_equal(__A						,					config.pad_token_id       )						,					tf.inta       )
       if decoder_attention_mask is None:
              a_      :	Optional[Any]							=						tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape						,					dtype=tf.inta       ),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,					config.pad_token_id       )						,					tf.inta       ),
                  ]						,					axis=-1						,					)
       if head_mask is None:
              a_      :	List[Any]							=						tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
       if decoder_head_mask is None:
              a_      :	Optional[int]							=						tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
       if cross_attn_head_mask is None:
              a_      :	List[Any]							=						tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }
@require_tf
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Optional[Any]       =		(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
      snake_case__       :  Optional[Any]       =		(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
      snake_case__       :  Dict       =		(
          {
              '''conversational''': TFPegasusForConditionalGeneration,
              '''feature-extraction''': TFPegasusModel,
              '''summarization''': TFPegasusForConditionalGeneration,
              '''text2text-generation''': TFPegasusForConditionalGeneration,
              '''translation''': TFPegasusForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      snake_case__       :  List[Any]       =		True
      snake_case__       :  List[Any]       =		False
      snake_case__       :  List[Any]       =		False
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Union[str, Any]:
             a_      :	Optional[int]							=						TFPegasusModelTester(self  )
             a_      :	str							=						ConfigTester(self  ,							config_class=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    int:
             self.config_tester.run_common_tests()

      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    List[str]:
             a_      :	List[str]							=						self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__  )
@require_sentencepiece
@require_tokenizers
@require_tf
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      snake_case__       :  Any       =		[
          ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
          ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
      ]
      snake_case__       :  List[Any]       =		[
          '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
          ''' reduce the risk of wildfires.''',
          '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
      ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
      snake_case__       :  int       =		'''google/pegasus-xsum'''
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[int]:
             return AutoTokenizer.from_pretrained(self.model_name  )
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Any:
             a_      :	Optional[int]							=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
             return model
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  ,							**SCREAMING_SNAKE_CASE__       : List[str]  )	->    List[str]:
             a_      :	Optional[int]							=						self.translate_src_text(**SCREAMING_SNAKE_CASE__  )
             assert self.expected_text == generated_words
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							**SCREAMING_SNAKE_CASE__       : Optional[int]  )	->    Tuple:
             a_      :	List[Any]							=						self.tokenizer(self.src_text  ,							**SCREAMING_SNAKE_CASE__  ,							padding=SCREAMING_SNAKE_CASE__  ,							return_tensors='tf'  )
             a_      :	int							=						self.model.generate(
                 model_inputs.input_ids  ,							attention_mask=model_inputs.attention_mask  ,							num_beams=2  ,							use_cache=SCREAMING_SNAKE_CASE__  ,							)
             a_      :	List[Any]							=						self.tokenizer.batch_decode(generated_ids.numpy()  ,							skip_special_tokens=SCREAMING_SNAKE_CASE__  )
             return generated_words

      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Union[str, Any]:
             self._assert_generated_batch_equal_expected()






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_   :				int						      = datasets.logging.get_logger(__name__)


UpperCAmelCase_   :				Dict						      = '\\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'

UpperCAmelCase_   :				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'

UpperCAmelCase_   :				Optional[Any]						      = '\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  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    Any						,					__A				:    List[Any]=False						,					__A				:    Tuple=False						,					__A				:    Any=True						,					__A				:    Any=False						,					__A				:    Any="dummy_doc"       )			->	List[str]:

       """simple docstring"""

       a_      :	List[str]							=						{doc: key_lines}
       a_      :	Optional[Any]							=						{doc: sys_lines}

       a_      :	List[Any]							=						{}

       a_      :	Tuple							=						0
       a_      :	List[str]							=						0
       a_      :	Union[str, Any]							=						0
       a_      :	List[Any]							=						0
       a_      :	List[str]							=						0
       a_      :	Union[str, Any]							=						0

       a_							,	a_      :	List[Any]							=						reader.get_doc_mentions(__A						,					key_doc_lines[doc]						,					__A       )
       key_singletons_num += singletons_num

       if NP_only or min_span:
              a_      :	Union[str, Any]							=						reader.set_annotated_parse_trees(__A						,					key_doc_lines[doc]						,					__A						,					__A       )

       a_							,	a_      :	Union[str, Any]							=						reader.get_doc_mentions(__A						,					sys_doc_lines[doc]						,					__A       )
       sys_singletons_num += singletons_num

       if NP_only or min_span:
              a_      :	Dict							=						reader.set_annotated_parse_trees(__A						,					key_doc_lines[doc]						,					__A						,					__A       )

       if remove_nested:
              a_							,	a_      :	Optional[Any]							=						reader.remove_nested_coref_mentions(__A						,					__A       )
              key_nested_coref_num += nested_mentions
              key_removed_nested_clusters += removed_clusters

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

       a_      :	int							=						reader.get_mention_assignments(__A						,					__A       )
       a_      :	List[Any]							=						reader.get_mention_assignments(__A						,					__A       )

       a_      :	List[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  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    Optional[int]						,					__A				:    Optional[Any]						,					__A				:    Optional[int]						,					__A				:    Tuple						,					__A				:    Dict						,					__A				:    Optional[int]       )			->	List[Any]:

       """simple docstring"""

       a_      :	int							=						get_coref_infos(__A						,					__A						,					__A						,					__A						,					__A						,					__A       )

       a_      :	List[Any]							=						{}
       a_      :	int							=						0
       a_      :	Optional[int]							=						0

       for name, metric in metrics:
              a_							,	a_							,	a_      :	Tuple							=						evaluator.evaluate_documents(__A						,					__A						,					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:
              a_      :	List[str]							=						(conll / 3) * 1_00
              logger.info(F"""CoNLL score: {conll:.2f}"""       )
              output_scores.update({'conll_score': conll}       )

       return output_scores







def  SCREAMING_SNAKE_CASE_		(    __A				:    int       )			->	Dict:

       """simple docstring"""

       a_      :	List[Any]							=						False
       for line in key_lines:
              if not line.startswith('#'       ):
                     if len(line.split()       ) > 6:
                            a_      :	List[Any]							=						line.split()[5]
                            if not parse_col == "-":
                                   a_      :	List[Any]							=						True
                                   break
                            else:
                                   break
       return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,					_KWARGS_DESCRIPTION    )
class 				SCREAMING_SNAKE_CASE__						(       datasets.Metric    ):
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    int:
             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  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							SCREAMING_SNAKE_CASE__       : Tuple=False  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=False  ,							SCREAMING_SNAKE_CASE__       : int=False  )	->    Tuple:
             a_      :	List[str]							=						[
                 ('mentions', evaluator.mentions),
                 ('muc', evaluator.muc),
                 ('bcub', evaluator.b_cubed),
                 ('ceafe', evaluator.ceafe),
                 ('lea', evaluator.lea),
             ]

             if min_span:
                    a_      :	Union[str, Any]							=						util.check_gold_parse_annotation(SCREAMING_SNAKE_CASE__  )
                    if not has_gold_parse:
                           raise NotImplementedError('References should have gold parse annotation to use \'min_span\'.'  )
                           # util.parse_key_file(key_file)
                           # key_file = key_file + ".parsed"

             a_      :	List[Any]							=						evaluate(
                 key_lines=SCREAMING_SNAKE_CASE__  ,							sys_lines=SCREAMING_SNAKE_CASE__  ,							metrics=SCREAMING_SNAKE_CASE__  ,							NP_only=SCREAMING_SNAKE_CASE__  ,							remove_nested=SCREAMING_SNAKE_CASE__  ,							keep_singletons=SCREAMING_SNAKE_CASE__  ,							min_span=SCREAMING_SNAKE_CASE__  ,							)

             return score


