


'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
  from huggingface_hub import snapshot_download
  from pyctcdecode import BeamSearchDecoderCTC

  from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
  from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
  from transformers import WavaVecaForCTC




@require_pyctcdecode
class 			lowerCamelCase_   (	unittest.TestCase      ):






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				'''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
          UpperCAmelCase__			:		str													=				dict(zip(_A						,						range(len(_A ) ) ) )

          UpperCAmelCase__			:		int													=				{
              '''unk_token''': '''<unk>''',
              '''bos_token''': '''<s>''',
              '''eos_token''': '''</s>''',
          }
          UpperCAmelCase__			:		List[Any]													=				{
              '''feature_size''': 1,
              '''padding_value''': 0.0,
              '''sampling_rate''': 16_000,
              '''return_attention_mask''': False,
              '''do_normalize''': True,
          }

          UpperCAmelCase__			:		str													=				tempfile.mkdtemp()
          UpperCAmelCase__			:		int													=				os.path.join(self.tmpdirname						,						VOCAB_FILES_NAMES['''vocab_file'''] )
          UpperCAmelCase__			:		List[Any]													=				os.path.join(self.tmpdirname						,						_A )
          with open(self.vocab_file						,						'''w'''						,						encoding='''utf-8''' ) as fp:
              fp.write(json.dumps(_A ) + '''\n''' )

          with open(self.feature_extraction_file						,						'''w'''						,						encoding='''utf-8''' ) as fp:
              fp.write(json.dumps(_A ) + '''\n''' )

          # load decoder from hub
          UpperCAmelCase__			:		Tuple													=				'''hf-internal-testing/ngram-beam-search-decoder'''






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


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				self.add_kwargs_tokens_map.copy()
          kwargs.update(_A )
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname						,						**_A )






      def lowercase_							(					self				:    Dict						,						**_A				:    str ):


          '''simple docstring'''


          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname						,						**_A )






      def lowercase_							(					self				:    Tuple						,						**_A				:    int ):


          '''simple docstring'''


          return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name						,						**_A )






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          shutil.rmtree(self.tmpdirname )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				self.get_tokenizer()
          UpperCAmelCase__			:		Any													=				self.get_feature_extractor()
          UpperCAmelCase__			:		Dict													=				self.get_decoder()

          UpperCAmelCase__			:		Dict													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          processor.save_pretrained(self.tmpdirname )
          UpperCAmelCase__			:		Optional[Any]													=				WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )

          # tokenizer
          self.assertEqual(processor.tokenizer.get_vocab()						,						tokenizer.get_vocab() )
          self.assertIsInstance(processor.tokenizer						,						_A )

          # feature extractor
          self.assertEqual(processor.feature_extractor.to_json_string()						,						feature_extractor.to_json_string() )
          self.assertIsInstance(processor.feature_extractor						,						_A )

          # decoder
          self.assertEqual(processor.decoder._alphabet.labels						,						decoder._alphabet.labels )
          self.assertEqual(
              processor.decoder.model_container[decoder._model_key]._unigram_set						,						decoder.model_container[decoder._model_key]._unigram_set						,						)
          self.assertIsInstance(processor.decoder						,						_A )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM(
              tokenizer=self.get_tokenizer()						,						feature_extractor=self.get_feature_extractor()						,						decoder=self.get_decoder() )
          processor.save_pretrained(self.tmpdirname )

          # make sure that error is thrown when decoder alphabet doesn't match
          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM.from_pretrained(
              self.tmpdirname						,						alpha=5.0						,						beta=3.0						,						score_boundary=-7.0						,						unk_score_offset=3 )

          # decoder
          self.assertEqual(processor.language_model.alpha						,						5.0 )
          self.assertEqual(processor.language_model.beta						,						3.0 )
          self.assertEqual(processor.language_model.score_boundary						,						-7.0 )
          self.assertEqual(processor.language_model.unk_score_offset						,						3 )






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				self.get_tokenizer()
          # add token to trigger raise
          tokenizer.add_tokens(['''xx'''] )
          with self.assertRaisesRegex(_A						,						'''include''' ):
              WavaVecaProcessorWithLM(
                  tokenizer=_A						,						feature_extractor=self.get_feature_extractor()						,						decoder=self.get_decoder() )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				self.get_feature_extractor()
          UpperCAmelCase__			:		List[Any]													=				self.get_tokenizer()
          UpperCAmelCase__			:		int													=				self.get_decoder()

          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          UpperCAmelCase__			:		str													=				floats_list((3, 1_000) )

          UpperCAmelCase__			:		str													=				feature_extractor(_A						,						return_tensors='''np''' )
          UpperCAmelCase__			:		Optional[Any]													=				processor(_A						,						return_tensors='''np''' )

          for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()						,						input_processor[key].sum()						,						delta=1e-2 )






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				self.get_feature_extractor()
          UpperCAmelCase__			:		Any													=				self.get_tokenizer()
          UpperCAmelCase__			:		Any													=				self.get_decoder()

          UpperCAmelCase__			:		str													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          UpperCAmelCase__			:		Union[str, Any]													=				'''This is a test string'''

          UpperCAmelCase__			:		Union[str, Any]													=				processor(text=_A )

          UpperCAmelCase__			:		Dict													=				tokenizer(_A )

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]						,						encoded_processor[key] )






      def lowercase_							(					self				:    str						,						_A				:    int=(2, 10, 16)						,						_A				:    Optional[int]=77 ):


          '''simple docstring'''


          np.random.seed(_A )
          return np.random.rand(*_A )






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				self.get_feature_extractor()
          UpperCAmelCase__			:		Dict													=				self.get_tokenizer()
          UpperCAmelCase__			:		Tuple													=				self.get_decoder()

          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          UpperCAmelCase__			:		Tuple													=				self._get_dummy_logits(shape=(10, 16)						,						seed=13 )

          UpperCAmelCase__			:		Union[str, Any]													=				processor.decode(_A )

          UpperCAmelCase__			:		Union[str, Any]													=				decoder.decode_beams(_A )[0]

          self.assertEqual(decoded_decoder[0]						,						decoded_processor.text )
          self.assertEqual('''</s> <s> </s>'''						,						decoded_processor.text )
          self.assertEqual(decoded_decoder[-2]						,						decoded_processor.logit_score )
          self.assertEqual(decoded_decoder[-1]						,						decoded_processor.lm_score )






      @parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
      def lowercase_							(					self				:    Dict						,						_A				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				self.get_feature_extractor()
          UpperCAmelCase__			:		Union[str, Any]													=				self.get_tokenizer()
          UpperCAmelCase__			:		Union[str, Any]													=				self.get_decoder()

          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          UpperCAmelCase__			:		List[str]													=				self._get_dummy_logits()

          # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
          #       otherwise, the LM won't be available to the pool's sub-processes.
          # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
          if pool_context is None:
              UpperCAmelCase__			:		Union[str, Any]													=				processor.batch_decode(_A )
          else:
              with get_context(_A ).Pool() as pool:
                  UpperCAmelCase__			:		List[Any]													=				processor.batch_decode(_A						,						_A )

          UpperCAmelCase__			:		List[str]													=				list(_A )

          with get_context('''fork''' ).Pool() as p:
              UpperCAmelCase__			:		List[Any]													=				decoder.decode_beams_batch(_A						,						_A )

          UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		List[Any]													=				[], [], []
          for beams in decoded_beams:
              texts_decoder.append(beams[0][0] )
              logit_scores_decoder.append(beams[0][-2] )
              lm_scores_decoder.append(beams[0][-1] )

          self.assertListEqual(_A						,						decoded_processor.text )
          self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']						,						decoded_processor.text )
          self.assertListEqual(_A						,						decoded_processor.logit_score )
          self.assertListEqual(_A						,						decoded_processor.lm_score )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				self.get_feature_extractor()
          UpperCAmelCase__			:		str													=				self.get_tokenizer()
          UpperCAmelCase__			:		Optional[int]													=				self.get_decoder()

          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          UpperCAmelCase__			:		List[Any]													=				self._get_dummy_logits()

          UpperCAmelCase__			:		Optional[Any]													=				15
          UpperCAmelCase__			:		Union[str, Any]													=				-2_0.0
          UpperCAmelCase__			:		List[str]													=				-4.0

          UpperCAmelCase__			:		str													=				processor.batch_decode(
              _A						,						beam_width=_A						,						beam_prune_logp=_A						,						token_min_logp=_A						,						)
          UpperCAmelCase__			:		Union[str, Any]													=				decoded_processor_out.text

          UpperCAmelCase__			:		Tuple													=				list(_A )

          with get_context('''fork''' ).Pool() as pool:
              UpperCAmelCase__			:		Tuple													=				decoder.decode_beams_batch(
                  _A						,						_A						,						beam_width=_A						,						beam_prune_logp=_A						,						token_min_logp=_A						,						)

          UpperCAmelCase__			:		Optional[Any]													=				[d[0][0] for d in decoded_decoder_out]
          UpperCAmelCase__			:		Any													=				[d[0][2] for d in decoded_decoder_out]
          UpperCAmelCase__			:		Tuple													=				[d[0][3] for d in decoded_decoder_out]

          self.assertListEqual(_A						,						_A )
          self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>''']						,						_A )

          self.assertTrue(np.array_equal(_A						,						decoded_processor_out.logit_score ) )
          self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7]						,						_A						,						atol=1e-3 ) )

          self.assertTrue(np.array_equal(_A						,						decoded_processor_out.lm_score ) )
          self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4]						,						_A						,						atol=1e-3 ) )






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.get_feature_extractor()
          UpperCAmelCase__			:		str													=				self.get_tokenizer()
          UpperCAmelCase__			:		List[str]													=				self.get_decoder()

          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          UpperCAmelCase__			:		Optional[Any]													=				self._get_dummy_logits()

          UpperCAmelCase__			:		List[Any]													=				2.0
          UpperCAmelCase__			:		Union[str, Any]													=				5.0
          UpperCAmelCase__			:		Any													=				-2_0.0
          UpperCAmelCase__			:		List[Any]													=				True

          UpperCAmelCase__			:		Any													=				processor.batch_decode(
              _A						,						alpha=_A						,						beta=_A						,						unk_score_offset=_A						,						lm_score_boundary=_A						,						)
          UpperCAmelCase__			:		List[Any]													=				decoded_processor_out.text

          UpperCAmelCase__			:		List[Any]													=				list(_A )
          decoder.reset_params(
              alpha=_A						,						beta=_A						,						unk_score_offset=_A						,						lm_score_boundary=_A						,						)

          with get_context('''fork''' ).Pool() as pool:
              UpperCAmelCase__			:		Any													=				decoder.decode_beams_batch(
                  _A						,						_A						,						)

          UpperCAmelCase__			:		Union[str, Any]													=				[d[0][0] for d in decoded_decoder_out]

          self.assertListEqual(_A						,						_A )
          self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']						,						_A )
          UpperCAmelCase__			:		Any													=				processor.decoder.model_container[processor.decoder._model_key]
          self.assertEqual(lm_model.alpha						,						2.0 )
          self.assertEqual(lm_model.beta						,						5.0 )
          self.assertEqual(lm_model.unk_score_offset						,						-2_0.0 )
          self.assertEqual(lm_model.score_boundary						,						_A )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )

          UpperCAmelCase__			:		List[Any]													=				processor.decoder.model_container[processor.decoder._model_key]
          UpperCAmelCase__			:		Optional[Any]													=				Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()

          UpperCAmelCase__			:		Dict													=				os.listdir(_A )
          UpperCAmelCase__			:		str													=				['''alphabet.json''', '''language_model''']

          downloaded_decoder_files.sort()
          expected_decoder_files.sort()

          # test that only decoder relevant files from
          # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
          # are downloaded and none of the rest (e.g. README.md, ...)
          self.assertListEqual(_A						,						_A )






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				snapshot_download('''hf-internal-testing/processor_with_lm''' )

          UpperCAmelCase__			:		Tuple													=				WavaVecaProcessorWithLM.from_pretrained(_A )

          UpperCAmelCase__			:		Optional[Any]													=				processor.decoder.model_container[processor.decoder._model_key]
          UpperCAmelCase__			:		Union[str, Any]													=				Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()

          UpperCAmelCase__			:		Optional[Any]													=				os.listdir(_A )
          UpperCAmelCase__			:		int													=				os.listdir(_A )

          local_decoder_files.sort()
          expected_decoder_files.sort()

          # test that both decoder form hub and local files in cache are the same
          self.assertListEqual(_A						,						_A )






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
          UpperCAmelCase__			:		Any													=				AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )

          UpperCAmelCase__			:		Tuple													=				floats_list((3, 1_000) )

          UpperCAmelCase__			:		Tuple													=				processor_wavaveca(_A						,						return_tensors='''np''' )
          UpperCAmelCase__			:		Union[str, Any]													=				processor_auto(_A						,						return_tensors='''np''' )

          for key in input_wavaveca.keys():
              self.assertAlmostEqual(input_wavaveca[key].sum()						,						input_auto[key].sum()						,						delta=1e-2 )

          UpperCAmelCase__			:		str													=				self._get_dummy_logits()

          UpperCAmelCase__			:		List[Any]													=				processor_wavaveca.batch_decode(_A )
          UpperCAmelCase__			:		List[Any]													=				processor_auto.batch_decode(_A )

          self.assertListEqual(decoded_wavaveca.text						,						decoded_auto.text )






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


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				self.get_feature_extractor()
          UpperCAmelCase__			:		Any													=				self.get_tokenizer()
          UpperCAmelCase__			:		List[str]													=				self.get_decoder()

          UpperCAmelCase__			:		Dict													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          self.assertListEqual(
              processor.model_input_names						,						feature_extractor.model_input_names						,						msg='''`processor` and `feature_extractor` model input names do not match'''						,						)






      @staticmethod
      def lowercase_							(					_A				:    Tuple						,						_A				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				[d[key] for d in offsets]
          return retrieved_list






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
          UpperCAmelCase__			:		Tuple													=				self._get_dummy_logits()[0]

          UpperCAmelCase__			:		int													=				processor.decode(_A						,						output_word_offsets=_A )
          # check Wav2Vec2CTCTokenizerOutput keys for word
          self.assertEqual(len(outputs.keys() )						,						4 )
          self.assertTrue('''text''' in outputs )
          self.assertTrue('''word_offsets''' in outputs )
          self.assertTrue(isinstance(_A						,						_A ) )

          self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets''']						,						'''word''' ) )						,						outputs.text )
          self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']						,						'''word''' )						,						['''<s>''', '''<s>''', '''</s>'''] )
          self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']						,						'''start_offset''' )						,						[0, 2, 4] )
          self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']						,						'''end_offset''' )						,						[1, 3, 5] )






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
          UpperCAmelCase__			:		List[str]													=				self._get_dummy_logits()

          UpperCAmelCase__			:		Dict													=				processor.batch_decode(_A						,						output_word_offsets=_A )

          # check Wav2Vec2CTCTokenizerOutput keys for word
          self.assertEqual(len(outputs.keys() )						,						4 )
          self.assertTrue('''text''' in outputs )
          self.assertTrue('''word_offsets''' in outputs )
          self.assertTrue(isinstance(_A						,						_A ) )

          self.assertListEqual(
              [''' '''.join(self.get_from_offsets(_A						,						'''word''' ) ) for o in outputs['''word_offsets''']]						,						outputs.text )
          self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]						,						'''word''' )						,						['''<s>''', '''<s>''', '''</s>'''] )
          self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]						,						'''start_offset''' )						,						[0, 2, 4] )
          self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]						,						'''end_offset''' )						,						[1, 3, 5] )






      @slow
      @require_torch
      @require_torchaudio
      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          import torch

          UpperCAmelCase__			:		Dict													=				load_dataset('''common_voice'''						,						'''en'''						,						split='''train'''						,						streaming=_A )
          UpperCAmelCase__			:		Optional[Any]													=				ds.cast_column('''audio'''						,						datasets.Audio(sampling_rate=16_000 ) )
          UpperCAmelCase__			:		Any													=				iter(_A )
          UpperCAmelCase__			:		Dict													=				next(_A )

          UpperCAmelCase__			:		Optional[int]													=				AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
          UpperCAmelCase__			:		List[Any]													=				WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )

          # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
          UpperCAmelCase__			:		Union[str, Any]													=				processor(sample['''audio''']['''array''']						,						return_tensors='''pt''' ).input_values

          with torch.no_grad():
              UpperCAmelCase__			:		Dict													=				model(_A ).logits.cpu().numpy()

          UpperCAmelCase__			:		List[str]													=				processor.decode(logits[0]						,						output_word_offsets=_A )

          UpperCAmelCase__			:		Tuple													=				model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
          UpperCAmelCase__			:		List[str]													=				[
              {
                  '''start_time''': d['''start_offset'''] * time_offset,
                  '''end_time''': d['''end_offset'''] * time_offset,
                  '''word''': d['''word'''],
              }
              for d in output['''word_offsets''']
          ]

          UpperCAmelCase__			:		Tuple													=				'''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

          # output words
          self.assertEqual(''' '''.join(self.get_from_offsets(_A						,						'''word''' ) )						,						_A )
          self.assertEqual(''' '''.join(self.get_from_offsets(_A						,						'''word''' ) )						,						output.text )

          # output times
          UpperCAmelCase__			:		List[Any]													=				torch.tensor(self.get_from_offsets(_A						,						'''start_time''' ) )
          UpperCAmelCase__			:		Dict													=				torch.tensor(self.get_from_offsets(_A						,						'''end_time''' ) )

          # fmt: off
          UpperCAmelCase__			:		Any													=				torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9] )
          UpperCAmelCase__			:		Any													=				torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4] )
          # fmt: on

          self.assertTrue(torch.allclose(_A						,						_A						,						atol=0.0_1 ) )
          self.assertTrue(torch.allclose(_A						,						_A						,						atol=0.0_1 ) )




'''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
