
'''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   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			"""| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
												_lowerCAmelCase  						=			dict(zip(lowerCAmelCase_   ,       range(len(lowerCAmelCase_				)				)				)				)

												_lowerCAmelCase  						=			{
												    """unk_token""": """<unk>""",
												    """bos_token""": """<s>""",
												    """eos_token""": """</s>""",
												}
												_lowerCAmelCase  						=			{
												    """feature_size""": 1,
												    """padding_value""": 0.0,
												    """sampling_rate""": 16_000,
												    """return_attention_mask""": False,
												    """do_normalize""": True,
												}

												_lowerCAmelCase  						=			tempfile.mkdtemp()
												_lowerCAmelCase  						=			os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES["""vocab_file"""]				)
												_lowerCAmelCase  						=			os.path.join(self.tmpdirname   ,       lowerCAmelCase_				)
												with open(self.vocab_file   ,       """w"""   ,       encoding="""utf-8"""				) as fp:
																		fp.write(json.dumps(lowerCAmelCase_				) + """\n"""				)

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

												# load decoder from hub
												_lowerCAmelCase  						=			"""hf-internal-testing/ngram-beam-search-decoder"""




						def  _lowercase     ( self   ,       **_lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.add_kwargs_tokens_map.copy()
												kwargs.update(lowerCAmelCase_				)
												return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname   ,       **lowerCAmelCase_				)




						def  _lowercase     ( self   ,       **_lowercase				):

												"""simple docstring"""



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




						def  _lowercase     ( self   ,       **_lowercase				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



												shutil.rmtree(self.tmpdirname				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_tokenizer()
												_lowerCAmelCase  						=			self.get_feature_extractor()
												_lowerCAmelCase  						=			self.get_decoder()

												_lowerCAmelCase  						=			WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_   ,       feature_extractor=lowerCAmelCase_   ,       decoder=lowerCAmelCase_				)

												processor.save_pretrained(self.tmpdirname				)
												_lowerCAmelCase  						=			WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname				)

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

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

												# 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   ,       lowerCAmelCase_				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			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				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_tokenizer()
												# add token to trigger raise
												tokenizer.add_tokens(["""xx"""]				)
												with self.assertRaisesRegex(lowerCAmelCase_   ,       """include"""				):
																		WavaVecaProcessorWithLM(
																		    tokenizer=lowerCAmelCase_   ,       feature_extractor=self.get_feature_extractor()   ,       decoder=self.get_decoder()				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_feature_extractor()
												_lowerCAmelCase  						=			self.get_tokenizer()
												_lowerCAmelCase  						=			self.get_decoder()

												_lowerCAmelCase  						=			WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_   ,       feature_extractor=lowerCAmelCase_   ,       decoder=lowerCAmelCase_				)

												_lowerCAmelCase  						=			floats_list((3, 1_000)				)

												_lowerCAmelCase  						=			feature_extractor(lowerCAmelCase_   ,       return_tensors="""np"""				)
												_lowerCAmelCase  						=			processor(lowerCAmelCase_   ,       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				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_feature_extractor()
												_lowerCAmelCase  						=			self.get_tokenizer()
												_lowerCAmelCase  						=			self.get_decoder()

												_lowerCAmelCase  						=			WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_   ,       feature_extractor=lowerCAmelCase_   ,       decoder=lowerCAmelCase_				)

												_lowerCAmelCase  						=			"""This is a test string"""

												_lowerCAmelCase  						=			processor(text=lowerCAmelCase_				)

												_lowerCAmelCase  						=			tokenizer(lowerCAmelCase_				)

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




						def  _lowercase     ( self   ,       _lowercase=(2, 10, 16)   ,       _lowercase=77				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_feature_extractor()
												_lowerCAmelCase  						=			self.get_tokenizer()
												_lowerCAmelCase  						=			self.get_decoder()

												_lowerCAmelCase  						=			WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_   ,       feature_extractor=lowerCAmelCase_   ,       decoder=lowerCAmelCase_				)

												_lowerCAmelCase  						=			self._get_dummy_logits(shape=(10, 16)   ,       seed=13				)

												_lowerCAmelCase  						=			processor.decode(lowerCAmelCase_				)

												_lowerCAmelCase  						=			decoder.decode_beams(lowerCAmelCase_				)[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   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_feature_extractor()
												_lowerCAmelCase  						=			self.get_tokenizer()
												_lowerCAmelCase  						=			self.get_decoder()

												_lowerCAmelCase  						=			WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_   ,       feature_extractor=lowerCAmelCase_   ,       decoder=lowerCAmelCase_				)

												_lowerCAmelCase  						=			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:
																		_lowerCAmelCase  						=			processor.batch_decode(lowerCAmelCase_				)
												else:
																		with get_context(lowerCAmelCase_				).Pool() as pool:
																								_lowerCAmelCase  						=			processor.batch_decode(lowerCAmelCase_   ,       lowerCAmelCase_				)

												_lowerCAmelCase  						=			list(lowerCAmelCase_				)

												with get_context("""fork"""				).Pool() as p:
																		_lowerCAmelCase  						=			decoder.decode_beams_batch(lowerCAmelCase_   ,       lowerCAmelCase_				)

												_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			[], [], []
												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(lowerCAmelCase_   ,       decoded_processor.text				)
												self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""]   ,       decoded_processor.text				)
												self.assertListEqual(lowerCAmelCase_   ,       decoded_processor.logit_score				)
												self.assertListEqual(lowerCAmelCase_   ,       decoded_processor.lm_score				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_feature_extractor()
												_lowerCAmelCase  						=			self.get_tokenizer()
												_lowerCAmelCase  						=			self.get_decoder()

												_lowerCAmelCase  						=			WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_   ,       feature_extractor=lowerCAmelCase_   ,       decoder=lowerCAmelCase_				)

												_lowerCAmelCase  						=			self._get_dummy_logits()

												_lowerCAmelCase  						=			15
												_lowerCAmelCase  						=			-20.0
												_lowerCAmelCase  						=			-4.0

												_lowerCAmelCase  						=			processor.batch_decode(
												    lowerCAmelCase_   ,       beam_width=lowerCAmelCase_   ,       beam_prune_logp=lowerCAmelCase_   ,       token_min_logp=lowerCAmelCase_   ,       )
												_lowerCAmelCase  						=			decoded_processor_out.text

												_lowerCAmelCase  						=			list(lowerCAmelCase_				)

												with get_context("""fork"""				).Pool() as pool:
																		_lowerCAmelCase  						=			decoder.decode_beams_batch(
																		    lowerCAmelCase_   ,       lowerCAmelCase_   ,       beam_width=lowerCAmelCase_   ,       beam_prune_logp=lowerCAmelCase_   ,       token_min_logp=lowerCAmelCase_   ,       )

												_lowerCAmelCase  						=			[d[0][0] for d in decoded_decoder_out]
												_lowerCAmelCase  						=			[d[0][2] for d in decoded_decoder_out]
												_lowerCAmelCase  						=			[d[0][3] for d in decoded_decoder_out]

												self.assertListEqual(lowerCAmelCase_   ,       lowerCAmelCase_				)
												self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""]   ,       lowerCAmelCase_				)

												self.assertTrue(np.array_equal(lowerCAmelCase_   ,       decoded_processor_out.logit_score				)				)
												self.assertTrue(np.allclose([-20.054, -18.447]   ,       lowerCAmelCase_   ,       atol=1e-3				)				)

												self.assertTrue(np.array_equal(lowerCAmelCase_   ,       decoded_processor_out.lm_score				)				)
												self.assertTrue(np.allclose([-15.554, -13.9474]   ,       lowerCAmelCase_   ,       atol=1e-3				)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_feature_extractor()
												_lowerCAmelCase  						=			self.get_tokenizer()
												_lowerCAmelCase  						=			self.get_decoder()

												_lowerCAmelCase  						=			WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_   ,       feature_extractor=lowerCAmelCase_   ,       decoder=lowerCAmelCase_				)

												_lowerCAmelCase  						=			self._get_dummy_logits()

												_lowerCAmelCase  						=			2.0
												_lowerCAmelCase  						=			5.0
												_lowerCAmelCase  						=			-20.0
												_lowerCAmelCase  						=			True

												_lowerCAmelCase  						=			processor.batch_decode(
												    lowerCAmelCase_   ,       alpha=lowerCAmelCase_   ,       beta=lowerCAmelCase_   ,       unk_score_offset=lowerCAmelCase_   ,       lm_score_boundary=lowerCAmelCase_   ,       )
												_lowerCAmelCase  						=			decoded_processor_out.text

												_lowerCAmelCase  						=			list(lowerCAmelCase_				)
												decoder.reset_params(
												    alpha=lowerCAmelCase_   ,       beta=lowerCAmelCase_   ,       unk_score_offset=lowerCAmelCase_   ,       lm_score_boundary=lowerCAmelCase_   ,       )

												with get_context("""fork"""				).Pool() as pool:
																		_lowerCAmelCase  						=			decoder.decode_beams_batch(
																		    lowerCAmelCase_   ,       lowerCAmelCase_   ,       )

												_lowerCAmelCase  						=			[d[0][0] for d in decoded_decoder_out]

												self.assertListEqual(lowerCAmelCase_   ,       lowerCAmelCase_				)
												self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""]   ,       lowerCAmelCase_				)
												_lowerCAmelCase  						=			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   ,       -20.0				)
												self.assertEqual(lm_model.score_boundary   ,       lowerCAmelCase_				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""				)

												_lowerCAmelCase  						=			processor.decoder.model_container[processor.decoder._model_key]
												_lowerCAmelCase  						=			Path(language_model._kenlm_model.path.decode("""utf-8"""				)				).parent.parent.absolute()

												_lowerCAmelCase  						=			os.listdir(lowerCAmelCase_				)
												_lowerCAmelCase  						=			["""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(lowerCAmelCase_   ,       lowerCAmelCase_				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			snapshot_download("""hf-internal-testing/processor_with_lm"""				)

												_lowerCAmelCase  						=			WavaVecaProcessorWithLM.from_pretrained(lowerCAmelCase_				)

												_lowerCAmelCase  						=			processor.decoder.model_container[processor.decoder._model_key]
												_lowerCAmelCase  						=			Path(language_model._kenlm_model.path.decode("""utf-8"""				)				).parent.parent.absolute()

												_lowerCAmelCase  						=			os.listdir(lowerCAmelCase_				)
												_lowerCAmelCase  						=			os.listdir(lowerCAmelCase_				)

												local_decoder_files.sort()
												expected_decoder_files.sort()

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""				)
												_lowerCAmelCase  						=			AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""				)

												_lowerCAmelCase  						=			floats_list((3, 1_000)				)

												_lowerCAmelCase  						=			processor_wavaveca(lowerCAmelCase_   ,       return_tensors="""np"""				)
												_lowerCAmelCase  						=			processor_auto(lowerCAmelCase_   ,       return_tensors="""np"""				)

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

												_lowerCAmelCase  						=			self._get_dummy_logits()

												_lowerCAmelCase  						=			processor_wavaveca.batch_decode(lowerCAmelCase_				)
												_lowerCAmelCase  						=			processor_auto.batch_decode(lowerCAmelCase_				)

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_feature_extractor()
												_lowerCAmelCase  						=			self.get_tokenizer()
												_lowerCAmelCase  						=			self.get_decoder()

												_lowerCAmelCase  						=			WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_   ,       feature_extractor=lowerCAmelCase_   ,       decoder=lowerCAmelCase_				)

												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     ( _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			[d[key] for d in offsets]
												return retrieved_list




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""				)
												_lowerCAmelCase  						=			self._get_dummy_logits()[0]

												_lowerCAmelCase  						=			processor.decode(lowerCAmelCase_   ,       output_word_offsets=lowerCAmelCase_				)
												# 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(lowerCAmelCase_   ,       lowerCAmelCase_				)				)

												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				):

												"""simple docstring"""



												_lowerCAmelCase  						=			WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""				)
												_lowerCAmelCase  						=			self._get_dummy_logits()

												_lowerCAmelCase  						=			processor.batch_decode(lowerCAmelCase_   ,       output_word_offsets=lowerCAmelCase_				)

												# 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(lowerCAmelCase_   ,       lowerCAmelCase_				)				)

												self.assertListEqual(
												    [""" """.join(self.get_from_offsets(lowerCAmelCase_   ,       """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				):

												"""simple docstring"""



												import torch

												_lowerCAmelCase  						=			load_dataset("""common_voice"""   ,       """en"""   ,       split="""train"""   ,       streaming=lowerCAmelCase_				)
												_lowerCAmelCase  						=			ds.cast_column("""audio"""   ,       datasets.Audio(sampling_rate=16_000				)				)
												_lowerCAmelCase  						=			iter(lowerCAmelCase_				)
												_lowerCAmelCase  						=			next(lowerCAmelCase_				)

												_lowerCAmelCase  						=			AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""				)
												_lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			processor(sample["""audio"""]["""array"""]   ,       return_tensors="""pt"""				).input_values

												with torch.no_grad():
																		_lowerCAmelCase  						=			model(lowerCAmelCase_				).logits.cpu().numpy()

												_lowerCAmelCase  						=			processor.decode(logits[0]   ,       output_word_offsets=lowerCAmelCase_				)

												_lowerCAmelCase  						=			model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
												_lowerCAmelCase  						=			[
												    {
												        """start_time""": d["""start_offset"""] * time_offset,
												        """end_time""": d["""end_offset"""] * time_offset,
												        """word""": d["""word"""],
												    }
												    for d in output["""word_offsets"""]
												]

												_lowerCAmelCase  						=			"""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(lowerCAmelCase_   ,       """word"""				)				)   ,       lowerCAmelCase_				)
												self.assertEqual(""" """.join(self.get_from_offsets(lowerCAmelCase_   ,       """word"""				)				)   ,       output.text				)

												# output times
												_lowerCAmelCase  						=			torch.tensor(self.get_from_offsets(lowerCAmelCase_   ,       """start_time"""				)				)
												_lowerCAmelCase  						=			torch.tensor(self.get_from_offsets(lowerCAmelCase_   ,       """end_time"""				)				)

												# fmt: off
												_lowerCAmelCase  						=			torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599]				)
												_lowerCAmelCase  						=			torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94]				)
												# fmt: on

												self.assertTrue(torch.allclose(lowerCAmelCase_   ,       lowerCAmelCase_   ,       atol=0.01				)				)
												self.assertTrue(torch.allclose(lowerCAmelCase_   ,       lowerCAmelCase_   ,       atol=0.01				)				)



'''simple docstring'''



# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

_lowercase    =		"""2.13.1"""

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("""3.7"""):
						raise ImportWarning(
						    """To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
						)

if version.parse(pyarrow.__version__).major < 8:
						raise ImportWarning(
						    """To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
						    """If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
						)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

_lowercase    =		concatenate_datasets
_lowercase    =		DownloadConfig
_lowercase    =		DownloadManager
_lowercase    =		DownloadMode
_lowercase    =		DownloadConfig
_lowercase    =		DownloadMode
_lowercase    =		DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager
