




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    lowercase_		(unittest.TestCase ):



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							{
								    'unk_token': '<unk>',
								    'bos_token': '<s>',
								    'eos_token': '</s>',
								}
								SCREAMING_SNAKE_CASE_					      =							{
								    'feature_size': 1,
								    'padding_value': 0.0,
								    'sampling_rate': 1_60_00,
								    'return_attention_mask': False,
								    'do_normalize': True,
								}

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

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

								# load decoder from hub
								SCREAMING_SNAKE_CASE_					      =							'hf-internal-testing/ngram-beam-search-decoder'



						def 	__a					(			self	:   Any					,							**snake_case__	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.add_kwargs_tokens_map.copy()
								kwargs.update(snake_case__  )
								return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname					,							**snake_case__  )



						def 	__a					(			self	:   Dict					,							**snake_case__	:   List[Any]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[Any]					,							**snake_case__	:   int  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								shutil.rmtree(self.tmpdirname  )



						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							WavaVecaProcessorWithLM(tokenizer=snake_case__					,							feature_extractor=snake_case__					,							decoder=snake_case__  )

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

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

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

								# 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					,							snake_case__  )



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							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
								SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							WavaVecaProcessorWithLM(tokenizer=snake_case__					,							feature_extractor=snake_case__					,							decoder=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							floats_list((3, 10_00)  )

								SCREAMING_SNAKE_CASE_					      =							feature_extractor(snake_case__					,							return_tensors='np'  )
								SCREAMING_SNAKE_CASE_					      =							processor(snake_case__					,							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 	__a					(			self	:   Any  ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							WavaVecaProcessorWithLM(tokenizer=snake_case__					,							feature_extractor=snake_case__					,							decoder=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							'This is a test string'

								SCREAMING_SNAKE_CASE_					      =							processor(text=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							tokenizer(snake_case__  )

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



						def 	__a					(			self	:   Dict					,							snake_case__	:   Any=(2, 10, 16)					,							snake_case__	:   Union[str, Any]=77  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							WavaVecaProcessorWithLM(tokenizer=snake_case__					,							feature_extractor=snake_case__					,							decoder=snake_case__  )

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

								SCREAMING_SNAKE_CASE_					      =							processor.decode(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							decoder.decode_beams(snake_case__  )[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 	__a					(			self	:   Dict					,							snake_case__	:   Dict  ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							WavaVecaProcessorWithLM(tokenizer=snake_case__					,							feature_extractor=snake_case__					,							decoder=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							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:
										SCREAMING_SNAKE_CASE_					      =							processor.batch_decode(snake_case__  )
								else:
										with get_context(snake_case__  ).Pool() as pool:
												SCREAMING_SNAKE_CASE_					      =							processor.batch_decode(snake_case__					,							snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							list(snake_case__  )

								with get_context('fork'  ).Pool() as p:
										SCREAMING_SNAKE_CASE_					      =							decoder.decode_beams_batch(snake_case__					,							snake_case__  )

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



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							WavaVecaProcessorWithLM(tokenizer=snake_case__					,							feature_extractor=snake_case__					,							decoder=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							self._get_dummy_logits()

								SCREAMING_SNAKE_CASE_					      =							15
								SCREAMING_SNAKE_CASE_					      =							-20.0
								SCREAMING_SNAKE_CASE_					      =							-4.0

								SCREAMING_SNAKE_CASE_					      =							processor.batch_decode(
								    snake_case__					,							beam_width=snake_case__					,							beam_prune_logp=snake_case__					,							token_min_logp=snake_case__					,							)
								SCREAMING_SNAKE_CASE_					      =							decoded_processor_out.text

								SCREAMING_SNAKE_CASE_					      =							list(snake_case__  )

								with get_context('fork'  ).Pool() as pool:
										SCREAMING_SNAKE_CASE_					      =							decoder.decode_beams_batch(
										    snake_case__					,							snake_case__					,							beam_width=snake_case__					,							beam_prune_logp=snake_case__					,							token_min_logp=snake_case__					,							)

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

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

								self.assertTrue(np.array_equal(snake_case__					,							decoded_processor_out.logit_score  )  )
								self.assertTrue(np.allclose([-20.0_54, -18.4_47]					,							snake_case__					,							atol=1e-3  )  )

								self.assertTrue(np.array_equal(snake_case__					,							decoded_processor_out.lm_score  )  )
								self.assertTrue(np.allclose([-15.5_54, -13.94_74]					,							snake_case__					,							atol=1e-3  )  )



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							WavaVecaProcessorWithLM(tokenizer=snake_case__					,							feature_extractor=snake_case__					,							decoder=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							self._get_dummy_logits()

								SCREAMING_SNAKE_CASE_					      =							2.0
								SCREAMING_SNAKE_CASE_					      =							5.0
								SCREAMING_SNAKE_CASE_					      =							-20.0
								SCREAMING_SNAKE_CASE_					      =							True

								SCREAMING_SNAKE_CASE_					      =							processor.batch_decode(
								    snake_case__					,							alpha=snake_case__					,							beta=snake_case__					,							unk_score_offset=snake_case__					,							lm_score_boundary=snake_case__					,							)
								SCREAMING_SNAKE_CASE_					      =							decoded_processor_out.text

								SCREAMING_SNAKE_CASE_					      =							list(snake_case__  )
								decoder.reset_params(
								    alpha=snake_case__					,							beta=snake_case__					,							unk_score_offset=snake_case__					,							lm_score_boundary=snake_case__					,							)

								with get_context('fork'  ).Pool() as pool:
										SCREAMING_SNAKE_CASE_					      =							decoder.decode_beams_batch(
										    snake_case__					,							snake_case__					,							)

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

								self.assertListEqual(snake_case__					,							snake_case__  )
								self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>']					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							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					,							snake_case__  )



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'  )

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

								SCREAMING_SNAKE_CASE_					      =							os.listdir(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							['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(snake_case__					,							snake_case__  )



						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							snapshot_download('hf-internal-testing/processor_with_lm'  )

								SCREAMING_SNAKE_CASE_					      =							WavaVecaProcessorWithLM.from_pretrained(snake_case__  )

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

								SCREAMING_SNAKE_CASE_					      =							os.listdir(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							os.listdir(snake_case__  )

								local_decoder_files.sort()
								expected_decoder_files.sort()

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



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'  )
								SCREAMING_SNAKE_CASE_					      =							AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm'  )

								SCREAMING_SNAKE_CASE_					      =							floats_list((3, 10_00)  )

								SCREAMING_SNAKE_CASE_					      =							processor_wavaveca(snake_case__					,							return_tensors='np'  )
								SCREAMING_SNAKE_CASE_					      =							processor_auto(snake_case__					,							return_tensors='np'  )

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

								SCREAMING_SNAKE_CASE_					      =							self._get_dummy_logits()

								SCREAMING_SNAKE_CASE_					      =							processor_wavaveca.batch_decode(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							processor_auto.batch_decode(snake_case__  )

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



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							WavaVecaProcessorWithLM(tokenizer=snake_case__					,							feature_extractor=snake_case__					,							decoder=snake_case__  )

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



						@staticmethod
						def 	__a					(			snake_case__	:   Optional[int]					,							snake_case__	:   Union[str, Any]  ):

								"""simple docstring"""

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



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

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							processor.decode(snake_case__					,							output_word_offsets=snake_case__  )
								# 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(snake_case__					,							snake_case__  )  )

								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 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'  )
								SCREAMING_SNAKE_CASE_					      =							self._get_dummy_logits()

								SCREAMING_SNAKE_CASE_					      =							processor.batch_decode(snake_case__					,							output_word_offsets=snake_case__  )

								# 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(snake_case__					,							snake_case__  )  )

								self.assertListEqual(
								    [' '.join(self.get_from_offsets(snake_case__					,							'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 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								import torch

								SCREAMING_SNAKE_CASE_					      =							load_dataset('common_voice'					,							'en'					,							split='train'					,							streaming=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							ds.cast_column('audio'					,							datasets.Audio(sampling_rate=1_60_00  )  )
								SCREAMING_SNAKE_CASE_					      =							iter(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							next(snake_case__  )

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

								with torch.no_grad():
										SCREAMING_SNAKE_CASE_					      =							model(snake_case__  ).logits.cpu().numpy()

								SCREAMING_SNAKE_CASE_					      =							processor.decode(logits[0]					,							output_word_offsets=snake_case__  )

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

								SCREAMING_SNAKE_CASE_					      =							'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(snake_case__					,							'word'  )  )					,							snake_case__  )
								self.assertEqual(' '.join(self.get_from_offsets(snake_case__					,							'word'  )  )					,							output.text  )

								# output times
								SCREAMING_SNAKE_CASE_					      =							torch.tensor(self.get_from_offsets(snake_case__					,							'start_time'  )  )
								SCREAMING_SNAKE_CASE_					      =							torch.tensor(self.get_from_offsets(snake_case__					,							'end_time'  )  )

								# fmt: off
								SCREAMING_SNAKE_CASE_					      =							torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99]  )
								SCREAMING_SNAKE_CASE_					      =							torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94]  )
								# fmt: on

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Tuple									= ['''RoFormerTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Any									= [
						    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RoFormerForCausalLM''',
						    '''RoFormerForMaskedLM''',
						    '''RoFormerForMultipleChoice''',
						    '''RoFormerForQuestionAnswering''',
						    '''RoFormerForSequenceClassification''',
						    '''RoFormerForTokenClassification''',
						    '''RoFormerLayer''',
						    '''RoFormerModel''',
						    '''RoFormerPreTrainedModel''',
						    '''load_tf_weights_in_roformer''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Dict									= [
						    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFRoFormerForCausalLM''',
						    '''TFRoFormerForMaskedLM''',
						    '''TFRoFormerForMultipleChoice''',
						    '''TFRoFormerForQuestionAnswering''',
						    '''TFRoFormerForSequenceClassification''',
						    '''TFRoFormerForTokenClassification''',
						    '''TFRoFormerLayer''',
						    '''TFRoFormerModel''',
						    '''TFRoFormerPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     str									= [
						    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlaxRoFormerForMaskedLM''',
						    '''FlaxRoFormerForMultipleChoice''',
						    '''FlaxRoFormerForQuestionAnswering''',
						    '''FlaxRoFormerForSequenceClassification''',
						    '''FlaxRoFormerForTokenClassification''',
						    '''FlaxRoFormerModel''',
						    '''FlaxRoFormerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
						from .tokenization_roformer import RoFormerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roformer_fast import RoFormerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roformer import (
												    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoFormerForCausalLM,
												    RoFormerForMaskedLM,
												    RoFormerForMultipleChoice,
												    RoFormerForQuestionAnswering,
												    RoFormerForSequenceClassification,
												    RoFormerForTokenClassification,
												    RoFormerLayer,
												    RoFormerModel,
												    RoFormerPreTrainedModel,
												    load_tf_weights_in_roformer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roformer import (
												    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRoFormerForCausalLM,
												    TFRoFormerForMaskedLM,
												    TFRoFormerForMultipleChoice,
												    TFRoFormerForQuestionAnswering,
												    TFRoFormerForSequenceClassification,
												    TFRoFormerForTokenClassification,
												    TFRoFormerLayer,
												    TFRoFormerModel,
												    TFRoFormerPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roformer import (
												    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxRoFormerForMaskedLM,
												    FlaxRoFormerForMultipleChoice,
												    FlaxRoFormerForQuestionAnswering,
												    FlaxRoFormerForSequenceClassification,
												    FlaxRoFormerForTokenClassification,
												    FlaxRoFormerModel,
												    FlaxRoFormerPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE:     Any									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)