
'''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 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		def       lowerCAmelCase				(      self				:			Tuple      )						->			int:






				"""simple docstring"""







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

				snake_case  :       Any     			=						{
				    '''unk_token''': '''<unk>''',
				    '''bos_token''': '''<s>''',
				    '''eos_token''': '''</s>''',
				}
				snake_case  :       str     			=						{
				    '''feature_size''': 1,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 1_6000,
				    '''return_attention_mask''': False,
				    '''do_normalize''': True,
				}

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

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

				# load decoder from hub
				snake_case  :       List[Any]     			=						'''hf-internal-testing/ngram-beam-search-decoder'''



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	**UpperCamelCase__				:			str      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.add_kwargs_tokens_map.copy()
				kwargs.update(UpperCamelCase__      )
				return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname  ,	**UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Dict  ,	**UpperCamelCase__				:			Union[str, Any]      )						->			Optional[int]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			List[str]  ,	**UpperCamelCase__				:			int      )						->			List[Any]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			Tuple      )						->			int:






				"""simple docstring"""







				shutil.rmtree(self.tmpdirname      )



		def       lowerCAmelCase				(      self				:			str      )						->			int:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.get_tokenizer()
				snake_case  :       Any     			=						self.get_feature_extractor()
				snake_case  :       List[Any]     			=						self.get_decoder()

				snake_case  :       Union[str, Any]     			=						WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__  ,	feature_extractor=UpperCamelCase__  ,	decoder=UpperCamelCase__      )

				processor.save_pretrained(self.tmpdirname      )
				snake_case  :       Union[str, Any]     			=						WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname      )

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

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

				# 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  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						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
				snake_case  :       Union[str, Any]     			=						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       lowerCAmelCase				(      self				:			int      )						->			Union[str, Any]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			List[str]      )						->			Tuple:






				"""simple docstring"""







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

				snake_case  :       List[Any]     			=						WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__  ,	feature_extractor=UpperCamelCase__  ,	decoder=UpperCamelCase__      )

				snake_case  :       List[str]     			=						floats_list((3, 1000)      )

				snake_case  :       Any     			=						feature_extractor(UpperCamelCase__  ,	return_tensors='''np'''      )
				snake_case  :       str     			=						processor(UpperCamelCase__  ,	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       lowerCAmelCase				(      self				:			str      )						->			Tuple:






				"""simple docstring"""







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

				snake_case  :       Optional[Any]     			=						WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__  ,	feature_extractor=UpperCamelCase__  ,	decoder=UpperCamelCase__      )

				snake_case  :       int     			=						'''This is a test string'''

				snake_case  :       List[Any]     			=						processor(text=UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						tokenizer(UpperCamelCase__      )

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



		def       lowerCAmelCase				(      self				:			Any  ,	UpperCamelCase__				:			Optional[int]=(2, 10, 16)  ,	UpperCamelCase__				:			Optional[int]=77      )						->			List[str]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       str     			=						self.get_feature_extractor()
				snake_case  :       Tuple     			=						self.get_tokenizer()
				snake_case  :       List[Any]     			=						self.get_decoder()

				snake_case  :       List[str]     			=						WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__  ,	feature_extractor=UpperCamelCase__  ,	decoder=UpperCamelCase__      )

				snake_case  :       List[Any]     			=						self._get_dummy_logits(shape=(10, 16)  ,	seed=13      )

				snake_case  :       List[Any]     			=						processor.decode(UpperCamelCase__      )

				snake_case  :       Union[str, Any]     			=						decoder.decode_beams(UpperCamelCase__      )[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       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			Optional[int]      )						->			int:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						self.get_feature_extractor()
				snake_case  :       Optional[Any]     			=						self.get_tokenizer()
				snake_case  :       str     			=						self.get_decoder()

				snake_case  :       Tuple     			=						WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__  ,	feature_extractor=UpperCamelCase__  ,	decoder=UpperCamelCase__      )

				snake_case  :       Any     			=						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:
						snake_case  :       Optional[Any]     			=						processor.batch_decode(UpperCamelCase__      )
				else:
						with get_context(UpperCamelCase__      ).Pool() as pool:
								snake_case  :       List[str]     			=						processor.batch_decode(UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       int     			=						list(UpperCamelCase__      )

				with get_context('''fork'''      ).Pool() as p:
						snake_case  :       Optional[Any]     			=						decoder.decode_beams_batch(UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case			,snake_case			,snake_case  :       str     			=						[], [], []
				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(UpperCamelCase__  ,	decoded_processor.text      )
				self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']  ,	decoded_processor.text      )
				self.assertListEqual(UpperCamelCase__  ,	decoded_processor.logit_score      )
				self.assertListEqual(UpperCamelCase__  ,	decoded_processor.lm_score      )



		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						self.get_feature_extractor()
				snake_case  :       Any     			=						self.get_tokenizer()
				snake_case  :       Tuple     			=						self.get_decoder()

				snake_case  :       str     			=						WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__  ,	feature_extractor=UpperCamelCase__  ,	decoder=UpperCamelCase__      )

				snake_case  :       Union[str, Any]     			=						self._get_dummy_logits()

				snake_case  :       Optional[int]     			=						15
				snake_case  :       Tuple     			=						-20.0
				snake_case  :       Tuple     			=						-4.0

				snake_case  :       Tuple     			=						processor.batch_decode(
				    UpperCamelCase__  ,	beam_width=UpperCamelCase__  ,	beam_prune_logp=UpperCamelCase__  ,	token_min_logp=UpperCamelCase__  ,	)
				snake_case  :       Optional[Any]     			=						decoded_processor_out.text

				snake_case  :       List[str]     			=						list(UpperCamelCase__      )

				with get_context('''fork'''      ).Pool() as pool:
						snake_case  :       Optional[Any]     			=						decoder.decode_beams_batch(
						    UpperCamelCase__  ,	UpperCamelCase__  ,	beam_width=UpperCamelCase__  ,	beam_prune_logp=UpperCamelCase__  ,	token_min_logp=UpperCamelCase__  ,	)

				snake_case  :       Dict     			=						[d[0][0] for d in decoded_decoder_out]
				snake_case  :       Optional[int]     			=						[d[0][2] for d in decoded_decoder_out]
				snake_case  :       Tuple     			=						[d[0][3] for d in decoded_decoder_out]

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

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

				self.assertTrue(np.array_equal(UpperCamelCase__  ,	decoded_processor_out.lm_score      )      )
				self.assertTrue(np.allclose([-15.554, -13.9_474]  ,	UpperCamelCase__  ,	atol=1e-3      )      )



		def       lowerCAmelCase				(      self				:			Tuple      )						->			int:






				"""simple docstring"""







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

				snake_case  :       Optional[int]     			=						WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__  ,	feature_extractor=UpperCamelCase__  ,	decoder=UpperCamelCase__      )

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

				snake_case  :       Any     			=						2.0
				snake_case  :       Any     			=						5.0
				snake_case  :       Optional[Any]     			=						-20.0
				snake_case  :       Optional[Any]     			=						True

				snake_case  :       int     			=						processor.batch_decode(
				    UpperCamelCase__  ,	alpha=UpperCamelCase__  ,	beta=UpperCamelCase__  ,	unk_score_offset=UpperCamelCase__  ,	lm_score_boundary=UpperCamelCase__  ,	)
				snake_case  :       Optional[Any]     			=						decoded_processor_out.text

				snake_case  :       List[str]     			=						list(UpperCamelCase__      )
				decoder.reset_params(
				    alpha=UpperCamelCase__  ,	beta=UpperCamelCase__  ,	unk_score_offset=UpperCamelCase__  ,	lm_score_boundary=UpperCamelCase__  ,	)

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

				snake_case  :       Optional[int]     			=						[d[0][0] for d in decoded_decoder_out]

				self.assertListEqual(UpperCamelCase__  ,	UpperCamelCase__      )
				self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']  ,	UpperCamelCase__      )
				snake_case  :       int     			=						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  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			str      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       str     			=						WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''      )

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

				snake_case  :       str     			=						os.listdir(UpperCamelCase__      )
				snake_case  :       Dict     			=						['''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(UpperCamelCase__  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[str]      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						snapshot_download('''hf-internal-testing/processor_with_lm'''      )

				snake_case  :       int     			=						WavaVecaProcessorWithLM.from_pretrained(UpperCamelCase__      )

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

				snake_case  :       Tuple     			=						os.listdir(UpperCamelCase__      )
				snake_case  :       int     			=						os.listdir(UpperCamelCase__      )

				local_decoder_files.sort()
				expected_decoder_files.sort()

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



		def       lowerCAmelCase				(      self				:			Dict      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''      )
				snake_case  :       List[Any]     			=						AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''      )

				snake_case  :       Dict     			=						floats_list((3, 1000)      )

				snake_case  :       Optional[int]     			=						processor_wavaveca(UpperCamelCase__  ,	return_tensors='''np'''      )
				snake_case  :       Optional[Any]     			=						processor_auto(UpperCamelCase__  ,	return_tensors='''np'''      )

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

				snake_case  :       int     			=						self._get_dummy_logits()

				snake_case  :       List[Any]     			=						processor_wavaveca.batch_decode(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						processor_auto.batch_decode(UpperCamelCase__      )

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



		def       lowerCAmelCase				(      self				:			str      )						->			Dict:






				"""simple docstring"""







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

				snake_case  :       Union[str, Any]     			=						WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__  ,	feature_extractor=UpperCamelCase__  ,	decoder=UpperCamelCase__      )

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



		@staticmethod
		def       lowerCAmelCase				(      UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Any      )						->			List[str]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''      )
				snake_case  :       Union[str, Any]     			=						self._get_dummy_logits()[0]

				snake_case  :       Optional[Any]     			=						processor.decode(UpperCamelCase__  ,	output_word_offsets=UpperCamelCase__      )
				# 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(UpperCamelCase__  ,	UpperCamelCase__      )      )

				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       lowerCAmelCase				(      self				:			int      )						->			str:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''      )
				snake_case  :       Optional[Any]     			=						self._get_dummy_logits()

				snake_case  :       str     			=						processor.batch_decode(UpperCamelCase__  ,	output_word_offsets=UpperCamelCase__      )

				# 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(UpperCamelCase__  ,	UpperCamelCase__      )      )

				self.assertListEqual(
				    [''' '''.join(self.get_from_offsets(UpperCamelCase__  ,	'''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       lowerCAmelCase				(      self				:			Tuple      )						->			List[str]:






				"""simple docstring"""







				import torch

				snake_case  :       Optional[Any]     			=						load_dataset('''common_voice'''  ,	'''en'''  ,	split='''train'''  ,	streaming=UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						ds.cast_column('''audio'''  ,	datasets.Audio(sampling_rate=1_6000      )      )
				snake_case  :       List[Any]     			=						iter(UpperCamelCase__      )
				snake_case  :       List[str]     			=						next(UpperCamelCase__      )

				snake_case  :       Union[str, Any]     			=						AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''      )
				snake_case  :       Dict     			=						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
				snake_case  :       Union[str, Any]     			=						processor(sample['''audio''']['''array''']  ,	return_tensors='''pt'''      ).input_values

				with torch.no_grad():
						snake_case  :       Any     			=						model(UpperCamelCase__      ).logits.cpu().numpy()

				snake_case  :       Optional[Any]     			=						processor.decode(logits[0]  ,	output_word_offsets=UpperCamelCase__      )

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

				snake_case  :       Optional[Any]     			=						'''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(UpperCamelCase__  ,	'''word'''      )      )  ,	UpperCamelCase__      )
				self.assertEqual(''' '''.join(self.get_from_offsets(UpperCamelCase__  ,	'''word'''      )      )  ,	output.text      )

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

				# fmt: off
				snake_case  :       Optional[Any]     			=						torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]      )
				snake_case  :       Tuple     			=						torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]      )
				# fmt: on

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




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

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import numpy as np
						import tensorflow as tf

						from transformers import TFCamembertModel






@require_tf
@require_sentencepiece
@require_tokenizers
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		@slow
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Any     			=						TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''      )

				snake_case  :       List[Any]     			=						tf.convert_to_tensor(
				    [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,	dtype=tf.intaa  ,	)  # J'aime le camembert !"

				snake_case  :       Any     			=						model(UpperCamelCase__      )['''last_hidden_state''']
				snake_case  :       int     			=						tf.TensorShape((1, 10, 768)      )
				self.assertEqual(output.shape  ,	UpperCamelCase__      )
				# compare the actual values for a slice.
				snake_case  :       Dict     			=						tf.convert_to_tensor(
				    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]  ,	dtype=tf.floataa  ,	)
				# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
				# camembert.eval()
				# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

				self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,	expected_slice.numpy()  ,	atol=1e-4      )      )


