




'''simple docstring'''
import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
							from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_SCREAMING_SNAKE_CASE =	get_tests_dir("fixtures/test_sentencepiece.model")
@require_torch
@require_torchaudio
@require_sentencepiece
class      __UpperCAmelCase							(    unittest.TestCase							):



					'''simple docstring'''





					def 						__snake_case	(  self    :	Tuple)						->       int:
								A_       		=   tempfile.mkdtemp()

								A_       		=   ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
								A_       		=   dict(zip(_lowercase			,					range(len(_lowercase))))
								A_       		=   Path(self.tmpdirname)
								save_json(_lowercase			,					save_dir / VOCAB_FILES_NAMES['vocab_file'])
								if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
											copyfile(_lowercase			,					save_dir / VOCAB_FILES_NAMES['spm_file'])

								A_       		=   SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
								tokenizer.save_pretrained(self.tmpdirname)

								A_       		=   {
								    'feature_size': 24,
								    'num_mel_bins': 24,
								    'padding_value': 0.0,
								    'sampling_rate': 16_000,
								    'return_attention_mask': False,
								    'do_normalize': True,
								}
								save_json(_lowercase			,					save_dir / FEATURE_EXTRACTOR_NAME)



					def 						__snake_case	(  self    :	Tuple			,					**_lowercase    :	Any)						->       Any:
								return SpeechaTextTokenizer.from_pretrained(self.tmpdirname			,					**_lowercase)



					def 						__snake_case	(  self    :	List[str]			,					**_lowercase    :	Any)						->       Union[str, Any]:
								return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname			,					**_lowercase)



					def 						__snake_case	(  self    :	Any)						->       Union[str, Any]:
								shutil.rmtree(self.tmpdirname)



					def 						__snake_case	(  self    :	Any)						->       List[str]:
								A_       		=   self.get_tokenizer()
								A_       		=   self.get_feature_extractor()

								A_       		=   SpeechaTextProcessor(tokenizer=_lowercase			,					feature_extractor=_lowercase)

								processor.save_pretrained(self.tmpdirname)
								A_       		=   SpeechaTextProcessor.from_pretrained(self.tmpdirname)

								self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer.get_vocab())
								self.assertIsInstance(processor.tokenizer			,					_lowercase)

								self.assertEqual(processor.feature_extractor.to_json_string()			,					feature_extractor.to_json_string())
								self.assertIsInstance(processor.feature_extractor			,					_lowercase)



					def 						__snake_case	(  self    :	List[str])						->       List[Any]:
								A_       		=   SpeechaTextProcessor(
								    tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor())
								processor.save_pretrained(self.tmpdirname)

								A_       		=   self.get_tokenizer(bos_token='(BOS)'			,					eos_token='(EOS)')
								A_       		=   self.get_feature_extractor(do_normalize=_lowercase			,					padding_value=1.0)

								A_       		=   SpeechaTextProcessor.from_pretrained(
								    self.tmpdirname			,					bos_token='(BOS)'			,					eos_token='(EOS)'			,					do_normalize=_lowercase			,					padding_value=1.0)

								self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer_add_kwargs.get_vocab())
								self.assertIsInstance(processor.tokenizer			,					_lowercase)

								self.assertEqual(processor.feature_extractor.to_json_string()			,					feature_extractor_add_kwargs.to_json_string())
								self.assertIsInstance(processor.feature_extractor			,					_lowercase)



					def 						__snake_case	(  self    :	Dict)						->       Any:
								A_       		=   self.get_feature_extractor()
								A_       		=   self.get_tokenizer()

								A_       		=   SpeechaTextProcessor(tokenizer=_lowercase			,					feature_extractor=_lowercase)

								A_       		=   floats_list((3, 1_000))

								A_       		=   feature_extractor(_lowercase			,					return_tensors='np')
								A_       		=   processor(_lowercase			,					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 						__snake_case	(  self    :	int)						->       Union[str, Any]:
								A_       		=   self.get_feature_extractor()
								A_       		=   self.get_tokenizer()

								A_       		=   SpeechaTextProcessor(tokenizer=_lowercase			,					feature_extractor=_lowercase)

								A_       		=   'This is a test string'

								A_       		=   processor(text=_lowercase)

								A_       		=   tokenizer(_lowercase)

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



					def 						__snake_case	(  self    :	Union[str, Any])						->       Optional[Any]:
								A_       		=   self.get_feature_extractor()
								A_       		=   self.get_tokenizer()

								A_       		=   SpeechaTextProcessor(tokenizer=_lowercase			,					feature_extractor=_lowercase)

								A_       		=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								A_       		=   processor.batch_decode(_lowercase)
								A_       		=   tokenizer.batch_decode(_lowercase)

								self.assertListEqual(_lowercase			,					_lowercase)



					def 						__snake_case	(  self    :	int)						->       str:
								A_       		=   self.get_feature_extractor()
								A_       		=   self.get_tokenizer()

								A_       		=   SpeechaTextProcessor(tokenizer=_lowercase			,					feature_extractor=_lowercase)

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










'''simple docstring'''
from unittest.mock import patch

import datasets
from datasets import Dataset





def  lowerCamelCase(       )		->		Any:
			A_       		=   Dataset.from_dict({'col_1': [3, 2, 0, 1]}    )

			with patch('tqdm.auto.tqdm'    ) as mock_tqdm:
						datasets.disable_progress_bar()
						dset.map(lambda SCREAMING_SNAKE_CASE_    : {"col_2": x["col_1"] + 1}    )
						mock_tqdm.assert_not_called()

						mock_tqdm.reset_mock()

						datasets.enable_progress_bar()
						dset.map(lambda SCREAMING_SNAKE_CASE_    : {"col_2": x["col_1"] + 1}    )
						mock_tqdm.assert_called()




