




'''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


snake_case__          =						get_tests_dir("""fixtures/test_sentencepiece.model""")






@require_torch
@require_torchaudio
@require_sentencepiece
class     UpperCamelCase_     (unittest.TestCase     ):







						"""simple docstring"""


						def       _a    (     self      :       List[Any]    ):





											"""simple docstring"""







											A_ :     Optional[int] =							tempfile.mkdtemp()

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

											A_ :     Union[str, Any] =							SpeechaTextTokenizer.from_pretrained(self.tmpdirname    )
											tokenizer.save_pretrained(self.tmpdirname    )

											A_ :     List[Any] =							{
											    '''feature_size''': 24,
											    '''num_mel_bins''': 24,
											    '''padding_value''': 0.0,
											    '''sampling_rate''': 16000,
											    '''return_attention_mask''': False,
											    '''do_normalize''': True,
											}
											save_json(_lowerCamelCase							, save_dir / FEATURE_EXTRACTOR_NAME    )







						def       _a    (     self      :       List[Any]							, **_lowerCamelCase      :       List[str]    ):





											"""simple docstring"""







											return SpeechaTextTokenizer.from_pretrained(self.tmpdirname							, **_lowerCamelCase    )







						def       _a    (     self      :       List[Any]							, **_lowerCamelCase      :       str    ):





											"""simple docstring"""







											return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname							, **_lowerCamelCase    )







						def       _a    (     self      :       Dict    ):





											"""simple docstring"""







											shutil.rmtree(self.tmpdirname    )







						def       _a    (     self      :       List[Any]    ):





											"""simple docstring"""







											A_ :     int =							self.get_tokenizer()
											A_ :     Dict =							self.get_feature_extractor()

											A_ :     Dict =							SpeechaTextProcessor(tokenizer=_lowerCamelCase							, feature_extractor=_lowerCamelCase    )

											processor.save_pretrained(self.tmpdirname    )
											A_ :     Optional[Any] =							SpeechaTextProcessor.from_pretrained(self.tmpdirname    )

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

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







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





											"""simple docstring"""







											A_ :     Any =							SpeechaTextProcessor(
											    tokenizer=self.get_tokenizer()							, feature_extractor=self.get_feature_extractor()    )
											processor.save_pretrained(self.tmpdirname    )

											A_ :     Union[str, Any] =							self.get_tokenizer(bos_token='''(BOS)'''							, eos_token='''(EOS)'''    )
											A_ :     Optional[int] =							self.get_feature_extractor(do_normalize=_lowerCamelCase							, padding_value=1.0    )

											A_ :     Union[str, Any] =							SpeechaTextProcessor.from_pretrained(
											    self.tmpdirname							, bos_token='''(BOS)'''							, eos_token='''(EOS)'''							, do_normalize=_lowerCamelCase							, padding_value=1.0    )

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

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







						def       _a    (     self      :       Optional[int]    ):





											"""simple docstring"""







											A_ :     List[Any] =							self.get_feature_extractor()
											A_ :     List[str] =							self.get_tokenizer()

											A_ :     Dict =							SpeechaTextProcessor(tokenizer=_lowerCamelCase							, feature_extractor=_lowerCamelCase    )

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

											A_ :     List[Any] =							feature_extractor(_lowerCamelCase							, return_tensors='''np'''    )
											A_ :     Any =							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       _a    (     self      :       Optional[int]    ):





											"""simple docstring"""







											A_ :     Optional[int] =							self.get_feature_extractor()
											A_ :     Tuple =							self.get_tokenizer()

											A_ :     Dict =							SpeechaTextProcessor(tokenizer=_lowerCamelCase							, feature_extractor=_lowerCamelCase    )

											A_ :     str =							'''This is a test string'''

											A_ :     Union[str, Any] =							processor(text=_lowerCamelCase    )

											A_ :     Optional[Any] =							tokenizer(_lowerCamelCase    )

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







						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







											A_ :     Any =							self.get_feature_extractor()
											A_ :     str =							self.get_tokenizer()

											A_ :     Any =							SpeechaTextProcessor(tokenizer=_lowerCamelCase							, feature_extractor=_lowerCamelCase    )

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

											A_ :     Optional[Any] =							processor.batch_decode(_lowerCamelCase    )
											A_ :     Dict =							tokenizer.batch_decode(_lowerCamelCase    )

											self.assertListEqual(_lowerCamelCase							, _lowerCamelCase    )







						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







											A_ :     Any =							self.get_feature_extractor()
											A_ :     int =							self.get_tokenizer()

											A_ :     Optional[Any] =							SpeechaTextProcessor(tokenizer=_lowerCamelCase							, feature_extractor=_lowerCamelCase    )

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









'''simple docstring'''




def 						snake_case__    ( lowerCamelCase__				:   float			)   ->   float:
					return -num if num < 0 else num




def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   int:
					if len(lowerCamelCase__			) == 0:
										raise ValueError('''abs_min() arg is an empty sequence'''			)
					A_ :     Any =							x[0]
					for i in x:
										if abs_val(lowerCamelCase__			) < abs_val(lowerCamelCase__			):
															A_ :     Tuple =							i
					return j




def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   int:
					if len(lowerCamelCase__			) == 0:
										raise ValueError('''abs_max() arg is an empty sequence'''			)
					A_ :     List[str] =							x[0]
					for i in x:
										if abs(lowerCamelCase__			) > abs(lowerCamelCase__			):
															A_ :     Any =							i
					return j




def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   int:
					if len(lowerCamelCase__			) == 0:
										raise ValueError('''abs_max_sort() arg is an empty sequence'''			)
					return sorted(lowerCamelCase__			,		key=lowerCamelCase__			)[-1]




def 						snake_case__    ( )   ->   Optional[Any]:
					assert abs_val(0			) == 0
					assert abs_val(3_4			) == 3_4
					assert abs_val(-1_0_0_0_0_0_0_0_0_0_0_0			) == 1_0_0_0_0_0_0_0_0_0_0_0

					A_ :     Dict =							[-3, -1, 2, -1_1]
					assert abs_max(lowerCamelCase__			) == -1_1
					assert abs_max_sort(lowerCamelCase__			) == -1_1
					assert abs_min(lowerCamelCase__			) == -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							test_abs_val()
							print(abs_val(-34))  # --> 34



