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


lowerCAmelCase_	     =     get_tests_dir('''fixtures/test_sentencepiece.model''')





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







			'''simple docstring'''

			def     snake_case__(					self			:    Dict							)				->List[Any]:
									snake_case_       =     tempfile.mkdtemp()

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

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

									snake_case_       =     {
									    '''feature_size''': 2_4,
									    '''num_mel_bins''': 2_4,
									    '''padding_value''': 0.0,
									    '''sampling_rate''': 1_6_0_0_0,
									    '''return_attention_mask''': False,
									    '''do_normalize''': True,
									}
									save_json(_UpperCamelCase							,       save_dir / FEATURE_EXTRACTOR_NAME							)







			def     snake_case__(					self			:    Union[str, Any]							,       **_UpperCamelCase			:    List[str]							)				->List[str]:
									return SpeechaTextTokenizer.from_pretrained(self.tmpdirname							,       **_UpperCamelCase							)







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







			def     snake_case__(					self			:    Optional[Any]							)				->int:
									shutil.rmtree(self.tmpdirname							)







			def     snake_case__(					self			:    List[Any]							)				->Tuple:
									snake_case_       =     self.get_tokenizer()
									snake_case_       =     self.get_feature_extractor()

									snake_case_       =     SpeechaTextProcessor(tokenizer=_UpperCamelCase							,       feature_extractor=_UpperCamelCase							)

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

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

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







			def     snake_case__(					self			:    Any							)				->Union[str, Any]:
									snake_case_       =     SpeechaTextProcessor(
									    tokenizer=self.get_tokenizer()							,       feature_extractor=self.get_feature_extractor()							)
									processor.save_pretrained(self.tmpdirname							)

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

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

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

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







			def     snake_case__(					self			:    Any							)				->List[Any]:
									snake_case_       =     self.get_feature_extractor()
									snake_case_       =     self.get_tokenizer()

									snake_case_       =     SpeechaTextProcessor(tokenizer=_UpperCamelCase							,       feature_extractor=_UpperCamelCase							)

									snake_case_       =     floats_list((3, 1_0_0_0)							)

									snake_case_       =     feature_extractor(_UpperCamelCase							,       return_tensors='''np'''							)
									snake_case_       =     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     snake_case__(					self			:    Union[str, Any]							)				->Dict:
									snake_case_       =     self.get_feature_extractor()
									snake_case_       =     self.get_tokenizer()

									snake_case_       =     SpeechaTextProcessor(tokenizer=_UpperCamelCase							,       feature_extractor=_UpperCamelCase							)

									snake_case_       =     '''This is a test string'''

									snake_case_       =     processor(text=_UpperCamelCase							)

									snake_case_       =     tokenizer(_UpperCamelCase							)

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







			def     snake_case__(					self			:    List[str]							)				->List[str]:
									snake_case_       =     self.get_feature_extractor()
									snake_case_       =     self.get_tokenizer()

									snake_case_       =     SpeechaTextProcessor(tokenizer=_UpperCamelCase							,       feature_extractor=_UpperCamelCase							)

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

									snake_case_       =     processor.batch_decode(_UpperCamelCase							)
									snake_case_       =     tokenizer.batch_decode(_UpperCamelCase							)

									self.assertListEqual(_UpperCamelCase							,       _UpperCamelCase							)







			def     snake_case__(					self			:    int							)				->Union[str, Any]:
									snake_case_       =     self.get_feature_extractor()
									snake_case_       =     self.get_tokenizer()

									snake_case_       =     SpeechaTextProcessor(tokenizer=_UpperCamelCase							,       feature_extractor=_UpperCamelCase							)

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


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						snake_case_       =     5381
						for x in s:
												snake_case_       =     ((hash_value << 5) + hash_value) + ord(SCREAMING_SNAKE_CASE__	)
						return hash_value & 0xFF_FF_FF_FF