


'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
						from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

						from .test_feature_extraction_speechta import floats_list


__magic_name__   :   List[str]  							=get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')






@require_torch
class 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						def 					__A					( self				:					Optional[Any]  )							->					int:
										__magic_name__	  = tempfile.mkdtemp()

										__magic_name__	  = SpeechTaTokenizer(_lowerCamelCase  )
										tokenizer.save_pretrained(self.tmpdirname  )

										__magic_name__	  = {
										    "feature_size": 1,
										    "padding_value": 0.0,
										    "sampling_rate": 1_60_00,
										    "do_normalize": False,
										    "num_mel_bins": 80,
										    "hop_length": 16,
										    "win_length": 64,
										    "win_function": "hann_window",
										    "fmin": 80,
										    "fmax": 76_00,
										    "mel_floor": 1e-10,
										    "reduction_factor": 2,
										    "return_attention_mask": True,
										}

										__magic_name__	  = os.path.join(self.tmpdirname	,				_lowerCamelCase  )
										with open(self.feature_extraction_file	,				"w"	,				encoding="utf-8"  ) as fp:
														fp.write(json.dumps(_lowerCamelCase  ) + "\n"  )
						def 					__A					( self				:					List[Any]	,				**_lowerCamelCase				:					List[str]  )							->					Optional[Any]:
										return SpeechTaTokenizer.from_pretrained(self.tmpdirname	,				**_lowerCamelCase  )
						def 					__A					( self				:					Any	,				**_lowerCamelCase				:					str  )							->					Any:
										return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname	,				**_lowerCamelCase  )
						def 					__A					( self				:					Optional[Any]  )							->					Any:
										shutil.rmtree(self.tmpdirname  )
						def 					__A					( self				:					Union[str, Any]  )							->					str:
										__magic_name__	  = self.get_tokenizer()
										__magic_name__	  = self.get_feature_extractor()

										__magic_name__	  = SpeechTaProcessor(tokenizer=_lowerCamelCase	,				feature_extractor=_lowerCamelCase  )

										processor.save_pretrained(self.tmpdirname  )
										__magic_name__	  = SpeechTaProcessor.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				:					List[str]  )							->					Any:
										__magic_name__	  = SpeechTaProcessor(tokenizer=self.get_tokenizer()	,				feature_extractor=self.get_feature_extractor()  )
										processor.save_pretrained(self.tmpdirname  )

										__magic_name__	  = self.get_tokenizer(bos_token="(BOS)"	,				eos_token="(EOS)"  )
										__magic_name__	  = self.get_feature_extractor(do_normalize=_lowerCamelCase	,				padding_value=1.0  )

										__magic_name__	  = SpeechTaProcessor.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				:					str  )							->					Any:
										__magic_name__	  = self.get_feature_extractor()
										__magic_name__	  = self.get_tokenizer()

										__magic_name__	  = SpeechTaProcessor(tokenizer=_lowerCamelCase	,				feature_extractor=_lowerCamelCase  )

										__magic_name__	  = floats_list((3, 10_00)  )

										__magic_name__	  = feature_extractor(audio=_lowerCamelCase	,				return_tensors="np"  )
										__magic_name__	  = processor(audio=_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				:					Any  )							->					Any:
										__magic_name__	  = self.get_feature_extractor()
										__magic_name__	  = self.get_tokenizer()

										__magic_name__	  = SpeechTaProcessor(tokenizer=_lowerCamelCase	,				feature_extractor=_lowerCamelCase  )

										__magic_name__	  = floats_list((3, 10_00)  )

										__magic_name__	  = feature_extractor(audio_target=_lowerCamelCase	,				return_tensors="np"  )
										__magic_name__	  = processor(audio_target=_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]  )							->					Optional[Any]:
										__magic_name__	  = self.get_feature_extractor()
										__magic_name__	  = self.get_tokenizer()

										__magic_name__	  = SpeechTaProcessor(tokenizer=_lowerCamelCase	,				feature_extractor=_lowerCamelCase  )

										__magic_name__	  = "This is a test string"

										__magic_name__	  = processor(text=_lowerCamelCase  )
										__magic_name__	  = tokenizer(_lowerCamelCase  )

										for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]	,				encoded_processor[key]  )
						def 					__A					( self				:					Tuple  )							->					Dict:
										__magic_name__	  = self.get_feature_extractor()
										__magic_name__	  = self.get_tokenizer()

										__magic_name__	  = SpeechTaProcessor(tokenizer=_lowerCamelCase	,				feature_extractor=_lowerCamelCase  )

										__magic_name__	  = "This is a test string"

										__magic_name__	  = processor(text_target=_lowerCamelCase  )
										__magic_name__	  = tokenizer(_lowerCamelCase  )

										for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]	,				encoded_processor[key]  )
						def 					__A					( self				:					List[str]  )							->					Dict:
										__magic_name__	  = self.get_feature_extractor()
										__magic_name__	  = self.get_tokenizer()

										__magic_name__	  = SpeechTaProcessor(tokenizer=_lowerCamelCase	,				feature_extractor=_lowerCamelCase  )

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

										__magic_name__	  = processor.batch_decode(_lowerCamelCase  )
										__magic_name__	  = tokenizer.batch_decode(_lowerCamelCase  )

										self.assertListEqual(_lowerCamelCase	,				_lowerCamelCase  )

						def 					__A					( self				:					List[str]  )							->					Union[str, Any]:
										__magic_name__	  = self.get_feature_extractor()
										__magic_name__	  = self.get_tokenizer()

										__magic_name__	  = SpeechTaProcessor(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'''
import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__magic_name__   :   int  							=logging.get_logger(__name__)






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						def __init__( self				:					List[str]	,				*_lowerCamelCase				:					List[str]	,				**_lowerCamelCase				:					Optional[Any]  )							->					None:
										warnings.warn(
										    "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use ConvNextImageProcessor instead."	,				_lowerCamelCase	,				)
										super().__init__(*_lowerCamelCase	,				**_lowerCamelCase  )



