


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


snake_case__  				=			get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')



@require_torch
class      snake_case__	(   unittest.TestCase	):
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				List[str]				=   tempfile.mkdtemp()

										__magic_name__					:				Dict				=   SpeechTaTokenizer(__lowercase   )
										tokenizer.save_pretrained(self.tmpdirname   )

										__magic_name__					:				int				=   {
										    '''feature_size''': 1,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 1_6000,
										    '''do_normalize''': False,
										    '''num_mel_bins''': 80,
										    '''hop_length''': 16,
										    '''win_length''': 64,
										    '''win_function''': '''hann_window''',
										    '''fmin''': 80,
										    '''fmax''': 7600,
										    '''mel_floor''': 1E-1_0,
										    '''reduction_factor''': 2,
										    '''return_attention_mask''': True,
										}

										__magic_name__					:				int				=   os.path.join(self.tmpdirname  ,   __lowercase   )
										with open(self.feature_extraction_file  ,   'w'  ,   encoding='utf-8'   ) as fp:
																fp.write(json.dumps(__lowercase   ) + '\n'   )
				def 			UpperCamelCase_    (	self  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										return SpeechTaTokenizer.from_pretrained(self.tmpdirname  ,   **__lowercase   )
				def 			UpperCamelCase_    (	self  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname  ,   **__lowercase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										shutil.rmtree(self.tmpdirname   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




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

										__magic_name__					:				Optional[Any]				=   SpeechTaProcessor(tokenizer=__lowercase  ,   feature_extractor=__lowercase   )

										processor.save_pretrained(self.tmpdirname   )
										__magic_name__					:				Tuple				=   SpeechTaProcessor.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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Optional[int]				=   SpeechTaProcessor(tokenizer=self.get_tokenizer()  ,   feature_extractor=self.get_feature_extractor()   )
										processor.save_pretrained(self.tmpdirname   )

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

										__magic_name__					:				Dict				=   SpeechTaProcessor.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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				str				=   self.get_feature_extractor()
										__magic_name__					:				Optional[int]				=   self.get_tokenizer()

										__magic_name__					:				Union[str, Any]				=   SpeechTaProcessor(tokenizer=__lowercase  ,   feature_extractor=__lowercase   )

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

										__magic_name__					:				int				=   feature_extractor(audio=__lowercase  ,   return_tensors='np'   )
										__magic_name__					:				Any				=   processor(audio=__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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Optional[Any]				=   self.get_feature_extractor()
										__magic_name__					:				Tuple				=   self.get_tokenizer()

										__magic_name__					:				Union[str, Any]				=   SpeechTaProcessor(tokenizer=__lowercase  ,   feature_extractor=__lowercase   )

										__magic_name__					:				Tuple				=   floats_list((3, 1000)   )

										__magic_name__					:				Optional[Any]				=   feature_extractor(audio_target=__lowercase  ,   return_tensors='np'   )
										__magic_name__					:				List[Any]				=   processor(audio_target=__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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Optional[Any]				=   self.get_feature_extractor()
										__magic_name__					:				str				=   self.get_tokenizer()

										__magic_name__					:				Tuple				=   SpeechTaProcessor(tokenizer=__lowercase  ,   feature_extractor=__lowercase   )

										__magic_name__					:				Optional[int]				=   '''This is a test string'''

										__magic_name__					:				Union[str, Any]				=   processor(text=__lowercase   )
										__magic_name__					:				int				=   tokenizer(__lowercase   )

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




										"""simple docstring"""




										__magic_name__					:				int				=   self.get_feature_extractor()
										__magic_name__					:				Union[str, Any]				=   self.get_tokenizer()

										__magic_name__					:				List[Any]				=   SpeechTaProcessor(tokenizer=__lowercase  ,   feature_extractor=__lowercase   )

										__magic_name__					:				Optional[int]				=   '''This is a test string'''

										__magic_name__					:				int				=   processor(text_target=__lowercase   )
										__magic_name__					:				str				=   tokenizer(__lowercase   )

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




										"""simple docstring"""




										__magic_name__					:				Tuple				=   self.get_feature_extractor()
										__magic_name__					:				List[str]				=   self.get_tokenizer()

										__magic_name__					:				Any				=   SpeechTaProcessor(tokenizer=__lowercase  ,   feature_extractor=__lowercase   )

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

										__magic_name__					:				Dict				=   processor.batch_decode(__lowercase   )
										__magic_name__					:				int				=   tokenizer.batch_decode(__lowercase   )

										self.assertListEqual(__lowercase  ,   __lowercase   )





				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




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

										__magic_name__					:				Dict				=   SpeechTaProcessor(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'  ,   )






from transformers import PretrainedConfig







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Optional[int]       =				"""custom"""




						def __init__(					self ,    __lowercase=1 ,    **__lowercase				)       ->     Dict:
										lowerCAmelCase_			:   List[Any]     					=					attribute
										super().__init__(**__lowercase				)







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	int       =				"""custom"""




						def __init__(					self ,    __lowercase=1 ,    **__lowercase				)       ->     List[Any]:
										lowerCAmelCase_			:   Optional[Any]     					=					attribute