
"""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


lowerCamelCase__     =						get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")




@require_torch
class 					A__			(      unittest.TestCase):

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		List[str]		     = tempfile.mkdtemp()

			__lowerCAmelCase     :		Optional[Any]		     = SpeechTaTokenizer(UpperCAmelCase__					)
			tokenizer.save_pretrained(self.tmpdirname					)

			__lowerCAmelCase     :		Optional[int]		     = {
			    '''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,
			}

			__lowerCAmelCase     :		Optional[Any]		     = os.path.join(self.tmpdirname			,						UpperCAmelCase__					)
			with open(self.feature_extraction_file			,						'w'			,						encoding='utf-8'					) as fp:
				fp.write(json.dumps(UpperCAmelCase__					) + '\n'					)

		def 				__lowerCamelCase				( self			,						**_SCREAMING_SNAKE_CASE					):
			return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,						**UpperCAmelCase__					)

		def 				__lowerCamelCase				( self			,						**_SCREAMING_SNAKE_CASE					):
			return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,						**UpperCAmelCase__					)

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

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Tuple		     = self.get_tokenizer()
			__lowerCAmelCase     :		Union[str, Any]		     = self.get_feature_extractor()

			__lowerCAmelCase     :		Union[str, Any]		     = SpeechTaProcessor(tokenizer=UpperCAmelCase__			,						feature_extractor=UpperCAmelCase__					)

			processor.save_pretrained(self.tmpdirname					)
			__lowerCAmelCase     :		List[str]		     = SpeechTaProcessor.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 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		int		     = SpeechTaProcessor(tokenizer=self.get_tokenizer()			,						feature_extractor=self.get_feature_extractor()					)
			processor.save_pretrained(self.tmpdirname					)

			__lowerCAmelCase     :		int		     = self.get_tokenizer(bos_token='(BOS)'			,						eos_token='(EOS)'					)
			__lowerCAmelCase     :		int		     = self.get_feature_extractor(do_normalize=UpperCAmelCase__			,						padding_value=1.0					)

			__lowerCAmelCase     :		str		     = SpeechTaProcessor.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 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[int]		     = self.get_feature_extractor()
			__lowerCAmelCase     :		str		     = self.get_tokenizer()

			__lowerCAmelCase     :		str		     = SpeechTaProcessor(tokenizer=UpperCAmelCase__			,						feature_extractor=UpperCAmelCase__					)

			__lowerCAmelCase     :		Tuple		     = floats_list((3, 10_00)					)

			__lowerCAmelCase     :		int		     = feature_extractor(audio=UpperCAmelCase__			,						return_tensors='np'					)
			__lowerCAmelCase     :		str		     = processor(audio=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 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		str		     = self.get_feature_extractor()
			__lowerCAmelCase     :		Optional[int]		     = self.get_tokenizer()

			__lowerCAmelCase     :		Optional[Any]		     = SpeechTaProcessor(tokenizer=UpperCAmelCase__			,						feature_extractor=UpperCAmelCase__					)

			__lowerCAmelCase     :		List[str]		     = floats_list((3, 10_00)					)

			__lowerCAmelCase     :		Dict		     = feature_extractor(audio_target=UpperCAmelCase__			,						return_tensors='np'					)
			__lowerCAmelCase     :		List[str]		     = processor(audio_target=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 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Dict		     = self.get_feature_extractor()
			__lowerCAmelCase     :		Optional[Any]		     = self.get_tokenizer()

			__lowerCAmelCase     :		Optional[int]		     = SpeechTaProcessor(tokenizer=UpperCAmelCase__			,						feature_extractor=UpperCAmelCase__					)

			__lowerCAmelCase     :		int		     = '''This is a test string'''

			__lowerCAmelCase     :		Union[str, Any]		     = processor(text=UpperCAmelCase__					)
			__lowerCAmelCase     :		List[Any]		     = tokenizer(UpperCAmelCase__					)

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

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Union[str, Any]		     = self.get_feature_extractor()
			__lowerCAmelCase     :		str		     = self.get_tokenizer()

			__lowerCAmelCase     :		Any		     = SpeechTaProcessor(tokenizer=UpperCAmelCase__			,						feature_extractor=UpperCAmelCase__					)

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

			__lowerCAmelCase     :		str		     = processor(text_target=UpperCAmelCase__					)
			__lowerCAmelCase     :		List[str]		     = tokenizer(UpperCAmelCase__					)

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

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		List[str]		     = self.get_feature_extractor()
			__lowerCAmelCase     :		Dict		     = self.get_tokenizer()

			__lowerCAmelCase     :		Tuple		     = SpeechTaProcessor(tokenizer=UpperCAmelCase__			,						feature_extractor=UpperCAmelCase__					)

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

			__lowerCAmelCase     :		str		     = processor.batch_decode(UpperCAmelCase__					)
			__lowerCAmelCase     :		Dict		     = tokenizer.batch_decode(UpperCAmelCase__					)

			self.assertListEqual(UpperCAmelCase__			,						UpperCAmelCase__					)







		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		int		     = self.get_feature_extractor()
			__lowerCAmelCase     :		Tuple		     = self.get_tokenizer()

			__lowerCAmelCase     :		Any		     = SpeechTaProcessor(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'			,						)






'''simple docstring'''






from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	def __init__(					self   ,     *UpperCAmelCase__   ,     **UpperCAmelCase__			)       ->				Optional[int]:
					super().__init__(*UpperCAmelCase__   ,     **UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Optional[int]:
					_A :				Union[str, Any]				=  (
					    next_prime(value % self.size_table			)
					    if not is_prime(value % self.size_table			)
					    else value % self.size_table
					)  # gt = bigger than
					return next_prime_gt - (data % next_prime_gt)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Union[str, Any]:
					return (increment * self.__hash_function_a(UpperCAmelCase__   ,     UpperCAmelCase__			)) % self.size_table




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None			)       ->				List[Any]:
					_A :				Optional[int]				=  1
					_A :				Optional[int]				=  self.hash_function(UpperCAmelCase__			)

					while self.values[new_key] is not None and self.values[new_key] != key:
									_A :				int				=  (
									    self.__hash_double_function(UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)
									    if self.balanced_factor() >= self.lim_charge
									    else None
									)
									if new_key is None:
													break
									else:
													i += 1

					return new_key
