






'''simple docstring'''





from .number_theory.prime_numbers import next_prime





class 		SCREAMING_SNAKE_CASE__   :



		def __init__(    self:    Dict				,   a:    int				,   a:    int | None = None				,   a:    float | None = None				,   )  ->None:






								'''simple docstring'''


								a_				     =   size_table
								a_				     =   [None] * self.size_table
								a_				     =   0.75 if lim_charge is None else lim_charge
								a_				     =   1 if charge_factor is None else charge_factor
								a_				     =   []
								a_				     =   {}



		def _lowerCAmelCase     (    self:    str)  ->Union[str, Any]:






								'''simple docstring'''


								return self._keys



		def _lowerCAmelCase     (    self:    List[str])  ->Any:






								'''simple docstring'''


								return sum(1 for slot in self.values if slot is not None) / (
								    self.size_table * self.charge_factor
								)



		def _lowerCAmelCase     (    self:    Optional[int]				,   a:    str)  ->Optional[Any]:






								'''simple docstring'''


								return key % self.size_table



		def _lowerCAmelCase     (    self:    Optional[Any]				,   a:    List[str])  ->List[str]:






								'''simple docstring'''


								print(f"""step {step_ord}""")
								print(list(range(len(self.values))))
								print(self.values)



		def _lowerCAmelCase     (    self:    int				,   a:    Optional[Any])  ->List[str]:






								'''simple docstring'''


								a_				     =   1
								a_				     =   values
								for value in values:
														self.insert_data(a)
														self._step_by_step(a)
														i += 1



		def _lowerCAmelCase     (    self:    Union[str, Any]				,   a:    List[str]				,   a:    Tuple)  ->Any:






								'''simple docstring'''


								a_				     =   data
								a_				     =   data



		def _lowerCAmelCase     (    self:    Any				,   a:    List[Any]				,   a:    Any=None)  ->Optional[int]:






								'''simple docstring'''


								a_				     =   self.hash_function(key + 1)

								while self.values[new_key] is not None and self.values[new_key] != key:
														if self.values.count(a) > 0:
																				a_				     =   self.hash_function(new_key + 1)
														else:
																				a_				     =   None
																				break

								return new_key



		def _lowerCAmelCase     (    self:    List[Any])  ->Optional[int]:






								'''simple docstring'''


								a_				     =   [value for value in self.values if value is not None]
								a_				     =   next_prime(self.size_table				,   factor=2)
								self._keys.clear()
								a_				     =   [None] * self.size_table  # hell's pointers D: don't DRY ;/
								for value in survivor_values:
														self.insert_data(a)




		def _lowerCAmelCase     (    self:    Optional[int]				,   a:    List[Any])  ->Optional[int]:






								'''simple docstring'''


								a_				     =   self.hash_function(a)

								if self.values[key] is None:
														self._set_value(a				,   a)

								elif self.values[key] == data:
														pass

								else:
														a_				     =   self._collision_resolution(a				,   a)
														if collision_resolution is not None:
																				self._set_value(a				,   a)
														else:
																				self.rehashing()
																				self.insert_data(a)











'''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


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





@require_torch
class 		SCREAMING_SNAKE_CASE__   (     unittest.TestCase    ):



		def _lowerCAmelCase     (    self:    str)  ->Optional[Any]:






								'''simple docstring'''


								a_				     =   tempfile.mkdtemp()

								a_				     =   SpeechTaTokenizer(a)
								tokenizer.save_pretrained(self.tmpdirname)

								a_				     =   {
								    "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,
								}

								a_				     =   os.path.join(self.tmpdirname				,   a)
								with open(self.feature_extraction_file				,   "w"				,   encoding="utf-8") as fp:
														fp.write(json.dumps(a) + "\n")



		def _lowerCAmelCase     (    self:    Any				,   **a:    List[Any])  ->Optional[Any]:






								'''simple docstring'''


								return SpeechTaTokenizer.from_pretrained(self.tmpdirname				,   **a)



		def _lowerCAmelCase     (    self:    Union[str, Any]				,   **a:    int)  ->str:






								'''simple docstring'''


								return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname				,   **a)



		def _lowerCAmelCase     (    self:    int)  ->Optional[Any]:






								'''simple docstring'''


								shutil.rmtree(self.tmpdirname)



		def _lowerCAmelCase     (    self:    Any)  ->str:






								'''simple docstring'''


								a_				     =   self.get_tokenizer()
								a_				     =   self.get_feature_extractor()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								processor.save_pretrained(self.tmpdirname)
								a_				     =   SpeechTaProcessor.from_pretrained(self.tmpdirname)

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

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



		def _lowerCAmelCase     (    self:    List[Any])  ->Optional[int]:






								'''simple docstring'''


								a_				     =   SpeechTaProcessor(tokenizer=self.get_tokenizer()				,   feature_extractor=self.get_feature_extractor())
								processor.save_pretrained(self.tmpdirname)

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

								a_				     =   SpeechTaProcessor.from_pretrained(
								    self.tmpdirname				,   bos_token="(BOS)"				,   eos_token="(EOS)"				,   do_normalize=a				,   padding_value=1.0)

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

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



		def _lowerCAmelCase     (    self:    Any)  ->List[Any]:






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   floats_list((3, 10_00))

								a_				     =   feature_extractor(audio=a				,   return_tensors="np")
								a_				     =   processor(audio=a				,   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:    Tuple)  ->Union[str, Any]:






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   floats_list((3, 10_00))

								a_				     =   feature_extractor(audio_target=a				,   return_tensors="np")
								a_				     =   processor(audio_target=a				,   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:    str)  ->Dict:






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   "This is a test string"

								a_				     =   processor(text=a)
								a_				     =   tokenizer(a)

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



		def _lowerCAmelCase     (    self:    List[Any])  ->Any:






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   "This is a test string"

								a_				     =   processor(text_target=a)
								a_				     =   tokenizer(a)

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



		def _lowerCAmelCase     (    self:    List[Any])  ->Any:






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

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

								a_				     =   processor.batch_decode(a)
								a_				     =   tokenizer.batch_decode(a)

								self.assertListEqual(a				,   a)




		def _lowerCAmelCase     (    self:    Optional[int])  ->Optional[int]:






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

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



