


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     :			Optional[Any]      =get_tests_dir('fixtures/test_sentencepiece.model')
@require_torch
@require_torchaudio
@require_sentencepiece
class 	_lowercase				(      unittest.TestCase	):






				'''simple docstring'''



				def 		__magic_name__(	self      :Any     )		->      Any:
										__SCREAMING_SNAKE_CASE						:					int		      =		tempfile.mkdtemp()

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

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
										tokenizer.save_pretrained(self.tmpdirname     )

										__SCREAMING_SNAKE_CASE						:					Any		      =		{
										    '''feature_size''': 24,
										    '''num_mel_bins''': 24,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 16_000,
										    '''return_attention_mask''': False,
										    '''do_normalize''': True,
										}
										save_json(lowerCAmelCase__      ,      save_dir / FEATURE_EXTRACTOR_NAME     )


				def 		__magic_name__(	self      :Any      ,      **lowerCAmelCase__      :Any     )		->      int:
										return SpeechaTextTokenizer.from_pretrained(self.tmpdirname      ,      **lowerCAmelCase__     )


				def 		__magic_name__(	self      :str      ,      **lowerCAmelCase__      :int     )		->      int:
										return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname      ,      **lowerCAmelCase__     )


				def 		__magic_name__(	self      :Union[str, Any]     )		->      Tuple:
										shutil.rmtree(self.tmpdirname     )


				def 		__magic_name__(	self      :Union[str, Any]     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.get_tokenizer()
										__SCREAMING_SNAKE_CASE						:					str		      =		self.get_feature_extractor()

										__SCREAMING_SNAKE_CASE						:					str		      =		SpeechaTextProcessor(tokenizer=lowerCAmelCase__      ,      feature_extractor=lowerCAmelCase__     )

										processor.save_pretrained(self.tmpdirname     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		SpeechaTextProcessor.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 		__magic_name__(	self      :Optional[Any]     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		SpeechaTextProcessor(
										    tokenizer=self.get_tokenizer()      ,      feature_extractor=self.get_feature_extractor()     )
										processor.save_pretrained(self.tmpdirname     )

										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.get_tokenizer(bos_token='''(BOS)'''      ,      eos_token='''(EOS)'''     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.get_feature_extractor(do_normalize=lowerCAmelCase__      ,      padding_value=1.0     )

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		SpeechaTextProcessor.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 		__magic_name__(	self      :List[Any]     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.get_feature_extractor()
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		SpeechaTextProcessor(tokenizer=lowerCAmelCase__      ,      feature_extractor=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					int		      =		floats_list((3, 1_000)     )

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		feature_extractor(lowerCAmelCase__      ,      return_tensors='''np'''     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		processor(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 		__magic_name__(	self      :Union[str, Any]     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.get_feature_extractor()
										__SCREAMING_SNAKE_CASE						:					str		      =		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		SpeechaTextProcessor(tokenizer=lowerCAmelCase__      ,      feature_extractor=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Any		      =		'''This is a test string'''

										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		processor(text=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Dict		      =		tokenizer(lowerCAmelCase__     )

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


				def 		__magic_name__(	self      :Optional[Any]     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.get_feature_extractor()
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:					str		      =		SpeechaTextProcessor(tokenizer=lowerCAmelCase__      ,      feature_extractor=lowerCAmelCase__     )

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

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		processor.batch_decode(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		tokenizer.batch_decode(lowerCAmelCase__     )

										self.assertListEqual(lowerCAmelCase__      ,      lowerCAmelCase__     )



				def 		__magic_name__(	self      :str     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.get_feature_extractor()
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		SpeechaTextProcessor(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'''      ,      )




from .number_theory.prime_numbers import next_prime
class 	_lowercase				:






				'''simple docstring'''



				def __init__(	self      :Tuple      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :int | None = None      ,      lowerCAmelCase__      :float | None = None      ,      )		->      None:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		size_table
										__SCREAMING_SNAKE_CASE						:					Dict		      =		[None] * self.size_table
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		0.75 if lim_charge is None else lim_charge
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		1 if charge_factor is None else charge_factor
										__SCREAMING_SNAKE_CASE						:					list		      =		[]
										__SCREAMING_SNAKE_CASE						:					dict		      =		{}


				def 		__magic_name__(	self      :List[Any]     )		->      Tuple:
										return self._keys


				def 		__magic_name__(	self      :Any     )		->      Dict:
										return sum(1 for slot in self.values if slot is not None     ) / (
										    self.size_table * self.charge_factor
										)


				def 		__magic_name__(	self      :Optional[Any]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      Optional[Any]:
										return key % self.size_table


				def 		__magic_name__(	self      :int      ,      lowerCAmelCase__      :Union[str, Any]     )		->      List[Any]:
										print(f'''step {step_ord}'''     )
										print(list(range(len(self.values     )     )     )     )
										print(self.values     )


				def 		__magic_name__(	self      :List[str]      ,      lowerCAmelCase__      :Optional[Any]     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		1
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		values
										for value in values:
																self.insert_data(lowerCAmelCase__     )
																self._step_by_step(lowerCAmelCase__     )
																i += 1


				def 		__magic_name__(	self      :Tuple      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		data
										__SCREAMING_SNAKE_CASE						:					Any		      =		data


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :List[str]=None     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.hash_function(key + 1     )

										while self.values[new_key] is not None and self.values[new_key] != key:
																if self.values.count(lowerCAmelCase__     ) > 0:
																						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.hash_function(new_key + 1     )
																else:
																						__SCREAMING_SNAKE_CASE						:					int		      =		None
																						break

										return new_key


				def 		__magic_name__(	self      :Optional[int]     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					str		      =		[value for value in self.values if value is not None]
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		next_prime(self.size_table      ,      factor=2     )
										self._keys.clear()
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		[None] * self.size_table  # hell's pointers D: don't DRY ;/
										for value in survivor_values:
																self.insert_data(lowerCAmelCase__     )



				def 		__magic_name__(	self      :Tuple      ,      lowerCAmelCase__      :str     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.hash_function(lowerCAmelCase__     )

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

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

										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		self._collision_resolution(lowerCAmelCase__      ,      lowerCAmelCase__     )
																if collision_resolution is not None:
																						self._set_value(lowerCAmelCase__      ,      lowerCAmelCase__     )
																else:
																						self.rehashing()
																						self.insert_data(lowerCAmelCase__     )
