
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


A		:   int			       =    get_tests_dir('''fixtures/test_sentencepiece.model''')



@require_torch
@require_torchaudio
@require_sentencepiece
class       a_   ( unittest.TestCase						):






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				tempfile.mkdtemp()

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

									_lowercase									=				SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
									tokenizer.save_pretrained(self.tmpdirname     )

									_lowercase									=				{
									    """feature_size""": 24,
									    """num_mel_bins""": 24,
									    """padding_value""": 0.0,
									    """sampling_rate""": 16_000,
									    """return_attention_mask""": False,
									    """do_normalize""": True,
									}
									save_json(__UpperCamelCase							, save_dir / FEATURE_EXTRACTOR_NAME     )






			def       UpperCamelCase_							(			self							, **__UpperCamelCase     ):
									return SpeechaTextTokenizer.from_pretrained(self.tmpdirname							, **__UpperCamelCase     )






			def       UpperCamelCase_							(			self							, **__UpperCamelCase     ):
									return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname							, **__UpperCamelCase     )






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






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.get_tokenizer()
									_lowercase									=				self.get_feature_extractor()

									_lowercase									=				SpeechaTextProcessor(tokenizer=__UpperCamelCase							, feature_extractor=__UpperCamelCase     )

									processor.save_pretrained(self.tmpdirname     )
									_lowercase									=				SpeechaTextProcessor.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       UpperCamelCase_							(			self     ):
									_lowercase									=				SpeechaTextProcessor(
									    tokenizer=self.get_tokenizer()							, feature_extractor=self.get_feature_extractor()     )
									processor.save_pretrained(self.tmpdirname     )

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

									_lowercase									=				SpeechaTextProcessor.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       UpperCamelCase_							(			self     ):
									_lowercase									=				self.get_feature_extractor()
									_lowercase									=				self.get_tokenizer()

									_lowercase									=				SpeechaTextProcessor(tokenizer=__UpperCamelCase							, feature_extractor=__UpperCamelCase     )

									_lowercase									=				floats_list((3, 1_000)     )

									_lowercase									=				feature_extractor(__UpperCamelCase							, return_tensors="""np"""     )
									_lowercase									=				processor(__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       UpperCamelCase_							(			self     ):
									_lowercase									=				self.get_feature_extractor()
									_lowercase									=				self.get_tokenizer()

									_lowercase									=				SpeechaTextProcessor(tokenizer=__UpperCamelCase							, feature_extractor=__UpperCamelCase     )

									_lowercase									=				"""This is a test string"""

									_lowercase									=				processor(text=__UpperCamelCase     )

									_lowercase									=				tokenizer(__UpperCamelCase     )

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






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.get_feature_extractor()
									_lowercase									=				self.get_tokenizer()

									_lowercase									=				SpeechaTextProcessor(tokenizer=__UpperCamelCase							, feature_extractor=__UpperCamelCase     )

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

									_lowercase									=				processor.batch_decode(__UpperCamelCase     )
									_lowercase									=				tokenizer.batch_decode(__UpperCamelCase     )

									self.assertListEqual(__UpperCamelCase							, __UpperCamelCase     )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.get_feature_extractor()
									_lowercase									=				self.get_tokenizer()

									_lowercase									=				SpeechaTextProcessor(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"""							, )

def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int					)     ->    int:
						return n * (2 * n - 1)




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int					)     ->    bool:
						_lowercase									=				(1 + 24 * n) ** 0.5
						return ((1 + root) / 6) % 1 == 0




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int = 1_44					)     ->    int:
						_lowercase									=				start
						_lowercase									=				hexagonal_num(SCREAMING_SNAKE_CASE_					)
						while not is_pentagonal(SCREAMING_SNAKE_CASE_					):
												n += 1
												_lowercase									=				hexagonal_num(SCREAMING_SNAKE_CASE_					)
						return num


if __name__ == "__main__":
		print(F'{solution()} = ')