
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


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


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





		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   Any							=       tempfile.mkdtemp()

							lowerCAmelCase						:   Dict							=       ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
							lowerCAmelCase						:   Optional[int]							=       dict(zip(lowerCamelCase__  ,					range(len(lowerCamelCase__			)			)			)			)
							lowerCAmelCase						:   Any							=       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''']			)

							lowerCAmelCase						:   Union[str, Any]							=       SpeechaTextTokenizer.from_pretrained(self.tmpdirname			)
							tokenizer.save_pretrained(self.tmpdirname			)

							lowerCAmelCase						:   Any							=       {
							    '''feature_size''': 2_4,
							    '''num_mel_bins''': 2_4,
							    '''padding_value''': 0.0,
							    '''sampling_rate''': 1_6_0_0_0,
							    '''return_attention_mask''': False,
							    '''do_normalize''': True,
							}
							save_json(lowerCamelCase__  ,					save_dir / FEATURE_EXTRACTOR_NAME			)





		def  _A							(							self      :		Tuple  ,					**lowerCamelCase__      :		List[str]			):
							return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,					**lowerCamelCase__			)





		def  _A							(							self      :		Optional[Any]  ,					**lowerCamelCase__      :		Tuple			):
							return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,					**lowerCamelCase__			)





		def  _A							(							self      :		Any			):
							shutil.rmtree(self.tmpdirname			)





		def  _A							(							self      :		int			):
							lowerCAmelCase						:   Any							=       self.get_tokenizer()
							lowerCAmelCase						:   int							=       self.get_feature_extractor()

							lowerCAmelCase						:   str							=       SpeechaTextProcessor(tokenizer=lowerCamelCase__  ,					feature_extractor=lowerCamelCase__			)

							processor.save_pretrained(self.tmpdirname			)
							lowerCAmelCase						:   List[Any]							=       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  _A							(							self      :		Tuple			):
							lowerCAmelCase						:   int							=       SpeechaTextProcessor(
							    tokenizer=self.get_tokenizer()  ,					feature_extractor=self.get_feature_extractor()			)
							processor.save_pretrained(self.tmpdirname			)

							lowerCAmelCase						:   Optional[int]							=       self.get_tokenizer(bos_token='''(BOS)'''  ,					eos_token='''(EOS)'''			)
							lowerCAmelCase						:   List[str]							=       self.get_feature_extractor(do_normalize=lowerCamelCase__  ,					padding_value=1.0			)

							lowerCAmelCase						:   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  _A							(							self      :		int			):
							lowerCAmelCase						:   Tuple							=       self.get_feature_extractor()
							lowerCAmelCase						:   Tuple							=       self.get_tokenizer()

							lowerCAmelCase						:   Optional[Any]							=       SpeechaTextProcessor(tokenizer=lowerCamelCase__  ,					feature_extractor=lowerCamelCase__			)

							lowerCAmelCase						:   List[str]							=       floats_list((3, 1_0_0_0)			)

							lowerCAmelCase						:   Dict							=       feature_extractor(lowerCamelCase__  ,					return_tensors='''np'''			)
							lowerCAmelCase						:   List[str]							=       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  _A							(							self      :		str			):
							lowerCAmelCase						:   int							=       self.get_feature_extractor()
							lowerCAmelCase						:   Dict							=       self.get_tokenizer()

							lowerCAmelCase						:   str							=       SpeechaTextProcessor(tokenizer=lowerCamelCase__  ,					feature_extractor=lowerCamelCase__			)

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

							lowerCAmelCase						:   Dict							=       processor(text=lowerCamelCase__			)

							lowerCAmelCase						:   Dict							=       tokenizer(lowerCamelCase__			)

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





		def  _A							(							self      :		int			):
							lowerCAmelCase						:   str							=       self.get_feature_extractor()
							lowerCAmelCase						:   List[str]							=       self.get_tokenizer()

							lowerCAmelCase						:   Union[str, Any]							=       SpeechaTextProcessor(tokenizer=lowerCamelCase__  ,					feature_extractor=lowerCamelCase__			)

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

							lowerCAmelCase						:   Dict							=       processor.batch_decode(lowerCamelCase__			)
							lowerCAmelCase						:   List[Any]							=       tokenizer.batch_decode(lowerCamelCase__			)

							self.assertListEqual(lowerCamelCase__  ,					lowerCamelCase__			)





		def  _A							(							self      :		int			):
							lowerCAmelCase						:   Union[str, Any]							=       self.get_feature_extractor()
							lowerCAmelCase						:   List[Any]							=       self.get_tokenizer()

							lowerCAmelCase						:   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'''  ,					)


def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       int					):


					'''simple docstring'''

					sequence.append(__magic_name__					)

					lowerCAmelCase						:   Tuple							=       0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__magic_name__					):
										return None

					return index


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				Dict             =       input('Enter numbers separated by comma:\n').strip()
		__SCREAMING_SNAKE_CASE     :				int             =       [int(item) for item in user_input.split(',')]

		__SCREAMING_SNAKE_CASE     :				str             =       input('Enter a single number to be found in the list:\n')
		__SCREAMING_SNAKE_CASE     :				Tuple             =       int(target_input)
		__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       sentinel_linear_search(sequence, target)
		if result is not None:
				print(f"""{target} found at positions: {result}""")
		else:
				print('Not found')
