





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				:							Any							= get_tests_dir("""fixtures/test_sentencepiece.model""")



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


    '''simple docstring'''







    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= tempfile.mkdtemp()

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

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

        SCREAMING_SNAKE_CASE__       							= {
            '''feature_size''': 24,
            '''num_mel_bins''': 24,
            '''padding_value''': 0.0,
            '''sampling_rate''': 1_60_00,
            '''return_attention_mask''': False,
            '''do_normalize''': True,
        }
        save_json(A_      ,							save_dir / FEATURE_EXTRACTOR_NAME    )



    def    lowercase_				(					self      ,							**A_    ):





        '''simple docstring'''

        return SpeechaTextTokenizer.from_pretrained(self.tmpdirname      ,							**A_    )



    def    lowercase_				(					self      ,							**A_    ):





        '''simple docstring'''

        return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname      ,							**A_    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        shutil.rmtree(self.tmpdirname    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.get_tokenizer()
        SCREAMING_SNAKE_CASE__       							= self.get_feature_extractor()

        SCREAMING_SNAKE_CASE__       							= SpeechaTextProcessor(tokenizer=A_      ,							feature_extractor=A_    )

        processor.save_pretrained(self.tmpdirname    )
        SCREAMING_SNAKE_CASE__       							= SpeechaTextProcessor.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    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= SpeechaTextProcessor(
            tokenizer=self.get_tokenizer()      ,							feature_extractor=self.get_feature_extractor()    )
        processor.save_pretrained(self.tmpdirname    )

        SCREAMING_SNAKE_CASE__       							= self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''    )
        SCREAMING_SNAKE_CASE__       							= self.get_feature_extractor(do_normalize=A_      ,							padding_value=1.0    )

        SCREAMING_SNAKE_CASE__       							= SpeechaTextProcessor.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    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.get_feature_extractor()
        SCREAMING_SNAKE_CASE__       							= self.get_tokenizer()

        SCREAMING_SNAKE_CASE__       							= SpeechaTextProcessor(tokenizer=A_      ,							feature_extractor=A_    )

        SCREAMING_SNAKE_CASE__       							= floats_list((3, 10_00)    )

        SCREAMING_SNAKE_CASE__       							= feature_extractor(A_      ,							return_tensors='''np'''    )
        SCREAMING_SNAKE_CASE__       							= processor(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    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.get_feature_extractor()
        SCREAMING_SNAKE_CASE__       							= self.get_tokenizer()

        SCREAMING_SNAKE_CASE__       							= SpeechaTextProcessor(tokenizer=A_      ,							feature_extractor=A_    )

        SCREAMING_SNAKE_CASE__       							= '''This is a test string'''

        SCREAMING_SNAKE_CASE__       							= processor(text=A_    )

        SCREAMING_SNAKE_CASE__       							= tokenizer(A_    )

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



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.get_feature_extractor()
        SCREAMING_SNAKE_CASE__       							= self.get_tokenizer()

        SCREAMING_SNAKE_CASE__       							= SpeechaTextProcessor(tokenizer=A_      ,							feature_extractor=A_    )

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

        SCREAMING_SNAKE_CASE__       							= processor.batch_decode(A_    )
        SCREAMING_SNAKE_CASE__       							= tokenizer.batch_decode(A_    )

        self.assertListEqual(A_      ,							A_    )




    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.get_feature_extractor()
        SCREAMING_SNAKE_CASE__       							= self.get_tokenizer()

        SCREAMING_SNAKE_CASE__       							= SpeechaTextProcessor(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'''      ,							)







from __future__ import annotations

from collections.abc import Generator


def        __snake_case       (						lowerCAmelCase_  )							->	Generator[int, None, None]:
    if not isinstance(lowerCAmelCase_   ,       lowerCAmelCase_  ) or n < 1:
        raise Exception('''Sequence only defined for positive integers'''  )

    yield n
    while n != 1:
        if n % 2 == 0:
            n //= 2
        else:
            SCREAMING_SNAKE_CASE__       							= 3 * n + 1
        yield n


def        __snake_case       (						)							->	str:
    SCREAMING_SNAKE_CASE__       							= 4_3
    SCREAMING_SNAKE_CASE__       							= tuple(collatz_sequence(lowerCAmelCase_  )  )
    print(lowerCAmelCase_  )
    print(f'''Collatz sequence from {n} took {len(lowerCAmelCase_  )} steps.'''  )


if __name__ == "__main__":
  main()
