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


UpperCAmelCase_    		=				get_tests_dir('''fixtures/test_sentencepiece.model''')





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



     def       a__	(       self							):
            _A=						tempfile.mkdtemp()

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

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

            _A=						{
                'feature_size': 24,
                'num_mel_bins': 24,
                'padding_value': 0.0,
                'sampling_rate': 16000,
                'return_attention_mask': False,
                'do_normalize': True,
            }
            save_json(snake_case__					,   save_dir / FEATURE_EXTRACTOR_NAME							)



     def       a__	(       self					,   **lowerCAmelCase__							):
            return SpeechaTextTokenizer.from_pretrained(self.tmpdirname					,   **snake_case__							)



     def       a__	(       self					,   **lowerCAmelCase__							):
            return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname					,   **snake_case__							)



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



     def       a__	(       self							):
            _A=						self.get_tokenizer()
            _A=						self.get_feature_extractor()

            _A=						SpeechaTextProcessor(tokenizer=snake_case__					,   feature_extractor=snake_case__							)

            processor.save_pretrained(self.tmpdirname							)
            _A=						SpeechaTextProcessor.from_pretrained(self.tmpdirname							)

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

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



     def       a__	(       self							):
            _A=						SpeechaTextProcessor(
                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=snake_case__					,   padding_value=1.0							)

            _A=						SpeechaTextProcessor.from_pretrained(
                self.tmpdirname					,   bos_token='(BOS)'					,   eos_token='(EOS)'					,   do_normalize=snake_case__					,   padding_value=1.0							)

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

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



     def       a__	(       self							):
            _A=						self.get_feature_extractor()
            _A=						self.get_tokenizer()

            _A=						SpeechaTextProcessor(tokenizer=snake_case__					,   feature_extractor=snake_case__							)

            _A=						floats_list((3, 1000)							)

            _A=						feature_extractor(snake_case__					,   return_tensors='np'							)
            _A=						processor(snake_case__					,   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							):
            _A=						self.get_feature_extractor()
            _A=						self.get_tokenizer()

            _A=						SpeechaTextProcessor(tokenizer=snake_case__					,   feature_extractor=snake_case__							)

            _A=						'This is a test string'

            _A=						processor(text=snake_case__							)

            _A=						tokenizer(snake_case__							)

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



     def       a__	(       self							):
            _A=						self.get_feature_extractor()
            _A=						self.get_tokenizer()

            _A=						SpeechaTextProcessor(tokenizer=snake_case__					,   feature_extractor=snake_case__							)

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

            _A=						processor.batch_decode(snake_case__							)
            _A=						tokenizer.batch_decode(snake_case__							)

            self.assertListEqual(snake_case__					,   snake_case__							)





     def       a__	(       self							):
            _A=						self.get_feature_extractor()
            _A=						self.get_tokenizer()

            _A=						SpeechaTextProcessor(tokenizer=snake_case__					,   feature_extractor=snake_case__							)

            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 enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowerCamelCase					    =	TypeVar('''T''', bound='''Enum''')

@unique
class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   __lowerCAmelCase  =    80
   __lowerCAmelCase  =    70
   __lowerCAmelCase  =    60
   __lowerCAmelCase  =    50
   __lowerCAmelCase  =    40
   __lowerCAmelCase  =    30
   __lowerCAmelCase  =    20
   __lowerCAmelCase  =    10






@unique
class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   __lowerCAmelCase  =    24
   __lowerCAmelCase  =    21
   __lowerCAmelCase  =    18
   __lowerCAmelCase  =    15
   __lowerCAmelCase  =    12
   __lowerCAmelCase  =    9
   __lowerCAmelCase  =    6
   __lowerCAmelCase  =    3
   __lowerCAmelCase  =    2
   __lowerCAmelCase  =    1
   __lowerCAmelCase  =    -1
   __lowerCAmelCase  =    -2
   __lowerCAmelCase  =    -3
   __lowerCAmelCase  =    -6
   __lowerCAmelCase  =    -9
   __lowerCAmelCase  =    -12
   __lowerCAmelCase  =    -15
   __lowerCAmelCase  =    -18
   __lowerCAmelCase  =    -21
   __lowerCAmelCase  =    -24

   @classmethod
   def       SCREAMING_SNAKE_CASE__					(     cls:						type[T]						)	->		dict:


       """simple docstring"""



       return {unit.name: unit.value for unit in cls if unit.value > 0}

   @classmethod
   def       SCREAMING_SNAKE_CASE__					(     cls:						type[T]						)	->		dict:


       """simple docstring"""



       return {unit.name: unit.value for unit in cls if unit.value < 0}






def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
    for name_prefix, value_prefix in prefixes.items():
        _lowercase     			=						value / (10**value_prefix)
        if numerical_part > 1:
            return f'''{numerical_part!s} {name_prefix}'''
    return str(_lowercase   )
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    for prefix in BinaryUnit:
        _lowercase     			=						value / (2**prefix.value)
        if numerical_part > 1:
            return f'''{numerical_part!s} {prefix.name}'''
    return str(_lowercase   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
