
'''simple docstring'''
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_     =  get_tests_dir("fixtures/test_sentencepiece.model")






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





 def     snake_case__						(      self):
     snake_case_	:      Tuple            =  tempfile.mkdtemp()

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

     snake_case_	:      int            =  SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
     tokenizer.save_pretrained(self.tmpdirname)

     snake_case_	:      Tuple            =  {
         "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(lowercase_	,    save_dir / FEATURE_EXTRACTOR_NAME)





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





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





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





 def     snake_case__						(      self):
     snake_case_	:      str            =  self.get_tokenizer()
     snake_case_	:      Optional[Any]            =  self.get_feature_extractor()

     snake_case_	:      List[str]            =  SpeechaTextProcessor(tokenizer=lowercase_	,    feature_extractor=lowercase_)

     processor.save_pretrained(self.tmpdirname)
     snake_case_	:      List[Any]            =  SpeechaTextProcessor.from_pretrained(self.tmpdirname)

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

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





 def     snake_case__						(      self):
     snake_case_	:      int            =  SpeechaTextProcessor(
         tokenizer=self.get_tokenizer()	,    feature_extractor=self.get_feature_extractor())
     processor.save_pretrained(self.tmpdirname)

     snake_case_	:      List[str]            =  self.get_tokenizer(bos_token="(BOS)"	,    eos_token="(EOS)")
     snake_case_	:      int            =  self.get_feature_extractor(do_normalize=lowercase_	,    padding_value=1.0)

     snake_case_	:      str            =  SpeechaTextProcessor.from_pretrained(
         self.tmpdirname	,    bos_token="(BOS)"	,    eos_token="(EOS)"	,    do_normalize=lowercase_	,    padding_value=1.0)

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

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





 def     snake_case__						(      self):
     snake_case_	:      Tuple            =  self.get_feature_extractor()
     snake_case_	:      List[str]            =  self.get_tokenizer()

     snake_case_	:      Dict            =  SpeechaTextProcessor(tokenizer=lowercase_	,    feature_extractor=lowercase_)

     snake_case_	:      Tuple            =  floats_list((3, 10_00))

     snake_case_	:      Dict            =  feature_extractor(lowercase_	,    return_tensors="np")
     snake_case_	:      Optional[Any]            =  processor(lowercase_	,    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     snake_case__						(      self):
     snake_case_	:      int            =  self.get_feature_extractor()
     snake_case_	:      int            =  self.get_tokenizer()

     snake_case_	:      Any            =  SpeechaTextProcessor(tokenizer=lowercase_	,    feature_extractor=lowercase_)

     snake_case_	:      List[str]            =  "This is a test string"

     snake_case_	:      Optional[Any]            =  processor(text=lowercase_)

     snake_case_	:      Dict            =  tokenizer(lowercase_)

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





 def     snake_case__						(      self):
     snake_case_	:      int            =  self.get_feature_extractor()
     snake_case_	:      List[str]            =  self.get_tokenizer()

     snake_case_	:      Optional[Any]            =  SpeechaTextProcessor(tokenizer=lowercase_	,    feature_extractor=lowercase_)

     snake_case_	:      Tuple            =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     snake_case_	:      Optional[Any]            =  processor.batch_decode(lowercase_)
     snake_case_	:      Union[str, Any]            =  tokenizer.batch_decode(lowercase_)

     self.assertListEqual(lowercase_	,    lowercase_)





 def     snake_case__						(      self):
     snake_case_	:      Any            =  self.get_feature_extractor()
     snake_case_	:      Any            =  self.get_tokenizer()

     snake_case_	:      List[str]            =  SpeechaTextProcessor(tokenizer=lowercase_	,    feature_extractor=lowercase_)

     self.assertListEqual(
         processor.model_input_names	,    feature_extractor.model_input_names	,    msg="`processor` and `feature_extractor` model input names do not match"	,    )






'''simple docstring'''
import math

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Tuple            =  math.log(2,						1_0    ) * number
    snake_case_	:      Dict            =  round((log_number - int(__SCREAMING_SNAKE_CASE    )),						1_5    )

    return difference

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE = 6_7_8_9_1_0    ):
    """simple docstring"""






    snake_case_	:      Any            =  9_0
    snake_case_	:      Optional[int]            =  0

    snake_case_	:      Union[str, Any]            =  math.log(1.23,						1_0    )
    snake_case_	:      Dict            =  math.log(1.24,						1_0    )
    snake_case_	:      List[Any]            =  0

    while position < number:
        snake_case_	:      Union[str, Any]            =  log_difference(__SCREAMING_SNAKE_CASE    )

        if difference >= upper_limit:
            power_iterator += 9_3

        elif difference < lower_limit:
            power_iterator += 1_9_6

        else:
            snake_case_	:      Optional[Any]            =  power_iterator
            power_iterator += 1_9_6
            position += 1

    return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(f"""{solution() = }""")




