

import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list



class    __snake_case	(     unittest.TestCase							):

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    '<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' ')
      SCREAMING_SNAKE_CASE_						=    dict(zip(_A    ,		range(len(_A))))

      SCREAMING_SNAKE_CASE_						=    {
          'pad_token': '<pad>',
          'unk_token': '<unk>',
          'bos_token': '<s>',
          'eos_token': '</s>',
      }
      SCREAMING_SNAKE_CASE_						=    {
          'feature_size': 1,
          'padding_value': 0.0,
          'sampling_rate': 16000,
          'return_attention_mask': False,
          'do_normalize': True,
      }

      SCREAMING_SNAKE_CASE_						=    tempfile.mkdtemp()
      SCREAMING_SNAKE_CASE_						=    os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['vocab_file'])
      SCREAMING_SNAKE_CASE_						=    os.path.join(self.tmpdirname    ,		_A)
      with open(self.vocab_file    ,		'w'    ,		encoding='utf-8') as fp:
          fp.write(json.dumps(_A) + '\n')

      with open(self.feature_extraction_file    ,		'w'    ,		encoding='utf-8') as fp:
          fp.write(json.dumps(_A) + '\n')

  def     lowerCAmelCase__  (       self    ,		**_A):
      SCREAMING_SNAKE_CASE_						=    self.add_kwargs_tokens_map.copy()
      kwargs.update(_A)
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname    ,		**_A)

  def     lowerCAmelCase__  (       self    ,		**_A):
      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname    ,		**_A)

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

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    self.get_tokenizer()
      SCREAMING_SNAKE_CASE_						=    self.get_feature_extractor()

      SCREAMING_SNAKE_CASE_						=    WavaVecaProcessor(tokenizer=_A    ,		feature_extractor=_A)

      processor.save_pretrained(self.tmpdirname)
      SCREAMING_SNAKE_CASE_						=    WavaVecaProcessor.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     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    WavaVecaProcessor(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_						=    WavaVecaProcessor.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     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    self.get_feature_extractor()
      SCREAMING_SNAKE_CASE_						=    self.get_tokenizer()

      SCREAMING_SNAKE_CASE_						=    WavaVecaProcessor(tokenizer=_A    ,		feature_extractor=_A)

      SCREAMING_SNAKE_CASE_						=    floats_list((3, 1000))

      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     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    self.get_feature_extractor()
      SCREAMING_SNAKE_CASE_						=    self.get_tokenizer()

      SCREAMING_SNAKE_CASE_						=    WavaVecaProcessor(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     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    self.get_feature_extractor()
      SCREAMING_SNAKE_CASE_						=    self.get_tokenizer()

      SCREAMING_SNAKE_CASE_						=    WavaVecaProcessor(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     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    self.get_feature_extractor()
      SCREAMING_SNAKE_CASE_						=    self.get_tokenizer()

      SCREAMING_SNAKE_CASE_						=    WavaVecaProcessor(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'    ,		)





import random


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		List[Any]							):





    """simple docstring"""


    def is_sorted(_SCREAMING_SNAKE_CASE       :		List[Any]							):
        for i in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
            if collection[i] > collection[i + 1]:
                return False
        return True

    while not is_sorted(_SCREAMING_SNAKE_CASE							):
        random.shuffle(_SCREAMING_SNAKE_CASE							)
    return collection


if __name__ == "__main__":
       UpperCamelCase__	:   Tuple								=     input("Enter numbers separated by a comma:\n").strip()
       UpperCamelCase__	:   List[str]								=     [int(item) for item in user_input.split(",")]
       print(bogo_sort(unsorted))


