from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass


@dataclass
class 					UpperCAmelCase		:



    """simple docstring"""

    _UpperCAmelCase			:int
    _UpperCAmelCase			:TreeNode | None							=				None
    _UpperCAmelCase			:TreeNode | None							=				None



def SCREAMING_SNAKE_CASE__    (     )						->						TreeNode:
   return TreeNode(3 ,					TreeNode(9				) ,					TreeNode(2_0 ,					TreeNode(1_5				) ,					TreeNode(7				)				)				)



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						list[int]:

   def depth_first_search(
       __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				) -> None:
      if not root:
         return

      if depth == len(__UpperCAmelCase				):
         right_view.append(root.val				)

      depth_first_search(root.right ,					depth + 1 ,					__UpperCAmelCase				)
      depth_first_search(root.left ,					depth + 1 ,					__UpperCAmelCase				)

   lowercase__:     list      =   []
   if not root:
      return right_view

   depth_first_search(__UpperCAmelCase ,					0 ,					__UpperCAmelCase				)
   return right_view



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						list[int]:

   def depth_first_search(
       __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				) -> None:
      if not root:
         return

      if depth == len(__UpperCAmelCase				):
         left_view.append(root.val				)

      depth_first_search(root.left ,					depth + 1 ,					__UpperCAmelCase				)
      depth_first_search(root.right ,					depth + 1 ,					__UpperCAmelCase				)

   lowercase__:     list      =   []
   if not root:
      return left_view

   depth_first_search(__UpperCAmelCase ,					0 ,					__UpperCAmelCase				)
   return left_view



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						list[int]:

   def breadth_first_search(__UpperCAmelCase ,					__UpperCAmelCase				) -> None:
      lowercase__:     Optional[int]      =   [(root, 0)]
      lowercase__:     Dict      =   defaultdict(__UpperCAmelCase				)

      while queue:
         lowercase__:     Dict      =   queue.pop(0				)
         lowercase__:     Optional[int]      =   first

         lookup[hd].append(node.val				)

         if node.left:
            queue.append((node.left, hd - 1)				)
         if node.right:
            queue.append((node.right, hd + 1)				)

      for pair in sorted(lookup.items() ,					key=lambda __UpperCAmelCase				: each[0]				):
         top_view.append(pair[1][0]				)

   lowercase__:     list      =   []
   if not root:
      return top_view

   breadth_first_search(__UpperCAmelCase ,					__UpperCAmelCase				)
   return top_view



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						list[int]:
   from collections import defaultdict

   def breadth_first_search(__UpperCAmelCase ,					__UpperCAmelCase				) -> None:
      lowercase__:     str      =   [(root, 0)]
      lowercase__:     List[Any]      =   defaultdict(__UpperCAmelCase				)

      while queue:
         lowercase__:     Optional[Any]      =   queue.pop(0				)
         lowercase__:     Optional[Any]      =   first
         lookup[hd].append(node.val				)

         if node.left:
            queue.append((node.left, hd - 1)				)
         if node.right:
            queue.append((node.right, hd + 1)				)

      for pair in sorted(lookup.items() ,					key=lambda __UpperCAmelCase				: each[0]				):
         bottom_view.append(pair[1][-1]				)

   lowercase__:     list      =   []
   if not root:
      return bottom_view

   breadth_first_search(__UpperCAmelCase ,					__UpperCAmelCase				)
   return bottom_view


if __name__ == "__main__":
       import doctest

       doctest.testmod()

"""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						):



    """simple docstring"""

    def _snake_case    (     self							):
       lowercase__:     Dict      =   tempfile.mkdtemp()

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

       lowercase__:     Tuple      =   SpeechaTextTokenizer.from_pretrained(self.tmpdirname							)
       tokenizer.save_pretrained(self.tmpdirname							)

       lowercase__:     Optional[int]      =   {
           '''feature_size''': 24,
           '''num_mel_bins''': 24,
           '''padding_value''': 0.0,
           '''sampling_rate''': 16000,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }
       save_json(_UpperCAmelCase					,    save_dir / FEATURE_EXTRACTOR_NAME							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       return SpeechaTextTokenizer.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase							)






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






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_tokenizer()
       lowercase__:     Optional[int]      =   self.get_feature_extractor()

       lowercase__:     int      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       processor.save_pretrained(self.tmpdirname							)
       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor.from_pretrained(self.tmpdirname							)

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

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






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   SpeechaTextProcessor(
           tokenizer=self.get_tokenizer()					,    feature_extractor=self.get_feature_extractor()							)
       processor.save_pretrained(self.tmpdirname							)

       lowercase__:     Any      =   self.get_tokenizer(bos_token='''(BOS)'''					,    eos_token='''(EOS)'''							)
       lowercase__:     Tuple      =   self.get_feature_extractor(do_normalize=_UpperCAmelCase					,    padding_value=1.0							)

       lowercase__:     List[str]      =   SpeechaTextProcessor.from_pretrained(
           self.tmpdirname					,    bos_token='''(BOS)'''					,    eos_token='''(EOS)'''					,    do_normalize=_UpperCAmelCase					,    padding_value=1.0							)

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

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






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_feature_extractor()
       lowercase__:     List[Any]      =   self.get_tokenizer()

       lowercase__:     Any      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     int      =   floats_list((3, 1000)							)

       lowercase__:     Optional[Any]      =   feature_extractor(_UpperCAmelCase					,    return_tensors='''np'''							)
       lowercase__:     Dict      =   processor(_UpperCAmelCase					,    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							):
       lowercase__:     Tuple      =   self.get_feature_extractor()
       lowercase__:     Dict      =   self.get_tokenizer()

       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     Dict      =   '''This is a test string'''

       lowercase__:     int      =   processor(text=_UpperCAmelCase							)

       lowercase__:     Union[str, Any]      =   tokenizer(_UpperCAmelCase							)

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






    def _snake_case    (     self							):
       lowercase__:     List[Any]      =   self.get_feature_extractor()
       lowercase__:     Union[str, Any]      =   self.get_tokenizer()

       lowercase__:     Tuple      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     List[str]      =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowercase__:     List[Any]      =   processor.batch_decode(_UpperCAmelCase							)
       lowercase__:     Dict      =   tokenizer.batch_decode(_UpperCAmelCase							)

       self.assertListEqual(_UpperCAmelCase					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__:     Optional[Any]      =   self.get_feature_extractor()
       lowercase__:     List[str]      =   self.get_tokenizer()

       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

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