






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 	a_(		unittest.TestCase	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		tempfile.mkdtemp()

      SCREAMING_SNAKE_CASE				    =		['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
      SCREAMING_SNAKE_CASE				    =		dict(zip(lowerCAmelCase__  ,  range(len(lowerCAmelCase__))))
      SCREAMING_SNAKE_CASE				    =		Path(self.tmpdirname)
      save_json(lowerCAmelCase__  ,  save_dir / VOCAB_FILES_NAMES['vocab_file'])
      if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
       copyfile(lowerCAmelCase__  ,  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': 2_4,
          'num_mel_bins': 2_4,
          'padding_value': 0.0,
          'sampling_rate': 1_6_0_0_0,
          'return_attention_mask': False,
          'do_normalize': True,
      }
      save_json(lowerCAmelCase__  ,  save_dir / FEATURE_EXTRACTOR_NAME)





     def     __UpperCamelCase						(		self					:	List[Any]  ,  **lowerCAmelCase__					:	int)							->	Optional[int]:

      """simple docstring"""
      return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	List[str]  ,  **lowerCAmelCase__					:	List[Any])							->	Dict:

      """simple docstring"""
      return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Union[str, Any])							->	List[str]:

      """simple docstring"""
      shutil.rmtree(self.tmpdirname)





     def     __UpperCamelCase						(		self					:	Any)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.get_tokenizer()
      SCREAMING_SNAKE_CASE				    =		self.get_feature_extractor()

      SCREAMING_SNAKE_CASE				    =		SpeechaTextProcessor(tokenizer=lowerCAmelCase__  ,  feature_extractor=lowerCAmelCase__)

      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  ,  lowerCAmelCase__)

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





     def     __UpperCamelCase						(		self					:	Tuple)							->	int:

      """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=lowerCAmelCase__  ,  padding_value=1.0)

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

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

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





     def     __UpperCamelCase						(		self					:	Tuple)							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.get_feature_extractor()
      SCREAMING_SNAKE_CASE				    =		self.get_tokenizer()

      SCREAMING_SNAKE_CASE				    =		SpeechaTextProcessor(tokenizer=lowerCAmelCase__  ,  feature_extractor=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		floats_list((3, 1_0_0_0))

      SCREAMING_SNAKE_CASE				    =		feature_extractor(lowerCAmelCase__  ,  return_tensors='np')
      SCREAMING_SNAKE_CASE				    =		processor(lowerCAmelCase__  ,  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     __UpperCamelCase						(		self					:	Union[str, Any])							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.get_feature_extractor()
      SCREAMING_SNAKE_CASE				    =		self.get_tokenizer()

      SCREAMING_SNAKE_CASE				    =		SpeechaTextProcessor(tokenizer=lowerCAmelCase__  ,  feature_extractor=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		'This is a test string'

      SCREAMING_SNAKE_CASE				    =		processor(text=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		tokenizer(lowerCAmelCase__)

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





     def     __UpperCamelCase						(		self					:	Union[str, Any])							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.get_feature_extractor()
      SCREAMING_SNAKE_CASE				    =		self.get_tokenizer()

      SCREAMING_SNAKE_CASE				    =		SpeechaTextProcessor(tokenizer=lowerCAmelCase__  ,  feature_extractor=lowerCAmelCase__)

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

      SCREAMING_SNAKE_CASE				    =		processor.batch_decode(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		tokenizer.batch_decode(lowerCAmelCase__)

      self.assertListEqual(lowerCAmelCase__  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Union[str, Any])							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.get_feature_extractor()
      SCREAMING_SNAKE_CASE				    =		self.get_tokenizer()

      SCREAMING_SNAKE_CASE				    =		SpeechaTextProcessor(tokenizer=lowerCAmelCase__  ,  feature_extractor=lowerCAmelCase__)

      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

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_ = 0		):
											a__					   =		value
											a__					   =		None
											a__					   =		None




def       A_   (						__a  :					Node | None			,		__a  :					Node | None		):

				"""simple docstring"""
				if treea is None:
								return treea
				if treea is None:
								return treea

				a__					   =		treea.value + treea.value
				a__					   =		merge_two_binary_trees(treea.left			,		treea.left		)
				a__					   =		merge_two_binary_trees(treea.right			,		treea.right		)
				return treea

def       A_   (						__a  :					Node | None		):

				"""simple docstring"""
				if root:
								print(root.value		)
								print_preorder(root.left		)
								print_preorder(root.right		)


if __name__ == "__main__":
	UpperCAmelCase							=       Node(1)
	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(3)
	UpperCAmelCase							=       Node(4)

	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(4)
	UpperCAmelCase							=       Node(6)
	UpperCAmelCase							=       Node(9)
	UpperCAmelCase							=       Node(5)

	print("""Tree1 is: """)
	print_preorder(treea)
	print("""Tree2 is: """)
	print_preorder(treea)
	UpperCAmelCase							=       merge_two_binary_trees(treea, treea)
	print("""Merged Tree is: """)
	print_preorder(merged_tree)

