





from __future__ import annotations

from dataclasses import dataclass





@dataclass
class 			lowerCAmelCase__:






       '''simple docstring'''


       __snake_case 			=    42
       __snake_case 			=    None
       __snake_case 			=    None






def     lowerCamelCase__			(__lowerCamelCase	):

     # Validation
     def is_valid_tree(__lowerCamelCase	) -> bool:
          if node is None:
               return True

          if not isinstance(__lowerCamelCase,       __lowerCamelCase	):
               return False

          try:
               float(node.data	)
          except (TypeError, ValueError):
               return False

          return is_valid_tree(node.left	) and is_valid_tree(node.right	)

     if not is_valid_tree(__lowerCamelCase	):
          raise ValueError(
              "Each node should be type of TreeNode and data should be float."	)

     def is_binary_search_tree_recursive_check(
         __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	) -> bool:

          if node is None:
               return True

          return (
              left_bound < node.data < right_bound
              and is_binary_search_tree_recursive_check(node.left,       __lowerCamelCase,       node.data	)
              and is_binary_search_tree_recursive_check(
                  node.right,       node.data,       __lowerCamelCase	)
          )

     return is_binary_search_tree_recursive_check(__lowerCamelCase,       -float("inf"	),       float("inf"	)	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							"ylacombe/bark-small"
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							tempfile.mkdtemp()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							"en_speaker_1"
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"This is a test string"
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							"speaker_embeddings_path.json"
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"speaker_embeddings"




       def     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     str:
            return AutoTokenizer.from_pretrained(self.checkpoint    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            shutil.rmtree(self.tmpdirname							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	Dict						    =							BarkProcessor(tokenizer=__lowerCamelCase							)

            processor.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							BarkProcessor.from_pretrained(self.tmpdirname							)

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




       @slow
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						)
            processor.save_pretrained(
                self.tmpdirname    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						speaker_embeddings_directory=self.speaker_embeddings_directory    ,						)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.get_tokenizer(bos_token="(BOS)"    ,						eos_token="(EOS)"							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							BarkProcessor.from_pretrained(
                self.tmpdirname    ,						self.speaker_embeddings_dict_path    ,						bos_token="(BOS)"    ,						eos_token="(EOS)"    ,						)

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




       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Any						    =							BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							3_5
            _SCREAMING_SNAKE_CASE       :	Any						    =							2
            _SCREAMING_SNAKE_CASE       :	int						    =							8

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{
                "semantic_prompt": np.ones(__lowerCamelCase							),
                "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)							),
                "fine_prompt": np.ones((nb_codebooks_total, seq_len)							),
            }

            # test providing already loaded voice_preset
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							processor(text=self.input_string    ,						voice_preset=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							inputs["history_prompt"]
            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()    ,						processed_voice_preset.get(__lowerCamelCase    ,						np.array([]							)							).tolist()							)

            # test loading voice preset from npz file
            _SCREAMING_SNAKE_CASE       :	Any						    =							os.path.join(self.tmpdirname    ,						"file.npz"							)
            np.savez(__lowerCamelCase    ,						**__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							processor(text=self.input_string    ,						voice_preset=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							inputs["history_prompt"]

            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()    ,						processed_voice_preset.get(__lowerCamelCase    ,						np.array([]							)							).tolist()							)

            # test loading voice preset from the hub
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							processor(text=self.input_string    ,						voice_preset=self.voice_preset							)







       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							BarkProcessor(tokenizer=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							processor(text=self.input_string							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tokenizer(
                self.input_string    ,						padding="max_length"    ,						max_length=2_5_6    ,						add_special_tokens=__lowerCamelCase    ,						return_attention_mask=__lowerCamelCase    ,						return_token_type_ids=__lowerCamelCase    ,						)

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