

'''simple docstring'''


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 					lowercase						(  unittest.TestCase ):


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


							def 						_snake_case							(			self    ,			**_snake_case)							->				List[Any]:
								return AutoTokenizer.from_pretrained(self.checkpoint    ,			**_snake_case)


							def 						_snake_case							(			self)							->				Tuple:
								shutil.rmtree(self.tmpdirname)


							def 						_snake_case							(			self)							->				List[str]:
								UpperCAmelCase_		:      int       	=					self.get_tokenizer()

								UpperCAmelCase_		:      Optional[Any]       	=					BarkProcessor(tokenizer=_snake_case)

								processor.save_pretrained(self.tmpdirname)
								UpperCAmelCase_		:      str       	=					BarkProcessor.from_pretrained(self.tmpdirname)

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


							@slow
							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      Optional[Any]       	=					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    ,			)

								UpperCAmelCase_		:      List[Any]       	=					self.get_tokenizer(bos_token='(BOS)'    ,			eos_token='(EOS)')

								UpperCAmelCase_		:      int       	=					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 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      Optional[int]       	=					BarkProcessor.from_pretrained(
								    pretrained_processor_name_or_path=self.checkpoint    ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,			)

								UpperCAmelCase_		:      Dict       	=					35
								UpperCAmelCase_		:      Optional[Any]       	=					2
								UpperCAmelCase_		:      Union[str, Any]       	=					8

								UpperCAmelCase_		:      str       	=					{
								    'semantic_prompt': np.ones(_snake_case),
								    'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)),
								    'fine_prompt': np.ones((nb_codebooks_total, seq_len)),
								}

								# test providing already loaded voice_preset
								UpperCAmelCase_		:      List[str]       	=					processor(text=self.input_string    ,			voice_preset=_snake_case)

								UpperCAmelCase_		:      List[Any]       	=					inputs['history_prompt']
								for key in voice_preset:
									self.assertListEqual(voice_preset[key].tolist()    ,			processed_voice_preset.get(_snake_case    ,			np.array([])).tolist())

								# test loading voice preset from npz file
								UpperCAmelCase_		:      Optional[Any]       	=					os.path.join(self.tmpdirname    ,			'file.npz')
								np.savez(_snake_case    ,			**_snake_case)
								UpperCAmelCase_		:      Tuple       	=					processor(text=self.input_string    ,			voice_preset=_snake_case)
								UpperCAmelCase_		:      Union[str, Any]       	=					inputs['history_prompt']

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

								# test loading voice preset from the hub
								UpperCAmelCase_		:      int       	=					processor(text=self.input_string    ,			voice_preset=self.voice_preset)





							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      str       	=					self.get_tokenizer()

								UpperCAmelCase_		:      List[str]       	=					BarkProcessor(tokenizer=_snake_case)

								UpperCAmelCase_		:      Optional[int]       	=					processor(text=self.input_string)

								UpperCAmelCase_		:      List[str]       	=					tokenizer(
								    self.input_string    ,			padding='max_length'    ,			max_length=256    ,			add_special_tokens=_snake_case    ,			return_attention_mask=_snake_case    ,			return_token_type_ids=_snake_case    ,			)

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









'''simple docstring'''


from __future__ import annotations

from typing import Any



class 					lowercase						(  a_ ):
							pass



class 					lowercase						:


							def __init__(			self    ,			_snake_case)							->				None:
								UpperCAmelCase_		:      Any       	=					data
								UpperCAmelCase_		:      Node | None       	=					None


							def __iter__(			self)							->				Optional[int]:
								UpperCAmelCase_		:      int       	=					self
								UpperCAmelCase_		:      List[str]       	=					[]
								while node:
									if node in visited:
										raise ContainsLoopError
									visited.append(_snake_case)
									yield node.data
									UpperCAmelCase_		:      Tuple       	=					node.next_node





							@property
							def 						_snake_case							(			self)							->				bool:
								try:
									list(self)
									return False
								except ContainsLoopError:
									return True


if __name__ == "__main__":
						lowerCAmelCase__										= Node(1)
						lowerCAmelCase__										= Node(2)
						lowerCAmelCase__										= Node(3)
						lowerCAmelCase__										= Node(4)
						print(root_node.has_loop)  # False
						lowerCAmelCase__										= root_node.next_node
						print(root_node.has_loop)  # True

						lowerCAmelCase__										= Node(5)
						lowerCAmelCase__										= Node(6)
						lowerCAmelCase__										= Node(5)
						lowerCAmelCase__										= Node(6)
						print(root_node.has_loop)  # False

						lowerCAmelCase__										= Node(1)
						print(root_node.has_loop)  # False






