



"""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    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									_UpperCAmelCase							 =		'ylacombe/bark-small'
									_UpperCAmelCase							 =		tempfile.mkdtemp()
									_UpperCAmelCase							 =		'en_speaker_1'
									_UpperCAmelCase							 =		'This is a test string'
									_UpperCAmelCase							 =		'speaker_embeddings_path.json'
									_UpperCAmelCase							 =		'speaker_embeddings'






							def        lowerCamelCase_   (  self	,       **snake_case						)			->							Optional[int]:
									return AutoTokenizer.from_pretrained(self.checkpoint	,       **snake_case						)






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






							def        lowerCamelCase_   (  self						)			->							Tuple:
									_UpperCAmelCase							 =		self.get_tokenizer()

									_UpperCAmelCase							 =		BarkProcessor(tokenizer=snake_case						)

									processor.save_pretrained(self.tmpdirname						)
									_UpperCAmelCase							 =		BarkProcessor.from_pretrained(self.tmpdirname						)

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






							@slow
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		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							 =		self.get_tokenizer(bos_token='(BOS)'	,       eos_token='(EOS)'						)

									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Optional[int]:
									_UpperCAmelCase							 =		BarkProcessor.from_pretrained(
									    pretrained_processor_name_or_path=self.checkpoint	,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path	,       )

									_UpperCAmelCase							 =		35
									_UpperCAmelCase							 =		2
									_UpperCAmelCase							 =		8

									_UpperCAmelCase							 =		{
									    '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							 =		processor(text=self.input_string	,       voice_preset=snake_case						)

									_UpperCAmelCase							 =		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							 =		os.path.join(self.tmpdirname	,       'file.npz'						)
									np.savez(snake_case	,       **snake_case						)
									_UpperCAmelCase							 =		processor(text=self.input_string	,       voice_preset=snake_case						)
									_UpperCAmelCase							 =		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							 =		processor(text=self.input_string	,       voice_preset=self.voice_preset						)






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		self.get_tokenizer()

									_UpperCAmelCase							 =		BarkProcessor(tokenizer=snake_case						)

									_UpperCAmelCase							 =		processor(text=self.input_string						)

									_UpperCAmelCase							 =		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"""





import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


lowercase  							=					logging.get_logger(__name__)




class 			lowercase__			(      A    ):
							'''simple docstring'''







							def __init__(  self	,       *snake_case	,       **snake_case						)			->							None:
									warnings.warn(
									    'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use YolosImageProcessor instead.'	,       snake_case	,       )
									super().__init__(*snake_case	,       **snake_case						)


