



'''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 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






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







	def      A	(						self			:     Optional[int]		,			**__snake_case			:     Union[str, Any]		)			->		int:
								return AutoTokenizer.from_pretrained(self.checkpoint		,			**__snake_case		)







	def      A	(						self			:     str		)			->		int:
								shutil.rmtree(self.tmpdirname		)







	def      A	(						self			:     int		)			->		Optional[int]:
								UpperCAmelCase :       int 							=		self.get_tokenizer()

								UpperCAmelCase :       Tuple 							=		BarkProcessor(tokenizer=__snake_case		)

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

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







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

								UpperCAmelCase :       List[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      A	(						self			:     Any		)			->		str:
								UpperCAmelCase :       int 							=		BarkProcessor.from_pretrained(
								    pretrained_processor_name_or_path=self.checkpoint		,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,			)

								UpperCAmelCase :       str 							=		35
								UpperCAmelCase :       Tuple 							=		2
								UpperCAmelCase :       Optional[Any] 							=		8

								UpperCAmelCase :       int 							=		{
								    '''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 :       Dict 							=		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 :       Any 							=		os.path.join(self.tmpdirname		,			'''file.npz'''		)
								np.savez(__snake_case		,			**__snake_case		)
								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 the hub
								UpperCAmelCase :       Any 							=		processor(text=self.input_string		,			voice_preset=self.voice_preset		)







	def      A	(						self			:     List[str]		)			->		str:
								UpperCAmelCase :       int 							=		self.get_tokenizer()

								UpperCAmelCase :       List[Any] 							=		BarkProcessor(tokenizer=__snake_case		)

								UpperCAmelCase :       List[str] 							=		processor(text=self.input_string		)

								UpperCAmelCase :       Union[str, Any] 							=		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"""




# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


__UpperCamelCase						=      open  # noqa: we just need to have a builtin inside this module to test it properly


