




'''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       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self			):
								_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 				UpperCAmelCase (    self						,		**_SCREAMING_SNAKE_CASE			):
								return AutoTokenizer.from_pretrained(self.checkpoint						,		**UpperCamelCase__			)






		def 				UpperCAmelCase (    self			):
								shutil.rmtree(self.tmpdirname			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.get_tokenizer()

								_UpperCAmelCase      =			BarkProcessor(tokenizer=UpperCamelCase__			)

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

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






		@slow
		def 				UpperCAmelCase (    self			):
								_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 				UpperCAmelCase (    self			):
								_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(UpperCamelCase__			),
								    '''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=UpperCamelCase__			)

								_UpperCAmelCase      =			inputs['''history_prompt''']
								for key in voice_preset:
														self.assertListEqual(voice_preset[key].tolist()						,		processed_voice_preset.get(UpperCamelCase__						,		np.array([]			)			).tolist()			)

								# test loading voice preset from npz file
								_UpperCAmelCase      =			os.path.join(self.tmpdirname						,		"""file.npz"""			)
								np.savez(UpperCamelCase__						,		**UpperCamelCase__			)
								_UpperCAmelCase      =			processor(text=self.input_string						,		voice_preset=UpperCamelCase__			)
								_UpperCAmelCase      =			inputs['''history_prompt''']

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

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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.get_tokenizer()

								_UpperCAmelCase      =			BarkProcessor(tokenizer=UpperCamelCase__			)

								_UpperCAmelCase      =			processor(text=self.input_string			)

								_UpperCAmelCase      =			tokenizer(
								    self.input_string						,		padding="""max_length"""						,		max_length=256						,		add_special_tokens=UpperCamelCase__						,		return_attention_mask=UpperCamelCase__						,		return_token_type_ids=UpperCamelCase__						,		)

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





# flake8: noqa
# Lint as: python3
a													=			[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental