




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 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				def       __snake_case		( self						):
											lowerCAmelCase   =		'''ylacombe/bark-small'''
											lowerCAmelCase   =		tempfile.mkdtemp()
											lowerCAmelCase   =		'''en_speaker_1'''
											lowerCAmelCase   =		'''This is a test string'''
											lowerCAmelCase   =		'''speaker_embeddings_path.json'''
											lowerCAmelCase   =		'''speaker_embeddings'''



				def       __snake_case		( self       , **UpperCAmelCase_						):
											return AutoTokenizer.from_pretrained(self.checkpoint       , **UpperCAmelCase_						)



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



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.get_tokenizer()

											lowerCAmelCase   =		BarkProcessor(tokenizer=UpperCAmelCase_						)

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

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



				@slow
				def       __snake_case		( self						):
											lowerCAmelCase   =		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       , )

											lowerCAmelCase   =		self.get_tokenizer(bos_token='''(BOS)'''       , eos_token='''(EOS)'''						)

											lowerCAmelCase   =		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						):
											lowerCAmelCase   =		BarkProcessor.from_pretrained(
											    pretrained_processor_name_or_path=self.checkpoint       , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       , )

											lowerCAmelCase   =		35
											lowerCAmelCase   =		2
											lowerCAmelCase   =		8

											lowerCAmelCase   =		{
											    '''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
											lowerCAmelCase   =		processor(text=self.input_string       , voice_preset=UpperCAmelCase_						)

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






				def       __snake_case		( self						):
											lowerCAmelCase   =		self.get_tokenizer()

											lowerCAmelCase   =		BarkProcessor(tokenizer=UpperCAmelCase_						)

											lowerCAmelCase   =		processor(text=self.input_string						)

											lowerCAmelCase   =		tokenizer(
											    self.input_string       , padding='''max_length'''       , max_length=2_56       , 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()						)









from collections.abc import Sequence



def       UpperCAmelCase       (     _snake_case						, _snake_case = False							):
							if not arr:
														return 0

							lowerCAmelCase   =		0 if allow_empty_subarrays else float('''-inf'''							)
							lowerCAmelCase   =		0.0
							for num in arr:
														lowerCAmelCase   =		max(0 if allow_empty_subarrays else num						, curr_sum + num							)
														lowerCAmelCase   =		max(_snake_case						, _snake_case							)

							return max_sum


if __name__ == "__main__":
		from doctest import testmod

		testmod()

		UpperCAmelCase_						=[-2, 1, -3, 4, -1, 2, 1, -5, 4]
		print(F'''{max_subarray_sum(nums) = }''')



