
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			)->   str:





													'''simple docstring'''






													__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     A__		(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Union[str, Any]:





													'''simple docstring'''






													return AutoTokenizer.from_pretrained(self.checkpoint     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													shutil.rmtree(self.tmpdirname			)



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_			)

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

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



							@slow
							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__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     A__		(  self			)->   int:





													'''simple docstring'''






													__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(SCREAMING_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=SCREAMING_SNAKE_CASE_			)

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

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

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

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



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			processor(text=self.input_string			)

													__UpperCamelCase       =			tokenizer(
													    self.input_string     ,	padding='''max_length'''     ,	max_length=256     ,	add_special_tokens=SCREAMING_SNAKE_CASE_     ,	return_attention_mask=SCREAMING_SNAKE_CASE_     ,	return_token_type_ids=SCREAMING_SNAKE_CASE_     ,	)

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






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def 	A_ (							snake_case							:   str ,      snake_case							:   str ,      **snake_case							:   List[str] )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			AutoConfig.from_pretrained(snake_case ,      **snake_case )
						__UpperCamelCase       =			AutoModelForSeqaSeqLM.from_config(snake_case )
						model.save_pretrained(snake_case )
						AutoTokenizer.from_pretrained(snake_case ).save_pretrained(snake_case )
						return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)




