
"""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    _UpperCAmelCase(   unittest.TestCase     ):



		def 						UpperCAmelCase							( self)	->	int:






							'''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 						UpperCAmelCase							( self    ,		**__a)	->	int:






							'''simple docstring'''
							return AutoTokenizer.from_pretrained(self.checkpoint    ,		**__lowerCAmelCase)



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							shutil.rmtree(self.tmpdirname)



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






							'''simple docstring'''
							_UpperCamelCase										=  self.get_tokenizer()

							_UpperCamelCase										=  BarkProcessor(tokenizer=__lowerCAmelCase)

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

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



		@slow
		def 						UpperCAmelCase							( 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 						UpperCAmelCase							( 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    ,		)

							_UpperCamelCase										=  35
							_UpperCamelCase										=  2
							_UpperCamelCase										=  8

							_UpperCamelCase										=  {
							    '''semantic_prompt''': np.ones(__lowerCAmelCase),
							    '''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=__lowerCAmelCase)

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

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

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

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




		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.get_tokenizer()

							_UpperCamelCase										=  BarkProcessor(tokenizer=__lowerCAmelCase)

							_UpperCamelCase										=  processor(text=self.input_string)

							_UpperCamelCase										=  tokenizer(
							    self.input_string    ,		padding='''max_length'''    ,		max_length=2_56    ,		add_special_tokens=__lowerCAmelCase    ,		return_attention_mask=__lowerCAmelCase    ,		return_token_type_ids=__lowerCAmelCase    ,		)

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




"""simple docstring"""





import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    """The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionInpaintPipeline` instead."""
)






