






'''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							:						Dict			)							->    Optional[Any]:

									'''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							:						Optional[Any] ,      **lowercase							:						Any			)							->    Optional[Any]:

									'''simple docstring'''







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






			def 				A      (   self							:						List[str]			)							->    Optional[Any]:

									'''simple docstring'''







									shutil.rmtree(self.tmpdirname			)






			def 				A      (   self							:						Optional[Any]			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = self.get_tokenizer()

									UpperCamelCase__         = BarkProcessor(tokenizer=__a			)

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

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






			@slow
			def 				A      (   self							:						Dict			)							->    List[str]:

									'''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			)							->    Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__         = BarkProcessor.from_pretrained(
									    pretrained_processor_name_or_path=self.checkpoint ,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,      )

									UpperCamelCase__         = 3_5
									UpperCamelCase__         = 2
									UpperCamelCase__         = 8

									UpperCamelCase__         = {
									    """semantic_prompt""": np.ones(__a			),
									    """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=__a			)

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

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

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

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







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

									'''simple docstring'''







									UpperCamelCase__         = self.get_tokenizer()

									UpperCamelCase__         = BarkProcessor(tokenizer=__a			)

									UpperCamelCase__         = processor(text=self.input_string			)

									UpperCamelCase__         = tokenizer(
									    self.input_string ,      padding="""max_length""" ,      max_length=2_5_6 ,      add_special_tokens=__a ,      return_attention_mask=__a ,      return_token_type_ids=__a ,      )

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








'''simple docstring'''


import inspect
import unittest
class    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			def 				A      (   self							:						Tuple			)							->    Any:

									'''simple docstring'''







									try:
															import diffusers  # noqa: F401
									except ImportError:
															assert False







			def 				A      (   self							:						Optional[int]			)							->    Union[str, Any]:

									'''simple docstring'''







									import diffusers
									from diffusers.dependency_versions_table import deps

									UpperCamelCase__         = inspect.getmembers(lowercase ,      inspect.isclass			)

									for cls_name, cls_module in all_classes:
															if "dummy_" in cls_module.__module__:
																					for backend in cls_module._backends:
																											if backend == "k_diffusion":
																																	UpperCamelCase__         = """k-diffusion"""
																											elif backend == "invisible_watermark":
																																	UpperCamelCase__         = """invisible-watermark"""
																											assert backend in deps, f"{backend} is not in the deps table!"
