




"""simple docstring"""





import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       **UpperCamelCase							)			->			int:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[int]		=					AutoConfig.from_pretrained(UpperCamelCase				,       **UpperCamelCase							)
			__UpperCAmelCase	: List[str]		=					AutoModelForSeqaSeqLM.from_config(UpperCamelCase							)
			model.save_pretrained(UpperCamelCase							)
			AutoTokenizer.from_pretrained(UpperCamelCase							).save_pretrained(UpperCamelCase							)
			return model


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








"""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       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					"ylacombe/bark-small"
						__UpperCAmelCase	: Any		=					tempfile.mkdtemp()
						__UpperCAmelCase	: List[str]		=					"en_speaker_1"
						__UpperCAmelCase	: List[Any]		=					"This is a test string"
						__UpperCAmelCase	: List[str]		=					"speaker_embeddings_path.json"
						__UpperCAmelCase	: Tuple		=					"speaker_embeddings"


			def       a_     (      self      :    Optional[int]      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




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


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						shutil.rmtree(self.tmpdirname)


			def       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.get_tokenizer()

						__UpperCAmelCase	: Optional[Any]		=					BarkProcessor(tokenizer=UpperCamelCase_)

						processor.save_pretrained(self.tmpdirname)
						__UpperCAmelCase	: Optional[int]		=					BarkProcessor.from_pretrained(self.tmpdirname)

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


			@slow
			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					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	: Optional[Any]		=					self.get_tokenizer(bos_token="(BOS)"      ,  eos_token="(EOS)")

						__UpperCAmelCase	: Optional[Any]		=					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      :    Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					BarkProcessor.from_pretrained(
						    pretrained_processor_name_or_path=self.checkpoint      ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,  )

						__UpperCAmelCase	: Optional[int]		=					35
						__UpperCAmelCase	: Union[str, Any]		=					2
						__UpperCAmelCase	: List[Any]		=					8

						__UpperCAmelCase	: str		=					{
						    "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	: Union[str, Any]		=					processor(text=self.input_string      ,  voice_preset=UpperCamelCase_)

						__UpperCAmelCase	: str		=					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	: int		=					os.path.join(self.tmpdirname      ,  "file.npz")
						np.savez(UpperCamelCase_      ,  **UpperCamelCase_)
						__UpperCAmelCase	: List[Any]		=					processor(text=self.input_string      ,  voice_preset=UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					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	: Tuple		=					processor(text=self.input_string      ,  voice_preset=self.voice_preset)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_tokenizer()

						__UpperCAmelCase	: Any		=					BarkProcessor(tokenizer=UpperCamelCase_)

						__UpperCAmelCase	: List[Any]		=					processor(text=self.input_string)

						__UpperCAmelCase	: List[Any]		=					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())


