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 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			'''ylacombe/bark-small'''
    _UpperCamelCase:  Tuple    						=			tempfile.mkdtemp()
    _UpperCamelCase:  Tuple    						=			'''en_speaker_1'''
    _UpperCamelCase:  Optional[int]    						=			'''This is a test string'''
    _UpperCamelCase:  List[str]    						=			'''speaker_embeddings_path.json'''
    _UpperCamelCase:  Optional[int]    						=			'''speaker_embeddings'''



   def 	lowerCAmelCase    (						self       :	List[Any]		,	**_lowercase       :	Tuple				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    shutil.rmtree(self.tmpdirname				)



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			self.get_tokenizer()

    _UpperCamelCase:  Optional[Any]    						=			BarkProcessor(tokenizer=_lowercase				)

    processor.save_pretrained(self.tmpdirname				)
    _UpperCamelCase:  Union[str, Any]    						=			BarkProcessor.from_pretrained(self.tmpdirname				)

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



   @slow
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			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:  Dict    						=			self.get_tokenizer(bos_token='''(BOS)'''		,	eos_token='''(EOS)'''				)

    _UpperCamelCase:  Dict    						=			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 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint		,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,	)

    _UpperCamelCase:  str    						=			35
    _UpperCamelCase:  List[str]    						=			2
    _UpperCamelCase:  int    						=			8

    _UpperCamelCase:  Union[str, Any]    						=			{
        '''semantic_prompt''': np.ones(_lowercase				),
        '''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:  Optional[int]    						=			processor(text=self.input_string		,	voice_preset=_lowercase				)

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

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

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

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



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			self.get_tokenizer()

    _UpperCamelCase:  Tuple    						=			BarkProcessor(tokenizer=_lowercase				)

    _UpperCamelCase:  Dict    						=			processor(text=self.input_string				)

    _UpperCamelCase:  Any    						=			tokenizer(
        self.input_string		,	padding='''max_length'''		,	max_length=256		,	add_special_tokens=_lowercase		,	return_attention_mask=_lowercase		,	return_token_type_ids=_lowercase		,	)

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




def    lowerCAmelCase_  (		)     ->					int:







 '''simple docstring'''
 if torch.cuda.is_available():
  _UpperCamelCase:  Any    						=			torch.cuda.device_count()
 else:
  _UpperCamelCase:  Union[str, Any]    						=			0
 print(F"""Successfully ran on {num_gpus} GPUs"""		)


if __name__ == "__main__":
  main()