

'''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        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           A_						       =   """ylacombe/bark-small"""
           A_						       =   tempfile.mkdtemp()
           A_						       =   """en_speaker_1"""
           A_						       =   """This is a test string"""
           A_						       =   """speaker_embeddings_path.json"""
           A_						       =   """speaker_embeddings"""



    def        snake_case_      (       self		,					**UpperCamelCase__							)    ->   int:





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



    def        snake_case_      (       self							)    ->   List[str]:





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



    def        snake_case_      (       self							)    ->   Tuple:





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

           A_						       =   BarkProcessor(tokenizer=UpperCamelCase__							)

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

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



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





           '''simple docstring'''
           A_						       =   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		,					)

           A_						       =   self.get_tokenizer(bos_token="""(BOS)"""		,					eos_token="""(EOS)"""							)

           A_						       =   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        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   BarkProcessor.from_pretrained(
               pretrained_processor_name_or_path=self.checkpoint		,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,					)

           A_						       =   35
           A_						       =   2
           A_						       =   8

           A_						       =   {
               """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
           A_						       =   processor(text=self.input_string		,					voice_preset=UpperCamelCase__							)

           A_						       =   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
           A_						       =   os.path.join(self.tmpdirname		,					"""file.npz"""							)
           np.savez(UpperCamelCase__		,					**UpperCamelCase__							)
           A_						       =   processor(text=self.input_string		,					voice_preset=UpperCamelCase__							)
           A_						       =   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
           A_						       =   processor(text=self.input_string		,					voice_preset=self.voice_preset							)







    def        snake_case_      (       self							)    ->   List[str]:





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

           A_						       =   BarkProcessor(tokenizer=UpperCamelCase__							)

           A_						       =   processor(text=self.input_string							)

           A_						       =   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()							)



'''simple docstring'''







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""",							[None, 4_00 * 2**20, 6_00 * 2**20]					)
@pytest.mark.parametrize("""input_in_memory_max_size""",							["""default""", 0, 1_00 * 2**20, 9_00 * 2**20]					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if input_in_memory_max_size != "default":
              monkeypatch.setattr(datasets.config,							"""IN_MEMORY_MAX_SIZE""",							UpperCAmelCase__					)
       A_						       =   datasets.config.IN_MEMORY_MAX_SIZE
       if input_in_memory_max_size == "default":
              assert in_memory_max_size == 0
       else:
              assert in_memory_max_size == input_in_memory_max_size
       if dataset_size and in_memory_max_size:
              A_						       =   dataset_size < in_memory_max_size
       else:
              A_						       =   False
       A_						       =   is_small_dataset(UpperCAmelCase__					)
       assert result == expected
