



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      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					def     _a							(     self					:       int					):






								"""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     _a							(     self					:       Dict				,					**_snake_case					:       int					):






								"""simple docstring"""


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



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								shutil.rmtree(self.tmpdirname					)



					def     _a							(     self					:       Optional[Any]					):






								"""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     _a							(     self					:       List[Any]					):






								"""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     _a							(     self					:       Tuple					):






								"""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     _a							(     self					:       Any					):






								"""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=2_56				,					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()					)









from typing import TYPE_CHECKING

from ..utils import _LazyModule


SCREAMING_SNAKE_CASE__       =      {
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
							from .config import (
							    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
							    OnnxConfig,
							    OnnxConfigWithPast,
							    OnnxSeqaSeqConfigWithPast,
							    PatchingSpec,
							)
							from .convert import export, validate_model_outputs
							from .features import FeaturesManager
							from .utils import ParameterFormat, compute_serialized_parameters_size

else:
							import sys

							SCREAMING_SNAKE_CASE__       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




