




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  ):






    """simple docstring"""





    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				Optional[Any]  =     '''ylacombe/bark-small'''
         UpperCAmelCase_     :				int  =     tempfile.mkdtemp()
         UpperCAmelCase_     :				Union[str, Any]  =     '''en_speaker_1'''
         UpperCAmelCase_     :				List[str]  =     '''This is a test string'''
         UpperCAmelCase_     :				Any  =     '''speaker_embeddings_path.json'''
         UpperCAmelCase_     :				List[str]  =     '''speaker_embeddings'''





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						List[str]:
         return AutoTokenizer.from_pretrained(self.checkpoint  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						List[str]:
         shutil.rmtree(self.tmpdirname    )





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				Tuple  =     self.get_tokenizer()

         UpperCAmelCase_     :				List[Any]  =     BarkProcessor(tokenizer=_SCREAMING_SNAKE_CASE    )

         processor.save_pretrained(self.tmpdirname    )
         UpperCAmelCase_     :				Optional[Any]  =     BarkProcessor.from_pretrained(self.tmpdirname    )

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





    @slow
    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				List[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_     :				List[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    )					->						Optional[int]:
         UpperCAmelCase_     :				Optional[Any]  =     BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint  ,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  ,)

         UpperCAmelCase_     :				Tuple  =     35
         UpperCAmelCase_     :				List[str]  =     2
         UpperCAmelCase_     :				List[str]  =     8

         UpperCAmelCase_     :				List[Any]  =     {
             '''semantic_prompt''': np.ones(_SCREAMING_SNAKE_CASE    ),
             '''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_     :				Dict  =     processor(text=self.input_string  ,voice_preset=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[str]  =     inputs['''history_prompt''']
         for key in voice_preset:
              self.assertListEqual(voice_preset[key].tolist()  ,processed_voice_preset.get(_SCREAMING_SNAKE_CASE  ,np.array([]    )    ).tolist()    )

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

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

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





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				Any  =     self.get_tokenizer()

         UpperCAmelCase_     :				Any  =     BarkProcessor(tokenizer=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				str  =     processor(text=self.input_string    )

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

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a							     =					{}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					['NllbTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					['NllbTokenizerFast']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb import NllbTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb_fast import NllbTokenizerFast

else:
     import sys

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