


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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		"ylacombe/bark-small"
             _lowerCamelCase							:     Dict	=		tempfile.mkdtemp()
             _lowerCamelCase							:     Dict	=		"en_speaker_1"
             _lowerCamelCase							:     List[str]	=		"This is a test string"
             _lowerCamelCase							:     Any	=		"speaker_embeddings_path.json"
             _lowerCamelCase							:     int	=		"speaker_embeddings"


      def 				__snake_case			(		self      ,							**a__):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             shutil.rmtree(self.tmpdirname)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		self.get_tokenizer()

             _lowerCamelCase							:     Optional[int]	=		BarkProcessor(tokenizer=__lowerCamelCase)

             processor.save_pretrained(self.tmpdirname)
             _lowerCamelCase							:     str	=		BarkProcessor.from_pretrained(self.tmpdirname)

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


      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     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      ,							)

             _lowerCamelCase							:     Optional[Any]	=		self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)''')

             _lowerCamelCase							:     Optional[int]	=		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):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		BarkProcessor.from_pretrained(
                 pretrained_processor_name_or_path=self.checkpoint      ,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,							)

             _lowerCamelCase							:     Tuple	=		35
             _lowerCamelCase							:     Dict	=		2
             _lowerCamelCase							:     Dict	=		8

             _lowerCamelCase							:     str	=		{
                 "semantic_prompt": np.ones(__lowerCamelCase),
                 "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)),
                 "fine_prompt": np.ones((nb_codebooks_total, seq_len)),
             }

             # test providing already loaded voice_preset
             _lowerCamelCase							:     int	=		processor(text=self.input_string      ,							voice_preset=__lowerCamelCase)

             _lowerCamelCase							:     List[Any]	=		inputs["history_prompt"]
             for key in voice_preset:
                    self.assertListEqual(voice_preset[key].tolist()      ,							processed_voice_preset.get(__lowerCamelCase      ,							np.array([])).tolist())

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

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

             # test loading voice preset from the hub
             _lowerCamelCase							:     List[Any]	=		processor(text=self.input_string      ,							voice_preset=self.voice_preset)



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     str	=		self.get_tokenizer()

             _lowerCamelCase							:     Optional[int]	=		BarkProcessor(tokenizer=__lowerCamelCase)

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

             _lowerCamelCase							:     List[Any]	=		tokenizer(
                 self.input_string      ,							padding='''max_length'''      ,							max_length=256      ,							add_special_tokens=__lowerCamelCase      ,							return_attention_mask=__lowerCamelCase      ,							return_token_type_ids=__lowerCamelCase      ,							)

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








import warnings

from .generation import TFGenerationMixin







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      warnings.warn(
          """Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
          """be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.""" ,lowerCamelCase__ ,)

