





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							:     Optional[Any]	=		tempfile.mkdtemp()
             _lowerCamelCase							:     Tuple	=		'''en_speaker_1'''
             _lowerCamelCase							:     str	=		'''This is a test string'''
             _lowerCamelCase							:     Any	=		'''speaker_embeddings_path.json'''
             _lowerCamelCase							:     List[str]	=		'''speaker_embeddings'''


      def 				__snake_case			(		self      ,							**a__):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             shutil.rmtree(self.tmpdirname)


      def 				__snake_case			(		self):



             """simple docstring"""





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

             _lowerCamelCase							:     Dict	=		BarkProcessor(tokenizer=a__)

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

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


      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     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      ,							)

             _lowerCamelCase							:     str	=		self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)''')

             _lowerCamelCase							:     Union[str, 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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		BarkProcessor.from_pretrained(
                 pretrained_processor_name_or_path=self.checkpoint      ,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,							)

             _lowerCamelCase							:     Tuple	=		35
             _lowerCamelCase							:     List[str]	=		2
             _lowerCamelCase							:     int	=		8

             _lowerCamelCase							:     Tuple	=		{
                 '''semantic_prompt''': np.ones(a__),
                 '''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=a__)

             _lowerCamelCase							:     Dict	=		inputs['''history_prompt''']
             for key in voice_preset:
                    self.assertListEqual(voice_preset[key].tolist()      ,							processed_voice_preset.get(a__      ,							np.array([])).tolist())

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

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

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



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.get_tokenizer()

             _lowerCamelCase							:     List[str]	=		BarkProcessor(tokenizer=a__)

             _lowerCamelCase							:     Union[str, Any]	=		processor(text=self.input_string)

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

             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_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowerCamelCase			       =      {
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RobertaPreLayerNormForCausalLM',
           'RobertaPreLayerNormForMaskedLM',
           'RobertaPreLayerNormForMultipleChoice',
           'RobertaPreLayerNormForQuestionAnswering',
           'RobertaPreLayerNormForSequenceClassification',
           'RobertaPreLayerNormForTokenClassification',
           'RobertaPreLayerNormModel',
           'RobertaPreLayerNormPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRobertaPreLayerNormForCausalLM',
           'TFRobertaPreLayerNormForMaskedLM',
           'TFRobertaPreLayerNormForMultipleChoice',
           'TFRobertaPreLayerNormForQuestionAnswering',
           'TFRobertaPreLayerNormForSequenceClassification',
           'TFRobertaPreLayerNormForTokenClassification',
           'TFRobertaPreLayerNormMainLayer',
           'TFRobertaPreLayerNormModel',
           'TFRobertaPreLayerNormPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxRobertaPreLayerNormForCausalLM',
           'FlaxRobertaPreLayerNormForMaskedLM',
           'FlaxRobertaPreLayerNormForMultipleChoice',
           'FlaxRobertaPreLayerNormForQuestionAnswering',
           'FlaxRobertaPreLayerNormForSequenceClassification',
           'FlaxRobertaPreLayerNormForTokenClassification',
           'FlaxRobertaPreLayerNormModel',
           'FlaxRobertaPreLayerNormPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roberta_prelayernorm import (
           ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
           RobertaPreLayerNormConfig,
           RobertaPreLayerNormOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta_prelayernorm import (
                  ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaPreLayerNormForCausalLM,
                  RobertaPreLayerNormForMaskedLM,
                  RobertaPreLayerNormForMultipleChoice,
                  RobertaPreLayerNormForQuestionAnswering,
                  RobertaPreLayerNormForSequenceClassification,
                  RobertaPreLayerNormForTokenClassification,
                  RobertaPreLayerNormModel,
                  RobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta_prelayernorm import (
                  TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaPreLayerNormForCausalLM,
                  TFRobertaPreLayerNormForMaskedLM,
                  TFRobertaPreLayerNormForMultipleChoice,
                  TFRobertaPreLayerNormForQuestionAnswering,
                  TFRobertaPreLayerNormForSequenceClassification,
                  TFRobertaPreLayerNormForTokenClassification,
                  TFRobertaPreLayerNormMainLayer,
                  TFRobertaPreLayerNormModel,
                  TFRobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta_prelayernorm import (
                  FlaxRobertaPreLayerNormForCausalLM,
                  FlaxRobertaPreLayerNormForMaskedLM,
                  FlaxRobertaPreLayerNormForMultipleChoice,
                  FlaxRobertaPreLayerNormForQuestionAnswering,
                  FlaxRobertaPreLayerNormForSequenceClassification,
                  FlaxRobertaPreLayerNormForTokenClassification,
                  FlaxRobertaPreLayerNormModel,
                  FlaxRobertaPreLayerNormPreTrainedModel,
              )

else:
       import sys

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

