

'''simple docstring'''



import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import EfficientNetForImageClassification, EfficientNetModel
  from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor







class 		lowerCAmelCase__     :



  """simple docstring"""



  def __init__(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : int      ,							__SCREAMING_SNAKE_CASE      : Tuple=13      ,							__SCREAMING_SNAKE_CASE      : List[str]=32      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=3      ,							__SCREAMING_SNAKE_CASE      : Dict=[3, 3, 5]      ,							__SCREAMING_SNAKE_CASE      : Tuple=[32, 16, 24]      ,							__SCREAMING_SNAKE_CASE      : Dict=[16, 24, 40]      ,							__SCREAMING_SNAKE_CASE      : int=[1, 1, 2]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=[1, 1, 2]      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=[1, 6, 6]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=True      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=True      ,							__SCREAMING_SNAKE_CASE      : Any=37      ,							__SCREAMING_SNAKE_CASE      : List[str]="gelu"      ,							__SCREAMING_SNAKE_CASE      : str=10      ,							)						->				List[Any]:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   parent
       __SCREAMING_SNAKE_CASE						     =   batch_size
       __SCREAMING_SNAKE_CASE						     =   image_size
       __SCREAMING_SNAKE_CASE						     =   num_channels
       __SCREAMING_SNAKE_CASE						     =   kernel_sizes
       __SCREAMING_SNAKE_CASE						     =   in_channels
       __SCREAMING_SNAKE_CASE						     =   out_channels
       __SCREAMING_SNAKE_CASE						     =   strides
       __SCREAMING_SNAKE_CASE						     =   num_block_repeats
       __SCREAMING_SNAKE_CASE						     =   expand_ratios
       __SCREAMING_SNAKE_CASE						     =   is_training
       __SCREAMING_SNAKE_CASE						     =   hidden_act
       __SCREAMING_SNAKE_CASE						     =   num_labels
       __SCREAMING_SNAKE_CASE						     =   use_labels
  def    UpperCAmelCase__	(	self      : Tuple    )						->				Dict:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

       __SCREAMING_SNAKE_CASE						     =   None
       if self.use_labels:
            __SCREAMING_SNAKE_CASE						     =   ids_tensor([self.batch_size]      ,							self.num_labels    )

       __SCREAMING_SNAKE_CASE						     =   self.get_config()
       return config, pixel_values, labels
  def    UpperCAmelCase__	(	self      : List[Any]    )						->				Dict:


       """simple docstring"""




       return EfficientNetConfig(
           num_channels=self.num_channels      ,							kernel_sizes=self.kernel_sizes      ,							in_channels=self.in_channels      ,							out_channels=self.out_channels      ,							strides=self.strides      ,							num_block_repeats=self.num_block_repeats      ,							expand_ratios=self.expand_ratios      ,							hidden_act=self.hidden_act      ,							num_labels=self.num_labels      ,							)
  def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : Any      ,							__SCREAMING_SNAKE_CASE      : List[str]    )						->				int:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   EfficientNetModel(config=lowercase_    )
       model.to(lowercase_    )
       model.eval()
       __SCREAMING_SNAKE_CASE						     =   model(lowercase_    )
       # expected last hidden states: B, C, H // 4, W // 4
       self.parent.assertEqual(
           result.last_hidden_state.shape      ,							(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)      ,							)
  def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				Dict:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   EfficientNetForImageClassification(lowercase_    )
       model.to(lowercase_    )
       model.eval()
       __SCREAMING_SNAKE_CASE						     =   model(lowercase_      ,							labels=lowercase_    )
       self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)    )


  def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				int:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   self.prepare_config_and_inputs()
       __SCREAMING_SNAKE_CASE						     =   config_and_inputs
       __SCREAMING_SNAKE_CASE						     =   {'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class 		lowerCAmelCase__     (   __UpperCAmelCase				,      __UpperCAmelCase				,      unittest.TestCase      ):



  """simple docstring"""



  lowerCAmelCase__						      =   (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
  lowerCAmelCase__						      =   (
      {"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
      if is_torch_available()
      else {}
  )

  lowerCAmelCase__						      =   False
  lowerCAmelCase__						      =   False
  lowerCAmelCase__						      =   False
  lowerCAmelCase__						      =   False
  lowerCAmelCase__						      =   False
  def    UpperCAmelCase__	(	self      : List[Any]    )						->				Dict:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   EfficientNetModelTester(self    )
       __SCREAMING_SNAKE_CASE						     =   ConfigTester(
           self      ,							config_class=lowercase_      ,							has_text_modality=lowercase_      ,							hidden_size=37    )
  def    UpperCAmelCase__	(	self      : List[Any]    )						->				Any:


       """simple docstring"""




       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()
  def    UpperCAmelCase__	(	self      : List[str]    )						->				Optional[Any]:


       """simple docstring"""




       return
  @unittest.skip(reason="""EfficientNet does not use inputs_embeds"""    )
  def    UpperCAmelCase__	(	self      : Any    )						->				List[str]:


       """simple docstring"""




       pass
  @unittest.skip(reason="""EfficientNet does not support input and output embeddings"""    )
  def    UpperCAmelCase__	(	self      : Tuple    )						->				List[Any]:


       """simple docstring"""




       pass
  @unittest.skip(reason="""EfficientNet does not use feedforward chunking"""    )
  def    UpperCAmelCase__	(	self      : str    )						->				Dict:


       """simple docstring"""




       pass
  def    UpperCAmelCase__	(	self      : Optional[int]    )						->				Dict:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            __SCREAMING_SNAKE_CASE						     =   model_class(lowercase_    )
            __SCREAMING_SNAKE_CASE						     =   inspect.signature(model.forward    )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __SCREAMING_SNAKE_CASE						     =   [*signature.parameters.keys()]

            __SCREAMING_SNAKE_CASE						     =   ['''pixel_values''']
            self.assertListEqual(arg_names[:1]      ,							lowercase_    )
  def    UpperCAmelCase__	(	self      : Optional[int]    )						->				Union[str, Any]:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_    )
  def    UpperCAmelCase__	(	self      : List[Any]    )						->				Tuple:


       """simple docstring"""




       def check_hidden_states_output(__SCREAMING_SNAKE_CASE      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Any      ,							__SCREAMING_SNAKE_CASE      : Optional[int]    ):
            __SCREAMING_SNAKE_CASE						     =   model_class(lowercase_    )
            model.to(lowercase_    )
            model.eval()

            with torch.no_grad():
                 __SCREAMING_SNAKE_CASE						     =   model(**self._prepare_for_class(lowercase_      ,							lowercase_    )    )

            __SCREAMING_SNAKE_CASE						     =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
            __SCREAMING_SNAKE_CASE						     =   sum(config.num_block_repeats    ) * 4
            self.assertEqual(len(lowercase_    )      ,							lowercase_    )

            # EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]    )      ,							[self.model_tester.image_size // 2, self.model_tester.image_size // 2]      ,							)

       __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            __SCREAMING_SNAKE_CASE						     =   True
            check_hidden_states_output(lowercase_      ,							lowercase_      ,							lowercase_    )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            __SCREAMING_SNAKE_CASE						     =   True

            check_hidden_states_output(lowercase_      ,							lowercase_      ,							lowercase_    )
  def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				List[str]:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowercase_    )
  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""    )
  def    UpperCAmelCase__	(	self      : Tuple    )						->				str:


       """simple docstring"""




       pass
  @slow
  def    UpperCAmelCase__	(	self      : List[Any]    )						->				Optional[Any]:


       """simple docstring"""




       for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __SCREAMING_SNAKE_CASE						     =   EfficientNetModel.from_pretrained(lowercase_    )
            self.assertIsNotNone(lowercase_    )


  @is_pipeline_test
  @require_vision
  @slow
  def    UpperCAmelCase__	(	self      : int    )						->				int:


       """simple docstring"""




       super().test_pipeline_image_classification()







def      a__ (						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
     return image







@require_torch
@require_vision
class 		lowerCAmelCase__     (   unittest.TestCase      ):



  """simple docstring"""



  @cached_property
  def    UpperCAmelCase__	(	self      : Tuple    )						->				Tuple:


       """simple docstring"""




       return AutoImageProcessor.from_pretrained("""google/efficientnet-b7"""    ) if is_vision_available() else None


  @slow
  def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Any:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   EfficientNetForImageClassification.from_pretrained("""google/efficientnet-b7"""    ).to(lowercase_    )

       __SCREAMING_SNAKE_CASE						     =   self.default_image_processor
       __SCREAMING_SNAKE_CASE						     =   prepare_img()
       __SCREAMING_SNAKE_CASE						     =   image_processor(images=lowercase_      ,							return_tensors="""pt"""    ).to(lowercase_    )

       # forward pass
       with torch.no_grad():
            __SCREAMING_SNAKE_CASE						     =   model(**lowercase_    )

       # verify the logits
       __SCREAMING_SNAKE_CASE						     =   torch.Size((1, 1_000)    )
       self.assertEqual(outputs.logits.shape      ,							lowercase_    )

       __SCREAMING_SNAKE_CASE						     =   torch.tensor([0.0001, 0.0002, 0.0002]    ).to(lowercase_    )
       self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,							lowercase_      ,							atol=1E-4    )    )









'''simple docstring'''



import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


UpperCAmelCase				: List[Any]         =  logging.get_logger(__name__)


UpperCAmelCase				: int         =  {
    'suno/bark-small': 'https://huggingface.co/suno/bark-small/resolve/main/config.json',
    'suno/bark': 'https://huggingface.co/suno/bark/resolve/main/config.json',
}

UpperCAmelCase				: Optional[Any]         =  '\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n'







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "bark_module"
 lowerCAmelCase__						      =   ["past_key_values"]

 lowerCAmelCase__						      =   {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
     "vocab_size": "input_vocab_size",
     "window_size": "block_size",
 }
 def __init__(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=1_024      ,							__SCREAMING_SNAKE_CASE      : Dict=10_048      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=10_048      ,							__SCREAMING_SNAKE_CASE      : Tuple=12      ,							__SCREAMING_SNAKE_CASE      : Dict=12      ,							__SCREAMING_SNAKE_CASE      : List[str]=768      ,							__SCREAMING_SNAKE_CASE      : Tuple=0.0      ,							__SCREAMING_SNAKE_CASE      : List[Any]=True      ,							__SCREAMING_SNAKE_CASE      : Dict=0.02      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=True      ,							**__SCREAMING_SNAKE_CASE      : Tuple      ,							)						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   block_size
      __SCREAMING_SNAKE_CASE						     =   input_vocab_size
      __SCREAMING_SNAKE_CASE						     =   output_vocab_size
      __SCREAMING_SNAKE_CASE						     =   num_layers
      __SCREAMING_SNAKE_CASE						     =   num_heads
      __SCREAMING_SNAKE_CASE						     =   hidden_size
      __SCREAMING_SNAKE_CASE						     =   dropout
      __SCREAMING_SNAKE_CASE						     =   bias
      __SCREAMING_SNAKE_CASE						     =   use_cache
      __SCREAMING_SNAKE_CASE						     =   initializer_range

      super().__init__(**__SCREAMING_SNAKE_CASE    )


 @classmethod
 def    UpperCAmelCase__	(	cls      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : Union[str, os.PathLike]      ,							__SCREAMING_SNAKE_CASE      : Optional[Union[str, os.PathLike]] = None      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : Optional[Union[str, bool]] = None      ,							__SCREAMING_SNAKE_CASE      : str = "main"      ,							**__SCREAMING_SNAKE_CASE      : Tuple      ,							)						->				"PretrainedConfig":


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   cache_dir
      __SCREAMING_SNAKE_CASE						     =   force_download
      __SCREAMING_SNAKE_CASE						     =   local_files_only
      __SCREAMING_SNAKE_CASE						     =   revision

      cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   cls.get_config_dict(__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )

      # get the config dict if we are loading from Bark
      if config_dict.get("""model_type"""    ) == "bark":
           __SCREAMING_SNAKE_CASE						     =   config_dict[f'{cls.model_type}_config']

      if "model_type" in config_dict and hasattr(cls      ,							"""model_type"""    ) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
               f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

      return cls.from_dict(__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkSemanticConfig"				,      model="BarkSemanticModel"      )				,      "\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				,      )
class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "semantic"







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkCoarseConfig"				,      model="BarkCoarseModel"      )				,      "\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				,      )
class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "coarse_acoustics"







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkFineConfig"				,      model="BarkFineModel"      )				,      "\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				,      )
class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "fine_acoustics"
 def __init__(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=True      ,							__SCREAMING_SNAKE_CASE      : Tuple=8      ,							__SCREAMING_SNAKE_CASE      : Tuple=1      ,							**__SCREAMING_SNAKE_CASE      : Any    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   n_codes_total
      __SCREAMING_SNAKE_CASE						     =   n_codes_given

      super().__init__(tie_word_embeddings=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "bark"
 lowerCAmelCase__						      =   True
 def __init__(	self      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : List[Any]=0.02      ,							**__SCREAMING_SNAKE_CASE      : List[str]      ,							)						->				Union[str, Any]:


      """simple docstring"""




      if semantic_config is None:
           __SCREAMING_SNAKE_CASE						     =   {}
           logger.info("""semantic_config is None. initializing the semantic model with default values."""    )

      if coarse_acoustics_config is None:
           __SCREAMING_SNAKE_CASE						     =   {}
           logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""    )

      if fine_acoustics_config is None:
           __SCREAMING_SNAKE_CASE						     =   {}
           logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""    )

      if codec_config is None:
           __SCREAMING_SNAKE_CASE						     =   {}
           logger.info("""codec_config is None. initializing the codec model with default values."""    )

      __SCREAMING_SNAKE_CASE						     =   BarkSemanticConfig(**__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   BarkCoarseConfig(**__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   BarkFineConfig(**__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   AutoConfig.for_model(**__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   initializer_range

      super().__init__(**__SCREAMING_SNAKE_CASE    )
 @classmethod
 def    UpperCAmelCase__	(	cls      : str      ,							__SCREAMING_SNAKE_CASE      : BarkSemanticConfig      ,							__SCREAMING_SNAKE_CASE      : BarkCoarseConfig      ,							__SCREAMING_SNAKE_CASE      : BarkFineConfig      ,							__SCREAMING_SNAKE_CASE      : AutoConfig      ,							**__SCREAMING_SNAKE_CASE      : int      ,							)						->				Optional[int]:


      """simple docstring"""




      return cls(
          semantic_config=semantic_config.to_dict()      ,							coarse_acoustics_config=coarse_acoustics_config.to_dict()      ,							fine_acoustics_config=fine_acoustics_config.to_dict()      ,							codec_config=codec_config.to_dict()      ,							**__SCREAMING_SNAKE_CASE      ,							)


 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   copy.deepcopy(self.__dict__    )

      __SCREAMING_SNAKE_CASE						     =   self.semantic_config.to_dict()
      __SCREAMING_SNAKE_CASE						     =   self.coarse_acoustics_config.to_dict()
      __SCREAMING_SNAKE_CASE						     =   self.fine_acoustics_config.to_dict()
      __SCREAMING_SNAKE_CASE						     =   self.codec_config.to_dict()

      __SCREAMING_SNAKE_CASE						     =   self.__class__.model_type
      return output






