code: 
  
  
  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
  
  
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  
  
  lowerCAmelCase__   	=				{
      'suno/bark-small': 'https://huggingface.co/suno/bark-small/resolve/main/config.json',
      'suno/bark': 'https://huggingface.co/suno/bark/resolve/main/config.json',
  }
  
  lowerCAmelCase__   	=				'\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'''
       __SCREAMING_SNAKE_CASE						    =				"bark_module"
       __SCREAMING_SNAKE_CASE						    =				["past_key_values"]
  
       __SCREAMING_SNAKE_CASE						    =				{
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
           "vocab_size": "input_vocab_size",
           "window_size": "block_size",
       }
  
  
       def __init__(		self				,		__lowerCamelCase=1_0_2_4				,		__lowerCamelCase=1_0_0_4_8				,		__lowerCamelCase=1_0_0_4_8				,		__lowerCamelCase=1_2				,		__lowerCamelCase=1_2				,		__lowerCamelCase=7_6_8				,		__lowerCamelCase=0.0				,		__lowerCamelCase=True				,		__lowerCamelCase=0.0_2				,		__lowerCamelCase=True				,		**__lowerCamelCase				,		)    ->       Union[str, Any]:
              _A   : Optional[int]       					=				block_size
              _A   : int       					=				input_vocab_size
              _A   : List[Any]       					=				output_vocab_size
              _A   : Optional[Any]       					=				num_layers
              _A   : str       					=				num_heads
              _A   : List[str]       					=				hidden_size
              _A   : Tuple       					=				dropout
              _A   : str       					=				bias
              _A   : List[str]       					=				use_cache
              _A   : Optional[int]       					=				initializer_range
  
              super().__init__(**__lowerCamelCase)
  
  
       @classmethod
       def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase = None				,		__lowerCamelCase = False				,		__lowerCamelCase = False				,		__lowerCamelCase = None				,		__lowerCamelCase = "main"				,		**__lowerCamelCase				,		)    ->       "PretrainedConfig":
              _A   : Optional[int]       					=				cache_dir
              _A   : Tuple       					=				force_download
              _A   : Optional[int]       					=				local_files_only
              _A   : Any       					=				revision
  
              cls._set_token_in_kwargs(__lowerCamelCase				,		__lowerCamelCase)
  
              _A   : str       					=				cls.get_config_dict(__lowerCamelCase				,		**__lowerCamelCase)
  
              # get the config dict if we are loading from Bark
              if config_dict.get("model_type") == "bark":
                     _A   : Optional[int]       					=				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(__lowerCamelCase				,		**__lowerCamelCase)
  
  
  
  
  
  
  
  @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'''
       __SCREAMING_SNAKE_CASE						    =				"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'''
       __SCREAMING_SNAKE_CASE						    =				"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'''
       __SCREAMING_SNAKE_CASE						    =				"fine_acoustics"
  
  
       def __init__(		self				,		__lowerCamelCase=True				,		__lowerCamelCase=8				,		__lowerCamelCase=1				,		**__lowerCamelCase)    ->       Optional[Any]:
              _A   : Tuple       					=				n_codes_total
              _A   : Optional[int]       					=				n_codes_given
  
              super().__init__(tie_word_embeddings=__lowerCamelCase				,		**__lowerCamelCase)
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				"bark"
       __SCREAMING_SNAKE_CASE						    =				True
  
  
       def __init__(		self				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase=0.0_2				,		**__lowerCamelCase				,		)    ->       Any:
              if semantic_config is None:
                     _A   : List[Any]       					=				{}
                     logger.info("semantic_config is None. initializing the semantic model with default values.")
  
              if coarse_acoustics_config is None:
                     _A   : List[Any]       					=				{}
                     logger.info("coarse_acoustics_config is None. initializing the coarse model with default values.")
  
              if fine_acoustics_config is None:
                     _A   : Dict       					=				{}
                     logger.info("fine_acoustics_config is None. initializing the fine model with default values.")
  
              if codec_config is None:
                     _A   : Union[str, Any]       					=				{}
                     logger.info("codec_config is None. initializing the codec model with default values.")
  
              _A   : Dict       					=				BarkSemanticConfig(**__lowerCamelCase)
              _A   : Optional[int]       					=				BarkCoarseConfig(**__lowerCamelCase)
              _A   : Union[str, Any]       					=				BarkFineConfig(**__lowerCamelCase)
              _A   : Optional[int]       					=				AutoConfig.for_model(**__lowerCamelCase)
  
              _A   : Any       					=				initializer_range
  
              super().__init__(**__lowerCamelCase)
  
  
       @classmethod
       def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		**__lowerCamelCase				,		)    ->       Tuple:
              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()				,		**__lowerCamelCase				,		)
  
  
       def 				_lowerCamelCase				(		self)    ->       Any:
              _A   : Union[str, Any]       					=				copy.deepcopy(self.__dict__)
  
              _A   : int       					=				self.semantic_config.to_dict()
              _A   : Any       					=				self.coarse_acoustics_config.to_dict()
              _A   : List[str]       					=				self.fine_acoustics_config.to_dict()
              _A   : int       					=				self.codec_config.to_dict()
  
              _A   : Union[str, Any]       					=				self.__class__.model_type
              return output
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCAmelCase__   	=				{'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['DetrFeatureExtractor']
      lowerCAmelCase__   	=				['DetrImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
          'DetrForObjectDetection',
          'DetrForSegmentation',
          'DetrModel',
          'DetrPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .feature_extraction_detr import DetrFeatureExtractor
          from .image_processing_detr import DetrImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_detr import (
              DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
              DetrForObjectDetection,
              DetrForSegmentation,
              DetrModel,
              DetrPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  