




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    : Dict		= logging.get_logger(__name__)


_lowerCAmelCase    : Any		= {
    "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    : Union[str, 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       _UpperCamelCase   (					UpperCamelCase__		):
      UpperCAmelCase_          =		"bark_module"
      UpperCAmelCase_          =		["past_key_values"]

      UpperCAmelCase_          =		{
          "num_attention_heads": "num_heads",
          "num_hidden_layers": "num_layers",
          "vocab_size": "input_vocab_size",
          "window_size": "block_size",
      }
      def __init__(      self :List[Any]			,   lowerCamelCase :int=1024			,   lowerCamelCase :Dict=1_0048			,   lowerCamelCase :List[str]=1_0048			,   lowerCamelCase :int=12			,   lowerCamelCase :int=12			,   lowerCamelCase :Optional[int]=768			,   lowerCamelCase :Optional[Any]=0.0			,   lowerCamelCase :Optional[int]=True			,   lowerCamelCase :Optional[int]=0.02			,   lowerCamelCase :Optional[Any]=True			,   **lowerCamelCase :Optional[Any]			,   )  ->      str:
       UpperCAmelCase__							=    block_size
       UpperCAmelCase__							=    input_vocab_size
       UpperCAmelCase__							=    output_vocab_size
       UpperCAmelCase__							=    num_layers
       UpperCAmelCase__							=    num_heads
       UpperCAmelCase__							=    hidden_size
       UpperCAmelCase__							=    dropout
       UpperCAmelCase__							=    bias
       UpperCAmelCase__							=    use_cache
       UpperCAmelCase__							=    initializer_range

       super().__init__(**_UpperCAmelCase   )







      @classmethod
      def 						UpperCAmelCase_    (      cls :Dict			,   lowerCamelCase :str			,   lowerCamelCase :Optional[int] = None			,   lowerCamelCase :Any = False			,   lowerCamelCase :Dict = False			,   lowerCamelCase :str = None			,   lowerCamelCase :Optional[Any] = "main"			,   **lowerCamelCase :int			,   )  ->      Union[str, Any]:
       UpperCAmelCase__							=    cache_dir
       UpperCAmelCase__							=    force_download
       UpperCAmelCase__							=    local_files_only
       UpperCAmelCase__							=    revision

       cls._set_token_in_kwargs(_UpperCAmelCase			,   _UpperCAmelCase   )

       UpperCAmelCase__							=    cls.get_config_dict(_UpperCAmelCase			,   **_UpperCAmelCase   )

       # get the config dict if we are loading from Bark
       if config_dict.get("model_type"   ) == "bark":
        UpperCAmelCase__							=    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(_UpperCAmelCase			,   **_UpperCAmelCase   )



@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       _UpperCamelCase   (					UpperCamelCase__		):
      UpperCAmelCase_          =		"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       _UpperCamelCase   (					UpperCamelCase__		):
      UpperCAmelCase_          =		"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       _UpperCamelCase   (					UpperCamelCase__		):
      UpperCAmelCase_          =		"fine_acoustics"
      def __init__(      self :Optional[Any]			,   lowerCamelCase :str=True			,   lowerCamelCase :Any=8			,   lowerCamelCase :List[str]=1			,   **lowerCamelCase :Optional[int]   )  ->      List[str]:
       UpperCAmelCase__							=    n_codes_total
       UpperCAmelCase__							=    n_codes_given

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



class       _UpperCamelCase   (					UpperCamelCase__		):
      UpperCAmelCase_          =		"bark"
      UpperCAmelCase_          =		True
      def __init__(      self :Union[str, Any]			,   lowerCamelCase :List[Any] = None			,   lowerCamelCase :Any = None			,   lowerCamelCase :Dict = None			,   lowerCamelCase :List[Any] = None			,   lowerCamelCase :Any=0.02			,   **lowerCamelCase :Optional[Any]			,   )  ->      List[Any]:
       if semantic_config is None:
        UpperCAmelCase__							=    {}
        logger.info("semantic_config is None. initializing the semantic model with default values."   )

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

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

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

       UpperCAmelCase__							=    BarkSemanticConfig(**_UpperCAmelCase   )
       UpperCAmelCase__							=    BarkCoarseConfig(**_UpperCAmelCase   )
       UpperCAmelCase__							=    BarkFineConfig(**_UpperCAmelCase   )
       UpperCAmelCase__							=    AutoConfig.for_model(**_UpperCAmelCase   )

       UpperCAmelCase__							=    initializer_range

       super().__init__(**_UpperCAmelCase   )
      @classmethod
      def 						UpperCAmelCase_    (      cls :Any			,   lowerCamelCase :Tuple			,   lowerCamelCase :Tuple			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :List[str]			,   **lowerCamelCase :Any			,   )  ->      Optional[Any]:
       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()			,   **_UpperCAmelCase			,   )







      def 						UpperCAmelCase_    (      self :int   )  ->      Any:
       UpperCAmelCase__							=    copy.deepcopy(self.__dict__   )

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

       UpperCAmelCase__							=    self.__class__.model_type
       return output













'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  import torch

  from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 					__UpperCAmelCase (		unittest.TestCase	):

      '''simple docstring'''





      @slow
      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Optional[int]												=	CamembertModel.from_pretrained('''camembert-base'''						)
             model.to(_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[int]												=	torch.tensor(
                 [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,		device=_UpperCAmelCase  ,		dtype=torch.long  ,		)  # J'aime le camembert !
             with torch.no_grad():
                    UpperCAmelCase__	:   Tuple												=	model(_UpperCAmelCase						)['''last_hidden_state''']
             UpperCAmelCase__	:   List[str]												=	torch.Size((1, 10, 768)						)
             self.assertEqual(output.shape  ,		_UpperCAmelCase						)
             # compare the actual values for a slice.
             UpperCAmelCase__	:   List[Any]												=	torch.tensor(
                 [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]  ,		device=_UpperCAmelCase  ,		dtype=torch.float  ,		)
             # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
             # camembert.eval()
             # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

             self.assertTrue(torch.allclose(output[:, :3, :3]  ,		_UpperCAmelCase  ,		atol=1E-4						)						)