

'''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


a       :      Dict  	=			logging.get_logger(__name__)


a       :      Optional[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",
}

a       :      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   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Any       =							"bark_module"
		SCREAMING_SNAKE_CASE__  :     Any       =							["past_key_values"]

		SCREAMING_SNAKE_CASE__  :     Union[str, Any]       =							{
		    "num_attention_heads": "num_heads",
		    "num_hidden_layers": "num_layers",
		    "vocab_size": "input_vocab_size",
		    "window_size": "block_size",
		}







		def __init__(			self   ,      snake_case=1_0_2_4   ,      snake_case=1_0_0_4_8   ,      snake_case=1_0_0_4_8   ,      snake_case=1_2   ,      snake_case=1_2   ,      snake_case=7_6_8   ,      snake_case=0.0   ,      snake_case=True   ,      snake_case=0.02   ,      snake_case=True   ,      **snake_case   ,      ):


							'''simple docstring'''

							UpperCAmelCase		:							Dict        =       block_size
							UpperCAmelCase		:							Tuple        =       input_vocab_size
							UpperCAmelCase		:							List[str]        =       output_vocab_size
							UpperCAmelCase		:							Optional[Any]        =       num_layers
							UpperCAmelCase		:							List[Any]        =       num_heads
							UpperCAmelCase		:							Optional[int]        =       hidden_size
							UpperCAmelCase		:							Union[str, Any]        =       dropout
							UpperCAmelCase		:							Optional[int]        =       bias
							UpperCAmelCase		:							List[Any]        =       use_cache
							UpperCAmelCase		:							Optional[Any]        =       initializer_range

							super().__init__(**snake_case      )







		@classmethod
		def 				A_	(			cls   ,      snake_case   ,      snake_case = None   ,      snake_case = False   ,      snake_case = False   ,      snake_case = None   ,      snake_case = "main"   ,      **snake_case   ,      ):


							'''simple docstring'''

							UpperCAmelCase		:							int        =       cache_dir
							UpperCAmelCase		:							str        =       force_download
							UpperCAmelCase		:							Tuple        =       local_files_only
							UpperCAmelCase		:							Union[str, Any]        =       revision

							cls._set_token_in_kwargs(snake_case   ,      snake_case      )

							UpperCAmelCase ,			UpperCAmelCase		:							Union[str, Any]        =       cls.get_config_dict(snake_case   ,      **snake_case      )

							# get the config dict if we are loading from Bark
							if config_dict.get("model_type"      ) == "bark":
												UpperCAmelCase		:							Any        =       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(snake_case   ,      **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   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     str       =							"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__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     int       =							"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__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     int       =							"fine_acoustics"







		def __init__(			self   ,      snake_case=True   ,      snake_case=8   ,      snake_case=1   ,      **snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[Any]        =       n_codes_total
							UpperCAmelCase		:							List[Any]        =       n_codes_given

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







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     List[Any]       =							"bark"
		SCREAMING_SNAKE_CASE__  :     List[str]       =							True







		def __init__(			self   ,      snake_case = None   ,      snake_case = None   ,      snake_case = None   ,      snake_case = None   ,      snake_case=0.02   ,      **snake_case   ,      ):


							'''simple docstring'''

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

							if coarse_acoustics_config is None:
												UpperCAmelCase		:							Optional[Any]        =       {}
												logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."      )

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

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

							UpperCAmelCase		:							int        =       BarkSemanticConfig(**snake_case      )
							UpperCAmelCase		:							List[Any]        =       BarkCoarseConfig(**snake_case      )
							UpperCAmelCase		:							Optional[int]        =       BarkFineConfig(**snake_case      )
							UpperCAmelCase		:							Union[str, Any]        =       AutoConfig.for_model(**snake_case      )

							UpperCAmelCase		:							Dict        =       initializer_range

							super().__init__(**snake_case      )







		@classmethod
		def 				A_	(			cls   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case   ,      **snake_case   ,      ):


							'''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()   ,      **snake_case   ,      )







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Tuple        =       copy.deepcopy(self.__dict__      )

							UpperCAmelCase		:							List[str]        =       self.semantic_config.to_dict()
							UpperCAmelCase		:							Tuple        =       self.coarse_acoustics_config.to_dict()
							UpperCAmelCase		:							int        =       self.fine_acoustics_config.to_dict()
							UpperCAmelCase		:							Optional[int]        =       self.codec_config.to_dict()

							UpperCAmelCase		:							Optional[Any]        =       self.__class__.model_type
							return output



'''simple docstring'''

from ..utils import DummyObject, requires_backends







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     List[Any]       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     int       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )
