
"""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


lowerCAmelCase :    Dict								=						logging.get_logger(__name__)


lowerCAmelCase :    List[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 :    Dict								=						"\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    A_     (					UpperCAmelCase__       ):



							"""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  :List[str]					,							lowerCamelCase_  :Optional[int]=1_024					,							lowerCamelCase_  :Tuple=10_048					,							lowerCamelCase_  :str=10_048					,							lowerCamelCase_  :Dict=12					,							lowerCamelCase_  :List[str]=12					,							lowerCamelCase_  :Dict=768					,							lowerCamelCase_  :Optional[int]=0.0					,							lowerCamelCase_  :str=True					,							lowerCamelCase_  :Union[str, Any]=0.02					,							lowerCamelCase_  :Any=True					,							**lowerCamelCase_  :Union[str, Any]					,							):





										"""simple docstring"""

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

										super().__init__(**__lowercase	)





							@classmethod
							def 				UpperCAmelCase__							(  cls  :str					,							lowerCamelCase_  :Union[str, os.PathLike]					,							lowerCamelCase_  :Optional[Union[str, os.PathLike]] = None					,							lowerCamelCase_  :bool = False					,							lowerCamelCase_  :bool = False					,							lowerCamelCase_  :Optional[Union[str, bool]] = None					,							lowerCamelCase_  :str = "main"					,							**lowerCamelCase_  :Union[str, Any]					,							):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[int]		=cache_dir
										lowerCamelCase__     :					List[Any]		=force_download
										lowerCamelCase__     :					Any		=local_files_only
										lowerCamelCase__     :					Tuple		=revision

										cls._set_token_in_kwargs(__lowercase					,							__lowercase	)

										lowerCamelCase__	,    lowerCamelCase__     :					List[Any]		=cls.get_config_dict(__lowercase					,							**__lowercase	)

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






@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkSemanticConfig"""				, model="""BarkSemanticModel"""       )				, """
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""				, )
class    A_     (					UpperCAmelCase__       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       """semantic"""






@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkCoarseConfig"""				, model="""BarkCoarseModel"""       )				, """
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""				, )
class    A_     (					UpperCAmelCase__       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       """coarse_acoustics"""






@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkFineConfig"""				, model="""BarkFineModel"""       )				, """
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""				, )
class    A_     (					UpperCAmelCase__       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       """fine_acoustics"""





							def __init__(  self  :List[Any]					,							lowerCamelCase_  :str=True					,							lowerCamelCase_  :str=8					,							lowerCamelCase_  :List[str]=1					,							**lowerCamelCase_  :Union[str, Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					int		=n_codes_total
										lowerCamelCase__     :					List[Any]		=n_codes_given

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






class    A_     (					UpperCAmelCase__       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       """bark"""
							SCREAMING_SNAKE_CASE_     				=       True





							def __init__(  self  :int					,							lowerCamelCase_  :Dict = None					,							lowerCamelCase_  :Dict = None					,							lowerCamelCase_  :Dict = None					,							lowerCamelCase_  :Dict = None					,							lowerCamelCase_  :List[Any]=0.02					,							**lowerCamelCase_  :int					,							):





										"""simple docstring"""

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

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

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

										if codec_config is None:
													lowerCamelCase__     :					List[Any]		={}
													logger.info('codec_config is None. initializing the codec model with default values.'	)

										lowerCamelCase__     :					Optional[Any]		=BarkSemanticConfig(**__lowercase	)
										lowerCamelCase__     :					List[Any]		=BarkCoarseConfig(**__lowercase	)
										lowerCamelCase__     :					Union[str, Any]		=BarkFineConfig(**__lowercase	)
										lowerCamelCase__     :					Dict		=AutoConfig.for_model(**__lowercase	)

										lowerCamelCase__     :					Dict		=initializer_range

										super().__init__(**__lowercase	)





							@classmethod
							def 				UpperCAmelCase__							(  cls  :List[Any]					,							lowerCamelCase_  :BarkSemanticConfig					,							lowerCamelCase_  :BarkCoarseConfig					,							lowerCamelCase_  :BarkFineConfig					,							lowerCamelCase_  :AutoConfig					,							**lowerCamelCase_  :Dict					,							):





										"""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()					,							**__lowercase					,							)





							def 				UpperCAmelCase__							(  self  :Dict	):





										"""simple docstring"""

										lowerCamelCase__     :					Union[str, Any]		=copy.deepcopy(self.__dict__	)

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

										lowerCamelCase__     :					Optional[int]		=self.__class__.model_type
										return output

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase								=						{
    """configuration_decision_transformer""": [
        """DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DecisionTransformerConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DecisionTransformerGPT2Model""",
							    """DecisionTransformerGPT2PreTrainedModel""",
							    """DecisionTransformerModel""",
							    """DecisionTransformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_decision_transformer import (
							    DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DecisionTransformerConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_decision_transformer import (
														    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DecisionTransformerGPTaModel,
														    DecisionTransformerGPTaPreTrainedModel,
														    DecisionTransformerModel,
														    DecisionTransformerPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)