






"""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_						:					str      				=			logging.get_logger(__name__)


UpperCamelCase_						:					Optional[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_						:					Dict      				=			"""
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the \"intermediate\" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
"""


class   _lowercase			(  lowerCAmelCase    ):
      _a :  List[str]										= '''bark_module'''
      _a :  Tuple										= ['''past_key_values''']

      _a :  Optional[int]										= {
          '''num_attention_heads''': '''num_heads''',
          '''num_hidden_layers''': '''num_layers''',
          '''vocab_size''': '''input_vocab_size''',
          '''window_size''': '''block_size''',
      }


      def __init__(     self					:						str				, a					:						Union[str, Any]=1_0_2_4				, a					:						Union[str, Any]=1_0_0_4_8				, a					:						Optional[int]=1_0_0_4_8				, a					:						Optional[Any]=1_2				, a					:						List[str]=1_2				, a					:						List[Any]=7_6_8				, a					:						Optional[Any]=0.0				, a					:						List[Any]=True				, a					:						Optional[Any]=0.0_2				, a					:						List[str]=True				, **a					:						Tuple				, ):





            """simple docstring"""







            __snake_case   :     str	      =block_size
            __snake_case   :     Any	      =input_vocab_size
            __snake_case   :     str	      =output_vocab_size
            __snake_case   :     Any	      =num_layers
            __snake_case   :     Any	      =num_heads
            __snake_case   :     Any	      =hidden_size
            __snake_case   :     Tuple	      =dropout
            __snake_case   :     int	      =bias
            __snake_case   :     Any	      =use_cache
            __snake_case   :     Any	      =initializer_range

            super().__init__(**a    )







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





            """simple docstring"""







            __snake_case   :     Optional[int]	      =cache_dir
            __snake_case   :     List[Any]	      =force_download
            __snake_case   :     Optional[Any]	      =local_files_only
            __snake_case   :     Dict	      =revision

            cls._set_token_in_kwargs(a				, a    )

            __snake_case      ,					__snake_case   :     Optional[Any]	      =cls.get_config_dict(a				, **a    )

            # get the config dict if we are loading from Bark
            if config_dict.get('''model_type'''    ) == "bark":
                  __snake_case   :     Optional[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(a				, **a    )



@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   _lowercase			(  lowerCAmelCase    ):
      _a :  int										= '''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   _lowercase			(  lowerCAmelCase    ):
      _a :  Tuple										= '''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   _lowercase			(  lowerCAmelCase    ):
      _a :  int										= '''fine_acoustics'''


      def __init__(     self					:						Dict				, a					:						Dict=True				, a					:						Dict=8				, a					:						Tuple=1				, **a					:						Union[str, Any]    ):





            """simple docstring"""







            __snake_case   :     str	      =n_codes_total
            __snake_case   :     int	      =n_codes_given

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



class   _lowercase			(  lowerCAmelCase    ):
      _a :  Tuple										= '''bark'''
      _a :  Any										= True


      def __init__(     self					:						List[Any]				, a					:						Dict = None				, a					:						Dict = None				, a					:						Dict = None				, a					:						Dict = None				, a					:						Dict=0.0_2				, **a					:						Union[str, Any]				, ):





            """simple docstring"""







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

            if coarse_acoustics_config is None:
                  __snake_case   :     Union[str, Any]	      ={}
                  logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''    )

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

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

            __snake_case   :     Dict	      =BarkSemanticConfig(**a    )
            __snake_case   :     Dict	      =BarkCoarseConfig(**a    )
            __snake_case   :     List[Any]	      =BarkFineConfig(**a    )
            __snake_case   :     Dict	      =AutoConfig.for_model(**a    )

            __snake_case   :     str	      =initializer_range

            super().__init__(**a    )


      @classmethod
      def _UpperCamelCase    (     cls					:						Dict				, a					:						BarkSemanticConfig				, a					:						BarkCoarseConfig				, a					:						BarkFineConfig				, a					:						AutoConfig				, **a					:						Tuple				, ):





            """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()				, **a				, )







      def _UpperCamelCase    (     self					:						List[Any]    ):





            """simple docstring"""







            __snake_case   :     int	      =copy.deepcopy(self.__dict__    )

            __snake_case   :     Tuple	      =self.semantic_config.to_dict()
            __snake_case   :     Any	      =self.coarse_acoustics_config.to_dict()
            __snake_case   :     Optional[Any]	      =self.fine_acoustics_config.to_dict()
            __snake_case   :     Dict	      =self.codec_config.to_dict()

            __snake_case   :     Any	      =self.__class__.model_type
            return output










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_						:					Dict      				=			{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					List[Any]      				=			["""LayoutLMTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[Any]      				=			[
        """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """LayoutLMForMaskedLM""",
        """LayoutLMForSequenceClassification""",
        """LayoutLMForTokenClassification""",
        """LayoutLMForQuestionAnswering""",
        """LayoutLMModel""",
        """LayoutLMPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[int]      				=			[
        """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFLayoutLMForMaskedLM""",
        """TFLayoutLMForSequenceClassification""",
        """TFLayoutLMForTokenClassification""",
        """TFLayoutLMForQuestionAnswering""",
        """TFLayoutLMMainLayer""",
        """TFLayoutLMModel""",
        """TFLayoutLMPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlm import (
            LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMForMaskedLM,
            LayoutLMForQuestionAnswering,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMModel,
            LayoutLMPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlm import (
            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForQuestionAnswering,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForTokenClassification,
            TFLayoutLMMainLayer,
            TFLayoutLMModel,
            TFLayoutLMPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_						:					Optional[Any]      				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


