




'''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_    :					Union[str, Any]					      =						logging.get_logger(__name__)


UpperCAmelCase_    :					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''',
}

UpperCAmelCase_    :					Union[str, Any]					      =						'''
    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       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: int	    =     '''bark_module'''
       __lowercase					: Optional[int]	    =     ['''past_key_values''']

       __lowercase					: Any	    =     {
           '''num_attention_heads''': '''num_heads''',
           '''num_hidden_layers''': '''num_layers''',
           '''vocab_size''': '''input_vocab_size''',
           '''window_size''': '''block_size''',
       }


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE=1024      ,					__SCREAMING_SNAKE_CASE=1_0048      ,					__SCREAMING_SNAKE_CASE=1_0048      ,					__SCREAMING_SNAKE_CASE=12      ,					__SCREAMING_SNAKE_CASE=12      ,					__SCREAMING_SNAKE_CASE=768      ,					__SCREAMING_SNAKE_CASE=0.0      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=0.02      ,					__SCREAMING_SNAKE_CASE=True      ,					**__SCREAMING_SNAKE_CASE      ,					) ->					Union[str, Any]:



            '''simple docstring'''




            __snake_case         =					block_size
            __snake_case         =					input_vocab_size
            __snake_case         =					output_vocab_size
            __snake_case         =					num_layers
            __snake_case         =					num_heads
            __snake_case         =					hidden_size
            __snake_case         =					dropout
            __snake_case         =					bias
            __snake_case         =					use_cache
            __snake_case         =					initializer_range

            super().__init__(**__SCREAMING_SNAKE_CASE					)




       @classmethod
       def  lowerCAmelCase (				cls      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = False      ,					__SCREAMING_SNAKE_CASE = False      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = "main"      ,					**__SCREAMING_SNAKE_CASE      ,					) ->					"PretrainedConfig":



            '''simple docstring'''




            __snake_case         =					cache_dir
            __snake_case         =					force_download
            __snake_case         =					local_files_only
            __snake_case         =					revision

            cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)

            __snake_case   ,      __snake_case         =					cls.get_config_dict(__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					)

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





@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       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: 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       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: Dict	    =     '''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       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: Optional[int]	    =     '''fine_acoustics'''


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=8      ,					__SCREAMING_SNAKE_CASE=1      ,					**__SCREAMING_SNAKE_CASE					) ->					str:



            '''simple docstring'''




            __snake_case         =					n_codes_total
            __snake_case         =					n_codes_given

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





class       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: List[str]	    =     '''bark'''
       __lowercase					: Any	    =     True


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE=0.02      ,					**__SCREAMING_SNAKE_CASE      ,					) ->					Any:



            '''simple docstring'''




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

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

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

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

            __snake_case         =					BarkSemanticConfig(**__SCREAMING_SNAKE_CASE					)
            __snake_case         =					BarkCoarseConfig(**__SCREAMING_SNAKE_CASE					)
            __snake_case         =					BarkFineConfig(**__SCREAMING_SNAKE_CASE					)
            __snake_case         =					AutoConfig.for_model(**__SCREAMING_SNAKE_CASE					)

            __snake_case         =					initializer_range

            super().__init__(**__SCREAMING_SNAKE_CASE					)


       @classmethod
       def  lowerCAmelCase (				cls      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE      ,					) ->					List[str]:



            '''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()      ,					**__SCREAMING_SNAKE_CASE      ,					)




       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					copy.deepcopy(self.__dict__					)

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

            __snake_case         =					self.__class__.model_type
            return output









'''simple docstring'''



from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class       lowerCAmelCase				:


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					data
            __snake_case         =					None




       def __str__(				self					) ->					str:



            '''simple docstring'''




            return F'''{self.data}'''





class       lowerCAmelCase				:


       def __init__(				self					) ->					None:



            '''simple docstring'''




            __snake_case         =					None
            __snake_case         =					None


       def __iter__(				self					) ->					Iterator[Any]:



            '''simple docstring'''




            __snake_case         =					self.front
            while node:
                 yield node.data
                 __snake_case         =					node.next


       def __len__(				self					) ->					int:



            '''simple docstring'''




            return len(tuple(iter(self					)					)					)


       def __str__(				self					) ->					str:



            '''simple docstring'''




            return " <- ".join(str(__SCREAMING_SNAKE_CASE					) for item in self					)


       def  lowerCAmelCase (				self					) ->					bool:



            '''simple docstring'''




            return len(self					) == 0


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					Node(__SCREAMING_SNAKE_CASE					)
            if self.is_empty():
                 __snake_case         =					__snake_case         =					node
            else:
                 assert isinstance(self.rear      ,					__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					node
                 __snake_case         =					node


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            if self.is_empty():
                 raise IndexError('''dequeue from empty queue'''					)
            assert isinstance(self.front      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.front
            __snake_case         =					self.front.next
            if self.front is None:
                 __snake_case         =					None
            return node.data




       def  lowerCAmelCase (				self					) ->					None:



            '''simple docstring'''




            __snake_case         =					__snake_case         =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()






