




'''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							:      Tuple 	=       logging.get_logger(__name__)


A							:      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""",
}

A							:      List[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        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[int]    =					"""bark_module"""
       snake_case_ :     Any    =					["""past_key_values"""]

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

       def __init__(    self					,		lowerCamelCase__=1024					,		lowerCamelCase__=1_0048					,		lowerCamelCase__=1_0048					,		lowerCamelCase__=12					,		lowerCamelCase__=12					,		lowerCamelCase__=768					,		lowerCamelCase__=0.0					,		lowerCamelCase__=True					,		lowerCamelCase__=0.02					,		lowerCamelCase__=True					,		**lowerCamelCase__					,		)    ->					Dict:

           """simple docstring"""



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

           super().__init__(**lowerCamelCase__     )




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

           """simple docstring"""



           __lowercase							=						cache_dir
           __lowercase							=						force_download
           __lowercase							=						local_files_only
           __lowercase							=						revision

           cls._set_token_in_kwargs(lowerCamelCase__					,		lowerCamelCase__     )

           __lowercase				,__lowercase							=						cls.get_config_dict(lowerCamelCase__					,		**lowerCamelCase__     )

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






@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        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Dict    =					"""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        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     str    =					"""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        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     int    =					"""fine_acoustics"""

       def __init__(    self					,		lowerCamelCase__=True					,		lowerCamelCase__=8					,		lowerCamelCase__=1					,		**lowerCamelCase__     )    ->					Any:

           """simple docstring"""



           __lowercase							=						n_codes_total
           __lowercase							=						n_codes_given

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






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""bark"""
       snake_case_ :     List[str]    =					True

       def __init__(    self					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__=0.02					,		**lowerCamelCase__					,		)    ->					Tuple:

           """simple docstring"""



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

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

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

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

           __lowercase							=						BarkSemanticConfig(**lowerCamelCase__     )
           __lowercase							=						BarkCoarseConfig(**lowerCamelCase__     )
           __lowercase							=						BarkFineConfig(**lowerCamelCase__     )
           __lowercase							=						AutoConfig.for_model(**lowerCamelCase__     )

           __lowercase							=						initializer_range

           super().__init__(**lowerCamelCase__     )

       @classmethod
       def     snake_case__			(    cls					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		**lowerCamelCase__					,		)    ->					Optional[int]:

           """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()					,		**lowerCamelCase__					,		)




       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           __lowercase							=						copy.deepcopy(self.__dict__     )

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

           __lowercase							=						self.__class__.model_type
           return output










'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class        SCREAMING_SNAKE_CASE(				__A   ,      __A   ,      __A     ):

       @register_to_config
       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = False					,		)    ->					Optional[int]:

           """simple docstring"""



           super().__init__()

           __lowercase							=						nn.Linear(lowerCamelCase__					,		lowerCamelCase__					,		bias=lowerCamelCase__     )

           __lowercase							=						nn.Embedding(lowerCamelCase__					,		lowerCamelCase__     )
           __lowercase							=						False

           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )

           __lowercase							=						TaConfig(
               d_model=lowerCamelCase__					,		num_heads=lowerCamelCase__					,		d_kv=lowerCamelCase__					,		d_ff=lowerCamelCase__					,		feed_forward_proj=lowerCamelCase__					,		dropout_rate=lowerCamelCase__					,		is_decoder=lowerCamelCase__					,		is_encoder_decoder=lowerCamelCase__					,		)
           __lowercase							=						nn.ModuleList()
           for lyr_num in range(lowerCamelCase__     ):
               __lowercase							=						TaBlock(lowerCamelCase__     )
               self.encoders.append(lowerCamelCase__     )

           __lowercase							=						TaLayerNorm(lowerCamelCase__     )
           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )




       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.input_proj(lowerCamelCase__     )

           # terminal relative positional encodings
           __lowercase							=						encoder_inputs.shape[1]
           __lowercase							=						torch.arange(lowerCamelCase__					,		device=encoder_inputs.device     )

           __lowercase							=						encoder_inputs_mask.sum(-1     )
           __lowercase							=						torch.roll(input_positions.unsqueeze(0     )					,		tuple(seq_lens.tolist()     )					,		dims=0     )
           x += self.position_encoding(lowerCamelCase__     )

           __lowercase							=						self.dropout_pre(lowerCamelCase__     )

           # inverted the attention mask
           __lowercase							=						encoder_inputs.size()
           __lowercase							=						self.get_extended_attention_mask(lowerCamelCase__					,		lowerCamelCase__     )

           for lyr in self.encoders:
               __lowercase							=						lyr(lowerCamelCase__					,		lowerCamelCase__     )[0]
           __lowercase							=						self.layer_norm(lowerCamelCase__     )

           return self.dropout_post(lowerCamelCase__     ), encoder_inputs_mask




