




'''simple docstring'''

import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp




def    snake_case__     (  a       ,      a       ,      a       ,      a       ,      a = 4096	)			->       Tuple:





			'''simple docstring'''






			snake_case__    ,			snake_case__    ,			snake_case__          =						key.shape[-3:]
			snake_case__          =						value.shape[-1]
			snake_case__          =						min(a       ,      a	)
			snake_case__          =						query / jnp.sqrt(a	)

			@functools.partial(jax.checkpoint       ,      prevent_cse=a	)
			def summarize_chunk(a       ,      a       ,      a	):
						snake_case__          =						jnp.einsum("""...qhd,...khd->...qhk"""       ,      a       ,      a       ,      precision=a	)

						snake_case__          =						jnp.max(a       ,      axis=-1       ,      keepdims=a	)
						snake_case__          =						jax.lax.stop_gradient(a	)
						snake_case__          =						jnp.exp(attn_weights - max_score	)

						snake_case__          =						jnp.einsum("""...vhf,...qhv->...qhf"""       ,      a       ,      a       ,      precision=a	)
						snake_case__          =						jnp.einsum("""...qhk->...qh"""       ,      a	)

						return (exp_values, exp_weights.sum(axis=-1	), max_score)

			def chunk_scanner(a	):
						# julienne key array
						snake_case__          =						jax.lax.dynamic_slice(
						    operand=a       ,      start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]       ,      slice_sizes=list(key.shape[:-3]	) + [key_chunk_size, num_heads, k_features]       ,      )

						# julienne value array
						snake_case__          =						jax.lax.dynamic_slice(
						    operand=a       ,      start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]       ,      slice_sizes=list(value.shape[:-3]	) + [key_chunk_size, num_heads, v_features]       ,      )

						return summarize_chunk(a       ,      a       ,      a	)

			snake_case__    ,			snake_case__    ,			snake_case__          =						jax.lax.map(f=a       ,      xs=jnp.arange(0       ,      a       ,      a	)	)

			snake_case__          =						jnp.max(a       ,      axis=0       ,      keepdims=a	)
			snake_case__          =						jnp.exp(chunk_max - global_max	)

			chunk_values *= jnp.expand_dims(a       ,      axis=-1	)
			chunk_weights *= max_diffs

			snake_case__          =						chunk_values.sum(axis=0	)
			snake_case__          =						jnp.expand_dims(a       ,      -1	).sum(axis=0	)

			return all_values / all_weights




def    snake_case__     (  a       ,      a       ,      a       ,      a=jax.lax.Precision.HIGHEST       ,      a = 1024       ,      a = 4096	)			->       List[str]:





			'''simple docstring'''






			snake_case__    ,			snake_case__    ,			snake_case__          =						query.shape[-3:]

			def chunk_scanner(a       ,      a	):
						# julienne query array
						snake_case__          =						jax.lax.dynamic_slice(
						    operand=a       ,      start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]       ,      slice_sizes=list(query.shape[:-3]	) + [min(a       ,      a	), num_heads, q_features]       ,      )

						return (
						    chunk_idx + query_chunk_size,  # unused ignore it
						    _query_chunk_attention(
						        query=a       ,      key=a       ,      value=a       ,      precision=a       ,      key_chunk_size=a	),
						)

			snake_case__    ,			snake_case__          =						jax.lax.scan(
			    f=a       ,      init=0       ,      xs=a       ,      length=math.ceil(num_q / query_chunk_size	)	)

			return jnp.concatenate(a       ,      axis=-3	)  # fuse the chunked result back





class __magic_name__(						nn.Module      ):
						UpperCAmelCase_		:					int
						UpperCAmelCase_		:					int    =  8
						UpperCAmelCase_		:					int    =  64
						UpperCAmelCase_		:					float    =  0.0
						UpperCAmelCase_		:					bool    =  False
						UpperCAmelCase_		:					jnp.dtype    =  jnp.floataa






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									snake_case__          =						self.dim_head * self.heads
									snake_case__          =						self.dim_head**-0.5

									# Weights were exported with old names {to_q, to_k, to_v, to_out}
									snake_case__          =						nn.Dense(__UpperCamelCase					,   use_bias=__UpperCamelCase					,   dtype=self.dtype					,   name="""to_q"""       )
									snake_case__          =						nn.Dense(__UpperCamelCase					,   use_bias=__UpperCamelCase					,   dtype=self.dtype					,   name="""to_k"""       )
									snake_case__          =						nn.Dense(__UpperCamelCase					,   use_bias=__UpperCamelCase					,   dtype=self.dtype					,   name="""to_v"""       )

									snake_case__          =						nn.Dense(self.query_dim					,   dtype=self.dtype					,   name="""to_out_0"""       )
									snake_case__          =						nn.Dropout(rate=self.dropout       )






						def       __lowerCAmelCase(   self						:						Optional[int]					,   __UpperCamelCase						:						str       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__    ,			snake_case__          =						tensor.shape
									snake_case__          =						self.heads
									snake_case__          =						tensor.reshape(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   dim // head_size       )
									snake_case__          =						jnp.transpose(__UpperCamelCase					,   (0, 2, 1, 3)       )
									snake_case__          =						tensor.reshape(batch_size * head_size					,   __UpperCamelCase					,   dim // head_size       )
									return tensor






						def       __lowerCAmelCase(   self						:						Optional[int]					,   __UpperCamelCase						:						Union[str, Any]       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__    ,			snake_case__          =						tensor.shape
									snake_case__          =						self.heads
									snake_case__          =						tensor.reshape(batch_size // head_size					,   __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase       )
									snake_case__          =						jnp.transpose(__UpperCamelCase					,   (0, 2, 1, 3)       )
									snake_case__          =						tensor.reshape(batch_size // head_size					,   __UpperCamelCase					,   dim * head_size       )
									return tensor







						def __call__(   self						:						Union[str, Any]					,   __UpperCamelCase						:						Tuple					,   __UpperCamelCase						:						Tuple=None					,   __UpperCamelCase						:						Dict=True       ):



									'''simple docstring'''






									snake_case__          =						hidden_states if context is None else context

									snake_case__          =						self.query(__UpperCamelCase       )
									snake_case__          =						self.key(__UpperCamelCase       )
									snake_case__          =						self.value(__UpperCamelCase       )

									snake_case__          =						self.reshape_heads_to_batch_dim(__UpperCamelCase       )
									snake_case__          =						self.reshape_heads_to_batch_dim(__UpperCamelCase       )
									snake_case__          =						self.reshape_heads_to_batch_dim(__UpperCamelCase       )

									if self.use_memory_efficient_attention:
												snake_case__          =						query_states.transpose(1					,   0					,   2       )
												snake_case__          =						key_states.transpose(1					,   0					,   2       )
												snake_case__          =						value_states.transpose(1					,   0					,   2       )

												# this if statement create a chunk size for each layer of the unet
												# the chunk size is equal to the query_length dimension of the deepest layer of the unet

												snake_case__          =						query_states.shape[-3]
												if flatten_latent_dim % 6_4 == 0:
															snake_case__          =						int(flatten_latent_dim / 6_4       )
												elif flatten_latent_dim % 1_6 == 0:
															snake_case__          =						int(flatten_latent_dim / 1_6       )
												elif flatten_latent_dim % 4 == 0:
															snake_case__          =						int(flatten_latent_dim / 4       )
												else:
															snake_case__          =						int(__UpperCamelCase       )

												snake_case__          =						jax_memory_efficient_attention(
												    __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   query_chunk_size=__UpperCamelCase					,   key_chunk_size=4_0_9_6 * 4       )

												snake_case__          =						hidden_states.transpose(1					,   0					,   2       )
									else:
												# compute attentions
												snake_case__          =						jnp.einsum("""b i d, b j d->b i j"""					,   __UpperCamelCase					,   __UpperCamelCase       )
												snake_case__          =						attention_scores * self.scale
												snake_case__          =						nn.softmax(__UpperCamelCase					,   axis=2       )

												# attend to values
												snake_case__          =						jnp.einsum("""b i j, b j d -> b i d"""					,   __UpperCamelCase					,   __UpperCamelCase       )

									snake_case__          =						self.reshape_batch_dim_to_heads(__UpperCamelCase       )
									snake_case__          =						self.proj_attn(__UpperCamelCase       )
									return self.dropout_layer(__UpperCamelCase					,   deterministic=__UpperCamelCase       )







class __magic_name__(						nn.Module      ):
						UpperCAmelCase_		:					int
						UpperCAmelCase_		:					int
						UpperCAmelCase_		:					int
						UpperCAmelCase_		:					float    =  0.0
						UpperCAmelCase_		:					bool    =  False
						UpperCAmelCase_		:					jnp.dtype    =  jnp.floataa
						UpperCAmelCase_		:					bool    =  False






						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									snake_case__          =						FlaxAttention(
									    self.dim					,   self.n_heads					,   self.d_head					,   self.dropout					,   self.use_memory_efficient_attention					,   dtype=self.dtype       )
									# cross attention
									snake_case__          =						FlaxAttention(
									    self.dim					,   self.n_heads					,   self.d_head					,   self.dropout					,   self.use_memory_efficient_attention					,   dtype=self.dtype       )
									snake_case__          =						FlaxFeedForward(dim=self.dim					,   dropout=self.dropout					,   dtype=self.dtype       )
									snake_case__          =						nn.LayerNorm(epsilon=1E-5					,   dtype=self.dtype       )
									snake_case__          =						nn.LayerNorm(epsilon=1E-5					,   dtype=self.dtype       )
									snake_case__          =						nn.LayerNorm(epsilon=1E-5					,   dtype=self.dtype       )
									snake_case__          =						nn.Dropout(rate=self.dropout       )







						def __call__(   self						:						str					,   __UpperCamelCase						:						List[Any]					,   __UpperCamelCase						:						Optional[Any]					,   __UpperCamelCase						:						Union[str, Any]=True       ):



									'''simple docstring'''






									snake_case__          =						hidden_states
									if self.only_cross_attention:
												snake_case__          =						self.attna(self.norma(__UpperCamelCase       )					,   __UpperCamelCase					,   deterministic=__UpperCamelCase       )
									else:
												snake_case__          =						self.attna(self.norma(__UpperCamelCase       )					,   deterministic=__UpperCamelCase       )
									snake_case__          =						hidden_states + residual

									# cross attention
									snake_case__          =						hidden_states
									snake_case__          =						self.attna(self.norma(__UpperCamelCase       )					,   __UpperCamelCase					,   deterministic=__UpperCamelCase       )
									snake_case__          =						hidden_states + residual

									# feed forward
									snake_case__          =						hidden_states
									snake_case__          =						self.ff(self.norma(__UpperCamelCase       )					,   deterministic=__UpperCamelCase       )
									snake_case__          =						hidden_states + residual

									return self.dropout_layer(__UpperCamelCase					,   deterministic=__UpperCamelCase       )







class __magic_name__(						nn.Module      ):
						UpperCAmelCase_		:					int
						UpperCAmelCase_		:					int
						UpperCAmelCase_		:					int
						UpperCAmelCase_		:					int    =  1
						UpperCAmelCase_		:					float    =  0.0
						UpperCAmelCase_		:					bool    =  False
						UpperCAmelCase_		:					bool    =  False
						UpperCAmelCase_		:					jnp.dtype    =  jnp.floataa
						UpperCAmelCase_		:					bool    =  False






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									snake_case__          =						nn.GroupNorm(num_groups=3_2					,   epsilon=1E-5       )

									snake_case__          =						self.n_heads * self.d_head
									if self.use_linear_projection:
												snake_case__          =						nn.Dense(__UpperCamelCase					,   dtype=self.dtype       )
									else:
												snake_case__          =						nn.Conv(
												    __UpperCamelCase					,   kernel_size=(1, 1)					,   strides=(1, 1)					,   padding="""VALID"""					,   dtype=self.dtype					,   )

									snake_case__          =						[
									    FlaxBasicTransformerBlock(
									        __UpperCamelCase					,   self.n_heads					,   self.d_head					,   dropout=self.dropout					,   only_cross_attention=self.only_cross_attention					,   dtype=self.dtype					,   use_memory_efficient_attention=self.use_memory_efficient_attention					,   )
									    for _ in range(self.depth       )
									]

									if self.use_linear_projection:
												snake_case__          =						nn.Dense(__UpperCamelCase					,   dtype=self.dtype       )
									else:
												snake_case__          =						nn.Conv(
												    __UpperCamelCase					,   kernel_size=(1, 1)					,   strides=(1, 1)					,   padding="""VALID"""					,   dtype=self.dtype					,   )

									snake_case__          =						nn.Dropout(rate=self.dropout       )







						def __call__(   self						:						Any					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						List[str]					,   __UpperCamelCase						:						str=True       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__          =						hidden_states.shape
									snake_case__          =						hidden_states
									snake_case__          =						self.norm(__UpperCamelCase       )
									if self.use_linear_projection:
												snake_case__          =						hidden_states.reshape(__UpperCamelCase					,   height * width					,   __UpperCamelCase       )
												snake_case__          =						self.proj_in(__UpperCamelCase       )
									else:
												snake_case__          =						self.proj_in(__UpperCamelCase       )
												snake_case__          =						hidden_states.reshape(__UpperCamelCase					,   height * width					,   __UpperCamelCase       )

									for transformer_block in self.transformer_blocks:
												snake_case__          =						transformer_block(__UpperCamelCase					,   __UpperCamelCase					,   deterministic=__UpperCamelCase       )

									if self.use_linear_projection:
												snake_case__          =						self.proj_out(__UpperCamelCase       )
												snake_case__          =						hidden_states.reshape(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase       )
									else:
												snake_case__          =						hidden_states.reshape(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase       )
												snake_case__          =						self.proj_out(__UpperCamelCase       )

									snake_case__          =						hidden_states + residual
									return self.dropout_layer(__UpperCamelCase					,   deterministic=__UpperCamelCase       )







class __magic_name__(						nn.Module      ):
						UpperCAmelCase_		:					int
						UpperCAmelCase_		:					float    =  0.0
						UpperCAmelCase_		:					jnp.dtype    =  jnp.floataa






						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									snake_case__          =						FlaxGEGLU(self.dim					,   self.dropout					,   self.dtype       )
									snake_case__          =						nn.Dense(self.dim					,   dtype=self.dtype       )







						def __call__(   self						:						Union[str, Any]					,   __UpperCamelCase						:						List[Any]					,   __UpperCamelCase						:						Any=True       ):



									'''simple docstring'''






									snake_case__          =						self.net_a(__UpperCamelCase					,   deterministic=__UpperCamelCase       )
									snake_case__          =						self.net_a(__UpperCamelCase       )
									return hidden_states







class __magic_name__(						nn.Module      ):
						UpperCAmelCase_		:					int
						UpperCAmelCase_		:					float    =  0.0
						UpperCAmelCase_		:					jnp.dtype    =  jnp.floataa






						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									snake_case__          =						self.dim * 4
									snake_case__          =						nn.Dense(inner_dim * 2					,   dtype=self.dtype       )
									snake_case__          =						nn.Dropout(rate=self.dropout       )







						def __call__(   self						:						Dict					,   __UpperCamelCase						:						Optional[Any]					,   __UpperCamelCase						:						Dict=True       ):



									'''simple docstring'''






									snake_case__          =						self.proj(__UpperCamelCase       )
									snake_case__    ,			snake_case__          =						jnp.split(__UpperCamelCase					,   2					,   axis=2       )
									return self.dropout_layer(hidden_linear * nn.gelu(__UpperCamelCase       )					,   deterministic=__UpperCamelCase       )







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_					:		Dict           = logging.get_logger(__name__)


UpperCAmelCase_					:		Dict           = {
    '''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_					:		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__				:	str	      =    """bark_module"""
	snake_case__				:	int	      =    ["""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							:   List[Any]					,    __lowerCamelCase							:   List[Any]=1_024					,    __lowerCamelCase							:   int=10_048					,    __lowerCamelCase							:   List[Any]=10_048					,    __lowerCamelCase							:   str=12					,    __lowerCamelCase							:   Dict=12					,    __lowerCamelCase							:   Tuple=768					,    __lowerCamelCase							:   Dict=0.0					,    __lowerCamelCase							:   List[str]=True					,    __lowerCamelCase							:   Tuple=0.02					,    __lowerCamelCase							:   List[str]=True					,    **__lowerCamelCase							:   str					,    ):
		UpperCamelCase	:Optional[int]		=      block_size
		UpperCamelCase	:List[Any]		=      input_vocab_size
		UpperCamelCase	:int		=      output_vocab_size
		UpperCamelCase	:int		=      num_layers
		UpperCamelCase	:Union[str, Any]		=      num_heads
		UpperCamelCase	:Dict		=      hidden_size
		UpperCamelCase	:Any		=      dropout
		UpperCamelCase	:List[str]		=      bias
		UpperCamelCase	:Tuple		=      use_cache
		UpperCamelCase	:Any		=      initializer_range

		super().__init__(**__lowerCamelCase      )





	@classmethod
	def 		_A					(						cls							:   int					,    __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							:   List[Any]					,    ):
		UpperCamelCase	:Dict		=      cache_dir
		UpperCamelCase	:List[Any]		=      force_download
		UpperCamelCase	:List[Any]		=      local_files_only
		UpperCamelCase	:int		=      revision

		cls._set_token_in_kwargs(__lowerCamelCase					,    __lowerCamelCase      )

		UpperCamelCase	, UpperCamelCase	:Dict		=      cls.get_config_dict(__lowerCamelCase					,    **__lowerCamelCase      )

		# get the config dict if we are loading from Bark
		if config_dict.get("""model_type"""      ) == "bark":
			UpperCamelCase	:List[str]		=      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__				:	Optional[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        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	int	      =    """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__				:	List[Any]	      =    """fine_acoustics"""





	def __init__(						self							:   Any					,    __lowerCamelCase							:   str=True					,    __lowerCamelCase							:   Optional[int]=8					,    __lowerCamelCase							:   Dict=1					,    **__lowerCamelCase							:   str      ):
		UpperCamelCase	:List[str]		=      n_codes_total
		UpperCamelCase	:str		=      n_codes_given

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

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Union[str, Any]	      =    """bark"""
	snake_case__				:	Union[str, Any]	      =    True





	def __init__(						self							:   int					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   str=0.02					,    **__lowerCamelCase							:   List[str]					,    ):
		if semantic_config is None:
			UpperCamelCase	:Union[str, Any]		=      {}
			logger.info("""semantic_config is None. initializing the semantic model with default values."""      )

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

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

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

		UpperCamelCase	:Tuple		=      BarkSemanticConfig(**__lowerCamelCase      )
		UpperCamelCase	:str		=      BarkCoarseConfig(**__lowerCamelCase      )
		UpperCamelCase	:List[Any]		=      BarkFineConfig(**__lowerCamelCase      )
		UpperCamelCase	:List[Any]		=      AutoConfig.for_model(**__lowerCamelCase      )

		UpperCamelCase	:Tuple		=      initializer_range

		super().__init__(**__lowerCamelCase      )





	@classmethod
	def 		_A					(						cls							:   int					,    __lowerCamelCase							:   BarkSemanticConfig					,    __lowerCamelCase							:   BarkCoarseConfig					,    __lowerCamelCase							:   BarkFineConfig					,    __lowerCamelCase							:   AutoConfig					,    **__lowerCamelCase							:   int					,    ):
		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 		_A					(						self							:   Dict      ):
		UpperCamelCase	:List[Any]		=      copy.deepcopy(self.__dict__      )

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

		UpperCamelCase	:List[str]		=      self.__class__.model_type
		return output
