






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)

@flax.struct.dataclass
class        _SCREAMING_SNAKE_CASE			:
	snake_case__				:	CommonSchedulerState

	# setable values
	snake_case__				:	jnp.ndarray
	snake_case__				:	jnp.ndarray
	snake_case__				:	jnp.ndarray
	snake_case__				:	Optional[int]	      =    None

	# running values
	snake_case__				:	Optional[jnp.ndarray]	      =    None





	@classmethod
	def 		_A					(						cls							:   Optional[int]					,    __lowerCamelCase							:   CommonSchedulerState					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   jnp.ndarray      ):
		return cls(common=__lowerCamelCase					,    init_noise_sigma=__lowerCamelCase					,    timesteps=__lowerCamelCase					,    sigmas=__lowerCamelCase      )

@dataclass
class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	LMSDiscreteSchedulerState

class        _SCREAMING_SNAKE_CASE			(     _a						,  _a ):
	snake_case__				:	Optional[Any]	      =    [e.name for e in FlaxKarrasDiffusionSchedulers]

	snake_case__				:	jnp.dtype





	@property
	def 		_A					(						self							:   Optional[Any]      ):
		return True





	@register_to_config
	def __init__(						self							:   Optional[Any]					,    __lowerCamelCase							:   int = 1_000					,    __lowerCamelCase							:   float = 0.0001					,    __lowerCamelCase							:   float = 0.02					,    __lowerCamelCase							:   str = "linear"					,    __lowerCamelCase							:   Optional[jnp.ndarray] = None					,    __lowerCamelCase							:   str = "epsilon"					,    __lowerCamelCase							:   jnp.dtype = jnp.floataa					,    ):
		UpperCamelCase	:Optional[Any]		=      dtype





	def 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   Optional[CommonSchedulerState] = None      ):
		if common is None:
			UpperCamelCase	:Optional[Any]		=      CommonSchedulerState.create(self      )

		UpperCamelCase	:Dict		=      jnp.arange(0					,    self.config.num_train_timesteps      ).round()[::-1]
		UpperCamelCase	:Optional[int]		=      ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

		# standard deviation of the initial noise distribution
		UpperCamelCase	:Union[str, Any]		=      sigmas.max()

		return LMSDiscreteSchedulerState.create(
		    common=__lowerCamelCase					,    init_noise_sigma=__lowerCamelCase					,    timesteps=__lowerCamelCase					,    sigmas=__lowerCamelCase					,    )





	def 		_A					(						self							:   List[Any]					,    __lowerCamelCase							:   LMSDiscreteSchedulerState					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   int      ):
		((UpperCamelCase)	, )	:Union[str, Any]		=      jnp.where(state.timesteps == timestep					,    size=1      )
		UpperCamelCase	:Dict		=      step_index[0]

		UpperCamelCase	:List[Any]		=      state.sigmas[step_index]
		UpperCamelCase	:Tuple		=      sample / ((sigma**2 + 1) ** 0.5)
		return sample





	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   LMSDiscreteSchedulerState					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Optional[int]					,    __lowerCamelCase							:   Optional[Any]      ):

		def lms_derivative(__lowerCamelCase							:   Dict      ):
			UpperCamelCase	:List[Any]		=      1.0
			for k in range(__lowerCamelCase      ):
				if current_order == k:
					continue
				prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
			return prod

		UpperCamelCase	:Tuple		=      integrate.quad(__lowerCamelCase					,    state.sigmas[t]					,    state.sigmas[t + 1]					,    epsrel=1E-4      )[0]

		return integrated_coeff





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   LMSDiscreteSchedulerState					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   Tuple = ()      ):
		UpperCamelCase	:Optional[Any]		=      jnp.linspace(self.config.num_train_timesteps - 1					,    0					,    __lowerCamelCase					,    dtype=self.dtype      )

		UpperCamelCase	:Tuple		=      jnp.floor(__lowerCamelCase      ).astype(jnp.intaa      )
		UpperCamelCase	:Optional[Any]		=      jnp.ceil(__lowerCamelCase      ).astype(jnp.intaa      )

		UpperCamelCase	:Optional[Any]		=      jnp.mod(__lowerCamelCase					,    1.0      )

		UpperCamelCase	:Optional[Any]		=      ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
		UpperCamelCase	:Tuple		=      (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
		UpperCamelCase	:List[str]		=      jnp.concatenate([sigmas, jnp.array([0.0]					,    dtype=self.dtype      )]      )

		UpperCamelCase	:Dict		=      timesteps.astype(jnp.intaa      )

		# initial running values
		UpperCamelCase	:str		=      jnp.zeros((0,) + shape					,    dtype=self.dtype      )

		return state.replace(
		    timesteps=__lowerCamelCase					,    sigmas=__lowerCamelCase					,    num_inference_steps=__lowerCamelCase					,    derivatives=__lowerCamelCase					,    )





	def 		_A					(						self							:   Any					,    __lowerCamelCase							:   LMSDiscreteSchedulerState					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   int = 4					,    __lowerCamelCase							:   bool = True					,    ):
		if state.num_inference_steps is None:
			raise ValueError(
			    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""      )

		UpperCamelCase	:Optional[int]		=      state.sigmas[timestep]

		# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
		if self.config.prediction_type == "epsilon":
			UpperCamelCase	:Union[str, Any]		=      sample - sigma * model_output
		elif self.config.prediction_type == "v_prediction":
			# * c_out + input * c_skip
			UpperCamelCase	:Any		=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
		else:
			raise ValueError(
			    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""      )

		# 2. Convert to an ODE derivative
		UpperCamelCase	:Dict		=      (sample - pred_original_sample) / sigma
		UpperCamelCase	:Union[str, Any]		=      state.replace(derivatives=jnp.append(state.derivatives					,    __lowerCamelCase      )      )
		if len(state.derivatives      ) > order:
			UpperCamelCase	:List[Any]		=      state.replace(derivatives=jnp.delete(state.derivatives					,    0      )      )

		# 3. Compute linear multistep coefficients
		UpperCamelCase	:List[str]		=      min(timestep + 1					,    __lowerCamelCase      )
		UpperCamelCase	:int		=      [self.get_lms_coefficient(__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase      ) for curr_order in range(__lowerCamelCase      )]

		# 4. Compute previous sample based on the derivatives path
		UpperCamelCase	:Dict		=      sample + sum(
		    coeff * derivative for coeff, derivative in zip(__lowerCamelCase					,    reversed(state.derivatives      )      )      )

		if not return_dict:
			return (prev_sample, state)

		return FlaxLMSSchedulerOutput(prev_sample=__lowerCamelCase					,    state=__lowerCamelCase      )





	def 		_A					(						self							:   Union[str, Any]					,    __lowerCamelCase							:   LMSDiscreteSchedulerState					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   jnp.ndarray					,    ):
		UpperCamelCase	:Optional[Any]		=      state.sigmas[timesteps].flatten()
		UpperCamelCase	:Union[str, Any]		=      broadcast_to_shape_from_left(__lowerCamelCase					,    noise.shape      )

		UpperCamelCase	:Any		=      original_samples + noise * sigma

		return noisy_samples





	def __len__(						self							:   Optional[int]      ):
		return self.config.num_train_timesteps








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
