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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left


@flax.struct.dataclass
class   lowercase			:
							# setable values
							lowercase__  :				Optional[jnp.ndarray]									=							None
							lowercase__  :				Optional[jnp.ndarray]									=							None
							lowercase__  :				Optional[jnp.ndarray]									=							None




							@classmethod
							def 		__snake_case(  cls      :    Tuple					)    -> str:



												'''simple docstring'''

												return cls()



@dataclass
class   lowercase			(   __A   ):
							lowercase__  :				ScoreSdeVeSchedulerState
							lowercase__  :				jnp.ndarray
							lowercase__  :				Optional[jnp.ndarray]									=							None



class   lowercase			(   __A ,				__A   ):




							@property
							def 		__snake_case(  self      :    Tuple					)    -> str:



												'''simple docstring'''

												return True




							@register_to_config
							def __init__(  self      :    Any		,							_UpperCamelCase      :    Optional[Any] = 2_000		,							_UpperCamelCase      :    Tuple = 0.1_5		,							_UpperCamelCase      :    Tuple = 0.0_1		,							_UpperCamelCase      :    List[str] = 1_348.0		,							_UpperCamelCase      :    List[str] = 1e-5		,							_UpperCamelCase      :    str = 1		,							)    -> Dict:



												'''simple docstring'''

												pass




							def 		__snake_case(  self      :    List[str]					)    -> Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				ScoreSdeVeSchedulerState.create()
												return self.set_sigmas(
												    lowerCamelCase__		,							self.config.num_train_timesteps		,							self.config.sigma_min		,							self.config.sigma_max		,							self.config.sampling_eps		,							)




							def 		__snake_case(  self      :    str		,							_UpperCamelCase      :    Optional[Any]		,							_UpperCamelCase      :    int		,							_UpperCamelCase      :    List[Any] = ()		,							_UpperCamelCase      :    Dict = None					)    -> ScoreSdeVeSchedulerState:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

												SCREAMING_SNAKE_CASE				=				jnp.linspace(1		,							lowerCamelCase__		,							lowerCamelCase__					)
												return state.replace(timesteps=lowerCamelCase__					)




							def 		__snake_case(  self      :    List[str]		,							_UpperCamelCase      :    Any		,							_UpperCamelCase      :    int		,							_UpperCamelCase      :    Dict = None		,							_UpperCamelCase      :    str = None		,							_UpperCamelCase      :    Any = None		,							)    -> ScoreSdeVeSchedulerState:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				sigma_min if sigma_min is not None else self.config.sigma_min
												SCREAMING_SNAKE_CASE				=				sigma_max if sigma_max is not None else self.config.sigma_max
												SCREAMING_SNAKE_CASE				=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
												if state.timesteps is None:
																	SCREAMING_SNAKE_CASE				=				self.set_timesteps(lowerCamelCase__		,							lowerCamelCase__		,							lowerCamelCase__					)

												SCREAMING_SNAKE_CASE				=				jnp.exp(jnp.linspace(jnp.log(lowerCamelCase__					)		,							jnp.log(lowerCamelCase__					)		,							lowerCamelCase__					)					)
												SCREAMING_SNAKE_CASE				=				jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]					)

												return state.replace(discrete_sigmas=lowerCamelCase__		,							sigmas=lowerCamelCase__					)




							def 		__snake_case(  self      :    List[Any]		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    Optional[Any]					)    -> List[str]:



												'''simple docstring'''

												return jnp.where(timesteps == 0		,							jnp.zeros_like(lowerCamelCase__					)		,							state.discrete_sigmas[timesteps - 1]					)




							def 		__snake_case(  self      :    Dict		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    Optional[Any]		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    List[str] = True		,							)    -> Union[FlaxSdeVeOutput, Tuple]:



												'''simple docstring'''

												if state.timesteps is None:
																	raise ValueError(
																	    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

												SCREAMING_SNAKE_CASE				=				timestep * jnp.ones(
												    sample.shape[0]		,							)
												SCREAMING_SNAKE_CASE				=				(timestep * (len(state.timesteps					) - 1)).long()

												SCREAMING_SNAKE_CASE				=				state.discrete_sigmas[timesteps]
												SCREAMING_SNAKE_CASE				=				self.get_adjacent_sigma(lowerCamelCase__		,							lowerCamelCase__		,							lowerCamelCase__					)
												SCREAMING_SNAKE_CASE				=				jnp.zeros_like(lowerCamelCase__					)
												SCREAMING_SNAKE_CASE				=				(sigma**2 - adjacent_sigma**2) ** 0.5

												# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
												# also equation 47 shows the analog from SDE models to ancestral sampling methods
												SCREAMING_SNAKE_CASE				=				diffusion.flatten()
												SCREAMING_SNAKE_CASE				=				broadcast_to_shape_from_left(lowerCamelCase__		,							sample.shape					)
												SCREAMING_SNAKE_CASE				=				drift - diffusion**2 * model_output

												#  equation 6: sample noise for the diffusion term of
												SCREAMING_SNAKE_CASE				=				random.split(lowerCamelCase__		,							num=1					)
												SCREAMING_SNAKE_CASE				=				random.normal(key=lowerCamelCase__		,							shape=sample.shape					)
												SCREAMING_SNAKE_CASE				=				sample - drift  # subtract because `dt` is a small negative timestep
												# TODO is the variable diffusion the correct scaling term for the noise?
												SCREAMING_SNAKE_CASE				=				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

												if not return_dict:
																	return (prev_sample, prev_sample_mean, state)

												return FlaxSdeVeOutput(prev_sample=lowerCamelCase__		,							prev_sample_mean=lowerCamelCase__		,							state=lowerCamelCase__					)




							def 		__snake_case(  self      :    Optional[int]		,							_UpperCamelCase      :    int		,							_UpperCamelCase      :    int		,							_UpperCamelCase      :    Any		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    Any = True		,							)    -> Union[FlaxSdeVeOutput, Tuple]:



												'''simple docstring'''

												if state.timesteps is None:
																	raise ValueError(
																	    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

												# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
												# sample noise for correction
												SCREAMING_SNAKE_CASE				=				random.split(lowerCamelCase__		,							num=1					)
												SCREAMING_SNAKE_CASE				=				random.normal(key=lowerCamelCase__		,							shape=sample.shape					)

												# compute step size from the model_output, the noise, and the snr
												SCREAMING_SNAKE_CASE				=				jnp.linalg.norm(lowerCamelCase__					)
												SCREAMING_SNAKE_CASE				=				jnp.linalg.norm(lowerCamelCase__					)
												SCREAMING_SNAKE_CASE				=				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
												SCREAMING_SNAKE_CASE				=				step_size * jnp.ones(sample.shape[0]					)

												# compute corrected sample: model_output term and noise term
												SCREAMING_SNAKE_CASE				=				step_size.flatten()
												SCREAMING_SNAKE_CASE				=				broadcast_to_shape_from_left(lowerCamelCase__		,							sample.shape					)
												SCREAMING_SNAKE_CASE				=				sample + step_size * model_output
												SCREAMING_SNAKE_CASE				=				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

												if not return_dict:
																	return (prev_sample, state)

												return FlaxSdeVeOutput(prev_sample=lowerCamelCase__		,							state=lowerCamelCase__					)





							def __len__(  self      :    List[Any]					)    -> Tuple:



												'''simple docstring'''

												return self.config.num_train_timesteps










'''simple docstring'''
import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


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






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     List[Any]    =					"""semantic"""

       def __init__(    self					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=True					,		lowerCamelCase__=768					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_0048					,		lowerCamelCase__=12_9595					,		lowerCamelCase__=12_9599					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=256					,		lowerCamelCase__=49.9					,		**lowerCamelCase__					,		)    ->					List[Any]:

           """simple docstring"""



           super().__init__(
               temperature=lowerCamelCase__					,		do_sample=lowerCamelCase__					,		eos_token_id=lowerCamelCase__					,		renormalize_logits=lowerCamelCase__					,		max_new_tokens=lowerCamelCase__					,		output_scores=lowerCamelCase__					,		return_dict_in_generate=lowerCamelCase__					,		output_hidden_states=lowerCamelCase__					,		output_attentions=lowerCamelCase__					,		**lowerCamelCase__					,		)

           __lowercase							=						text_encoding_offset
           __lowercase							=						text_pad_token
           __lowercase							=						eos_token_id
           __lowercase							=						semantic_infer_token
           __lowercase							=						semantic_vocab_size
           __lowercase							=						max_input_semantic_length
           __lowercase							=						semantic_rate_hz






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[int]    =					"""coarse_acoustics"""

       def __init__(    self					,		lowerCamelCase__=True					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_2048					,		lowerCamelCase__=75					,		lowerCamelCase__=2					,		lowerCamelCase__=1_2050					,		lowerCamelCase__=256					,		lowerCamelCase__ = 630					,		lowerCamelCase__ = 60					,		**lowerCamelCase__					,		)    ->					Dict:

           """simple docstring"""



           super().__init__(
               temperature=lowerCamelCase__					,		do_sample=lowerCamelCase__					,		renormalize_logits=lowerCamelCase__					,		output_scores=lowerCamelCase__					,		return_dict_in_generate=lowerCamelCase__					,		output_hidden_states=lowerCamelCase__					,		output_attentions=lowerCamelCase__					,		**lowerCamelCase__					,		)

           __lowercase							=						coarse_semantic_pad_token
           __lowercase							=						coarse_rate_hz
           __lowercase							=						n_coarse_codebooks
           __lowercase							=						coarse_infer_token
           __lowercase							=						max_coarse_input_length
           __lowercase							=						max_coarse_history
           __lowercase							=						sliding_window_len






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""fine_acoustics"""

       def __init__(    self					,		lowerCamelCase__=0.5					,		lowerCamelCase__=512					,		lowerCamelCase__=1024					,		lowerCamelCase__=8					,		**lowerCamelCase__					,		)    ->					str:

           """simple docstring"""



           super().__init__(temperature=lowerCamelCase__     )

           __lowercase							=						max_fine_history_length
           __lowercase							=						max_fine_input_length
           __lowercase							=						n_fine_codebooks






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

       def __init__(    self					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__=2_4000					,		lowerCamelCase__=1024					,		**lowerCamelCase__					,		)    ->					Optional[int]:

           """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."""     )

           __lowercase							=						BarkSemanticGenerationConfig(**lowerCamelCase__     )
           __lowercase							=						BarkCoarseGenerationConfig(**lowerCamelCase__     )
           __lowercase							=						BarkFineGenerationConfig(**lowerCamelCase__     )

           __lowercase							=						sample_rate
           __lowercase							=						codebook_size

       @classmethod
       def     snake_case__			(    cls					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		**lowerCamelCase__					,		)    ->					Union[str, Any]:

           """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()					,		**lowerCamelCase__					,		)




       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """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.__class__.model_type
           return output




