


'''simple docstring'''



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

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






@flax.struct.dataclass
class  UpperCamelCase__:
				__magic_name__       :   CommonSchedulerState

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




				@classmethod
				def   a__(	cls    :				str   ,					lowerCAmelCase    :				CommonSchedulerState   ,					lowerCAmelCase    :				jnp.ndarray   ,					lowerCAmelCase    :				jnp.ndarray   )->  List[str]:

										"""simple docstring"""




										return cls(common=lowerCAmelCase   ,					init_noise_sigma=lowerCAmelCase   ,					timesteps=lowerCAmelCase   )







@dataclass
class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   DDPMSchedulerState







class  UpperCamelCase__(     lowerCAmelCase		,       lowerCAmelCase ):
				__magic_name__       :   List[str]							= [e.name for e in FlaxKarrasDiffusionSchedulers]

				__magic_name__       :   jnp.dtype




				@property
				def   a__(	self    :				str   )->  str:

										"""simple docstring"""




										return True




				@register_to_config
				def __init__(	self    :				List[Any]   ,					lowerCAmelCase    :				int = 1000   ,					lowerCAmelCase    :				float = 0.0001   ,					lowerCAmelCase    :				float = 0.02   ,					lowerCAmelCase    :				str = "linear"   ,					lowerCAmelCase    :				Optional[jnp.ndarray] = None   ,					lowerCAmelCase    :				str = "fixed_small"   ,					lowerCAmelCase    :				bool = True   ,					lowerCAmelCase    :				str = "epsilon"   ,					lowerCAmelCase    :				jnp.dtype = jnp.floataa   ,					)->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			dtype




				def   a__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				Optional[CommonSchedulerState] = None   )->  DDPMSchedulerState:

										"""simple docstring"""




										if common is None:
																UpperCAmelCase         =			CommonSchedulerState.create(self   )

										# standard deviation of the initial noise distribution
										UpperCAmelCase         =			jnp.array(1.0   ,					dtype=self.dtype   )

										UpperCAmelCase         =			jnp.arange(0   ,					self.config.num_train_timesteps   ).round()[::-1]

										return DDPMSchedulerState.create(
										    common=lowerCAmelCase   ,					init_noise_sigma=lowerCAmelCase   ,					timesteps=lowerCAmelCase   ,					)




				def   a__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				DDPMSchedulerState   ,					lowerCAmelCase    :				jnp.ndarray   ,					lowerCAmelCase    :				Optional[int] = None   )->  jnp.ndarray:

										"""simple docstring"""




										return sample




				def   a__(	self    :				str   ,					lowerCAmelCase    :				DDPMSchedulerState   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				Tuple = ()   )->  DDPMSchedulerState:

										"""simple docstring"""




										UpperCAmelCase         =			self.config.num_train_timesteps // num_inference_steps
										# creates integer timesteps by multiplying by ratio
										# rounding to avoid issues when num_inference_step is power of 3
										UpperCAmelCase         =			(jnp.arange(0   ,					lowerCAmelCase   ) * step_ratio).round()[::-1]

										return state.replace(
										    num_inference_steps=lowerCAmelCase   ,					timesteps=lowerCAmelCase   ,					)




				def   a__(	self    :				Any   ,					lowerCAmelCase    :				DDPMSchedulerState   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Any=None   ,					lowerCAmelCase    :				Any=None   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			state.common.alphas_cumprod[t]
										UpperCAmelCase         =			jnp.where(t > 0   ,					state.common.alphas_cumprod[t - 1]   ,					jnp.array(1.0   ,					dtype=self.dtype   )   )

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
										# and sample from it to get previous sample
										# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
										UpperCAmelCase         =			(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

										if variance_type is None:
																UpperCAmelCase         =			self.config.variance_type

										# hacks - were probably added for training stability
										if variance_type == "fixed_small":
																UpperCAmelCase         =			jnp.clip(lowerCAmelCase   ,					a_min=1E-20   )
										# for rl-diffuser https://arxiv.org/abs/2205.09991
										elif variance_type == "fixed_small_log":
																UpperCAmelCase         =			jnp.log(jnp.clip(lowerCAmelCase   ,					a_min=1E-20   )   )
										elif variance_type == "fixed_large":
																UpperCAmelCase         =			state.common.betas[t]
										elif variance_type == "fixed_large_log":
																# Glide max_log
																UpperCAmelCase         =			jnp.log(state.common.betas[t]   )
										elif variance_type == "learned":
																return predicted_variance
										elif variance_type == "learned_range":
																UpperCAmelCase         =			variance
																UpperCAmelCase         =			state.common.betas[t]
																UpperCAmelCase         =			(predicted_variance + 1) / 2
																UpperCAmelCase         =			frac * max_log + (1 - frac) * min_log

										return variance




				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				DDPMSchedulerState   ,					lowerCAmelCase    :				jnp.ndarray   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				jnp.ndarray   ,					lowerCAmelCase    :				Optional[jax.random.KeyArray] = None   ,					lowerCAmelCase    :				bool = True   ,					)->  Union[FlaxDDPMSchedulerOutput, Tuple]:

										"""simple docstring"""




										UpperCAmelCase         =			timestep

										if key is None:
																UpperCAmelCase         =			jax.random.PRNGKey(0   )

										if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																UpperCAmelCase       ,       UpperCAmelCase         =			jnp.split(lowerCAmelCase   ,					sample.shape[1]   ,					axis=1   )
										else:
																UpperCAmelCase         =			None

										# 1. compute alphas, betas
										UpperCAmelCase         =			state.common.alphas_cumprod[t]
										UpperCAmelCase         =			jnp.where(t > 0   ,					state.common.alphas_cumprod[t - 1]   ,					jnp.array(1.0   ,					dtype=self.dtype   )   )
										UpperCAmelCase         =			1 - alpha_prod_t
										UpperCAmelCase         =			1 - alpha_prod_t_prev

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										if self.config.prediction_type == "epsilon":
																UpperCAmelCase         =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
																UpperCAmelCase         =			model_output
										elif self.config.prediction_type == "v_prediction":
																UpperCAmelCase         =			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
										else:
																raise ValueError(
																    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
																    ''' for the FlaxDDPMScheduler.'''   )

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
																UpperCAmelCase         =			jnp.clip(lowerCAmelCase   ,					-1   ,					1   )

										# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										UpperCAmelCase         =			(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
										UpperCAmelCase         =			state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

										# 5. Compute predicted previous sample µ_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										UpperCAmelCase         =			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

										# 6. Add noise
										def random_variance():
																UpperCAmelCase         =			jax.random.split(lowerCAmelCase   ,					num=1   )
																UpperCAmelCase         =			jax.random.normal(lowerCAmelCase   ,					shape=model_output.shape   ,					dtype=self.dtype   )
																return (self._get_variance(lowerCAmelCase   ,					lowerCAmelCase   ,					predicted_variance=lowerCAmelCase   ) ** 0.5) * noise

										UpperCAmelCase         =			jnp.where(t > 0   ,					random_variance()   ,					jnp.zeros(model_output.shape   ,					dtype=self.dtype   )   )

										UpperCAmelCase         =			pred_prev_sample + variance

										if not return_dict:
																return (pred_prev_sample, state)

										return FlaxDDPMSchedulerOutput(prev_sample=lowerCAmelCase   ,					state=lowerCAmelCase   )




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				DDPMSchedulerState   ,					lowerCAmelCase    :				jnp.ndarray   ,					lowerCAmelCase    :				jnp.ndarray   ,					lowerCAmelCase    :				jnp.ndarray   ,					)->  jnp.ndarray:

										"""simple docstring"""




										return add_noise_common(state.common   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )




				def   a__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				DDPMSchedulerState   ,					lowerCAmelCase    :				jnp.ndarray   ,					lowerCAmelCase    :				jnp.ndarray   ,					lowerCAmelCase    :				jnp.ndarray   ,					)->  jnp.ndarray:

										"""simple docstring"""




										return get_velocity_common(state.common   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )





				def __len__(	self    :				str   )->  str:

										"""simple docstring"""




										return self.config.num_train_timesteps









'''simple docstring'''



import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_lowercase    :    List[str]			     =  {
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   List[str]							= "encodec"




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				int=[1.5, 3.0, 6.0, 12.0, 24.0]   ,					lowerCAmelCase    :				Tuple=24000   ,					lowerCAmelCase    :				List[Any]=1   ,					lowerCAmelCase    :				Union[str, Any]=False   ,					lowerCAmelCase    :				str=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=128   ,					lowerCAmelCase    :				Any=32   ,					lowerCAmelCase    :				Any=1   ,					lowerCAmelCase    :				List[Any]=[8, 5, 4, 2]   ,					lowerCAmelCase    :				Union[str, Any]="weight_norm"   ,					lowerCAmelCase    :				str=7   ,					lowerCAmelCase    :				Optional[int]=7   ,					lowerCAmelCase    :				Any=3   ,					lowerCAmelCase    :				Tuple=2   ,					lowerCAmelCase    :				Optional[Any]=True   ,					lowerCAmelCase    :				List[str]="reflect"   ,					lowerCAmelCase    :				Optional[int]=2   ,					lowerCAmelCase    :				List[Any]=2   ,					lowerCAmelCase    :				Union[str, Any]=1.0   ,					lowerCAmelCase    :				Optional[Any]=1024   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				str=True   ,					**lowerCAmelCase    :				str   ,					)->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			target_bandwidths
										UpperCAmelCase         =			sampling_rate
										UpperCAmelCase         =			audio_channels
										UpperCAmelCase         =			normalize
										UpperCAmelCase         =			chunk_length_s
										UpperCAmelCase         =			overlap
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_filters
										UpperCAmelCase         =			num_residual_layers
										UpperCAmelCase         =			upsampling_ratios
										UpperCAmelCase         =			norm_type
										UpperCAmelCase         =			kernel_size
										UpperCAmelCase         =			last_kernel_size
										UpperCAmelCase         =			residual_kernel_size
										UpperCAmelCase         =			dilation_growth_rate
										UpperCAmelCase         =			use_causal_conv
										UpperCAmelCase         =			pad_mode
										UpperCAmelCase         =			compress
										UpperCAmelCase         =			num_lstm_layers
										UpperCAmelCase         =			trim_right_ratio
										UpperCAmelCase         =			codebook_size
										UpperCAmelCase         =			codebook_dim if codebook_dim is not None else hidden_size
										UpperCAmelCase         =			use_conv_shortcut

										if self.norm_type not in ["weight_norm", "time_group_norm"]:
																raise ValueError(
																    F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""   )

										super().__init__(**lowerCAmelCase   )




				@property
				def   a__(	self    :				str   )->  Optional[int]:

										"""simple docstring"""




										if self.chunk_length_s is None:
																return None
										else:
																return int(self.chunk_length_s * self.sampling_rate   )




				@property
				def   a__(	self    :				List[str]   )->  Optional[int]:

										"""simple docstring"""




										if self.chunk_length_s is None or self.overlap is None:
																return None
										else:
																return max(1   ,					int((1.0 - self.overlap) * self.chunk_length   )   )




				@property
				def   a__(	self    :				List[Any]   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			np.prod(self.upsampling_ratios   )
										return math.ceil(self.sampling_rate / hop_length   )





				@property
				def   a__(	self    :				int   )->  int:

										"""simple docstring"""




										return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )





