

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     a__  :
			"""simple docstring"""







			__lowerCamelCase       				=				42

			# setable values
			__lowerCamelCase       				=				42
			__lowerCamelCase       				=				42
			__lowerCamelCase       				=				None


			@classmethod
			def    UpperCamelCase						(			cls					,					lowercase					,					lowercase					,					lowercase				)     ->  List[Any]:
							'''simple docstring'''






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

@dataclass
class     a__  (    _UpperCAmelCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				42

class     a__  (    _UpperCAmelCase							,		_UpperCAmelCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				[e.name for e in FlaxKarrasDiffusionSchedulers]

			__lowerCamelCase       				=				42


			@property
			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							return True


			@register_to_config
			def __init__(			self					,					lowercase = 1000					,					lowercase = 0.0001					,					lowercase = 0.02					,					lowercase = "linear"					,					lowercase = None					,					lowercase = "fixed_small"					,					lowercase = True					,					lowercase = "epsilon"					,					lowercase = jnp.floataa					,					)     ->  List[Any]:
							'''simple docstring'''






							A__						=	dtype


			def    UpperCamelCase						(			self					,					lowercase = None				)     ->  DDPMSchedulerState:
							'''simple docstring'''






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

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

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

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


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase = None				)     ->  jnp.ndarray:
							'''simple docstring'''






							return sample


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase = ()				)     ->  DDPMSchedulerState:
							'''simple docstring'''






							A__						=	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
							A__						=	(jnp.arange(0					,					lowercase				) * step_ratio).round()[::-1]

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


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase=None					,					lowercase=None				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	state.common.alphas_cumprod[t]
							A__						=	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
							A__						=	(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

							return variance


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase					,					lowercase					,					lowercase = None					,					lowercase = True					,					)     ->  Union[FlaxDDPMSchedulerOutput, Tuple]:
							'''simple docstring'''






							A__						=	timestep

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

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

							# 1. compute alphas, betas
							A__						=	state.common.alphas_cumprod[t]
							A__						=	jnp.where(t > 0					,					state.common.alphas_cumprod[t - 1]					,					jnp.array(1.0					,					dtype=self.dtype				)				)
							A__						=	1 - alpha_prod_t
							A__						=	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":
											A__						=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
											A__						=	model_output
							elif self.config.prediction_type == "v_prediction":
											A__						=	(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:
											A__						=	jnp.clip(lowercase					,					-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
							A__						=	(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							A__						=	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
							A__						=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

							A__						=	pred_prev_sample + variance

							if not return_dict:
											return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=lowercase					,					state=lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase					,					lowercase					,					)     ->  jnp.ndarray:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase					,					lowercase					,					)     ->  jnp.ndarray:
							'''simple docstring'''






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


			def __len__(			self				)     ->  str:
							'''simple docstring'''






							return self.config.num_train_timesteps








"""simple docstring"""





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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer

@dataclass
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase					):





      @register_to_config
      def __init__(       self      ,						lowercase = 3      ,						lowercase = 3      ,						lowercase = ("DownEncoderBlock2D",)      ,						lowercase = ("UpDecoderBlock2D",)      ,						lowercase = (64,)      ,						lowercase = 1      ,						lowercase = "silu"      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 256      ,						lowercase = 32      ,						lowercase = None      ,						lowercase = 0.18_215      ,						lowercase = "group"      ,						)		->     Union[str, Any]:
            super().__init__()

            # pass init params to Encoder
            lowerCAmelCase  						=       Encoder(
                in_channels=lowercase      ,						out_channels=lowercase      ,						down_block_types=lowercase      ,						block_out_channels=lowercase      ,						layers_per_block=lowercase      ,						act_fn=lowercase      ,						norm_num_groups=lowercase      ,						double_z=lowercase      ,						)

            lowerCAmelCase  						=       vq_embed_dim if vq_embed_dim is not None else latent_channels

            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )
            lowerCAmelCase  						=       VectorQuantizer(lowercase      ,						lowercase      ,						beta=0.25      ,						remap=lowercase      ,						sane_index_shape=lowercase )
            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )

            # pass init params to Decoder
            lowerCAmelCase  						=       Decoder(
                in_channels=lowercase      ,						out_channels=lowercase      ,						up_block_types=lowercase      ,						block_out_channels=lowercase      ,						layers_per_block=lowercase      ,						act_fn=lowercase      ,						norm_num_groups=lowercase      ,						norm_type=lowercase      ,						)





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     VQEncoderOutput:
            lowerCAmelCase  						=       self.encoder(lowercase )
            lowerCAmelCase  						=       self.quant_conv(lowercase )

            if not return_dict:
                  return (h,)

            return VQEncoderOutput(latents=lowercase )





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = False      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            # also go through quantization layer
            if not force_not_quantize:
                  lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       self.quantize(lowercase )
            else:
                  lowerCAmelCase  						=       h
            lowerCAmelCase  						=       self.post_quant_conv(lowercase )
            lowerCAmelCase  						=       self.decoder(lowercase      ,						quant if self.config.norm_type == """spatial""" else None )

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            lowerCAmelCase  						=       sample
            lowerCAmelCase  						=       self.encode(lowercase ).latents
            lowerCAmelCase  						=       self.decode(lowercase ).sample

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )






