





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 ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin





@flax.struct.dataclass
class 			lowerCAmelCase__:






       '''simple docstring'''


       __snake_case 			=    None
       __snake_case 			=    None
       __snake_case 			=    None  # sigma(t_i)




       @classmethod
       def     UpperCamelCase_			(      cls							)			->     str:
            return cls()





@dataclass
class 			lowerCAmelCase__( snake_case_    ):






       '''simple docstring'''


       __snake_case 			=    42
       __snake_case 			=    42
       __snake_case 			=    42





class 			lowerCAmelCase__( snake_case_  ,     snake_case_    ):






       '''simple docstring'''


       @property
       def     UpperCamelCase_			(      self							)			->     Tuple:
            return True




       @register_to_config
       def __init__(      self    ,						__lowerCamelCase = 0.02    ,						__lowerCamelCase = 1_0_0    ,						__lowerCamelCase = 1.007    ,						__lowerCamelCase = 8_0    ,						__lowerCamelCase = 0.05    ,						__lowerCamelCase = 5_0    ,						)			->     str:
            pass




       def     UpperCamelCase_			(      self							)			->     List[str]:
            return KarrasVeSchedulerState.create()




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase = ()							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	str						    =							jnp.arange(0    ,						__lowerCamelCase							)[::-1].copy()
            _SCREAMING_SNAKE_CASE       :	Dict						    =							[
                (
                    self.config.sigma_max**2
                    * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
                )
                for i in timesteps
            ]

            return state.replace(
                num_inference_steps=__lowerCamelCase    ,						schedule=jnp.array(__lowerCamelCase    ,						dtype=jnp.floataa							)    ,						timesteps=__lowerCamelCase    ,						)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						)			->     Union[str, Any]:
            if self.config.s_min <= sigma <= self.config.s_max:
                 _SCREAMING_SNAKE_CASE       :	int						    =							min(self.config.s_churn / state.num_inference_steps    ,						2**0.5 - 1							)
            else:
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							0

            # sample eps ~ N(0, S_noise^2 * I)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							random.split(__lowerCamelCase    ,						num=1							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.config.s_noise * random.normal(key=__lowerCamelCase    ,						shape=sample.shape							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							sigma + gamma * sigma
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

            return sample_hat, sigma_hat




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase = True    ,						)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							sample_hat + sigma_hat * model_output
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							(sample_hat - pred_original_sample) / sigma_hat
            _SCREAMING_SNAKE_CASE       :	Any						    =							sample_hat + (sigma_prev - sigma_hat) * derivative

            if not return_dict:
                 return (sample_prev, derivative, state)

            return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase    ,						derivative=__lowerCamelCase    ,						state=__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase = True    ,						)			->     int:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							sample_prev + sigma_prev * model_output
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							(sample_prev - pred_original_sample) / sigma_prev
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

            if not return_dict:
                 return (sample_prev, derivative, state)

            return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase    ,						derivative=__lowerCamelCase    ,						state=__lowerCamelCase							)







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     str:
            raise NotImplementedError()







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 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    42





class 			lowerCAmelCase__( __lowercase  ,     __lowercase    ):






       '''simple docstring'''


       @register_to_config
       def __init__(      self    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = ("DownEncoderBlock2D",)    ,						__lowerCamelCase = ("UpDecoderBlock2D",)    ,						__lowerCamelCase = (6_4,)    ,						__lowerCamelCase = 1    ,						__lowerCamelCase = "silu"    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = 2_5_6    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = None    ,						__lowerCamelCase = 0.1_8215    ,						__lowerCamelCase = "group"    ,						)			->     List[str]:
            super().__init__()

            # pass init params to Encoder
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							Encoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						down_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						double_z=__lowerCamelCase    ,						)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							vq_embed_dim if vq_embed_dim is not None else latent_channels

            _SCREAMING_SNAKE_CASE       :	str						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							VectorQuantizer(__lowerCamelCase    ,						__lowerCamelCase    ,						beta=0.25    ,						remap=__lowerCamelCase    ,						sane_index_shape=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)

            # pass init params to Decoder
            _SCREAMING_SNAKE_CASE       :	Dict						    =							Decoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						up_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						norm_type=__lowerCamelCase    ,						)




       @apply_forward_hook
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     VQEncoderOutput:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.encoder(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.quant_conv(__lowerCamelCase							)

            if not return_dict:
                 return (h,)

            return VQEncoderOutput(latents=__lowerCamelCase							)




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

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     Union[DecoderOutput, torch.FloatTensor]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							sample
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.encode(__lowerCamelCase							).latents
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.decode(__lowerCamelCase							).sample

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)