


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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					torch.FloatTensor
			_UpperCamelCase		:					torch.FloatTensor
			_UpperCamelCase		:					Optional[torch.FloatTensor]    =		None







class        lowerCamelCase__	(							UpperCAmelCase					,	UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Any    =		2




			@register_to_config
			def __init__(							self      ,						snake_case = 0.02      ,						snake_case = 100      ,						snake_case = 1.007      ,						snake_case = 80      ,						snake_case = 0.05      ,						snake_case = 50      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       sigma_max

						# setable values
						UpperCamelCase__					=       None
						UpperCamelCase__					=       None
						UpperCamelCase__					=       None  # sigma(t_i)




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


						return sample




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


						UpperCamelCase__					=       num_inference_steps
						UpperCamelCase__					=       np.arange(0      ,						self.num_inference_steps     )[::-1].copy()
						UpperCamelCase__					=       torch.from_numpy(snake_case     ).to(snake_case     )
						UpperCamelCase__					=       [
						    (
						        self.config.sigma_max**2
						        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
						    )
						    for i in self.timesteps
						]
						UpperCamelCase__					=       torch.tensor(snake_case      ,						dtype=torch.floataa      ,						device=snake_case     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


						if self.config.s_min <= sigma <= self.config.s_max:
									UpperCamelCase__					=       min(self.config.s_churn / self.num_inference_steps      ,						2**0.5 - 1     )
						else:
									UpperCamelCase__					=       0

						# sample eps ~ N(0, S_noise^2 * I)
						UpperCamelCase__					=       self.config.s_noise * randn_tensor(sample.shape      ,						generator=snake_case     ).to(sample.device     )
						UpperCamelCase__					=       sigma + gamma * sigma
						UpperCamelCase__					=       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

						return sample_hat, sigma_hat




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case = True      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       sample_hat + sigma_hat * model_output
						UpperCamelCase__					=       (sample_hat - pred_original_sample) / sigma_hat
						UpperCamelCase__					=       sample_hat + (sigma_prev - sigma_hat) * derivative

						if not return_dict:
									return (sample_prev, derivative)

						return KarrasVeOutput(
						    prev_sample=snake_case      ,						derivative=snake_case      ,						pred_original_sample=snake_case     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case = True      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       sample_prev + sigma_prev * model_output
						UpperCamelCase__					=       (sample_prev - pred_original_sample) / sigma_prev
						UpperCamelCase__					=       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

						if not return_dict:
									return (sample_prev, derivative)

						return KarrasVeOutput(
						    prev_sample=snake_case      ,						derivative=snake_case      ,						pred_original_sample=snake_case     )







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						raise NotImplementedError()






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
						import jax







@require_flax
class        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					List[str]    =		FlaxAutoencoderKL




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       4
						UpperCamelCase__					=       3
						UpperCamelCase__					=       (32, 32)

						UpperCamelCase__					=       jax.random.PRNGKey(0     )
						UpperCamelCase__					=       jax.random.uniform(snake_case      ,						((batch_size, num_channels) + sizes)     )

						return {"sample": image, "prng_key": prng_key}







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       {
						    "block_out_channels": [32, 64],
						    "in_channels": 3,
						    "out_channels": 3,
						    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
						    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "latent_channels": 4,
						}
						UpperCamelCase__					=       self.dummy_input
						return init_dict, inputs_dict


