


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				42
					__UpperCAmelCase            =				None




def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=0.999       ,			_UpperCamelCase="cosine"       ,			):


			'''simple docstring'''







			if alpha_transform_type == "cosine":

						def alpha_bar_fn(_UpperCamelCase  ):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(_UpperCamelCase  ):
									return math.exp(t * -12.0  )

			else:
						raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'  )

			__lowercase 					=   []
			for i in range(_UpperCamelCase  ):
						__lowercase 					=   i / num_diffusion_timesteps
						__lowercase 					=   (i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(_UpperCamelCase  ) / alpha_bar_fn(_UpperCamelCase  )       ,			_UpperCamelCase  )  )
			return torch.tensor(_UpperCamelCase       ,			dtype=torch.floataa  )





class 	lowerCamelCase_       (				lowerCAmelCase__     ,     lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				1






					@register_to_config
					def __init__(				self ,       snake_case_ = 1_0_0_0 ,       snake_case_ = 0.0_0_0_1 ,       snake_case_ = 0.0_2 ,       snake_case_ = "linear" ,       snake_case_ = None ,       snake_case_ = True ,       snake_case_ = True ,       snake_case_ = 0 ,       snake_case_ = "epsilon" ,       snake_case_ = 1.0 ,       **snake_case_ ,       )   ->			Any:







								'''simple docstring'''

								if kwargs.get('''set_alpha_to_one''' ,       snake_case_						) is not None:
											__lowercase 					=   (
											    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
											)
											deprecate('''set_alpha_to_one''' ,       '''1.0.0''' ,       snake_case_ ,       standard_warn=snake_case_						)
											__lowercase 					=   kwargs['''set_alpha_to_one''']
								if trained_betas is not None:
											__lowercase 					=   torch.tensor(snake_case_ ,       dtype=torch.floataa						)
								elif beta_schedule == "linear":
											__lowercase 					=   torch.linspace(snake_case_ ,       snake_case_ ,       snake_case_ ,       dtype=torch.floataa						)
								elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											__lowercase 					=   (
											    torch.linspace(beta_start**0.5 ,       beta_end**0.5 ,       snake_case_ ,       dtype=torch.floataa						) ** 2
											)
								elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											__lowercase 					=   betas_for_alpha_bar(snake_case_						)
								else:
											raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'						)

								__lowercase 					=   1.0 - self.betas
								__lowercase 					=   torch.cumprod(self.alphas ,       dim=0						)

								# At every step in inverted ddim, we are looking into the next alphas_cumprod
								# For the final step, there is no next alphas_cumprod, and the index is out of bounds
								# `set_alpha_to_zero` decides whether we set this parameter simply to zero
								# in this case, self.step() just output the predicted noise
								# or whether we use the final alpha of the "non-previous" one.
								__lowercase 					=   torch.tensor(0.0						) if set_alpha_to_zero else self.alphas_cumprod[-1]

								# standard deviation of the initial noise distribution
								__lowercase 					=   1.0

								# setable values
								__lowercase 					=   None
								__lowercase 					=   torch.from_numpy(np.arange(0 ,       snake_case_						).copy().astype(np.intaa						)						)






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			torch.FloatTensor:







								'''simple docstring'''

								return sample






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			int:







								'''simple docstring'''

								if num_inference_steps > self.config.num_train_timesteps:
											raise ValueError(
											    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
											    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
											    F' maximal {self.config.num_train_timesteps} timesteps.'						)

								__lowercase 					=   num_inference_steps
								__lowercase 					=   self.config.num_train_timesteps // self.num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# casting to int to avoid issues when num_inference_step is power of 3
								__lowercase 					=   (np.arange(0 ,       snake_case_						) * step_ratio).round().copy().astype(np.intaa						)
								__lowercase 					=   torch.from_numpy(snake_case_						).to(snake_case_						)
								self.timesteps += self.config.steps_offset






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ = 0.0 ,       snake_case_ = False ,       snake_case_ = None ,       snake_case_ = True ,       )   ->			Union[DDIMSchedulerOutput, Tuple]:







								'''simple docstring'''

								__lowercase 					=   timestep + self.config.num_train_timesteps // self.num_inference_steps

								# 2. compute alphas, betas
								# change original implementation to exactly match noise levels for analogous forward process
								__lowercase 					=   self.alphas_cumprod[timestep]
								__lowercase 					=   (
								    self.alphas_cumprod[prev_timestep]
								    if prev_timestep < self.config.num_train_timesteps
								    else self.final_alpha_cumprod
								)

								__lowercase 					=   1 - alpha_prod_t

								# 3. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								if self.config.prediction_type == "epsilon":
											__lowercase 					=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											__lowercase 					=   model_output
								elif self.config.prediction_type == "sample":
											__lowercase 					=   model_output
											__lowercase 					=   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
								elif self.config.prediction_type == "v_prediction":
											__lowercase 					=   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											__lowercase 					=   (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
								else:
											raise ValueError(
											    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
											    ''' `v_prediction`'''						)

								# 4. Clip or threshold "predicted x_0"
								if self.config.clip_sample:
											__lowercase 					=   pred_original_sample.clamp(
											    -self.config.clip_sample_range ,       self.config.clip_sample_range						)

								# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								__lowercase 					=   (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

								# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								__lowercase 					=   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

								if not return_dict:
											return (prev_sample, pred_original_sample)
								return DDIMSchedulerOutput(prev_sample=snake_case_ ,       pred_original_sample=snake_case_						)






					def __len__(				self						)   ->			Any:







								'''simple docstring'''

								return self.config.num_train_timesteps






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_       (				lowerCAmelCase__     ,     unittest.TestCase				):



					'''simple docstring'''







					__UpperCAmelCase            =				FlaxAutoencoderKL






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







								'''simple docstring'''

								__lowercase 					=   4
								__lowercase 					=   3
								__lowercase 					=   (3_2, 3_2)

								__lowercase 					=   jax.random.PRNGKey(0						)
								__lowercase 					=   jax.random.uniform(snake_case_ ,       ((batch_size, num_channels) + sizes)						)

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






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   {
								    '''block_out_channels''': [3_2, 6_4],
								    '''in_channels''': 3,
								    '''out_channels''': 3,
								    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
								    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
								    '''latent_channels''': 4,
								}
								__lowercase 					=   self.dummy_input
								return init_dict, inputs_dict


