
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							_snake_case  		=	42
							_snake_case  		=	42

							# setable values
							_snake_case  		=	42
							_snake_case  		=	42
							_snake_case  		=	None



							@classmethod
							def      A__		(	cls  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> Union[str, Any]:





													'''simple docstring'''






													return cls(
													    common=SCREAMING_SNAKE_CASE_  ,						final_alpha_cumprod=SCREAMING_SNAKE_CASE_  ,						init_noise_sigma=SCREAMING_SNAKE_CASE_  ,						timesteps=SCREAMING_SNAKE_CASE_  ,						)






@dataclass
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	42






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	[e.name for e in FlaxKarrasDiffusionSchedulers]

							_snake_case  		=	42



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





													'''simple docstring'''






													return True



							@register_to_config
							def __init__(	self  ,						SCREAMING_SNAKE_CASE_ = 1000  ,						SCREAMING_SNAKE_CASE_ = 0.0_0_0_1  ,						SCREAMING_SNAKE_CASE_ = 0.0_2  ,						SCREAMING_SNAKE_CASE_ = "linear"  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = 0  ,						SCREAMING_SNAKE_CASE_ = "epsilon"  ,						SCREAMING_SNAKE_CASE_ = jnp.floataa  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      dtype



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_ = None      )-> DDIMSchedulerState:





													'''simple docstring'''






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

													# At every step in ddim, we are looking into the previous alphas_cumprod
													# For the final step, there is no previous alphas_cumprod because we are already at 0
													# `set_alpha_to_one` decides whether we set this parameter simply to one or
													# whether we use the final alpha of the "non-previous" one.
													__UpperCamelCase  	=      (
													    jnp.array(1.0  ,						dtype=self.dtype      ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
													)

													# 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 DDIMSchedulerState.create(
													    common=SCREAMING_SNAKE_CASE_  ,						final_alpha_cumprod=SCREAMING_SNAKE_CASE_  ,						init_noise_sigma=SCREAMING_SNAKE_CASE_  ,						timesteps=SCREAMING_SNAKE_CASE_  ,						)



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None      )-> jnp.ndarray:





													'''simple docstring'''






													return sample



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = ()      )-> DDIMSchedulerState:





													'''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  ,						SCREAMING_SNAKE_CASE_      ) * step_ratio).round()[::-1] + self.config.steps_offset

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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      state.common.alphas_cumprod[timestep]
													__UpperCamelCase  	=      jnp.where(
													    prev_timestep >= 0  ,						state.common.alphas_cumprod[prev_timestep]  ,						state.final_alpha_cumprod      )
													__UpperCamelCase  	=      1 - alpha_prod_t
													__UpperCamelCase  	=      1 - alpha_prod_t_prev

													__UpperCamelCase  	=      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

													return variance



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = 0.0  ,						SCREAMING_SNAKE_CASE_ = True  ,						)-> Union[FlaxDDIMSchedulerOutput, Tuple]:





													'''simple docstring'''






													if state.num_inference_steps is None:
																			raise ValueError(
																			    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''      )

													# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
													# Ideally, read DDIM paper in-detail understanding

													# Notation (<variable name> -> <name in paper>
													# - pred_noise_t -> e_theta(x_t, t)
													# - pred_original_sample -> f_theta(x_t, t) or x_0
													# - std_dev_t -> sigma_t
													# - eta -> η
													# - pred_sample_direction -> "direction pointing to x_t"
													# - pred_prev_sample -> "x_t-1"

													# 1. get previous step value (=t-1)
													__UpperCamelCase  	=      timestep - self.config.num_train_timesteps // state.num_inference_steps

													__UpperCamelCase  	=      state.common.alphas_cumprod
													__UpperCamelCase  	=      state.final_alpha_cumprod

													# 2. compute alphas, betas
													__UpperCamelCase  	=      alphas_cumprod[timestep]
													__UpperCamelCase  	=      jnp.where(prev_timestep >= 0  ,						alphas_cumprod[prev_timestep]  ,						SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      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":
																			__UpperCamelCase  	=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																			__UpperCamelCase  	=      model_output
													elif self.config.prediction_type == "sample":
																			__UpperCamelCase  	=      model_output
																			__UpperCamelCase  	=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
													elif self.config.prediction_type == "v_prediction":
																			__UpperCamelCase  	=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																			__UpperCamelCase  	=      (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. compute variance: "sigma_t(η)" -> see formula (16)
													# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
													__UpperCamelCase  	=      self._get_variance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      eta * variance ** 0.5

													# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													__UpperCamelCase  	=      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

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

													if not return_dict:
																			return (prev_sample, state)

													return FlaxDDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_  ,						state=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> jnp.ndarray:





													'''simple docstring'''






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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> jnp.ndarray:





													'''simple docstring'''






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



							def __len__(	self      )-> Union[str, Any]:





													'''simple docstring'''






													return self.config.num_train_timesteps










import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()




class  __lowerCAmelCase							(				unittest.TestCase):
       _a			       =    '''hf-internal-testing/tiny-stable-diffusion-pipe'''
       _a			       =    1
       _a			       =    77
       _a			       =    32
       _a			       =    4
       _a			       =    _a			       =    64
       _a			       =    torch.manual_seed(0)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       int=0.99_99					):
         lowercase     :Optional[int]   						=    UNetaDConditionModel.from_pretrained(self.model_id							,       subfolder="unet"					)
         lowercase     :Union[str, Any]   						=    unet.to(_lowerCAmelCase					)
         lowercase     :Any   						=    EMAModel(unet.parameters()							,       decay=_lowerCAmelCase							,       model_cls=_lowerCAmelCase							,       model_config=unet.config					)
         return unet, ema_unet






       def        SCREAMING_SNAKE_CASE     ( self:       List[str]					):
         lowercase     :Optional[Any]   						=    torch.randn(
             self.batch_size							,       self.num_in_channels							,       self.latent_height							,       self.latent_width							,       generator=self.generator					).to(_lowerCAmelCase					)
         lowercase     :Dict   						=    torch.randint(0							,       10_00							,       size=(self.batch_size,)							,       generator=self.generator					).to(_lowerCAmelCase					)
         lowercase     :List[Any]   						=    torch.randn(
             self.batch_size							,       self.prompt_length							,       self.text_encoder_hidden_dim							,       generator=self.generator					).to(_lowerCAmelCase					)
         return noisy_latents, timesteps, encoder_hidden_states






       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       Optional[Any]					):
         lowercase     :Any   						=    {}
         for k, param in unet.state_dict().items():
           lowercase     :Optional[int]   						=    torch.randn_like(_lowerCAmelCase					) + (param * torch.randn_like(_lowerCAmelCase					))
           updated_state_dict.update({k: updated_param}					)
         unet.load_state_dict(_lowerCAmelCase					)
         return unet






       def        SCREAMING_SNAKE_CASE     ( self:       int					):
         lowercase						,     lowercase     :List[str]   						=    self.get_models()
         # Take the first (hypothetical) EMA step.
         ema_unet.step(unet.parameters()					)
         assert ema_unet.optimization_step == 1

         # Take two more.
         for _ in range(2					):
           ema_unet.step(unet.parameters()					)
         assert ema_unet.optimization_step == 3






       def        SCREAMING_SNAKE_CASE     ( self:       Any					):
         lowercase						,     lowercase     :str   						=    self.get_models()
         # Since the `unet` is not being updated (i.e., backprop'd)
         # there won't be any difference between the `params` of `unet`
         # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
         ema_unet.step(unet.parameters()					)
         lowercase     :Tuple   						=    list(unet.parameters()					)
         for s_param, param in zip(ema_unet.shadow_params							,       _lowerCAmelCase					):
           assert torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)

         # The above holds true even if we call `ema.step()` multiple times since
         # `unet` params are still not being updated.
         for _ in range(4					):
           ema_unet.step(unet.parameters()					)
         for s_param, param in zip(ema_unet.shadow_params							,       _lowerCAmelCase					):
           assert torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Dict					):
         lowercase						,     lowercase     :Optional[int]   						=    self.get_models()
         # Here we simulate the parameter updates for `unet`. Since there might
         # be some parameters which are initialized to zero we take extra care to
         # initialize their values to something non-zero before the multiplication.
         lowercase     :List[str]   						=    self.simulate_backprop(_lowerCAmelCase					)

         # Take the EMA step.
         ema_unet.step(unet_pseudo_updated_step_one.parameters()					)

         # Now the EMA'd parameters won't be equal to the original model parameters.
         lowercase     :str   						=    list(unet_pseudo_updated_step_one.parameters()					)
         for s_param, param in zip(ema_unet.shadow_params							,       _lowerCAmelCase					):
           assert ~torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)

         # Ensure this is the case when we take multiple EMA steps.
         for _ in range(4					):
           ema_unet.step(unet.parameters()					)
         for s_param, param in zip(ema_unet.shadow_params							,       _lowerCAmelCase					):
           assert ~torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]					):
         # If we call EMA step after a backpropagation consecutively for two times,
         # the shadow params from those two steps should be different.
         lowercase						,     lowercase     :Optional[Any]   						=    self.get_models()

         # First backprop + EMA
         lowercase     :Union[str, Any]   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_one.parameters()					)
         lowercase     :int   						=    ema_unet.shadow_params

         # Second backprop + EMA
         lowercase     :Optional[int]   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_two.parameters()					)
         lowercase     :Optional[int]   						=    ema_unet.shadow_params

         for step_one, step_two in zip(_lowerCAmelCase							,       _lowerCAmelCase					):
           assert ~torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]					):
         # If there's no decay even if there are backprops, EMA steps
         # won't take any effect i.e., the shadow params would remain the
         # same.
         lowercase						,     lowercase     :Any   						=    self.get_models(decay=0.0					)
         lowercase     :List[Any]   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_one.parameters()					)
         lowercase     :List[str]   						=    ema_unet.shadow_params

         lowercase     :str   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_two.parameters()					)
         lowercase     :List[str]   						=    ema_unet.shadow_params

         for step_one, step_two in zip(_lowerCAmelCase							,       _lowerCAmelCase					):
           assert torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)






       @skip_mps
       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]					):
         lowercase						,     lowercase     :Optional[Any]   						=    self.get_models()
         lowercase						,     lowercase						,     lowercase     :str   						=    self.get_dummy_inputs()

         with tempfile.TemporaryDirectory() as tmpdir:
           ema_unet.save_pretrained(_lowerCAmelCase					)
           lowercase     :Optional[int]   						=    UNetaDConditionModel.from_pretrained(_lowerCAmelCase							,       model_cls=_lowerCAmelCase					)
           lowercase     :Tuple   						=    loaded_unet.to(unet.device					)

         # Since no EMA step has been performed the outputs should match.
         lowercase     :Dict   						=    unet(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					).sample
         lowercase     :Any   						=    loaded_unet(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					).sample

         assert torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase							,       atol=1e-4					)



