'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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 .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left




@flax.struct.dataclass
class    lowerCamelCase__  :
   # setable values
   __UpperCAmelCase           =						None
   __UpperCAmelCase           =						None
   __UpperCAmelCase           =						None



   @classmethod
   def  _UpperCamelCase		(    cls     )				->     List[str]:
        """simple docstring"""





        return cls()




@dataclass
class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						42
   __UpperCAmelCase           =						42
   __UpperCAmelCase           =						None




class    lowerCamelCase__  (   __snake_case       ,				__snake_case     ):



   @property
   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        return True



   @register_to_config
   def __init__(    self					,      lowerCAmelCase__ = 2_000					,      lowerCAmelCase__ = 0.15					,      lowerCAmelCase__ = 0.01					,      lowerCAmelCase__ = 1348.0					,      lowerCAmelCase__ = 1e-5					,      lowerCAmelCase__ = 1					,      )				->     int:
        """simple docstring"""





        pass



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Dict       =ScoreSdeVeSchedulerState.create()
        return self.set_sigmas(
            lowerCAmelCase__					,      self.config.num_train_timesteps					,      self.config.sigma_min					,      self.config.sigma_max					,      self.config.sampling_eps					,      )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = ()					,      lowerCAmelCase__ = None     )				->     ScoreSdeVeSchedulerState:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =sampling_eps if sampling_eps is not None else self.config.sampling_eps

        _UpperCamelCase  :List[Any]       =jnp.linspace(1					,      lowerCAmelCase__					,      lowerCAmelCase__     )
        return state.replace(timesteps=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      )				->     ScoreSdeVeSchedulerState:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =sigma_min if sigma_min is not None else self.config.sigma_min
        _UpperCamelCase  :List[Any]       =sigma_max if sigma_max is not None else self.config.sigma_max
        _UpperCamelCase  :List[Any]       =sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if state.timesteps is None:
             _UpperCamelCase  :List[str]       =self.set_timesteps(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =jnp.exp(jnp.linspace(jnp.log(lowerCAmelCase__     )					,      jnp.log(lowerCAmelCase__     )					,      lowerCAmelCase__     )     )
        _UpperCamelCase  :List[Any]       =jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]     )

        return state.replace(discrete_sigmas=lowerCAmelCase__					,      sigmas=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        return jnp.where(timesteps == 0					,      jnp.zeros_like(lowerCAmelCase__     )					,      state.discrete_sigmas[timesteps - 1]     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = True					,      )				->     Union[FlaxSdeVeOutput, Tuple]:
        """simple docstring"""





        if state.timesteps is None:
             raise ValueError(
                 """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

        _UpperCamelCase  :int       =timestep * jnp.ones(
            sample.shape[0]					,      )
        _UpperCamelCase  :List[Any]       =(timestep * (len(state.timesteps     ) - 1)).long()

        _UpperCamelCase  :List[str]       =state.discrete_sigmas[timesteps]
        _UpperCamelCase  :List[Any]       =self.get_adjacent_sigma(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =jnp.zeros_like(lowerCAmelCase__     )
        _UpperCamelCase  :Tuple       =(sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        _UpperCamelCase  :Optional[Any]       =diffusion.flatten()
        _UpperCamelCase  :List[str]       =broadcast_to_shape_from_left(lowerCAmelCase__					,      sample.shape     )
        _UpperCamelCase  :Tuple       =drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        _UpperCamelCase  :str       =random.split(lowerCAmelCase__					,      num=1     )
        _UpperCamelCase  :Optional[Any]       =random.normal(key=lowerCAmelCase__					,      shape=sample.shape     )
        _UpperCamelCase  :List[str]       =sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        _UpperCamelCase  :Optional[Any]       =prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
             return (prev_sample, prev_sample_mean, state)

        return FlaxSdeVeOutput(prev_sample=lowerCAmelCase__					,      prev_sample_mean=lowerCAmelCase__					,      state=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = True					,      )				->     Union[FlaxSdeVeOutput, Tuple]:
        """simple docstring"""





        if state.timesteps is None:
             raise ValueError(
                 """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        _UpperCamelCase  :Dict       =random.split(lowerCAmelCase__					,      num=1     )
        _UpperCamelCase  :Union[str, Any]       =random.normal(key=lowerCAmelCase__					,      shape=sample.shape     )

        # compute step size from the model_output, the noise, and the snr
        _UpperCamelCase  :int       =jnp.linalg.norm(lowerCAmelCase__     )
        _UpperCamelCase  :List[str]       =jnp.linalg.norm(lowerCAmelCase__     )
        _UpperCamelCase  :Tuple       =(self.config.snr * noise_norm / grad_norm) ** 2 * 2
        _UpperCamelCase  :int       =step_size * jnp.ones(sample.shape[0]     )

        # compute corrected sample: model_output term and noise term
        _UpperCamelCase  :List[Any]       =step_size.flatten()
        _UpperCamelCase  :Optional[int]       =broadcast_to_shape_from_left(lowerCAmelCase__					,      sample.shape     )
        _UpperCamelCase  :str       =sample + step_size * model_output
        _UpperCamelCase  :List[Any]       =prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
             return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=lowerCAmelCase__					,      state=lowerCAmelCase__     )




   def __len__(    self     )				->     List[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      snake_case__	(   unittest.TestCase	):
				__lowerCAmelCase		=   '''hf-internal-testing/tiny-stable-diffusion-pipe'''
				__lowerCAmelCase		=   1
				__lowerCAmelCase		=   77
				__lowerCAmelCase		=   32
				__lowerCAmelCase		=   4
				__lowerCAmelCase		=   __lowerCAmelCase		=   64
				__lowerCAmelCase		=   torch.manual_seed(0	)
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase=0.9999   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   UNetaDConditionModel.from_pretrained(self.model_id  ,   subfolder='unet'   )
										__magic_name__					:				int				=   unet.to(_UpperCAmelCase   )
										__magic_name__					:				Optional[int]				=   EMAModel(unet.parameters()  ,   decay=_UpperCAmelCase  ,   model_cls=_UpperCAmelCase  ,   model_config=unet.config   )
										return unet, ema_unet
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				List[str]				=   torch.randn(
										    self.batch_size  ,   self.num_in_channels  ,   self.latent_height  ,   self.latent_width  ,   generator=self.generator   ).to(_UpperCAmelCase   )
										__magic_name__					:				List[str]				=   torch.randint(0  ,   1000  ,   size=(self.batch_size,)  ,   generator=self.generator   ).to(_UpperCAmelCase   )
										__magic_name__					:				Any				=   torch.randn(
										    self.batch_size  ,   self.prompt_length  ,   self.text_encoder_hidden_dim  ,   generator=self.generator   ).to(_UpperCAmelCase   )
										return noisy_latents, timesteps, encoder_hidden_states
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   {}
										for k, param in unet.state_dict().items():
																__magic_name__					:				str				=   torch.randn_like(_UpperCAmelCase   ) + (param * torch.randn_like(_UpperCAmelCase   ))
																updated_state_dict.update({k: updated_param}   )
										unet.load_state_dict(_UpperCAmelCase   )
										return unet
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				int				=   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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Union[str, Any]				=   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()   )
										__magic_name__					:				Any				=   list(unet.parameters()   )
										for s_param, param in zip(ema_unet.shadow_params  ,   _UpperCAmelCase   ):
																assert torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase   )

										# 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  ,   _UpperCAmelCase   ):
																assert torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Optional[Any]				=   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.
										__magic_name__					:				Any				=   self.simulate_backprop(_UpperCAmelCase   )

										# 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.
										__magic_name__					:				Any				=   list(unet_pseudo_updated_step_one.parameters()   )
										for s_param, param in zip(ema_unet.shadow_params  ,   _UpperCAmelCase   ):
																assert ~torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase   )

										# 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  ,   _UpperCAmelCase   ):
																assert ~torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				List[str]				=   self.get_models()

										# First backprop + EMA
										__magic_name__					:				int				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_one.parameters()   )
										__magic_name__					:				Any				=   ema_unet.shadow_params

										# Second backprop + EMA
										__magic_name__					:				Any				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_two.parameters()   )
										__magic_name__					:				Union[str, Any]				=   ema_unet.shadow_params

										for step_one, step_two in zip(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																assert ~torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Optional[Any]				=   self.get_models(decay=0.0   )
										__magic_name__					:				str				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_one.parameters()   )
										__magic_name__					:				List[Any]				=   ema_unet.shadow_params

										__magic_name__					:				Optional[Any]				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_two.parameters()   )
										__magic_name__					:				Optional[Any]				=   ema_unet.shadow_params

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





				@skip_mps
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Optional[int]				=   self.get_models()
										__magic_name__		,			__magic_name__		,			__magic_name__					:				str				=   self.get_dummy_inputs()

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

										# Since no EMA step has been performed the outputs should match.
										__magic_name__					:				Union[str, Any]				=   unet(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ).sample
										__magic_name__					:				List[Any]				=   loaded_unet(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ).sample

										assert torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase  ,   atol=1E-4   )


