# 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	:



 '''simple docstring'''
 _lowerCamelCase    :						Optional[jnp.ndarray]					     =			None
 _lowerCamelCase    :						Optional[jnp.ndarray]					     =			None
 _lowerCamelCase    :						Optional[jnp.ndarray]					     =			None

 @classmethod
 def        __snake_case       (      cls							:		Union[str, Any]	)			->				List[str]:
        return cls()







@dataclass
class  __lowerCAmelCase	(							_a    ):



 '''simple docstring'''
 _lowerCamelCase    :						ScoreSdeVeSchedulerState
 _lowerCamelCase    :						jnp.ndarray
 _lowerCamelCase    :						Optional[jnp.ndarray]					     =			None







class  __lowerCAmelCase	(							_a,						_a    ):



 '''simple docstring'''

 @property
 def        __snake_case       (      self							:		Optional[int]	)			->				Any:
        return True

 @register_to_config
 def __init__(      self							:		Optional[Any]							,      lowerCamelCase							:		Tuple = 2_0_0_0							,      lowerCamelCase							:		Tuple = 0.1_5							,      lowerCamelCase							:		List[str] = 0.0_1							,      lowerCamelCase							:		int = 1_3_4_8.0							,      lowerCamelCase							:		Union[str, Any] = 1e-5							,      lowerCamelCase							:		Dict = 1							,      )			->				str:
        pass

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Any:
        _UpperCamelCase  :							str	    =	ScoreSdeVeSchedulerState.create()
        return self.set_sigmas(
            __SCREAMING_SNAKE_CASE							,      self.config.num_train_timesteps							,      self.config.sigma_min							,      self.config.sigma_max							,      self.config.sampling_eps							,      )

 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		Any = ()							,      lowerCamelCase							:		Dict = None	)			->				str:
        _UpperCamelCase  :							List[str]	    =	sampling_eps if sampling_eps is not None else self.config.sampling_eps

        _UpperCamelCase  :							Tuple	    =	jnp.linspace(1							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE	)
        return state.replace(timesteps=__SCREAMING_SNAKE_CASE	)

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		int							,      lowerCamelCase							:		str							,      lowerCamelCase							:		str = None							,      lowerCamelCase							:		Optional[int] = None							,      lowerCamelCase							:		List[str] = None							,      )			->				Any:
        _UpperCamelCase  :							Union[str, Any]	    =	sigma_min if sigma_min is not None else self.config.sigma_min
        _UpperCamelCase  :							Tuple	    =	sigma_max if sigma_max is not None else self.config.sigma_max
        _UpperCamelCase  :							Optional[int]	    =	sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if state.timesteps is None:
               _UpperCamelCase  :							int	    =	self.set_timesteps(__SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE	)

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

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

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		List[Any]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[Any]	)			->				int:
        return jnp.where(timesteps == 0							,      jnp.zeros_like(__SCREAMING_SNAKE_CASE	)							,      state.discrete_sigmas[timesteps - 1]	)

 def        __snake_case       (      self							:		str							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		str							,      lowerCamelCase							:		List[Any]							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Any = True							,      )			->				Tuple:




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

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

        _UpperCamelCase  :							List[Any]	    =	state.discrete_sigmas[timesteps]
        _UpperCamelCase  :							int	    =	self.get_adjacent_sigma(__SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE	)
        _UpperCamelCase  :							List[Any]	    =	jnp.zeros_like(__SCREAMING_SNAKE_CASE	)
        _UpperCamelCase  :							int	    =	(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  :							str	    =	broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE							,      sample.shape	)
        _UpperCamelCase  :							Optional[int]	    =	drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        _UpperCamelCase  :							Any	    =	random.split(__SCREAMING_SNAKE_CASE							,      num=1	)
        _UpperCamelCase  :							Optional[int]	    =	random.normal(key=__SCREAMING_SNAKE_CASE							,      shape=sample.shape	)
        _UpperCamelCase  :							int	    =	sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        _UpperCamelCase  :							Tuple	    =	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=__SCREAMING_SNAKE_CASE							,      prev_sample_mean=__SCREAMING_SNAKE_CASE							,      state=__SCREAMING_SNAKE_CASE	)

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		Union[str, Any]							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		int							,      lowerCamelCase							:		Dict							,      lowerCamelCase							:		Any = True							,      )			->				int:




        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  :							Optional[int]	    =	random.split(__SCREAMING_SNAKE_CASE							,      num=1	)
        _UpperCamelCase  :							Optional[int]	    =	random.normal(key=__SCREAMING_SNAKE_CASE							,      shape=sample.shape	)

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

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

        if not return_dict:
               return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=__SCREAMING_SNAKE_CASE							,      state=__SCREAMING_SNAKE_CASE	)

 def __len__(      self							:		Optional[Any]	)			->				Any:
        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  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       "hf-internal-testing/tiny-stable-diffusion-pipe"
       __UpperCamelCase   :  Optional[Any]				=       1
       __UpperCamelCase   :  str				=       77
       __UpperCamelCase   :  Tuple				=       32
       __UpperCamelCase   :  List[str]				=       4
       __UpperCamelCase   :  Optional[int]				=       64
       __UpperCamelCase   :  Union[str, Any]				=       torch.manual_seed(0)






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE=0.9_999    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		UNetaDConditionModel.from_pretrained(self.model_id			,       subfolder='''unet'''    )
              UpperCamelCase     :						Any 						=		unet.to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Dict 						=		EMAModel(unet.parameters()			,       decay=__SCREAMING_SNAKE_CASE			,       model_cls=__SCREAMING_SNAKE_CASE			,       model_config=unet.config    )
              return unet, ema_unet






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		torch.randn(
                  self.batch_size			,       self.num_in_channels			,       self.latent_height			,       self.latent_width			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		torch.randint(0			,       1_000			,       size=(self.batch_size,)			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[Any] 						=		torch.randn(
                  self.batch_size			,       self.prompt_length			,       self.text_encoder_hidden_dim			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              return noisy_latents, timesteps, encoder_hidden_states






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		{}
              for k, param in unet.state_dict().items():
                     UpperCamelCase     :						int 						=		torch.randn_like(__SCREAMING_SNAKE_CASE    ) + (param * torch.randn_like(__SCREAMING_SNAKE_CASE    ))
                     updated_state_dict.update({k: updated_param}    )
              unet.load_state_dict(__SCREAMING_SNAKE_CASE    )
              return unet






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Optional[Any] 						=		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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						List[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()    )
              UpperCamelCase     :						int 						=		list(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              # 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			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						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.
              UpperCamelCase     :						Union[str, Any] 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )

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

              # 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			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Optional[int] 						=		self.get_models()

              # First backprop + EMA
              UpperCamelCase     :						Dict 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_one.parameters()    )
              UpperCamelCase     :						List[str] 						=		ema_unet.shadow_params

              # Second backprop + EMA
              UpperCamelCase     :						str 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_two.parameters()    )
              UpperCamelCase     :						List[Any] 						=		ema_unet.shadow_params

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						int 						=		self.get_models(decay=0.0    )
              UpperCamelCase     :						Optional[int] 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_one.parameters()    )
              UpperCamelCase     :						Union[str, Any] 						=		ema_unet.shadow_params

              UpperCamelCase     :						Any 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_two.parameters()    )
              UpperCamelCase     :						int 						=		ema_unet.shadow_params

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






       @skip_mps
       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						str 						=		self.get_models()
              UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		self.get_dummy_inputs()

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

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

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

