


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

import numpy as np
import torch

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


__UpperCAmelCase			:			List[str]			      =					logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class  UpperCAmelCase_						(				_a):
       '''simple docstring'''
       __UpperCamelCase   :  torch.FloatTensor
       __UpperCamelCase   :  Optional[torch.FloatTensor]				=       None




def   a						(				SCREAMING_SNAKE_CASE_   :		Optional[int]				,      SCREAMING_SNAKE_CASE_   :		Optional[int]=0.999				,      SCREAMING_SNAKE_CASE_   :		int="cosine"				,      ):






       """simple docstring"""





       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE_   :		Optional[int]							):
                     return math.exp(t * -12.0							)

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

       UpperCamelCase     :						int 						=		[]
       for i in range(SCREAMING_SNAKE_CASE_							):
              UpperCamelCase     :						Dict 						=		i / num_diffusion_timesteps
              UpperCamelCase     :						str 						=		(i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_							) / alpha_bar_fn(SCREAMING_SNAKE_CASE_							)				,      SCREAMING_SNAKE_CASE_							)							)
       return torch.tensor(SCREAMING_SNAKE_CASE_				,      dtype=torch.floataa							)



class  UpperCAmelCase_						(				_a,  _a):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       [e.name for e in KarrasDiffusionSchedulers]
       __UpperCamelCase   :  Dict				=       1






       @register_to_config
       def __init__(  self			,       __SCREAMING_SNAKE_CASE = 1_000			,       __SCREAMING_SNAKE_CASE = 0.0_001			,       __SCREAMING_SNAKE_CASE = 0.02			,       __SCREAMING_SNAKE_CASE = "linear"			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = "epsilon"			,       __SCREAMING_SNAKE_CASE = "linspace"			,       __SCREAMING_SNAKE_CASE = 0			,       ):




              """simple docstring"""




              if trained_betas is not None:
                     UpperCamelCase     :						str 						=		torch.tensor(__SCREAMING_SNAKE_CASE			,       dtype=torch.floataa    )
              elif beta_schedule == "linear":
                     UpperCamelCase     :						List[Any] 						=		torch.linspace(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       dtype=torch.floataa    )
              elif beta_schedule == "scaled_linear":
                     # this schedule is very specific to the latent diffusion model.
                     UpperCamelCase     :						Tuple 						=		(
                         torch.linspace(beta_start**0.5			,       beta_end**0.5			,       __SCREAMING_SNAKE_CASE			,       dtype=torch.floataa    ) ** 2
                     )
              elif beta_schedule == "squaredcos_cap_v2":
                     # Glide cosine schedule
                     UpperCamelCase     :						Dict 						=		betas_for_alpha_bar(__SCREAMING_SNAKE_CASE    )
              else:
                     raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""    )

              UpperCamelCase     :						str 						=		1.0 - self.betas
              UpperCamelCase     :						Tuple 						=		torch.cumprod(self.alphas			,       dim=0    )

              UpperCamelCase     :						Union[str, Any] 						=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
              UpperCamelCase     :						Optional[int] 						=		np.concatenate([sigmas[::-1], [0.0]]    ).astype(np.floataa    )
              UpperCamelCase     :						Optional[int] 						=		torch.from_numpy(__SCREAMING_SNAKE_CASE    )

              # setable values
              UpperCamelCase     :						Union[str, Any] 						=		None
              UpperCamelCase     :						Union[str, Any] 						=		np.linspace(0			,       num_train_timesteps - 1			,       __SCREAMING_SNAKE_CASE			,       dtype=__SCREAMING_SNAKE_CASE    )[::-1].copy()
              UpperCamelCase     :						str 						=		torch.from_numpy(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Union[str, Any] 						=		False






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              if self.config.timestep_spacing in ["linspace", "trailing"]:
                     return self.sigmas.max()

              return (self.sigmas.max() ** 2 + 1) ** 0.5






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              if isinstance(__SCREAMING_SNAKE_CASE			,       torch.Tensor    ):
                     UpperCamelCase     :						Optional[Any] 						=		timestep.to(self.timesteps.device    )
              UpperCamelCase     :						Dict 						=		(self.timesteps == timestep).nonzero().item()
              UpperCamelCase     :						Optional[Any] 						=		self.sigmas[step_index]
              UpperCamelCase     :						Any 						=		sample / ((sigma**2 + 1) ** 0.5)
              UpperCamelCase     :						List[str] 						=		True
              return sample






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = None    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		num_inference_steps

              # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
              if self.config.timestep_spacing == "linspace":
                     UpperCamelCase     :						Dict 						=		np.linspace(0			,       self.config.num_train_timesteps - 1			,       __SCREAMING_SNAKE_CASE			,       dtype=__SCREAMING_SNAKE_CASE    )[
                         ::-1
                     ].copy()
              elif self.config.timestep_spacing == "leading":
                     UpperCamelCase     :						Dict 						=		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
                     UpperCamelCase     :						Optional[Any] 						=		(np.arange(0			,       __SCREAMING_SNAKE_CASE    ) * step_ratio).round()[::-1].copy().astype(__SCREAMING_SNAKE_CASE    )
                     timesteps += self.config.steps_offset
              elif self.config.timestep_spacing == "trailing":
                     UpperCamelCase     :						Tuple 						=		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
                     UpperCamelCase     :						Dict 						=		(np.arange(self.config.num_train_timesteps			,       0			,       -step_ratio    )).round().copy().astype(__SCREAMING_SNAKE_CASE    )
                     timesteps -= 1
              else:
                     raise ValueError(
                         f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

              UpperCamelCase     :						List[str] 						=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
              UpperCamelCase     :						Optional[Any] 						=		np.interp(__SCREAMING_SNAKE_CASE			,       np.arange(0			,       len(__SCREAMING_SNAKE_CASE    )    )			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						int 						=		np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
              UpperCamelCase     :						int 						=		torch.from_numpy(__SCREAMING_SNAKE_CASE    ).to(device=__SCREAMING_SNAKE_CASE    )
              if str(__SCREAMING_SNAKE_CASE    ).startswith('''mps'''    ):
                     # mps does not support float64
                     UpperCamelCase     :						str 						=		torch.from_numpy(__SCREAMING_SNAKE_CASE    ).to(__SCREAMING_SNAKE_CASE			,       dtype=torch.floataa    )
              else:
                     UpperCamelCase     :						Optional[int] 						=		torch.from_numpy(__SCREAMING_SNAKE_CASE    ).to(device=__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = True			,       ):




              """simple docstring"""




              if (
                  isinstance(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
                  or isinstance(__SCREAMING_SNAKE_CASE			,       torch.IntTensor    )
                  or isinstance(__SCREAMING_SNAKE_CASE			,       torch.LongTensor    )
              ):
                     raise ValueError(
                         (
                             '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                             ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
                             ''' one of the `scheduler.timesteps` as a timestep.'''
                         )			,       )

              if not self.is_scale_input_called:
                     logger.warning(
                         '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                         '''See `StableDiffusionPipeline` for a usage example.'''    )

              if isinstance(__SCREAMING_SNAKE_CASE			,       torch.Tensor    ):
                     UpperCamelCase     :						Union[str, Any] 						=		timestep.to(self.timesteps.device    )

              UpperCamelCase     :						str 						=		(self.timesteps == timestep).nonzero().item()
              UpperCamelCase     :						str 						=		self.sigmas[step_index]

              # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
              if self.config.prediction_type == "epsilon":
                     UpperCamelCase     :						List[str] 						=		sample - sigma * model_output
              elif self.config.prediction_type == "v_prediction":
                     # * c_out + input * c_skip
                     UpperCamelCase     :						Dict 						=		model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
              elif self.config.prediction_type == "sample":
                     raise NotImplementedError('''prediction_type not implemented yet: sample'''    )
              else:
                     raise ValueError(
                         f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""    )

              UpperCamelCase     :						str 						=		self.sigmas[step_index]
              UpperCamelCase     :						List[Any] 						=		self.sigmas[step_index + 1]
              UpperCamelCase     :						str 						=		(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
              UpperCamelCase     :						str 						=		(sigma_to**2 - sigma_up**2) ** 0.5

              # 2. Convert to an ODE derivative
              UpperCamelCase     :						List[Any] 						=		(sample - pred_original_sample) / sigma

              UpperCamelCase     :						Optional[int] 						=		sigma_down - sigma

              UpperCamelCase     :						List[Any] 						=		sample + derivative * dt

              UpperCamelCase     :						Optional[Any] 						=		model_output.device
              UpperCamelCase     :						List[Any] 						=		randn_tensor(model_output.shape			,       dtype=model_output.dtype			,       device=__SCREAMING_SNAKE_CASE			,       generator=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						List[Any] 						=		prev_sample + noise * sigma_up

              if not return_dict:
                     return (prev_sample,)

              return EulerAncestralDiscreteSchedulerOutput(
                  prev_sample=__SCREAMING_SNAKE_CASE			,       pred_original_sample=__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		self.sigmas.to(device=original_samples.device			,       dtype=original_samples.dtype    )
              if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE    ):
                     # mps does not support float64
                     UpperCamelCase     :						List[Any] 						=		self.timesteps.to(original_samples.device			,       dtype=torch.floataa    )
                     UpperCamelCase     :						Optional[Any] 						=		timesteps.to(original_samples.device			,       dtype=torch.floataa    )
              else:
                     UpperCamelCase     :						Union[str, Any] 						=		self.timesteps.to(original_samples.device    )
                     UpperCamelCase     :						str 						=		timesteps.to(original_samples.device    )

              UpperCamelCase     :						Any 						=		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

              UpperCamelCase     :						Dict 						=		sigmas[step_indices].flatten()
              while len(sigma.shape    ) < len(original_samples.shape    ):
                     UpperCamelCase     :						Union[str, Any] 						=		sigma.unsqueeze(-1    )

              UpperCamelCase     :						str 						=		original_samples + noise * sigma
              return noisy_samples






       def __len__(  self    ):




              """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  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    )

