



'''simple docstring'''





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=0.9_99     ,				_UpperCAmelCase="cosine"     ,				):

    if alpha_transform_type == "cosine":

        def alpha_bar_fn(_UpperCAmelCase       ):
            return math.cos((t + 0.0_08) / 1.0_08 * 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}'       )

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

class 					_A (			__a   ,__a					):
       UpperCamelCase__						:					str		      =		[e.name for e in KarrasDiffusionSchedulers]
       UpperCamelCase__						:					Tuple		      =		2



       @register_to_config
       def __init__(	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  int = 1_000  ,	__SCREAMING_SNAKE_CASE							:  float = 0.0_00_85  ,	__SCREAMING_SNAKE_CASE							:  float = 0.0_12  ,	__SCREAMING_SNAKE_CASE							:  str = "linear"  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[np.ndarray, List[float]]] = None  ,	__SCREAMING_SNAKE_CASE							:  str = "epsilon"  ,	__SCREAMING_SNAKE_CASE							:  str = "linspace"  ,	__SCREAMING_SNAKE_CASE							:  int = 0  ,	):


           '''simple docstring'''

           if trained_betas is not None:
               __a		 =       torch.tensor(a_  ,	dtype=torch.floataa)
           elif beta_schedule == "linear":
               __a		 =       torch.linspace(a_  ,	a_  ,	a_  ,	dtype=torch.floataa)
           elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               __a		 =       (
                   torch.linspace(beta_start**0.5  ,	beta_end**0.5  ,	a_  ,	dtype=torch.floataa) ** 2
               )
           elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               __a		 =       betas_for_alpha_bar(a_)
           else:
               raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

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

           #  set all values
           self.set_timesteps(a_  ,	a_  ,	a_)



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Dict=None):


           '''simple docstring'''

           if schedule_timesteps is None:
               __a		 =       self.timesteps

           __a		 =       (schedule_timesteps == timestep).nonzero()

           # The sigma index that is taken for the **very** first `step`
           # is always the second index (or the last index if there is only 1)
           # This way we can ensure we don't accidentally skip a sigma in
           # case we start in the middle of the denoising schedule (e.g. for image-to-image)
           if len(self._index_counter) == 0:
               __a		 =       1 if len(a_) > 1 else 0
           else:
               __a		 =       timestep.cpu().item() if torch.is_tensor(a_) else timestep
               __a		 =       self._index_counter[timestep_int]

           return indices[pos].item()



       @property
       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

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

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



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  torch.FloatTensor  ,	__SCREAMING_SNAKE_CASE							:  Union[float, torch.FloatTensor]  ,	):


           '''simple docstring'''

           __a		 =       self.index_for_timestep(a_)

           if self.state_in_first_order:
               __a		 =       self.sigmas[step_index]
           else:
               __a		 =       self.sigmas_interpol[step_index]

           __a		 =       sample / ((sigma**2 + 1) ** 0.5)
           return sample



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  Union[str, torch.device] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = None  ,	):


           '''simple docstring'''

           __a		 =       num_inference_steps

           __a		 =       num_train_timesteps or self.config.num_train_timesteps

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

           __a		 =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
           __a		 =       torch.from_numpy(np.log(a_)).to(a_)

           __a		 =       np.interp(a_  ,	np.arange(0  ,	len(a_))  ,	a_)
           __a		 =       np.concatenate([sigmas, [0.0]]).astype(np.floataa)
           __a		 =       torch.from_numpy(a_).to(device=a_)

           # interpolate sigmas
           __a		 =       sigmas.log().lerp(sigmas.roll(1).log()  ,	0.5).exp()

           __a		 =       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
           __a		 =       torch.cat(
               [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])

           if str(a_).startswith('''mps'''):
               # mps does not support float64
               __a		 =       torch.from_numpy(a_).to(a_  ,	dtype=torch.floataa)
           else:
               __a		 =       torch.from_numpy(a_).to(a_)

           # interpolate timesteps
           __a		 =       self.sigma_to_t(a_).to(a_  ,	dtype=timesteps.dtype)
           __a		 =       torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])  ,	dim=-1).flatten()

           __a		 =       torch.cat([timesteps[:1], interleaved_timesteps])

           __a		 =       None

           # for exp beta schedules, such as the one for `pipeline_shap_e.py`
           # we need an index counter
           __a		 =       defaultdict(a_)



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           __a		 =       sigma.log()

           # get distribution
           __a		 =       log_sigma - self.log_sigmas[:, None]

           # get sigmas range
           __a		 =       dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
           __a		 =       low_idx + 1

           __a		 =       self.log_sigmas[low_idx]
           __a		 =       self.log_sigmas[high_idx]

           # interpolate sigmas
           __a		 =       (low - log_sigma) / (low - high)
           __a		 =       w.clamp(0  ,	1)

           # transform interpolation to time range
           __a		 =       (1 - w) * low_idx + w * high_idx
           __a		 =       t.view(sigma.shape)
           return t



       @property
       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           return self.sample is None



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Union[torch.FloatTensor, np.ndarray]  ,	__SCREAMING_SNAKE_CASE							:  Union[float, torch.FloatTensor]  ,	__SCREAMING_SNAKE_CASE							:  Union[torch.FloatTensor, np.ndarray]  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	):


           '''simple docstring'''

           __a		 =       self.index_for_timestep(a_)

           # advance index counter by 1
           __a		 =       timestep.cpu().item() if torch.is_tensor(a_) else timestep
           self._index_counter[timestep_int] += 1

           if self.state_in_first_order:
               __a		 =       self.sigmas[step_index]
               __a		 =       self.sigmas_interpol[step_index + 1]
               __a		 =       self.sigmas[step_index + 1]
           else:
               # 2nd order / KDPM2's method
               __a		 =       self.sigmas[step_index - 1]
               __a		 =       self.sigmas_interpol[step_index]
               __a		 =       self.sigmas[step_index]

           # currently only gamma=0 is supported. This usually works best anyways.
           # We can support gamma in the future but then need to scale the timestep before
           # passing it to the model which requires a change in API
           __a		 =       0
           __a		 =       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
               __a		 =       sigma_hat if self.state_in_first_order else sigma_interpol
               __a		 =       sample - sigma_input * model_output
           elif self.config.prediction_type == "v_prediction":
               __a		 =       sigma_hat if self.state_in_first_order else sigma_interpol
               __a		 =       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**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`')

           if self.state_in_first_order:
               # 2. Convert to an ODE derivative for 1st order
               __a		 =       (sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               __a		 =       sigma_interpol - sigma_hat

               # store for 2nd order step
               __a		 =       sample
           else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               __a		 =       (sample - pred_original_sample) / sigma_interpol

               # 3. delta timestep
               __a		 =       sigma_next - sigma_hat

               __a		 =       self.sample
               __a		 =       None

           __a		 =       sample + derivative * dt

           if not return_dict:
               return (prev_sample,)

           return SchedulerOutput(prev_sample=a_)



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  torch.FloatTensor  ,	__SCREAMING_SNAKE_CASE							:  torch.FloatTensor  ,	__SCREAMING_SNAKE_CASE							:  torch.FloatTensor  ,	):


           '''simple docstring'''

           __a		 =       self.sigmas.to(device=original_samples.device  ,	dtype=original_samples.dtype)
           if original_samples.device.type == "mps" and torch.is_floating_point(a_):
               # mps does not support float64
               __a		 =       self.timesteps.to(original_samples.device  ,	dtype=torch.floataa)
               __a		 =       timesteps.to(original_samples.device  ,	dtype=torch.floataa)
           else:
               __a		 =       self.timesteps.to(original_samples.device)
               __a		 =       timesteps.to(original_samples.device)

           __a		 =       [self.index_for_timestep(a_  ,	a_) for t in timesteps]

           __a		 =       sigmas[step_indices].flatten()
           while len(sigma.shape) < len(original_samples.shape):
               __a		 =       sigma.unsqueeze(-1)

           __a		 =       original_samples + noise * sigma
           return noisy_samples





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


           '''simple docstring'''

           return self.config.num_train_timesteps










import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def   __snake_case			(    _UpperCAmelCase       ):
    __a ,     __a		 =       image.size
    __a ,     __a		 =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    __a		 =       image.resize((w, h)     ,				resample=PIL_INTERPOLATION['''lanczos''']       )
    __a		 =       np.array(_UpperCAmelCase       ).astype(np.floataa       ) / 2_55.0
    __a		 =       image[None].transpose(0     ,				3     ,				1     ,				2       )
    __a		 =       torch.from_numpy(_UpperCAmelCase       )
    return 2.0 * image - 1.0

class 					_A (			__UpperCAmelCase					):



       def __init__(	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  VQModel  ,	__SCREAMING_SNAKE_CASE							:  UNetaDModel  ,	__SCREAMING_SNAKE_CASE							:  Union[
               DDIMScheduler,
               PNDMScheduler,
               LMSDiscreteScheduler,
               EulerDiscreteScheduler,
               EulerAncestralDiscreteScheduler,
               DPMSolverMultistepScheduler,
           ]  ,	):


           '''simple docstring'''

           super().__init__()
           self.register_modules(vqvae=__SCREAMING_SNAKE_CASE  ,	unet=__SCREAMING_SNAKE_CASE  ,	scheduler=__SCREAMING_SNAKE_CASE)





       @torch.no_grad()
       def __call__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[torch.Tensor, PIL.Image.Image] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 1  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 100  ,	__SCREAMING_SNAKE_CASE							:  Optional[float] = 0.0  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[str] = "pil"  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE  ,	PIL.Image.Image):
               __a		 =       1
           elif isinstance(__SCREAMING_SNAKE_CASE  ,	torch.Tensor):
               __a		 =       image.shape[0]
           else:
               raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__SCREAMING_SNAKE_CASE)}')

           if isinstance(__SCREAMING_SNAKE_CASE  ,	PIL.Image.Image):
               __a		 =       preprocess(__SCREAMING_SNAKE_CASE)

           __a ,     __a		 =       image.shape[-2:]

           # in_channels should be 6: 3 for latents, 3 for low resolution image
           __a		 =       (batch_size, self.unet.config.in_channels // 2, height, width)
           __a		 =       next(self.unet.parameters()).dtype

           __a		 =       randn_tensor(__SCREAMING_SNAKE_CASE  ,	generator=__SCREAMING_SNAKE_CASE  ,	device=self.device  ,	dtype=__SCREAMING_SNAKE_CASE)

           __a		 =       image.to(device=self.device  ,	dtype=__SCREAMING_SNAKE_CASE)

           # set timesteps and move to the correct device
           self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  ,	device=self.device)
           __a		 =       self.scheduler.timesteps

           # scale the initial noise by the standard deviation required by the scheduler
           __a		 =       latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           __a		 =       '''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
           __a		 =       {}
           if accepts_eta:
               __a		 =       eta

           for t in self.progress_bar(__SCREAMING_SNAKE_CASE):
               # concat latents and low resolution image in the channel dimension.
               __a		 =       torch.cat([latents, image]  ,	dim=1)
               __a		 =       self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
               # predict the noise residual
               __a		 =       self.unet(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE).sample
               # compute the previous noisy sample x_t -> x_t-1
               __a		 =       self.scheduler.step(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE).prev_sample

           # decode the image latents with the VQVAE
           __a		 =       self.vqvae.decode(__SCREAMING_SNAKE_CASE).sample
           __a		 =       torch.clamp(__SCREAMING_SNAKE_CASE  ,	-1.0  ,	1.0)
           __a		 =       image / 2 + 0.5
           __a		 =       image.cpu().permute(0  ,	2  ,	3  ,	1).numpy()

           if output_type == "pil":
               __a		 =       self.numpy_to_pil(__SCREAMING_SNAKE_CASE)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE)





