

"""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        _lowercase    (			__lowerCAmelCase     ,    __lowerCAmelCase=0.999     ,    __lowerCAmelCase="cosine"     ,    )				-> Optional[Any]:
      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(__lowerCAmelCase							):
                  return math.exp(t * -12.0							)

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

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



class    __a							(UpperCamelCase_      ,		UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE  :Tuple				=       [e.name for e in KarrasDiffusionSchedulers]
     _SCREAMING_SNAKE_CASE  :Union[str, Any]				=       2

     @register_to_config
     def __init__(  self		,			_a = 1_000		,			_a = 0.00_085		,			_a = 0.012		,			_a = "linear"		,			_a = None		,			_a = "epsilon"		,			_a = "linspace"		,			_a = 0		,			)     ->					Optional[int]:


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		1.0 - self.betas
           SCREAMING_SNAKE_CASE__      :					Tuple											=		torch.cumprod(self.alphas		,			dim=0     )

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

     def     _a	(  self		,			_a		,			_a=None     )     ->					List[Any]:


           """simple docstring"""


           if schedule_timesteps is None:
                 SCREAMING_SNAKE_CASE__      :					List[str]											=		self.timesteps

           SCREAMING_SNAKE_CASE__      :					str											=		(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:
                 SCREAMING_SNAKE_CASE__      :					Any											=		1 if len(__lowerCamelCase     ) > 1 else 0
           else:
                 SCREAMING_SNAKE_CASE__      :					List[Any]											=		timestep.cpu().item() if torch.is_tensor(__lowerCamelCase     ) else timestep
                 SCREAMING_SNAKE_CASE__      :					List[Any]											=		self._index_counter[timestep_int]

           return indices[pos].item()

     @property
     def     _a	(  self     )     ->					Union[str, Any]:


           """simple docstring"""


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

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

     def     _a	(  self		,			_a		,			_a		,			)     ->					torch.FloatTensor:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		self.index_for_timestep(__lowerCamelCase     )

           if self.state_in_first_order:
                 SCREAMING_SNAKE_CASE__      :					Tuple											=		self.sigmas[step_index]
           else:
                 SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		self.sigmas_interpol[step_index]

           SCREAMING_SNAKE_CASE__      :					Dict											=		sample / ((sigma**2 + 1) ** 0.5)
           return sample

     def     _a	(  self		,			_a		,			_a = None		,			_a = None		,			)     ->					Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					List[Any]											=		num_inference_steps

           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		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":
                 SCREAMING_SNAKE_CASE__      :					str											=		np.linspace(0		,			num_train_timesteps - 1		,			__lowerCamelCase		,			dtype=__lowerCamelCase     )[::-1].copy()
           elif self.config.timestep_spacing == "leading":
                 SCREAMING_SNAKE_CASE__      :					Optional[int]											=		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
                 SCREAMING_SNAKE_CASE__      :					List[Any]											=		(np.arange(0		,			__lowerCamelCase     ) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase     )
                 timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
                 SCREAMING_SNAKE_CASE__      :					int											=		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
                 SCREAMING_SNAKE_CASE__      :					str											=		(np.arange(__lowerCamelCase		,			0		,			-step_ratio     )).round().copy().astype(__lowerCamelCase     )
                 timesteps -= 1
           else:
                 raise ValueError(
                     f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''     )

           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
           SCREAMING_SNAKE_CASE__      :					str											=		torch.from_numpy(np.log(__lowerCamelCase     )     ).to(__lowerCamelCase     )

           SCREAMING_SNAKE_CASE__      :					str											=		np.interp(__lowerCamelCase		,			np.arange(0		,			len(__lowerCamelCase     )     )		,			__lowerCamelCase     )
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
           SCREAMING_SNAKE_CASE__      :					Dict											=		torch.from_numpy(__lowerCamelCase     ).to(device=__lowerCamelCase     )

           # interpolate sigmas
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		sigmas.log().lerp(sigmas.roll(1     ).log()		,			0.5     ).exp()

           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2     ), sigmas[-1:]]     )
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		torch.cat(
               [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2     ), sigmas_interpol[-1:]]     )

           if str(__lowerCamelCase     ).startswith("""mps"""     ):
                 # mps does not support float64
                 SCREAMING_SNAKE_CASE__      :					str											=		torch.from_numpy(__lowerCamelCase     ).to(__lowerCamelCase		,			dtype=torch.floataa     )
           else:
                 SCREAMING_SNAKE_CASE__      :					int											=		torch.from_numpy(__lowerCamelCase     ).to(__lowerCamelCase     )

           # interpolate timesteps
           SCREAMING_SNAKE_CASE__      :					Dict											=		self.sigma_to_t(__lowerCamelCase     ).to(__lowerCamelCase		,			dtype=timesteps.dtype     )
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])		,			dim=-1     ).flatten()

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

           SCREAMING_SNAKE_CASE__      :					List[str]											=		None

           # for exp beta schedules, such as the one for `pipeline_shap_e.py`
           # we need an index counter
           SCREAMING_SNAKE_CASE__      :					str											=		defaultdict(__lowerCamelCase     )

     def     _a	(  self		,			_a     )     ->					str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Any											=		sigma.log()

           # get distribution
           SCREAMING_SNAKE_CASE__      :					List[str]											=		log_sigma - self.log_sigmas[:, None]

           # get sigmas range
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		dists.ge(0     ).cumsum(dim=0     ).argmax(dim=0     ).clamp(max=self.log_sigmas.shape[0] - 2     )
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		low_idx + 1

           SCREAMING_SNAKE_CASE__      :					List[str]											=		self.log_sigmas[low_idx]
           SCREAMING_SNAKE_CASE__      :					Any											=		self.log_sigmas[high_idx]

           # interpolate sigmas
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		(low - log_sigma) / (low - high)
           SCREAMING_SNAKE_CASE__      :					Dict											=		w.clamp(0		,			1     )

           # transform interpolation to time range
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		(1 - w) * low_idx + w * high_idx
           SCREAMING_SNAKE_CASE__      :					Any											=		t.view(sigma.shape     )
           return t

     @property
     def     _a	(  self     )     ->					Any:


           """simple docstring"""


           return self.sample is None

     def     _a	(  self		,			_a		,			_a		,			_a		,			_a = True		,			)     ->					Union[SchedulerOutput, Tuple]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		self.index_for_timestep(__lowerCamelCase     )

           # advance index counter by 1
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		timestep.cpu().item() if torch.is_tensor(__lowerCamelCase     ) else timestep
           self._index_counter[timestep_int] += 1

           if self.state_in_first_order:
                 SCREAMING_SNAKE_CASE__      :					Any											=		self.sigmas[step_index]
                 SCREAMING_SNAKE_CASE__      :					Any											=		self.sigmas_interpol[step_index + 1]
                 SCREAMING_SNAKE_CASE__      :					str											=		self.sigmas[step_index + 1]
           else:
                 # 2nd order / KDPM2's method
                 SCREAMING_SNAKE_CASE__      :					Any											=		self.sigmas[step_index - 1]
                 SCREAMING_SNAKE_CASE__      :					List[str]											=		self.sigmas_interpol[step_index]
                 SCREAMING_SNAKE_CASE__      :					int											=		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
           SCREAMING_SNAKE_CASE__      :					Dict											=		0
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		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":
                 SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		sigma_hat if self.state_in_first_order else sigma_interpol
                 SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		sample - sigma_input * model_output
           elif self.config.prediction_type == "v_prediction":
                 SCREAMING_SNAKE_CASE__      :					List[Any]											=		sigma_hat if self.state_in_first_order else sigma_interpol
                 SCREAMING_SNAKE_CASE__      :					List[str]											=		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
                 SCREAMING_SNAKE_CASE__      :					Tuple											=		(sample - pred_original_sample) / sigma_hat
                 # 3. delta timestep
                 SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		sigma_interpol - sigma_hat

                 # store for 2nd order step
                 SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		sample
           else:
                 # DPM-Solver-2
                 # 2. Convert to an ODE derivative for 2nd order
                 SCREAMING_SNAKE_CASE__      :					int											=		(sample - pred_original_sample) / sigma_interpol

                 # 3. delta timestep
                 SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		sigma_next - sigma_hat

                 SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		self.sample
                 SCREAMING_SNAKE_CASE__      :					Any											=		None

           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		sample + derivative * dt

           if not return_dict:
                 return (prev_sample,)

           return SchedulerOutput(prev_sample=__lowerCamelCase     )

     def     _a	(  self		,			_a		,			_a		,			_a		,			)     ->					torch.FloatTensor:


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__      :					Dict											=		[self.index_for_timestep(__lowerCamelCase		,			__lowerCamelCase     ) for t in timesteps]

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

           SCREAMING_SNAKE_CASE__      :					int											=		original_samples + noise * sigma
           return noisy_samples





     def __len__(  self     )     ->					List[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 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]						):
       _A    ,			_A      :       Any				    = image.size
       _A    ,			_A      :       str				    = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
       _A      :       List[str]				    = image.resize((w, h)				,							resample=PIL_INTERPOLATION["lanczos"]						)
       _A      :       Any				    = np.array(UpperCamelCase__						).astype(np.floataa						) / 2_55.0
       _A      :       Optional[Any]				    = image[None].transpose(0				,							3				,							1				,							2						)
       _A      :       Union[str, Any]				    = torch.from_numpy(UpperCamelCase__						)
       return 2.0 * image - 1.0







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						)			->				Optional[int]:
            super().__init__()
            self.register_modules(vqvae=__lowerCamelCase ,						unet=__lowerCamelCase ,						scheduler=__lowerCamelCase)


     @torch.no_grad()
     def __call__(						self ,						__lowerCamelCase = None ,						__lowerCamelCase = 1 ,						__lowerCamelCase = 1_0_0 ,						__lowerCamelCase = 0.0 ,						__lowerCamelCase = None ,						__lowerCamelCase = "pil" ,						__lowerCamelCase = True ,						)			->				Union[Tuple, ImagePipelineOutput]:
            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Tuple				    = 1
            elif isinstance(__lowerCamelCase ,						torch.Tensor):
                   _A      :       Union[str, Any]				    = image.shape[0]
            else:
                   raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase)}")

            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Union[str, Any]				    = preprocess(__lowerCamelCase)

            _A    ,			_A      :       Union[str, Any]				    = image.shape[-2:]

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

            _A      :       Union[str, Any]				    = randn_tensor(__lowerCamelCase ,						generator=__lowerCamelCase ,						device=self.device ,						dtype=__lowerCamelCase)

            _A      :       List[Any]				    = image.to(device=self.device ,						dtype=__lowerCamelCase)

            # set timesteps and move to the correct device
            self.scheduler.set_timesteps(__lowerCamelCase ,						device=self.device)
            _A      :       Any				    = self.scheduler.timesteps

            # scale the initial noise by the standard deviation required by the scheduler
            _A      :       List[str]				    = 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      :       str				    = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
            _A      :       Optional[int]				    = {}
            if accepts_eta:
                   _A      :       List[Any]				    = eta

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

            # decode the image latents with the VQVAE
            _A      :       Union[str, Any]				    = self.vqvae.decode(__lowerCamelCase).sample
            _A      :       Dict				    = torch.clamp(__lowerCamelCase ,						-1.0 ,						1.0)
            _A      :       Tuple				    = image / 2 + 0.5
            _A      :       int				    = image.cpu().permute(0 ,						2 ,						3 ,						1).numpy()

            if output_type == "pil":
                   _A      :       Optional[int]				    = self.numpy_to_pil(__lowerCamelCase)

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=__lowerCamelCase)
