





"""simple docstring"""


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 SchedulerMixin


a__   :       int   						=			logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							torch.FloatTensor



class        lowercase_			(       __lowerCAmelCase			,__lowerCAmelCase							):
       snake_case__			:							Any										=       1

       @register_to_config
       def __init__(     self				,   __SCREAMING_SNAKE_CASE = 40				,   __SCREAMING_SNAKE_CASE = 0.002				,   __SCREAMING_SNAKE_CASE = 80.0				,   __SCREAMING_SNAKE_CASE = 0.5				,   __SCREAMING_SNAKE_CASE = 1.0				,   __SCREAMING_SNAKE_CASE = 7.0				,   __SCREAMING_SNAKE_CASE = True				,   ):






        '''simple docstring'''

        snake_case__      							=			sigma_max

        snake_case__      							=			np.linspace(0				,   1				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			self._convert_to_karras(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			self.sigma_to_t(__SCREAMING_SNAKE_CASE       )

        # setable values
        snake_case__      							=			None
        snake_case__      							=			torch.from_numpy(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			torch.from_numpy(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			False
        snake_case__      							=			False

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






        '''simple docstring'''

        if schedule_timesteps is None:
         snake_case__      							=			self.timesteps

        snake_case__      							=			(schedule_timesteps == timestep).nonzero()
        return indices.item()

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        if isinstance(__SCREAMING_SNAKE_CASE				,   torch.Tensor       ):
         snake_case__      							=			timestep.to(self.timesteps.device       )
        snake_case__      							=			self.index_for_timestep(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			self.sigmas[step_idx]

        snake_case__      							=			sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

        snake_case__      							=			True
        return sample

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        if not isinstance(__SCREAMING_SNAKE_CASE				,   np.ndarray       ):
         snake_case__      							=			np.array(__SCREAMING_SNAKE_CASE				,   dtype=np.floataa       )

        snake_case__      							=			1000 * 0.25 * np.log(sigmas + 1e-44       )

        return timesteps

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






        '''simple docstring'''

        if num_inference_steps is None and timesteps is None:
         raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."       )

        if num_inference_steps is not None and timesteps is not None:
         raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."       )

        # Follow DDPMScheduler custom timesteps logic
        if timesteps is not None:
         for i in range(1				,   len(__SCREAMING_SNAKE_CASE       )       ):
          if timesteps[i] >= timesteps[i - 1]:
           raise ValueError("`timesteps` must be in descending order."       )

         if timesteps[0] >= self.config.num_train_timesteps:
          raise ValueError(
              F"`timesteps` must start before `self.config.train_timesteps`:"
              F" {self.config.num_train_timesteps}."       )

         snake_case__      							=			np.array(__SCREAMING_SNAKE_CASE				,   dtype=np.intaa       )
         snake_case__      							=			True
        else:
         if num_inference_steps > self.config.num_train_timesteps:
          raise ValueError(
              F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
              F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
              F" maximal {self.config.num_train_timesteps} timesteps."       )

         snake_case__      							=			num_inference_steps

         snake_case__      							=			self.config.num_train_timesteps // self.num_inference_steps
         snake_case__      							=			(np.arange(0				,   __SCREAMING_SNAKE_CASE       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
         snake_case__      							=			False

        # Map timesteps to Karras sigmas directly for multistep sampling
        # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
        snake_case__      							=			self.config.num_train_timesteps
        snake_case__      							=			timesteps[::-1].copy()
        snake_case__      							=			ramp / (num_train_timesteps - 1)
        snake_case__      							=			self._convert_to_karras(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			self.sigma_to_t(__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			np.concatenate([sigmas, [self.sigma_min]]       ).astype(np.floataa       )
        snake_case__      							=			torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(device=__SCREAMING_SNAKE_CASE       )

        if str(__SCREAMING_SNAKE_CASE       ).startswith("mps"       ):
         # mps does not support float64
         snake_case__      							=			torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(__SCREAMING_SNAKE_CASE				,   dtype=torch.floataa       )
        else:
         snake_case__      							=			torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(device=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.config.sigma_min
        snake_case__      							=			self.config.sigma_max

        snake_case__      							=			self.config.rho
        snake_case__      							=			sigma_min ** (1 / rho)
        snake_case__      							=			sigma_max ** (1 / rho)
        snake_case__      							=			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.config.sigma_data

        snake_case__      							=			sigma_data**2 / (sigma**2 + sigma_data**2)
        snake_case__      							=			sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
        return c_skip, c_out

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.config.sigma_min
        snake_case__      							=			self.config.sigma_data

        snake_case__      							=			sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
        snake_case__      							=			(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
        return c_skip, c_out

       def 						_SCREAMING_SNAKE_CASE(     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"
                 F" `{self.__class__}.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       ):
         snake_case__      							=			timestep.to(self.timesteps.device       )

        snake_case__      							=			self.config.sigma_min
        snake_case__      							=			self.config.sigma_max

        snake_case__      							=			self.index_for_timestep(__SCREAMING_SNAKE_CASE       )

        # sigma_next corresponds to next_t in original implementation
        snake_case__      							=			self.sigmas[step_index]
        if step_index + 1 < self.config.num_train_timesteps:
         snake_case__      							=			self.sigmas[step_index + 1]
        else:
         # Set sigma_next to sigma_min
         snake_case__      							=			self.sigmas[-1]

        # Get scalings for boundary conditions
        snake_case__							,				snake_case__      							=			self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE       )

        # 1. Denoise model output using boundary conditions
        snake_case__      							=			c_out * model_output + c_skip * sample
        if self.config.clip_denoised:
         snake_case__      							=			denoised.clamp(-1				,   1       )

        # 2. Sample z ~ N(0, s_noise^2 * I)
        # Noise is not used for onestep sampling.
        if len(self.timesteps       ) > 1:
         snake_case__      							=			randn_tensor(
             model_output.shape				,   dtype=model_output.dtype				,   device=model_output.device				,   generator=__SCREAMING_SNAKE_CASE       )
        else:
         snake_case__      							=			torch.zeros_like(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			noise * self.config.s_noise

        snake_case__      							=			sigma_next.clamp(min=__SCREAMING_SNAKE_CASE				,   max=__SCREAMING_SNAKE_CASE       )

        # 3. Return noisy sample
        # tau = sigma_hat, eps = sigma_min
        snake_case__      							=			denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

        if not return_dict:
         return (prev_sample,)

        return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        snake_case__      							=			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
         snake_case__      							=			self.timesteps.to(original_samples.device				,   dtype=torch.floataa       )
         snake_case__      							=			timesteps.to(original_samples.device				,   dtype=torch.floataa       )
        else:
         snake_case__      							=			self.timesteps.to(original_samples.device       )
         snake_case__      							=			timesteps.to(original_samples.device       )

        snake_case__      							=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

        snake_case__      							=			original_samples + noise * sigma
        return noisy_samples

       def __len__(     self       ):






        '''simple docstring'''

        return self.config.num_train_timesteps








"""simple docstring"""


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)



class        lowercase_			(       __lowerCAmelCase							):

       def __init__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__()

        self.register_modules(vae=__SCREAMING_SNAKE_CASE				,   text_encoder=__SCREAMING_SNAKE_CASE				,   tokenizer=__SCREAMING_SNAKE_CASE				,   unet=__SCREAMING_SNAKE_CASE				,   scheduler=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        with torch.no_grad():
         snake_case__      							=			self.vae.encode(tfms.ToTensor()(__SCREAMING_SNAKE_CASE       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
         snake_case__      							=			0.18215 * latent.latent_dist.sample()
        return latent

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			(1 / 0.18215) * latent
        with torch.no_grad():
         snake_case__      							=			self.vae.decode(__SCREAMING_SNAKE_CASE       ).sample
        snake_case__      							=			(img / 2 + 0.5).clamp(0				,   1       )
        snake_case__      							=			img.detach().cpu().permute(0				,   2				,   3				,   1       ).numpy()
        snake_case__      							=			(img * 255).round().astype("uint8"       )
        return Image.fromarray(img[0]       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.tokenizer(
            __SCREAMING_SNAKE_CASE				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"				,   )

        snake_case__      							=			self.text_encoder(text_input.input_ids.to(self.device       )       )[0]

        snake_case__      							=			self.tokenizer(
            ""				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"				,   )

        snake_case__      							=			self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

        return torch.cat([uncond_embedding, text_embedding]       )

       def __call__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = 0.3				,   __SCREAMING_SNAKE_CASE = 0.6				,   __SCREAMING_SNAKE_CASE = 0.5				,   __SCREAMING_SNAKE_CASE = 42				,   __SCREAMING_SNAKE_CASE = 50				,   __SCREAMING_SNAKE_CASE = 7.5				,   ):






        '''simple docstring'''

        snake_case__      							=			steps - int(kmin * steps       )
        snake_case__      							=			steps - int(kmax * steps       )

        snake_case__      							=			self.prep_text(__SCREAMING_SNAKE_CASE       )

        self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

        snake_case__							,				snake_case__      							=			img.size
        snake_case__      							=			self.encode(__SCREAMING_SNAKE_CASE       )

        torch.manual_seed(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)				,   ).to(self.device       )

        snake_case__      							=			self.scheduler.add_noise(
            __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   timesteps=self.scheduler.timesteps[tmax]				,   )

        snake_case__      							=			torch.cat([latents] * 2       )

        snake_case__      							=			self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]       )

        with torch.no_grad():
         snake_case__      							=			self.unet(
             __SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]				,   encoder_hidden_states=__SCREAMING_SNAKE_CASE				,   ).sample

        snake_case__							,				snake_case__      							=			pred.chunk(2       )
        snake_case__      							=			pred_uncond + guidance_scale * (pred_text - pred_uncond)

        snake_case__      							=			self.scheduler.step(__SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]				,   __SCREAMING_SNAKE_CASE       ).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps       )       ):
         if i > tmax:
          if i < tmin:  # layout generation phase
           snake_case__      							=			self.scheduler.add_noise(
               __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   timesteps=__SCREAMING_SNAKE_CASE				,   )

           snake_case__      							=			(mix_factor * latents) + (
               1 - mix_factor
           ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
           snake_case__      							=			torch.cat([input] * 2       )

          else:  # content generation phase
           snake_case__      							=			torch.cat([latents] * 2       )

          snake_case__      							=			self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

          with torch.no_grad():
           snake_case__      							=			self.unet(
               __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   encoder_hidden_states=__SCREAMING_SNAKE_CASE				,   ).sample

          snake_case__							,				snake_case__      							=			pred.chunk(2       )
          snake_case__      							=			pred_uncond + guidance_scale * (pred_text - pred_uncond)

          snake_case__      							=			self.scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ).prev_sample

        return self.decode(__SCREAMING_SNAKE_CASE       )

