

"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					4_2
    UpperCAmelCase						 =					4_2

    def __init__(     self: Optional[int]    ,						UpperCamelCase: UNetaDModel    ,						UpperCamelCase: KarrasVeScheduler       ):






         """simple docstring"""


         super().__init__()
         self.register_modules(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )





    @torch.no_grad()
    def __call__(     self: Dict    ,						UpperCamelCase: int = 1    ,						UpperCamelCase: int = 50    ,						UpperCamelCase: Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,						UpperCamelCase: Optional[str] = "pil"    ,						UpperCamelCase: bool = True    ,						**UpperCamelCase: Any    ,						):






         """simple docstring"""


         A__										=  self.unet.config.sample_size
         A__										=  (batch_size, 3, img_size, img_size)

         A__										=  self.unet

         # sample x_0 ~ N(0, sigma_0^2 * I)
         A__										=  randn_tensor(UpperCamelCase    ,						generator=UpperCamelCase    ,						device=self.device       ) * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(UpperCamelCase       )

         for t in self.progress_bar(self.scheduler.timesteps       ):
              # here sigma_t == t_i from the paper
              A__										=  self.scheduler.schedule[t]
              A__										=  self.scheduler.schedule[t - 1] if t > 0 else 0

              # 1. Select temporarily increased noise level sigma_hat
              # 2. Add new noise to move from sample_i to sample_hat
              A__			,				A__										=  self.scheduler.add_noise_to_input(UpperCamelCase    ,						UpperCamelCase    ,						generator=UpperCamelCase       )

              # 3. Predict the noise residual given the noise magnitude `sigma_hat`
              # The model inputs and output are adjusted by following eq. (213) in [1].
              A__										=  (sigma_hat / 2) * model((sample_hat + 1) / 2    ,						sigma_hat / 2       ).sample

              # 4. Evaluate dx/dt at sigma_hat
              # 5. Take Euler step from sigma to sigma_prev
              A__										=  self.scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )

              if sigma_prev != 0:
                   # 6. Apply 2nd order correction
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   A__										=  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2    ,						sigma_prev / 2       ).sample
                   A__										=  self.scheduler.step_correct(
                       UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						step_output.prev_sample    ,						step_output["""derivative"""]    ,						)
              A__										=  step_output.prev_sample

         A__										=  (sample / 2 + 0.5).clamp(0    ,						1       )
         A__										=  sample.cpu().permute(0    ,						2    ,						3    ,						1       ).numpy()
         if output_type == "pil":
              A__										=  self.numpy_to_pil(UpperCamelCase       )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=UpperCamelCase       )









"""simple docstring"""



SCREAMING_SNAKE_CASE_					:	str							  =						'0.21.0'

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
 from .utils import rich






