


import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


_A		       =						logging.get_logger(__name__)






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def __init__(			self					,   A_    )  ->       Optional[int]:
         super().__init__()
         __UpperCamelCase    							=nn.ModuleList(A_    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = False					,   A_ = True					,   )  ->       Union[ControlNetOutput, Tuple]:
         for i, (image, scale, controlnet) in enumerate(zip(A_					,   A_					,   self.nets    )    ):
              __UpperCamelCase							,      __UpperCamelCase    							=controlnet(
                  A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   )

              # merge samples
              if i == 0:
                   __UpperCamelCase							,      __UpperCamelCase    							=down_samples, mid_sample
              else:
                   __UpperCamelCase    							=[
                       samples_prev + samples_curr
                       for samples_prev, samples_curr in zip(A_					,   A_    )
                   ]
                   mid_block_res_sample += mid_sample

         return down_block_res_samples, mid_block_res_sample






    def _a					(			self					,   A_					,   A_ = True					,   A_ = None					,   A_ = False					,   A_ = None					,   )  ->       Optional[Any]:
         __UpperCamelCase    							=0
         __UpperCamelCase    							=save_directory
         for controlnet in self.nets:
              controlnet.save_pretrained(
                  A_					,   is_main_process=A_					,   save_function=A_					,   safe_serialization=A_					,   variant=A_					,   )

              idx += 1
              __UpperCamelCase    							=model_path_to_save + f'_{idx}'






    @classmethod
    def _a					(			cls					,   A_					,   **A_    )  ->       List[str]:
         __UpperCamelCase    							=0
         __UpperCamelCase    							=[]

         # load controlnet and append to list until no controlnet directory exists anymore
         # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
         # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
         __UpperCamelCase    							=pretrained_model_path
         while os.path.isdir(A_    ):
              __UpperCamelCase    							=ControlNetModel.from_pretrained(A_					,   **A_    )
              controlnets.append(A_    )

              idx += 1
              __UpperCamelCase    							=pretrained_model_path + f'_{idx}'

         logger.info(f'{len(A_    )} controlnets loaded from {pretrained_model_path}.'    )

         if len(A_    ) == 0:
              raise ValueError(
                  f'No ControlNets found under {os.path.dirname(A_    )}. Expected at least {pretrained_model_path + "_0"}.'    )

         return cls(A_    )













"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      super().__init__()
      self.register_modules(unet=_lowerCamelCase							,		scheduler=_lowerCamelCase       )







    @torch.no_grad()
    def __call__(			self							,		_lowerCamelCase = 1							,		_lowerCamelCase = None							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      lowercase							    =       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,		generator=_lowerCamelCase							,		)
      lowercase							    =       image.to(self.device       )

      # set step values
      self.scheduler.set_timesteps(_lowerCamelCase       )

      for t in self.progress_bar(self.scheduler.timesteps       ):
        # 1. predict noise model_output
        lowercase							    =       self.unet(_lowerCamelCase							,		_lowerCamelCase       ).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        lowercase							    =       self.scheduler.step(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ).prev_sample

      lowercase							    =       (image / 2 + 0.5).clamp(0							,		1       )
      lowercase							    =       image.cpu().permute(0							,		2							,		3							,		1       ).numpy()
      if output_type == "pil":
        lowercase							    =       self.numpy_to_pil(_lowerCamelCase       )

      if not return_dict:
        return (image,), "This is a local test"

      return ImagePipelineOutput(images=_lowerCamelCase       ), "This is a local test"


