





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_snake_case  :   Any						   =  {
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_snake_case  :   Optional[int]						   =  logging.get_logger(__name__)



class     _UpperCAmelCase							(   __lowerCamelCase   ):

  """simple docstring"""


  a_      =			"""maskformer"""
  a_      =			{"""hidden_size""": """mask_feature_size"""}
  a_      =			["""resnet""", """swin"""]
  a_      =			["""detr"""]






  def __init__(    self			:		str ,  lowerCAmelCase_			:		int = 2_5_6 ,  lowerCAmelCase_			:		int = 2_5_6 ,  lowerCAmelCase_			:		float = 0.1 ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		Optional[Dict] = None ,  lowerCAmelCase_			:		Optional[Dict] = None ,  lowerCAmelCase_			:		float = 0.02 ,  lowerCAmelCase_			:		float = 1.0 ,  lowerCAmelCase_			:		float = 1.0 ,  lowerCAmelCase_			:		float = 1.0 ,  lowerCAmelCase_			:		float = 20.0 ,  lowerCAmelCase_			:		Optional[bool] = None ,  **lowerCAmelCase_			:		Tuple ,  )							->						int:
     if backbone_config is None:
        # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
        __lowerCAmelCase							     =     SwinConfig(
            image_size=3_8_4 ,  in_channels=3 ,  patch_size=4 ,  embed_dim=1_2_8 ,  depths=[2, 2, 1_8, 2] ,  num_heads=[4, 8, 1_6, 3_2] ,  window_size=1_2 ,  drop_path_rate=0.3 ,  out_features=['stage1', 'stage2', 'stage3', 'stage4'] ,  )

     if isinstance(__lowercase ,  __lowercase					):
        __lowerCAmelCase							     =     backbone_config.pop('model_type'					)
        __lowerCAmelCase							     =     CONFIG_MAPPING[backbone_model_type]
        __lowerCAmelCase							     =     config_class.from_dict(__lowercase					)

     # verify that the backbone is supported
     if backbone_config.model_type not in self.backbones_supported:
        logger.warning_once(
            f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
            f"""Supported model types: {",".join(self.backbones_supported					)}"""					)

     if decoder_config is None:
        # fall back to https://huggingface.co/facebook/detr-resnet-50
        __lowerCAmelCase							     =     DetrConfig()
     else:
        # verify that the decoder is supported
        __lowerCAmelCase							     =     (
            decoder_config.pop('model_type'					) if isinstance(__lowercase ,  __lowercase					) else decoder_config.model_type
        )
        if decoder_type not in self.decoders_supported:
           raise ValueError(
               f"""Transformer Decoder {decoder_type} not supported, please use one of"""
               f""" {",".join(self.decoders_supported					)}"""					)
        if isinstance(__lowercase ,  __lowercase					):
           __lowerCAmelCase							     =     CONFIG_MAPPING[decoder_type]
           __lowerCAmelCase							     =     config_class.from_dict(__lowercase					)

     __lowerCAmelCase							     =     backbone_config
     __lowerCAmelCase							     =     decoder_config
     # main feature dimension for the model
     __lowerCAmelCase							     =     fpn_feature_size
     __lowerCAmelCase							     =     mask_feature_size
     # initializer
     __lowerCAmelCase							     =     init_std
     __lowerCAmelCase							     =     init_xavier_std
     # Hungarian matcher && loss
     __lowerCAmelCase							     =     cross_entropy_weight
     __lowerCAmelCase							     =     dice_weight
     __lowerCAmelCase							     =     mask_weight
     __lowerCAmelCase							     =     use_auxiliary_loss
     __lowerCAmelCase							     =     no_object_weight
     __lowerCAmelCase							     =     output_auxiliary_logits

     __lowerCAmelCase							     =     self.decoder_config.encoder_attention_heads
     __lowerCAmelCase							     =     self.decoder_config.num_hidden_layers
     super().__init__(**__lowercase					)






  @classmethod
  def     lowercase		(    cls			:		Optional[int] ,  lowerCAmelCase_			:		PretrainedConfig ,  lowerCAmelCase_			:		PretrainedConfig ,  **lowerCAmelCase_			:		Optional[Any]					)							->						Union[str, Any]:
     return cls(
         backbone_config=__lowercase ,  decoder_config=__lowercase ,  **__lowercase ,  )






  def     lowercase		(    self			:		Optional[int]					)							->						Tuple:
     __lowerCAmelCase							     =     copy.deepcopy(self.__dict__					)
     __lowerCAmelCase							     =     self.backbone_config.to_dict()
     __lowerCAmelCase							     =     self.decoder_config.to_dict()
     __lowerCAmelCase							     =     self.__class__.model_type
     return output








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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_snake_case  :   Union[str, Any]						   =  logging.get_logger(__name__)  # pylint: disable=invalid-name

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   warnings.warn(
       'The preprocess method is deprecated and will be removed in a future version. Please'
       ' use VaeImageProcessor.preprocess instead',     lowerCAmelCase_,     )
   if isinstance(lowerCAmelCase_,     torch.Tensor     ):
      return image
   elif isinstance(lowerCAmelCase_,     PIL.Image.Image     ):
      __lowerCAmelCase							     =     [image]

   if isinstance(image[0],     PIL.Image.Image     ):
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     image[0].size
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

      __lowerCAmelCase							     =     [np.array(i.resize((w, h),     resample=PIL_INTERPOLATION['lanczos']     )     )[None, :] for i in image]
      __lowerCAmelCase							     =     np.concatenate(lowerCAmelCase_,     axis=0     )
      __lowerCAmelCase							     =     np.array(lowerCAmelCase_     ).astype(np.floataa     ) / 255.0
      __lowerCAmelCase							     =     image.transpose(0,     3,     1,     2     )
      __lowerCAmelCase							     =     2.0 * image - 1.0
      __lowerCAmelCase							     =     torch.from_numpy(lowerCAmelCase_     )
   elif isinstance(image[0],     torch.Tensor     ):
      __lowerCAmelCase							     =     torch.cat(lowerCAmelCase_,     dim=0     )
   return image

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   if isinstance(lowerCAmelCase_,     torch.Tensor     ):
      return mask
   elif isinstance(lowerCAmelCase_,     PIL.Image.Image     ):
      __lowerCAmelCase							     =     [mask]

   if isinstance(mask[0],     PIL.Image.Image     ):
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     mask[0].size
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
      __lowerCAmelCase							     =     [np.array(m.convert('L'     ).resize((w, h),     resample=PIL_INTERPOLATION['nearest']     )     )[None, :] for m in mask]
      __lowerCAmelCase							     =     np.concatenate(lowerCAmelCase_,     axis=0     )
      __lowerCAmelCase							     =     mask.astype(np.floataa     ) / 255.0
      __lowerCAmelCase							     =     0
      __lowerCAmelCase							     =     1
      __lowerCAmelCase							     =     torch.from_numpy(lowerCAmelCase_     )
   elif isinstance(mask[0],     torch.Tensor     ):
      __lowerCAmelCase							     =     torch.cat(lowerCAmelCase_,     dim=0     )
   return mask




class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42
  a_      =			42






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		List[Any]					)							->						Optional[int]:
     super().__init__()
     self.register_modules(unet=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)






  @torch.no_grad()
  def __call__(    self			:		Dict ,  lowerCAmelCase_			:		Union[torch.Tensor, PIL.Image.Image] ,  lowerCAmelCase_			:		Union[torch.Tensor, PIL.Image.Image] ,  lowerCAmelCase_			:		int = 2_5_0 ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		int = 1_0 ,  lowerCAmelCase_			:		int = 1_0 ,  lowerCAmelCase_			:		Optional[Union[torch.Generator, List[torch.Generator]]] = None ,  lowerCAmelCase_			:		Optional[str] = "pil" ,  lowerCAmelCase_			:		bool = True ,  )							->						Union[ImagePipelineOutput, Tuple]:
     __lowerCAmelCase							     =     image

     __lowerCAmelCase							     =     _preprocess_image(lowerCAmelCase_					)
     __lowerCAmelCase							     =     original_image.to(device=self.device ,  dtype=self.unet.dtype					)
     __lowerCAmelCase							     =     _preprocess_mask(lowerCAmelCase_					)
     __lowerCAmelCase							     =     mask_image.to(device=self.device ,  dtype=self.unet.dtype					)

     __lowerCAmelCase							     =     original_image.shape[0]

     # sample gaussian noise to begin the loop
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					) and len(lowerCAmelCase_					) != batch_size:
        raise ValueError(
            f"""You have passed a list of generators of length {len(lowerCAmelCase_					)}, but requested an effective batch"""
            f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""					)

     __lowerCAmelCase							     =     original_image.shape
     __lowerCAmelCase							     =     randn_tensor(lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  device=self.device ,  dtype=self.unet.dtype					)

     # set step values
     self.scheduler.set_timesteps(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  self.device					)
     __lowerCAmelCase							     =     eta

     __lowerCAmelCase							     =     self.scheduler.timesteps[0] + 1
     __lowerCAmelCase							     =     generator[0] if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					) else generator
     for i, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
        if t < t_last:
           # predict the noise residual
           __lowerCAmelCase							     =     self.unet(lowerCAmelCase_ ,  lowerCAmelCase_					).sample
           # compute previous image: x_t -> x_t-1
           __lowerCAmelCase							     =     self.scheduler.step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					).prev_sample

        else:
           # compute the reverse: x_t-1 -> x_t
           __lowerCAmelCase							     =     self.scheduler.undo_step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
        __lowerCAmelCase							     =     t

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

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase_					)

