





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 							_lowerCAmelCase     (      UpperCAmelCase_				):


  '''simple docstring'''
  a_				:							UNetaDModel
  a_				:							KarrasVeScheduler







  def __init__(   self   :       Tuple   ,					UpperCamelCase   :       Dict   ,					UpperCamelCase   :       int				):


    '''simple docstring'''





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







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


    '''simple docstring'''





    _snake_case :					List[str]								=						self.unet.config.sample_size
    _snake_case :					Tuple								=						(batch_size, 3, img_size, img_size)

    _snake_case :					Dict								=						self.unet

    # sample x_0 ~ N(0, sigma_0^2 * I)
    _snake_case :					Dict								=						randn_tensor(_lowercase   ,					generator=_lowercase   ,					device=self.device				) * self.scheduler.init_noise_sigma

    self.scheduler.set_timesteps(_lowercase				)

    for t in self.progress_bar(self.scheduler.timesteps				):
      # here sigma_t == t_i from the paper
      _snake_case :					Optional[int]								=						self.scheduler.schedule[t]
      _snake_case :					List[Any]								=						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
      _snake_case				,       _snake_case :					Tuple								=						self.scheduler.add_noise_to_input(_lowercase   ,					_lowercase   ,					generator=_lowercase				)

      # 3. Predict the noise residual given the noise magnitude `sigma_hat`
      # The model inputs and output are adjusted by following eq. (213) in [1].
      _snake_case :					Union[str, Any]								=						(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
      _snake_case :					Union[str, Any]								=						self.scheduler.step(_lowercase   ,					_lowercase   ,					_lowercase   ,					_lowercase				)

      if sigma_prev != 0:
        # 6. Apply 2nd order correction
        # The model inputs and output are adjusted by following eq. (213) in [1].
        _snake_case :					Dict								=						(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2   ,					sigma_prev / 2				).sample
        _snake_case :					Optional[int]								=						self.scheduler.step_correct(
            _lowercase   ,					_lowercase   ,					_lowercase   ,					_lowercase   ,					step_output.prev_sample   ,					step_output['derivative']   ,					)
      _snake_case :					List[Any]								=						step_output.prev_sample

    _snake_case :					Union[str, Any]								=						(sample / 2 + 0.5).clamp(0   ,					1				)
    _snake_case :					List[str]								=						sample.cpu().permute(0   ,					2   ,					3   ,					1				).numpy()
    if output_type == "pil":
      _snake_case :					Tuple								=						self.numpy_to_pil(_lowercase				)

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_lowercase				)







import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE		:Optional[Any]						=     {
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

SCREAMING_SNAKE_CASE		:Union[str, Any]						=     logging.get_logger(__name__)






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Optional[int]										=      """mask2former"""
     UpperCamelCase_	:Dict										=      ["""swin"""]
     UpperCamelCase_	:List[Any]										=      {"""hidden_size""": """hidden_dim"""}






     def __init__(				self		,						_lowercase = None		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 1_024		,						_lowercase = "relu"		,						_lowercase = 6		,						_lowercase = 10		,						_lowercase = 8		,						_lowercase = 0.0		,						_lowercase = 2_048		,						_lowercase = False		,						_lowercase = False		,						_lowercase = 4		,						_lowercase = 255		,						_lowercase = 100		,						_lowercase = 0.1		,						_lowercase = 2.0		,						_lowercase = 5.0		,						_lowercase = 5.0		,						_lowercase = 12_544		,						_lowercase = 3.0		,						_lowercase = 0.75		,						_lowercase = 0.02		,						_lowercase = 1.0		,						_lowercase = True		,						_lowercase = [4, 8, 16, 32]		,						_lowercase = None		,						**_lowercase		,						)->     Union[str, Any]:
         if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."	)
             UpperCamelCase_			  =						CONFIG_MAPPING["swin"](
                 image_size=224		,						in_channels=3		,						patch_size=4		,						embed_dim=96		,						depths=[2, 2, 18, 2]		,						num_heads=[3, 6, 12, 24]		,						window_size=7		,						drop_path_rate=0.3		,						use_absolute_embeddings=_lowercase		,						out_features=["stage1", "stage2", "stage3", "stage4"]		,						)

         if isinstance(_lowercase		,						_lowercase	):
             UpperCamelCase_			  =						backbone_config.pop("model_type"	)
             UpperCamelCase_			  =						CONFIG_MAPPING[backbone_model_type]
             UpperCamelCase_			  =						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 Mask2Former. "
                 F"Supported model types: {','.join(self.backbones_supported	)}"	)

         UpperCamelCase_			  =						backbone_config
         UpperCamelCase_			  =						feature_size
         UpperCamelCase_			  =						mask_feature_size
         UpperCamelCase_			  =						hidden_dim
         UpperCamelCase_			  =						encoder_feedforward_dim
         UpperCamelCase_			  =						activation_function
         UpperCamelCase_			  =						encoder_layers
         UpperCamelCase_			  =						decoder_layers
         UpperCamelCase_			  =						num_attention_heads
         UpperCamelCase_			  =						dropout
         UpperCamelCase_			  =						dim_feedforward
         UpperCamelCase_			  =						pre_norm
         UpperCamelCase_			  =						enforce_input_projection
         UpperCamelCase_			  =						common_stride
         UpperCamelCase_			  =						ignore_value
         UpperCamelCase_			  =						num_queries
         UpperCamelCase_			  =						no_object_weight
         UpperCamelCase_			  =						class_weight
         UpperCamelCase_			  =						mask_weight
         UpperCamelCase_			  =						dice_weight
         UpperCamelCase_			  =						train_num_points
         UpperCamelCase_			  =						oversample_ratio
         UpperCamelCase_			  =						importance_sample_ratio
         UpperCamelCase_			  =						init_std
         UpperCamelCase_			  =						init_xavier_std
         UpperCamelCase_			  =						use_auxiliary_loss
         UpperCamelCase_			  =						feature_strides
         UpperCamelCase_			  =						output_auxiliary_logits
         UpperCamelCase_			  =						decoder_layers

         super().__init__(**_lowercase	)






     @classmethod
     def 	UpperCAmelCase_				(				cls		,						_lowercase		,						**_lowercase	)->     Optional[int]:
         return cls(
             backbone_config=_lowercase		,						**_lowercase		,						)






     def 	UpperCAmelCase_				(				self	)->     Dict[str, any]:
         UpperCamelCase_			  =						copy.deepcopy(self.__dict__	)
         UpperCamelCase_			  =						self.backbone_config.to_dict()
         UpperCamelCase_			  =						self.__class__.model_type
         return output
