

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


SCREAMING_SNAKE_CASE		:Optional[int]						=     logging.get_logger(__name__)  # pylint: disable=invalid-name




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    warnings.warn(
        "The preprocess method is deprecated and will be removed in a future version. Please"
        " use VaeImageProcessor.preprocess instead"			,						SCREAMING_SNAKE_CASE_			,						)
    if isinstance(SCREAMING_SNAKE_CASE_			,						torch.Tensor			):
        return image
    elif isinstance(SCREAMING_SNAKE_CASE_			,						PIL.Image.Image			):
        UpperCamelCase_			  =						[image]

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

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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Union[str, Any]:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_			,						torch.Tensor			):
        return mask
    elif isinstance(SCREAMING_SNAKE_CASE_			,						PIL.Image.Image			):
        UpperCamelCase_			  =						[mask]

    if isinstance(mask[0]			,						PIL.Image.Image			):
        UpperCamelCase_  ,       UpperCamelCase_			  =						mask[0].size
        UpperCamelCase_  ,       UpperCamelCase_			  =						(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
        UpperCamelCase_			  =						[np.array(m.convert("L"			).resize((w, h)			,						resample=PIL_INTERPOLATION["nearest"]			)			)[None, :] for m in mask]
        UpperCamelCase_			  =						np.concatenate(SCREAMING_SNAKE_CASE_			,						axis=0			)
        UpperCamelCase_			  =						mask.astype(np.floataa			) / 255.0
        UpperCamelCase_			  =						0
        UpperCamelCase_			  =						1
        UpperCamelCase_			  =						torch.from_numpy(SCREAMING_SNAKE_CASE_			)
    elif isinstance(mask[0]			,						torch.Tensor			):
        UpperCamelCase_			  =						torch.cat(SCREAMING_SNAKE_CASE_			,						dim=0			)
    return mask






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:UNetaDModel
     UpperCamelCase_	:RePaintScheduler






     def __init__(				self		,						_lowercase		,						_lowercase	)->     List[Any]:
         super().__init__()
         self.register_modules(unet=_lowercase		,						scheduler=_lowercase	)






     @torch.no_grad()
     def __call__(				self		,						_lowercase		,						_lowercase		,						_lowercase = 250		,						_lowercase = 0.0		,						_lowercase = 10		,						_lowercase = 10		,						_lowercase = None		,						_lowercase = "pil"		,						_lowercase = True		,						)->     Union[ImagePipelineOutput, Tuple]:
         UpperCamelCase_			  =						image

         UpperCamelCase_			  =						_preprocess_image(_lowercase	)
         UpperCamelCase_			  =						original_image.to(device=self.device		,						dtype=self.unet.dtype	)
         UpperCamelCase_			  =						_preprocess_mask(_lowercase	)
         UpperCamelCase_			  =						mask_image.to(device=self.device		,						dtype=self.unet.dtype	)

         UpperCamelCase_			  =						original_image.shape[0]

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

         UpperCamelCase_			  =						original_image.shape
         UpperCamelCase_			  =						randn_tensor(_lowercase		,						generator=_lowercase		,						device=self.device		,						dtype=self.unet.dtype	)

         # set step values
         self.scheduler.set_timesteps(_lowercase		,						_lowercase		,						_lowercase		,						self.device	)
         UpperCamelCase_			  =						eta

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

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

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