
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


lowerCamelCase__	=							logging.get_logger(__name__)  # pylint: disable=invalid-name


def 							__A(lowerCAmelCase )		->				str:

		"""simple docstring"""






		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 ):
				_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(lowerCAmelCase						,  axis=0 )
				_UpperCamelCase						    =	np.array(lowerCAmelCase ).astype(np.floataa ) / 255.0
				_UpperCamelCase						    =	image.transpose(0						,  3						,  1						,  2 )
				_UpperCamelCase						    =	2.0 * image - 1.0
				_UpperCamelCase						    =	torch.from_numpy(lowerCAmelCase )
		elif isinstance(image[0]						,  torch.Tensor ):
				_UpperCamelCase						    =	torch.cat(lowerCAmelCase						,  dim=0 )
		return image


def 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		if isinstance(lowerCAmelCase						,  torch.Tensor ):
				return mask
		elif isinstance(lowerCAmelCase						,  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(lowerCAmelCase						,  axis=0 )
				_UpperCamelCase						    =	mask.astype(np.floataa ) / 255.0
				_UpperCamelCase						    =	0
				_UpperCamelCase						    =	1
				_UpperCamelCase						    =	torch.from_numpy(lowerCAmelCase )
		elif isinstance(mask[0]						,  torch.Tensor ):
				_UpperCamelCase						    =	torch.cat(lowerCAmelCase						,  dim=0 )
		return mask







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: UNetaDModel
						UpperCamelCase_							: RePaintScheduler

						def __init__(			self       ,							a       ,							a				)					->      Dict:


								'''simple docstring'''

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







						@torch.no_grad()
						def __call__(			self       ,							a       ,							a       ,							a = 2_50       ,							a = 0.0       ,							a = 10       ,							a = 10       ,							a = None       ,							a = "pil"       ,							a = True       ,							)					->      Union[ImagePipelineOutput, Tuple]:


								'''simple docstring'''

								_UpperCamelCase						    =	image

								_UpperCamelCase						    =	_preprocess_image(a				)
								_UpperCamelCase						    =	original_image.to(device=self.device       ,							dtype=self.unet.dtype				)
								_UpperCamelCase						    =	_preprocess_mask(a				)
								_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(a       ,							a				) and len(a				) != batch_size:
										raise ValueError(
										    F'You have passed a list of generators of length {len(a				)}, 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(a       ,							generator=a       ,							device=self.device       ,							dtype=self.unet.dtype				)

								# set step values
								self.scheduler.set_timesteps(a       ,							a       ,							a       ,							self.device				)
								_UpperCamelCase						    =	eta

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

										else:
												# compute the reverse: x_t-1 -> x_t
												_UpperCamelCase						    =	self.scheduler.undo_step(a       ,							a       ,							a				)
										_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(a				)

								if not return_dict:
										return (image,)

								return ImagePipelineOutput(images=a				)





import copy

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


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: int	=		"deta"
						UpperCamelCase_							: Union[str, Any]	=		{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}

						def __init__(			self       ,							a=None       ,							a=9_00       ,							a=20_48       ,							a=6       ,							a=20_48       ,							a=8       ,							a=6       ,							a=10_24       ,							a=8       ,							a=0.0       ,							a=True       ,							a="relu"       ,							a=2_56       ,							a=0.1       ,							a=0.0       ,							a=0.0       ,							a=0.02       ,							a=1.0       ,							a=True       ,							a=False       ,							a="sine"       ,							a=5       ,							a=4       ,							a=4       ,							a=True       ,							a=3_00       ,							a=True       ,							a=True       ,							a=1       ,							a=5       ,							a=2       ,							a=1       ,							a=1       ,							a=5       ,							a=2       ,							a=0.1       ,							a=0.25       ,							**a       ,							)					->      Dict:


								'''simple docstring'''

								if backbone_config is None:
										logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""				)
										_UpperCamelCase						    =	CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]				)
								else:
										if isinstance(a       ,							a				):
												_UpperCamelCase						    =	backbone_config.pop("""model_type"""				)
												_UpperCamelCase						    =	CONFIG_MAPPING[backbone_model_type]
												_UpperCamelCase						    =	config_class.from_dict(a				)

								_UpperCamelCase						    =	backbone_config
								_UpperCamelCase						    =	num_queries
								_UpperCamelCase						    =	max_position_embeddings
								_UpperCamelCase						    =	d_model
								_UpperCamelCase						    =	encoder_ffn_dim
								_UpperCamelCase						    =	encoder_layers
								_UpperCamelCase						    =	encoder_attention_heads
								_UpperCamelCase						    =	decoder_ffn_dim
								_UpperCamelCase						    =	decoder_layers
								_UpperCamelCase						    =	decoder_attention_heads
								_UpperCamelCase						    =	dropout
								_UpperCamelCase						    =	attention_dropout
								_UpperCamelCase						    =	activation_dropout
								_UpperCamelCase						    =	activation_function
								_UpperCamelCase						    =	init_std
								_UpperCamelCase						    =	init_xavier_std
								_UpperCamelCase						    =	encoder_layerdrop
								_UpperCamelCase						    =	auxiliary_loss
								_UpperCamelCase						    =	position_embedding_type
								# deformable attributes
								_UpperCamelCase						    =	num_feature_levels
								_UpperCamelCase						    =	encoder_n_points
								_UpperCamelCase						    =	decoder_n_points
								_UpperCamelCase						    =	two_stage
								_UpperCamelCase						    =	two_stage_num_proposals
								_UpperCamelCase						    =	with_box_refine
								_UpperCamelCase						    =	assign_first_stage
								if two_stage is True and with_box_refine is False:
										raise ValueError("""If two_stage is True, with_box_refine must be True."""				)
								# Hungarian matcher
								_UpperCamelCase						    =	class_cost
								_UpperCamelCase						    =	bbox_cost
								_UpperCamelCase						    =	giou_cost
								# Loss coefficients
								_UpperCamelCase						    =	mask_loss_coefficient
								_UpperCamelCase						    =	dice_loss_coefficient
								_UpperCamelCase						    =	bbox_loss_coefficient
								_UpperCamelCase						    =	giou_loss_coefficient
								_UpperCamelCase						    =	eos_coefficient
								_UpperCamelCase						    =	focal_alpha
								super().__init__(is_encoder_decoder=a       ,							**a				)

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.encoder_attention_heads

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.d_model







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	copy.deepcopy(self.__dict__				)
								_UpperCamelCase						    =	self.backbone_config.to_dict()
								_UpperCamelCase						    =	self.__class__.model_type
								return output



