




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize







class 				lowerCAmelCase_       :






				def __init__(			self      ,snake_case__      ,snake_case__=sys.maxsize ):
							SCREAMING_SNAKE_CASE_   : str					  =      'bilinear'
							SCREAMING_SNAKE_CASE_   : List[str]					  =      max_size
							SCREAMING_SNAKE_CASE_   : Dict					  =      short_edge_length






				def __call__(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      []
							for img in imgs:
										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      img.shape[:2]
										# later: provide list and randomly choose index for resize
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      np.random.randint(self.short_edge_length[0]      ,self.short_edge_length[1] + 1 )
										if size == 0:
													return img
										SCREAMING_SNAKE_CASE_   : str					  =      size * 1.0 / min(snake_case__      ,snake_case__ )
										if h < w:
													SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[str]					  =      size, scale * w
										else:
													SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      scale * h, size
										if max(snake_case__      ,snake_case__ ) > self.max_size:
													SCREAMING_SNAKE_CASE_   : Tuple					  =      self.max_size * 1.0 / max(snake_case__      ,snake_case__ )
													SCREAMING_SNAKE_CASE_   : List[Any]					  =      newh * scale
													SCREAMING_SNAKE_CASE_   : Tuple					  =      neww * scale
										SCREAMING_SNAKE_CASE_   : List[str]					  =      int(neww + 0.5 )
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      int(newh + 0.5 )

										if img.dtype == np.uinta:
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      Image.fromarray(snake_case__ )
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      pil_image.resize((neww, newh)      ,PILImageResampling.BILINEAR )
													SCREAMING_SNAKE_CASE_   : Dict					  =      np.asarray(snake_case__ )
										else:
													SCREAMING_SNAKE_CASE_   : List[Any]					  =      img.permute(2      ,0      ,1 ).unsqueeze(0 )  # 3, 0, 1)  # hw(c) -> nchw
													SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      nn.functional.interpolate(
													    snake_case__      ,(newh, neww)      ,mode=self.interp_method      ,align_corners=snake_case__ ).squeeze(0 )
										img_augs.append(snake_case__ )

							return img_augs







class 				lowerCAmelCase_       :






				def __init__(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      ,cfg.INPUT.MAX_SIZE_TEST )
							SCREAMING_SNAKE_CASE_   : str					  =      cfg.INPUT.FORMAT
							SCREAMING_SNAKE_CASE_   : Dict					  =      cfg.SIZE_DIVISIBILITY
							SCREAMING_SNAKE_CASE_   : Dict					  =      cfg.PAD_VALUE
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      cfg.INPUT.MAX_SIZE_TEST
							SCREAMING_SNAKE_CASE_   : str					  =      cfg.MODEL.DEVICE
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )      ,1      ,1 )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )      ,1      ,1 )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      lambda snake_case__ : (x - self.pixel_mean) / self.pixel_std






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Any					  =      tuple(max(snake_case__ ) for s in zip(*[img.shape for img in images] ) )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [im.shape[-2:] for im in images]
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [
							    nn.functional.pad(
							        snake_case__      ,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,value=self.pad_value      ,)
							    for size, im in zip(snake_case__      ,snake_case__ )
							]

							return torch.stack(snake_case__ ), torch.tensor(snake_case__ )






				def __call__(			self      ,snake_case__      ,snake_case__=False ):
							with torch.no_grad():
										if not isinstance(snake_case__      ,snake_case__ ):
													SCREAMING_SNAKE_CASE_   : List[str]					  =      [images]
										if single_image:
													assert len(snake_case__ ) == 1
										for i in range(len(snake_case__ ) ):
													if isinstance(images[i]      ,torch.Tensor ):
																images.insert(snake_case__      ,images.pop(snake_case__ ).to(self.device ).float() )
													elif not isinstance(images[i]      ,torch.Tensor ):
																images.insert(
																    snake_case__      ,torch.as_tensor(img_tensorize(images.pop(snake_case__ )      ,input_format=self.input_format ) )
																    .to(self.device )
																    .float()      ,)
            # resize smallest edge
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      torch.tensor([im.shape[:2] for im in images] )
										SCREAMING_SNAKE_CASE_   : Dict					  =      self.aug(snake_case__ )
										# transpose images and convert to torch tensors
										# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
										# now normalize before pad to avoid useless arithmetic
										SCREAMING_SNAKE_CASE_   : Any					  =      [self.normalizer(snake_case__ ) for x in images]
										# now pad them to do the following operations
										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.pad(snake_case__ )
										# Normalize

										if self.size_divisibility > 0:
													raise NotImplementedError()
										# pad
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      torch.true_divide(snake_case__      ,snake_case__ )
										if single_image:
													return images[0], sizes[0], scales_yx[0]
										else:
													return images, sizes, scales_yx





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     str  )       ->  Any:







			"""simple docstring"""





			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     Tuple[int, int]  )       ->  List[Any]:







			"""simple docstring"""





			assert torch.isfinite(lowerCamelCase_  ).all(), "Box tensor contains infinite or NaN!"
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      box_size
			tensor[:, 0].clamp_(min=0  ,							max=lowerCamelCase_  )
			tensor[:, 1].clamp_(min=0  ,							max=lowerCamelCase_  )
			tensor[:, 2].clamp_(min=0  ,							max=lowerCamelCase_  )
			tensor[:, 3].clamp_(min=0  ,							max=lowerCamelCase_  )







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE__     					=			False



class 						_UpperCAmelCase      (     unittest.TestCase							):






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Dict=32):
				set_seed(0)
				SCREAMING_SNAKE_CASE_				:Dict				     =						UNetaDModel(sample_size=UpperCAmelCase   ,  in_channels=3   ,  out_channels=3)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.optim.SGD(model.parameters()   ,  lr=0.0001)
				return model, optimizer







		@slow
		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				SCREAMING_SNAKE_CASE_				:Any				     =						DDPMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  beta_end=0.02   ,  beta_schedule="linear"   ,  clip_sample=UpperCAmelCase   ,  )
				SCREAMING_SNAKE_CASE_				:str				     =						DDIMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  beta_end=0.02   ,  beta_schedule="linear"   ,  clip_sample=UpperCAmelCase   ,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0)
				SCREAMING_SNAKE_CASE_				:Any				     =						[torch.randn((4, 3, 32, 32)).clip(-1   ,  1).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:str				     =						[torch.randn((4, 3, 32, 32)).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[torch.randint(0   ,  10_00   ,  (4,)).long().to(UpperCAmelCase) for _ in range(4)]

				# train with a DDPM scheduler
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.get_model_optimizer(resolution=32)
				model.train().to(UpperCAmelCase)
				for i in range(4):
						optimizer.zero_grad()
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						ddpm_scheduler.add_noise(clean_images[i]   ,  noise[i]   ,  timesteps[i])
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model(UpperCAmelCase   ,  timesteps[i]).sample
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						torch.nn.functional.mse_loss(UpperCAmelCase   ,  noise[i])
						loss.backward()
						optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.get_model_optimizer(resolution=32)
				model.train().to(UpperCAmelCase)
				for i in range(4):
						optimizer.zero_grad()
						SCREAMING_SNAKE_CASE_				:List[str]				     =						ddim_scheduler.add_noise(clean_images[i]   ,  noise[i]   ,  timesteps[i])
						SCREAMING_SNAKE_CASE_				:Tuple				     =						model(UpperCAmelCase   ,  timesteps[i]).sample
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.nn.functional.mse_loss(UpperCAmelCase   ,  noise[i])
						loss.backward()
						optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(UpperCAmelCase   ,  UpperCAmelCase   ,  atol=1E-5))
				self.assertTrue(torch.allclose(UpperCAmelCase   ,  UpperCAmelCase   ,  atol=1E-5))






