


"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()


class 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  @property
  def 							lowercase_     (						self						)				->					Optional[Any]:
     torch.manual_seed(0						)
     a   :	Any						=    UNetaDModel(
         block_out_channels=(32, 64)  ,					layers_per_block=2  ,					sample_size=32  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,					up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,					)
     return model
  def 							lowercase_     (						self						)				->					Optional[int]:
     a   :	Dict						=    'cpu'
     a   :	List[str]						=    self.dummy_uncond_unet
     a   :	Optional[int]						=    DDPMScheduler()

     a   :	List[str]						=    DDPMPipeline(unet=__a  ,					scheduler=__a						)
     ddpm.to(__a						)
     ddpm.set_progress_bar_config(disable=__a						)

     a   :	int						=    torch.Generator(device=__a						).manual_seed(0						)
     a   :	Tuple						=    ddpm(generator=__a  ,					num_inference_steps=2  ,					output_type='numpy'						).images

     a   :	Dict						=    torch.Generator(device=__a						).manual_seed(0						)
     a   :	Optional[Any]						=    ddpm(generator=__a  ,					num_inference_steps=2  ,					output_type='numpy'  ,					return_dict=__a						)[0]

     a   :	Union[str, Any]						=    image[0, -3:, -3:, -1]
     a   :	List[str]						=    image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     a   :	Any						=    np.array(
         [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.000, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]						)

     assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2






  def 							lowercase_     (						self						)				->					str:
     a   :	List[str]						=    self.dummy_uncond_unet
     a   :	Union[str, Any]						=    DDPMScheduler(prediction_type='sample'						)

     a   :	List[str]						=    DDPMPipeline(unet=__a  ,					scheduler=__a						)
     ddpm.to(__a						)
     ddpm.set_progress_bar_config(disable=__a						)

     a   :	Optional[int]						=    torch.manual_seed(0						)
     a   :	str						=    ddpm(generator=__a  ,					num_inference_steps=2  ,					output_type='numpy'						).images

     a   :	Tuple						=    torch.manual_seed(0						)
     a   :	Optional[int]						=    ddpm(generator=__a  ,					num_inference_steps=2  ,					output_type='numpy'						)[0]

     a   :	int						=    image[0, -3:, -3:, -1]
     a   :	Dict						=    image_eps[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     a   :	Optional[int]						=    1E-2 if torch_device != 'mps' else 3E-2
     assert np.abs(image_slice.flatten() - image_eps_slice.flatten()						).max() < tolerance



@slow
@require_torch_gpu
class 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Optional[Any]:
     a   :	str						=    'google/ddpm-cifar10-32'

     a   :	str						=    UNetaDModel.from_pretrained(__a						)
     a   :	Any						=    DDPMScheduler.from_pretrained(__a						)

     a   :	List[str]						=    DDPMPipeline(unet=__a  ,					scheduler=__a						)
     ddpm.to(__a						)
     ddpm.set_progress_bar_config(disable=__a						)

     a   :	Dict						=    torch.manual_seed(0						)
     a   :	int						=    ddpm(generator=__a  ,					output_type='numpy'						).images

     a   :	Tuple						=    image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     a   :	List[str]						=    np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]						)
     assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2












"""simple docstring"""





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 ,				__a ,				__a=sys.maxsize		)			-> Tuple:
								UpperCamelCase =						"bilinear"
								UpperCamelCase =						max_size
								UpperCamelCase =						short_edge_length




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

										if img.dtype == np.uinta:
												UpperCamelCase =						Image.fromarray(__a		)
												UpperCamelCase =						pil_image.resize((neww, newh) ,				PILImageResampling.BILINEAR		)
												UpperCamelCase =						np.asarray(__a		)
										else:
												UpperCamelCase =						img.permute(2 ,				0 ,				1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
												UpperCamelCase =						nn.functional.interpolate(
												    __a ,				(newh, neww) ,				mode=self.interp_method ,				align_corners=__a		).squeeze(0		)
										img_augs.append(__a		)

								return img_augs





class 							_lowerCamelCase   :


						def __init__(self ,				__a		)			-> Any:
								UpperCamelCase =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,				cfg.INPUT.MAX_SIZE_TEST		)
								UpperCamelCase =						cfg.INPUT.FORMAT
								UpperCamelCase =						cfg.SIZE_DIVISIBILITY
								UpperCamelCase =						cfg.PAD_VALUE
								UpperCamelCase =						cfg.INPUT.MAX_SIZE_TEST
								UpperCamelCase =						cfg.MODEL.DEVICE
								UpperCamelCase =						torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		) ,				1 ,				1		)
								UpperCamelCase =						torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		) ,				1 ,				1		)
								UpperCamelCase =						lambda __a		: (x - self.pixel_mean) / self.pixel_std


						def      snake_case_ (self ,				__a		)			-> List[Any]:
								UpperCamelCase =						tuple(max(__a		) for s in zip(*[img.shape for img in images]		)		)
								UpperCamelCase =						[im.shape[-2:] for im in images]
								UpperCamelCase =						[
								    nn.functional.pad(
								        __a ,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,				value=self.pad_value ,				)
								    for size, im in zip(__a ,				__a		)
								]

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




						def __call__(self ,				__a ,				__a=False		)			-> Optional[int]:
								with torch.no_grad():
										if not isinstance(__a ,				__a		):
												UpperCamelCase =						[images]
										if single_image:
												assert len(__a		) == 1
										for i in range(len(__a		)		):
												if isinstance(images[i] ,				torch.Tensor		):
														images.insert(__a ,				images.pop(__a		).to(self.device		).float()		)
												elif not isinstance(images[i] ,				torch.Tensor		):
														images.insert(
														    __a ,				torch.as_tensor(img_tensorize(images.pop(__a		) ,				input_format=self.input_format		)		)
														    .to(self.device		)
														    .float() ,				)
            # resize smallest edge
										UpperCamelCase =						torch.tensor([im.shape[:2] for im in images]		)
										UpperCamelCase =						self.aug(__a		)
										# 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
										UpperCamelCase =						[self.normalizer(__a		) for x in images]
										# now pad them to do the following operations
										UpperCamelCase ,					UpperCamelCase =						self.pad(__a		)
										# Normalize

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



def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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






def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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






