


'''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			,     _UpperCamelCase			,     _UpperCamelCase=sys.maxsize   )->      int:
            _A				=							'bilinear'
            _A				=							max_size
            _A				=							short_edge_length






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

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

            return img_augs






class        lowerCAmelCase_  :






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






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

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






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

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






def 					lowerCamelCase_ ( __UpperCamelCase					:       Dict							,    __UpperCamelCase					:       List[str]		)					->	List[str]:







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

def 					lowerCamelCase_ ( __UpperCamelCase					:       Dict							,    __UpperCamelCase					:       Tuple[int, int]		)					->	Optional[int]:







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









'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






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






       def   UpperCamelCase						( self   )->      Optional[int]:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_euler'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe([prompt]			,     generator=_UpperCamelCase			,     guidance_scale=9.0			,     num_inference_steps=20			,     output_type='np'   )

            _A				=							output.images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]   )

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






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_euler'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe([prompt]			,     generator=_UpperCamelCase			,     guidance_scale=9.0			,     num_inference_steps=20			,     output_type='np'   )

            _A				=							output.images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]   )

            assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-1






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_dpmpp_2m'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe(
                [prompt]			,     generator=_UpperCamelCase			,     guidance_scale=7.5			,     num_inference_steps=15			,     output_type='np'			,     use_karras_sigmas=_UpperCamelCase			,     )

            _A				=							output.images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array(
                [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]   )

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





