




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 :

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__=sys.maxsize							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    'bilinear'
							__lowerCamelCase		       =    max_size
							__lowerCamelCase		       =    short_edge_length






			def __call__(      self			,						lowerCamelCase__							)    ->      Optional[int]:



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

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

							return img_augs







class     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,						cfg.INPUT.MAX_SIZE_TEST							)
							__lowerCamelCase		       =    cfg.INPUT.FORMAT
							__lowerCamelCase		       =    cfg.SIZE_DIVISIBILITY
							__lowerCamelCase		       =    cfg.PAD_VALUE
							__lowerCamelCase		       =    cfg.INPUT.MAX_SIZE_TEST
							__lowerCamelCase		       =    cfg.MODEL.DEVICE
							__lowerCamelCase		       =    torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)			,						1			,						1							)
							__lowerCamelCase		       =    torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)			,						1			,						1							)
							__lowerCamelCase		       =    lambda lowerCamelCase__							: (x - self.pixel_mean) / self.pixel_std


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    tuple(max(lowerCamelCase__							) for s in zip(*[img.shape for img in images]							)							)
							__lowerCamelCase		       =    [im.shape[-2:] for im in images]
							__lowerCamelCase		       =    [
							    nn.functional.pad(
							        lowerCamelCase__			,						[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,						value=self.pad_value			,						)
							    for size, im in zip(lowerCamelCase__			,						lowerCamelCase__							)
							]

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






			def __call__(      self			,						lowerCamelCase__			,						lowerCamelCase__=False							)    ->      List[Any]:



							'''simple docstring'''
							with torch.no_grad():
											if not isinstance(lowerCamelCase__			,						lowerCamelCase__							):
															__lowerCamelCase		       =    [images]
											if single_image:
															assert len(lowerCamelCase__							) == 1
											for i in range(len(lowerCamelCase__							)							):
															if isinstance(images[i]			,						torch.Tensor							):
																			images.insert(lowerCamelCase__			,						images.pop(lowerCamelCase__							).to(self.device							).float()							)
															elif not isinstance(images[i]			,						torch.Tensor							):
																			images.insert(
																			    lowerCamelCase__			,						torch.as_tensor(img_tensorize(images.pop(lowerCamelCase__							)			,						input_format=self.input_format							)							)
																			    .to(self.device							)
																			    .float()			,						)
            # resize smallest edge
											__lowerCamelCase		       =    torch.tensor([im.shape[:2] for im in images]							)
											__lowerCamelCase		       =    self.aug(lowerCamelCase__							)
											# 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
											__lowerCamelCase		       =    [self.normalizer(lowerCamelCase__							) for x in images]
											# now pad them to do the following operations
											__lowerCamelCase       ,						__lowerCamelCase		       =    self.pad(lowerCamelCase__							)
											# Normalize

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      str      )     ->       List[Any]:





				"""simple docstring"""


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Tuple[int, int]      )     ->       Dict:





				"""simple docstring"""


				assert torch.isfinite(UpperCamelCase__      ).all(), "Box tensor contains infinite or NaN!"
				__lowerCamelCase       ,						__lowerCamelCase		       =    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__      )






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class        _a							(			UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			Union[str, Any]        = KandinskyVaaImgaImgPipeline
							_lowercase	:			Tuple        = ['''image_embeds''', '''negative_image_embeds''', '''image''']
							_lowercase	:			Any        = [
							    '''image_embeds''',
							    '''negative_image_embeds''',
							    '''image''',
							]
							_lowercase	:			Union[str, Any]        = [
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''strength''',
							    '''guidance_scale''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							_lowercase	:			Optional[Any]        = False

							@property
							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   Dict:





									"""simple docstring"""







									return 32

							@property
							def       lowerCamelCase_ (							self:	Optional[int]							)			->   Optional[Any]:





									"""simple docstring"""







									return 32

							@property
							def       lowerCamelCase_ (							self:	Any							)			->   Any:





									"""simple docstring"""







									return self.time_input_dim

							@property
							def       lowerCamelCase_ (							self:	Tuple							)			->   Any:





									"""simple docstring"""







									return self.time_input_dim * 4

							@property
							def       lowerCamelCase_ (							self:	List[Any]							)			->   Optional[Any]:





									"""simple docstring"""







									return 100

							@property
							def       lowerCamelCase_ (							self:	int							)			->   int:





									"""simple docstring"""







									torch.manual_seed(0							)

									lowercase__            =      {
									    '''in_channels''': 4,
									    # Out channels is double in channels because predicts mean and variance
									    '''out_channels''': 8,
									    '''addition_embed_type''': '''image''',
									    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
									    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
									    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
									    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
									    '''layers_per_block''': 1,
									    '''encoder_hid_dim''': self.text_embedder_hidden_size,
									    '''encoder_hid_dim_type''': '''image_proj''',
									    '''cross_attention_dim''': self.cross_attention_dim,
									    '''attention_head_dim''': 4,
									    '''resnet_time_scale_shift''': '''scale_shift''',
									    '''class_embed_type''': None,
									}

									lowercase__            =      UNetaDConditionModel(**UpperCamelCase_							)
									return model

							@property
							def       lowerCamelCase_ (							self:	Optional[int]							)			->   Union[str, Any]:





									"""simple docstring"""







									return {
									    "block_out_channels": [32, 64],
									    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 12,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}

							@property
							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   int:





									"""simple docstring"""







									torch.manual_seed(0							)
									lowercase__            =      VQModel(**self.dummy_movq_kwargs							)
									return model

							def       lowerCamelCase_ (							self:	Optional[int]							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      self.dummy_unet
									lowercase__            =      self.dummy_movq

									lowercase__            =      {
									    '''num_train_timesteps''': 1_000,
									    '''beta_schedule''': '''linear''',
									    '''beta_start''': 0.00085,
									    '''beta_end''': 0.012,
									    '''clip_sample''': False,
									    '''set_alpha_to_one''': False,
									    '''steps_offset''': 0,
									    '''prediction_type''': '''epsilon''',
									    '''thresholding''': False,
									}

									lowercase__            =      DDIMScheduler(**UpperCamelCase_							)

									lowercase__            =      {
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''movq''': movq,
									}

									return components

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Optional[int]=0							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(UpperCamelCase_							)							).to(UpperCamelCase_							)
									lowercase__            =      floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(seed + 1							)							).to(
									    UpperCamelCase_							)
									# create init_image
									lowercase__            =      floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(UpperCamelCase_							)							).to(UpperCamelCase_							)
									lowercase__            =      image.cpu().permute(0    ,						2    ,						3    ,						1							)[0]
									lowercase__            =      Image.fromarray(np.uinta(UpperCamelCase_							)							).convert('''RGB'''							).resize((256, 256)							)

									if str(UpperCamelCase_							).startswith('''mps'''							):
											lowercase__            =      torch.manual_seed(UpperCamelCase_							)
									else:
											lowercase__            =      torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
									lowercase__            =      {
									    '''image''': init_image,
									    '''image_embeds''': image_embeds,
									    '''negative_image_embeds''': negative_image_embeds,
									    '''generator''': generator,
									    '''height''': 64,
									    '''width''': 64,
									    '''num_inference_steps''': 10,
									    '''guidance_scale''': 7.0,
									    '''strength''': 0.2,
									    '''output_type''': '''np''',
									}
									return inputs


							def       lowerCamelCase_ (							self:	Optional[int]							)			->   Dict:





									"""simple docstring"""







									lowercase__            =      '''cpu'''

									lowercase__            =      self.get_dummy_components()

									lowercase__            =      self.pipeline_class(**UpperCamelCase_							)
									lowercase__            =      pipe.to(UpperCamelCase_							)

									pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__            =      pipe(**self.get_dummy_inputs(UpperCamelCase_							)							)
									lowercase__            =      output.images

									lowercase__            =      pipe(
									    **self.get_dummy_inputs(UpperCamelCase_							)    ,						return_dict=UpperCamelCase_    ,						)[0]

									lowercase__            =      image[0, -3:, -3:, -1]
									lowercase__            =      image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									lowercase__            =      np.array(
									    [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]							)
									assert (
									    np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
									), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
									assert (
									    np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
									), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'


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

							def       lowerCamelCase_ (							self:	str							)			->   List[Any]:





									"""simple docstring"""







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


							def       lowerCamelCase_ (							self:	List[str]							)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''							)

									lowercase__            =      load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''							)
									lowercase__            =      '''A red cartoon frog, 4k'''

									lowercase__            =      KandinskyVaaPriorPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-2-prior'''    ,						torch_dtype=torch.floataa							)
									pipe_prior.to(UpperCamelCase_							)

									lowercase__            =      KandinskyVaaImgaImgPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-2-decoder'''    ,						torch_dtype=torch.floataa							)
									lowercase__            =      pipeline.to(UpperCamelCase_							)

									pipeline.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__            =      torch.Generator(device='''cpu'''							).manual_seed(0							)
									lowercase__     ,		lowercase__            =      pipe_prior(
									    UpperCamelCase_    ,						generator=UpperCamelCase_    ,						num_inference_steps=5    ,						negative_prompt=''''''    ,						).to_tuple()

									lowercase__            =      pipeline(
									    image=UpperCamelCase_    ,						image_embeds=UpperCamelCase_    ,						negative_image_embeds=UpperCamelCase_    ,						generator=UpperCamelCase_    ,						num_inference_steps=100    ,						height=768    ,						width=768    ,						strength=0.2    ,						output_type='''np'''    ,						)

									lowercase__            =      output.images[0]

									assert image.shape == (768, 768, 3)

									assert_mean_pixel_difference(UpperCamelCase_    ,						UpperCamelCase_							)


