





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   _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

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



   '''simple docstring'''



   lowerCamelCase__: List[Any]         ='''bilinear'''
   lowerCamelCase__: Optional[int]         =max_size
   lowerCamelCase__: Tuple         =short_edge_length







 def __call__(self							:  Any			,					UpperCAmelCase_							:  Optional[Any])			->Tuple:



   '''simple docstring'''



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

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

   return img_augs



class   _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

 def __init__(self							:  List[Any]			,					UpperCAmelCase_							:  Tuple)			->Union[str, Any]:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  str			,					UpperCAmelCase_							:  List[str])			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =tuple(max(__lowerCamelCase) for s in zip(*[img.shape for img in images]))
   lowerCamelCase__: Tuple         =[im.shape[-2:] for im in images]
   lowerCamelCase__: List[str]         =[
       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							:  Union[str, Any]			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  Optional[Any]=False)			->Any:



   '''simple docstring'''



   with torch.no_grad():
     if not isinstance(__lowerCamelCase			,					__lowerCamelCase):
       lowerCamelCase__: List[str]         =[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__: Union[str, Any]         =torch.tensor([im.shape[:2] for im in images])
     lowerCamelCase__: int         =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__: Optional[int]         =[self.normalizer(__lowerCamelCase) for x in images]
     # now pad them to do the following operations
     lowerCamelCase__: Dict         =self.pad(__lowerCamelCase)
     # Normalize

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





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						Dict:







  """simple docstring"""





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






def 	lowerCAmelCase_					(			__a      ,       __a						)	->						Any:







  """simple docstring"""





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








"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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 						__snake_case   (						SCREAMING_SNAKE_CASE__				,				unittest.TestCase	):





 """simple docstring"""


 _lowerCamelCase								=	KandinskyVaaPipeline
 _lowerCamelCase								=	[
     """image_embeds""",
     """negative_image_embeds""",
 ]
 _lowerCamelCase								=	["""image_embeds""", """negative_image_embeds"""]
 _lowerCamelCase								=	[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 _lowerCamelCase								=	False

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return 32

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return 32

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return self.time_input_dim

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return self.time_input_dim * 4

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return 100

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     torch.manual_seed(0    )

     __A       :		Optional[Any]							=	{
         '''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,
     }

     __A       :		Optional[Any]							=	UNetaDConditionModel(**__lowerCamelCase    )
     return model

 @property
 def    UpperCamelCase__(       self    ):



     '''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    UpperCamelCase__(       self    ):



     '''simple docstring'''




     torch.manual_seed(0    )
     __A       :		Tuple							=	VQModel(**self.dummy_movq_kwargs    )
     return model

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	self.dummy_unet
     __A       :		Optional[int]							=	self.dummy_movq

     __A       :		List[Any]							=	DDIMScheduler(
         num_train_timesteps=1000   ,		beta_schedule='''linear'''   ,		beta_start=0.0_0_0_8_5   ,		beta_end=0.0_1_2   ,		clip_sample=__lowerCamelCase   ,		set_alpha_to_one=__lowerCamelCase   ,		steps_offset=1   ,		prediction_type='''epsilon'''   ,		thresholding=__lowerCamelCase   ,		)

     __A       :		Optional[int]							=	{
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''movq''': movq,
     }
     return components

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=0    ):



     '''simple docstring'''




     __A       :		Optional[int]							=	floats_tensor((1, self.text_embedder_hidden_size)   ,		rng=random.Random(__lowerCamelCase    )    ).to(__lowerCamelCase    )
     __A       :		List[Any]							=	floats_tensor((1, self.text_embedder_hidden_size)   ,		rng=random.Random(seed + 1    )    ).to(
         __lowerCamelCase    )
     if str(__lowerCamelCase    ).startswith('''mps'''    ):
         __A       :		Optional[Any]							=	torch.manual_seed(__lowerCamelCase    )
     else:
         __A       :		Dict							=	torch.Generator(device=__lowerCamelCase    ).manual_seed(__lowerCamelCase    )
     __A       :		Dict							=	{
         '''image_embeds''': image_embeds,
         '''negative_image_embeds''': negative_image_embeds,
         '''generator''': generator,
         '''height''': 64,
         '''width''': 64,
         '''guidance_scale''': 4.0,
         '''num_inference_steps''': 2,
         '''output_type''': '''np''',
     }
     return inputs







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		str							=	'''cpu'''

     __A       :		str							=	self.get_dummy_components()

     __A       :		Dict							=	self.pipeline_class(**__lowerCamelCase    )
     __A       :		Tuple							=	pipe.to(__lowerCamelCase    )

     pipe.set_progress_bar_config(disable=__lowerCamelCase    )

     __A       :		List[str]							=	pipe(**self.get_dummy_inputs(__lowerCamelCase    )    )
     __A       :		Tuple							=	output.images

     __A       :		Tuple							=	pipe(
         **self.get_dummy_inputs(__lowerCamelCase    )   ,		return_dict=__lowerCamelCase   ,		)[0]

     __A       :		Dict							=	image[0, -3:, -3:, -1]
     __A       :		Any							=	image_from_tuple[0, -3:, -3:, -1]

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

     __A       :		Optional[int]							=	np.array(
         [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6]    )

     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 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




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







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''    )

     __A       :		Optional[Any]							=	KandinskyVaaPriorPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-prior'''   ,		torch_dtype=torch.floataa    )
     pipe_prior.to(__lowerCamelCase    )

     __A       :		int							=	KandinskyVaaPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-decoder'''   ,		torch_dtype=torch.floataa    )
     __A       :		Union[str, Any]							=	pipeline.to(__lowerCamelCase    )
     pipeline.set_progress_bar_config(disable=__lowerCamelCase    )

     __A       :		List[Any]							=	'''red cat, 4k photo'''

     __A       :		Union[str, Any]							=	torch.Generator(device='''cuda'''    ).manual_seed(0    )
     __A					,			__A       :		List[str]							=	pipe_prior(
         __lowerCamelCase   ,		generator=__lowerCamelCase   ,		num_inference_steps=5   ,		negative_prompt=''''''   ,		).to_tuple()

     __A       :		Any							=	torch.Generator(device='''cuda'''    ).manual_seed(0    )
     __A       :		Optional[int]							=	pipeline(
         image_embeds=__lowerCamelCase   ,		negative_image_embeds=__lowerCamelCase   ,		generator=__lowerCamelCase   ,		num_inference_steps=100   ,		output_type='''np'''   ,		)

     __A       :		Union[str, Any]							=	output.images[0]

     assert image.shape == (512, 512, 3)

     assert_mean_pixel_difference(__lowerCamelCase   ,		__lowerCamelCase    )




