'''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    a__				(       UpperCAmelCase__							,       unittest.TestCase			):
     lowerCamelCase  :					int							=KandinskyVaaPipeline
     lowerCamelCase  :					Union[str, Any]							=[
         "image_embeds",
         "negative_image_embeds",
     ]
     lowerCamelCase  :					Tuple							=["image_embeds", "negative_image_embeds"]
     lowerCamelCase  :					Optional[int]							=[
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     lowerCamelCase  :					Union[str, Any]							=False






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         return 32






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         return 32






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         return self.time_input_dim






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         return self.time_input_dim * 4






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         return 1_00






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         torch.manual_seed(0   )

         __lowerCamelCase          =  {
             '''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,
         }

         __lowerCamelCase          =  UNetaDConditionModel(**a   )
         return model






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		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        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         __lowerCamelCase          =  self.dummy_unet
         __lowerCamelCase          =  self.dummy_movq

         __lowerCamelCase          =  DDIMScheduler(
             num_train_timesteps=10_00  ,    beta_schedule='''linear'''  ,    beta_start=0.0_00_85  ,    beta_end=0.0_12  ,    clip_sample=a  ,    set_alpha_to_one=a  ,    steps_offset=1  ,    prediction_type='''epsilon'''  ,    thresholding=a  ,    )

         __lowerCamelCase          =  {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }
         return components






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    a		:		int  ,    a		:		Optional[int]=0   ):





         """simple docstring"""



         __lowerCamelCase          =  floats_tensor((1, self.text_embedder_hidden_size)  ,    rng=random.Random(a   )   ).to(a   )
         __lowerCamelCase          =  floats_tensor((1, self.text_embedder_hidden_size)  ,    rng=random.Random(seed + 1   )   ).to(
             a   )
         if str(a   ).startswith('''mps'''   ):
             __lowerCamelCase          =  torch.manual_seed(a   )
         else:
             __lowerCamelCase          =  torch.Generator(device=a   ).manual_seed(a   )
         __lowerCamelCase          =  {
             '''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        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         __lowerCamelCase          =  '''cpu'''

         __lowerCamelCase          =  self.get_dummy_components()

         __lowerCamelCase          =  self.pipeline_class(**a   )
         __lowerCamelCase          =  pipe.to(a   )

         pipe.set_progress_bar_config(disable=a   )

         __lowerCamelCase          =  pipe(**self.get_dummy_inputs(a   )   )
         __lowerCamelCase          =  output.images

         __lowerCamelCase          =  pipe(
             **self.get_dummy_inputs(a   )  ,    return_dict=a  ,    )[0]

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

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

         __lowerCamelCase          =  np.array(
             [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]   )

         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        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



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

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

         __lowerCamelCase          =  KandinskyVaaPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-decoder'''  ,    torch_dtype=torch.floataa   )
         __lowerCamelCase          =  pipeline.to(a   )
         pipeline.set_progress_bar_config(disable=a   )

         __lowerCamelCase          =  '''red cat, 4k photo'''

         __lowerCamelCase          =  torch.Generator(device='''cuda'''   ).manual_seed(0   )
         __lowerCamelCase     ,  __lowerCamelCase          =  pipe_prior(
             a  ,    generator=a  ,    num_inference_steps=5  ,    negative_prompt=''''''  ,    ).to_tuple()

         __lowerCamelCase          =  torch.Generator(device='''cuda'''   ).manual_seed(0   )
         __lowerCamelCase          =  pipeline(
             image_embeds=a  ,    negative_image_embeds=a  ,    generator=a  ,    num_inference_steps=1_00  ,    output_type='''np'''  ,    )

         __lowerCamelCase          =  output.images[0]

         assert image.shape == (5_12, 5_12, 3)

         assert_mean_pixel_difference(a  ,    a   )

'''simple docstring'''




import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class    a__				:






     def __init__(			self		:		List[Any]  ,    a		:		Tuple  ,    a		:		int  ,    a		:		int   ):





         """simple docstring"""



         if dst_width < 0 or dst_height < 0:
             raise ValueError('''Destination width/height should be > 0'''   )

         __lowerCamelCase          =  img
         __lowerCamelCase          =  img.shape[1]
         __lowerCamelCase          =  img.shape[0]
         __lowerCamelCase          =  dst_width
         __lowerCamelCase          =  dst_height

         __lowerCamelCase          =  self.src_w / self.dst_w
         __lowerCamelCase          =  self.src_h / self.dst_h

         __lowerCamelCase          =  __lowerCamelCase          =  (
             np.ones((self.dst_h, self.dst_w, 3)  ,    np.uinta   ) * 2_55
         )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         for i in range(self.dst_h   ):
             for j in range(self.dst_w   ):
                 __lowerCamelCase          =  self.img[self.get_y(a   )][self.get_x(a   )]






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		int   ):





         """simple docstring"""



         return int(self.ratio_x * x   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		int   ):





         """simple docstring"""



         return int(self.ratio_y * y   )


if __name__ == "__main__":
  __UpperCAmelCase							,	__UpperCAmelCase												=8_0_0, 6_0_0
  __UpperCAmelCase												=imread("image_data/lena.jpg", 1)
  __UpperCAmelCase												=NearestNeighbour(im, dst_w, dst_h)
  n.process()

  imshow(
      f'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
  )
  waitKey(0)
  destroyAllWindows()
