


"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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     _UpperCamelCase    (  lowerCAmelCase__			,unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 =KandinskyVaaInpaintPipeline
							__UpperCAmelCase		:       List[Any]							 =["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
							__UpperCAmelCase		:       Union[str, Any]							 =[
							    """image_embeds""",
							    """negative_image_embeds""",
							    """image""",
							    """mask_image""",
							]
							__UpperCAmelCase		:       List[str]							 =[
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							__UpperCAmelCase		:       List[Any]							 =False

							@property
							def      snake_case							(	self      ):
										return 32

							@property
							def      snake_case							(	self      ):
										return 32

							@property
							def      snake_case							(	self      ):
										return self.time_input_dim

							@property
							def      snake_case							(	self      ):
										return self.time_input_dim * 4

							@property
							def      snake_case							(	self      ):
										return 1_00

							@property
							def      snake_case							(	self      ):
										torch.manual_seed(0      )

										__lowerCAmelCase							       =     {
										    "in_channels": 9,
										    # 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      snake_case							(	self      ):
										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      snake_case							(	self      ):
										torch.manual_seed(0      )
										__lowerCAmelCase							       =     VQModel(**self.dummy_movq_kwargs      )
										return model

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.dummy_unet
										__lowerCAmelCase							       =     self.dummy_movq

										__lowerCAmelCase							       =     DDIMScheduler(
										    num_train_timesteps=10_00       ,  beta_schedule="linear"       ,  beta_start=0.0_0_0_8_5       ,  beta_end=0.0_1_2       ,  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      snake_case							(	self       ,  __a       ,  __a=0      ):
										__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      )
										# create init_image
										__lowerCAmelCase							       =     floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(__a      )      ).to(__a      )
										__lowerCAmelCase							       =     image.cpu().permute(0       ,  2       ,  3       ,  1      )[0]
										__lowerCAmelCase							       =     Image.fromarray(np.uinta(__a      )      ).convert("RGB"      ).resize((2_56, 2_56)      )
										# create mask
										__lowerCAmelCase							       =     np.ones((64, 64)       ,  dtype=np.floataa      )
										__lowerCAmelCase							       =     0

										if str(__a      ).startswith("mps"      ):
													__lowerCAmelCase							       =     torch.manual_seed(__a      )
										else:
													__lowerCAmelCase							       =     torch.Generator(device=__a      ).manual_seed(__a      )
										__lowerCAmelCase							       =     {
										    "image": init_image,
										    "mask_image": mask,
										    "image_embeds": image_embeds,
										    "negative_image_embeds": negative_image_embeds,
										    "generator": generator,
										    "height": 64,
										    "width": 64,
										    "num_inference_steps": 2,
										    "guidance_scale": 4.0,
										    "output_type": "np",
										}
										return inputs

							def      snake_case							(	self      ):
										__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]

										print(f"image.shape {image.shape}"      )

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

										__lowerCAmelCase							       =     np.array(
										    [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]      )

										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()}"



							def      snake_case							(	self      ):
										super().test_inference_batch_single_identical(expected_max_diff=3e-3      )
@slow
@require_torch_gpu
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							def      snake_case							(	self      ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"      )

										__lowerCAmelCase							       =     load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
										__lowerCAmelCase							       =     np.ones((7_68, 7_68)       ,  dtype=np.floataa      )
										__lowerCAmelCase							       =     0

										__lowerCAmelCase							       =     "a hat"

										__lowerCAmelCase							       =     KandinskyVaaPriorPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-prior"       ,  torch_dtype=torch.floataa      )
										pipe_prior.to(__a      )

										__lowerCAmelCase							       =     KandinskyVaaInpaintPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-decoder-inpaint"       ,  torch_dtype=torch.floataa      )
										__lowerCAmelCase							       =     pipeline.to(__a      )
										pipeline.set_progress_bar_config(disable=__a      )

										__lowerCAmelCase							       =     torch.Generator(device="cpu"      ).manual_seed(0      )
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     pipe_prior(
										    __a       ,  generator=__a       ,  num_inference_steps=5       ,  negative_prompt=""       ,  ).to_tuple()

										__lowerCAmelCase							       =     pipeline(
										    image=__a       ,  mask_image=__a       ,  image_embeds=__a       ,  negative_image_embeds=__a       ,  generator=__a       ,  num_inference_steps=1_00       ,  height=7_68       ,  width=7_68       ,  output_type="np"       ,  )

										__lowerCAmelCase							       =     output.images[0]

										assert image.shape == (7_68, 7_68, 3)

										assert_mean_pixel_difference(__a       ,  __a      )








"""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     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							def      snake_case							(	self      ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

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

										__lowerCAmelCase							       =     output.images

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

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]      )

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

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

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

										__lowerCAmelCase							       =     output.images

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

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]      )

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



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_dpmpp_2m"      )

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

										__lowerCAmelCase							       =     output.images

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

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array(
										    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]      )

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




