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 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Dict								=							KandinskyVaaInpaintPipeline
							lowercase__						:       Optional[int]								=							["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
							lowercase__						:       Union[str, Any]								=							[
							    """image_embeds""",
							    """negative_image_embeds""",
							    """image""",
							    """mask_image""",
							]
							lowercase__						:       Union[str, Any]								=							[
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							lowercase__						:       int								=							False




							@property
							def 	__snake_case(						self   :					Tuple      )					->				List[Any]:



												'''simple docstring'''

												return 32




							@property
							def 	__snake_case(						self   :					Tuple      )					->				str:



												'''simple docstring'''

												return 32




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



												'''simple docstring'''

												return self.time_input_dim




							@property
							def 	__snake_case(						self   :					str      )					->				List[str]:



												'''simple docstring'''

												return self.time_input_dim * 4




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



												'''simple docstring'''

												return 100




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



												'''simple docstring'''

												torch.manual_seed(0      )

												SCREAMING_SNAKE_CASE    			=       {
												    "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,
												}

												SCREAMING_SNAKE_CASE    			=       UNetaDConditionModel(**_UpperCamelCase      )
												return model




							@property
							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''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 	__snake_case(						self   :					Dict      )					->				int:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					Optional[Any]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.dummy_unet
												SCREAMING_SNAKE_CASE    			=       self.dummy_movq

												SCREAMING_SNAKE_CASE    			=       DDIMScheduler(
												    num_train_timesteps=1_000	,							beta_schedule="linear"	,							beta_start=0.0_0_0_8_5	,							beta_end=0.0_1_2	,							clip_sample=_UpperCamelCase	,							set_alpha_to_one=_UpperCamelCase	,							steps_offset=1	,							prediction_type="epsilon"	,							thresholding=_UpperCamelCase	,							)

												SCREAMING_SNAKE_CASE    			=       {
												    "unet": unet,
												    "scheduler": scheduler,
												    "movq": movq,
												}

												return components




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Union[str, Any]=0      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       floats_tensor((1, self.text_embedder_hidden_size)	,							rng=random.Random(_UpperCamelCase      )      ).to(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       floats_tensor((1, self.text_embedder_hidden_size)	,							rng=random.Random(seed + 1      )      ).to(
												    _UpperCamelCase      )
												# create init_image
												SCREAMING_SNAKE_CASE    			=       floats_tensor((1, 3, 64, 64)	,							rng=random.Random(_UpperCamelCase      )      ).to(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       image.cpu().permute(0	,							2	,							3	,							1      )[0]
												SCREAMING_SNAKE_CASE    			=       Image.fromarray(np.uinta(_UpperCamelCase      )      ).convert("RGB"      ).resize((256, 256)      )
												# create mask
												SCREAMING_SNAKE_CASE    			=       np.ones((64, 64)	,							dtype=np.floataa      )
												SCREAMING_SNAKE_CASE    			=       0

												if str(_UpperCamelCase      ).startswith("mps"      ):
																	SCREAMING_SNAKE_CASE    			=       torch.manual_seed(_UpperCamelCase      )
												else:
																	SCREAMING_SNAKE_CASE    			=       torch.Generator(device=_UpperCamelCase      ).manual_seed(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       {
												    "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   :					List[str]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "cpu"

												SCREAMING_SNAKE_CASE    			=       self.get_dummy_components()

												SCREAMING_SNAKE_CASE    			=       self.pipeline_class(**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       pipe.to(_UpperCamelCase      )

												pipe.set_progress_bar_config(disable=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       pipe(**self.get_dummy_inputs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       output.images

												SCREAMING_SNAKE_CASE    			=       pipe(
												    **self.get_dummy_inputs(_UpperCamelCase      )	,							return_dict=_UpperCamelCase	,							)[0]

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

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

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

												SCREAMING_SNAKE_CASE    			=       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   :					Union[str, Any]      )					->				Optional[int]:



												'''simple docstring'''

												super().test_inference_batch_single_identical(expected_max_diff=3e-3      )



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




							def 	__snake_case(						self   :					Optional[int]      )					->				Tuple:



												'''simple docstring'''

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





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



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE    			=       load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
												SCREAMING_SNAKE_CASE    			=       np.ones((768, 768)	,							dtype=np.floataa      )
												SCREAMING_SNAKE_CASE    			=       0

												SCREAMING_SNAKE_CASE    			=       "a hat"

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

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

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

												SCREAMING_SNAKE_CASE    			=       pipeline(
												    image=_UpperCamelCase	,							mask_image=_UpperCamelCase	,							image_embeds=_UpperCamelCase	,							negative_image_embeds=_UpperCamelCase	,							generator=_UpperCamelCase	,							num_inference_steps=100	,							height=768	,							width=768	,							output_type="np"	,							)

												SCREAMING_SNAKE_CASE    			=       output.images[0]

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

												assert_mean_pixel_difference(_UpperCamelCase	,							_UpperCamelCase      )





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DPTImageProcessor


class 						lowercase    (     unittest.TestCase     ):




							def __init__(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					List[Any]=7	,							_UpperCamelCase   :					Any=3	,							_UpperCamelCase   :					str=18	,							_UpperCamelCase   :					Tuple=30	,							_UpperCamelCase   :					Optional[int]=400	,							_UpperCamelCase   :					Optional[int]=True	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					int=True	,							_UpperCamelCase   :					Optional[int]=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					List[str]=[0.5, 0.5, 0.5]	,							)					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"height": 18, "width": 18}
												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       image_size
												SCREAMING_SNAKE_CASE    			=       min_resolution
												SCREAMING_SNAKE_CASE    			=       max_resolution
												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												SCREAMING_SNAKE_CASE    			=       do_normalize
												SCREAMING_SNAKE_CASE    			=       image_mean
												SCREAMING_SNAKE_CASE    			=       image_std





							def 	__snake_case(						self   :					List[Any]      )					->				Any:



												'''simple docstring'''

												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												}



@require_torch
@require_vision
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Any								=							DPTImageProcessor if is_vision_available() else None




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       DPTImageProcessingTester(self      )




							@property
							def 	__snake_case(						self   :					List[Any]      )					->				List[str]:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_mean"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_std"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_normalize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_resize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"size"      )      )




							def 	__snake_case(						self   :					Dict      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size	,							{"height": 18, "width": 18}      )

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict	,							size=42      )
												self.assertEqual(image_processor.size	,							{"height": 42, "width": 42}      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PIL images
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							Image.Image      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)




							def 	__snake_case(						self   :					Optional[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase	,							numpify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							np.ndarray      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)





							def 	__snake_case(						self   :					Optional[Any]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase	,							torchify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							torch.Tensor      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)




