




import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE       ,   unittest.TestCase					):


 '''simple docstring'''







 lowercase_				    =    KandinskyVaaControlnetPipeline
 lowercase_				    =    ["image_embeds", "negative_image_embeds", "hint"]
 lowercase_				    =    ["image_embeds", "negative_image_embeds", "hint"]
 lowercase_				    =    [
     "generator",
     "height",
     "width",
     "latents",
     "guidance_scale",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 lowercase_				    =    False



 @property
 def       lowerCAmelCase_  (							self  :		int						):
       return 32



 @property
 def       lowerCAmelCase_  (							self  :		Dict						):
       return 32



 @property
 def       lowerCAmelCase_  (							self  :		List[Any]						):
       return self.time_input_dim



 @property
 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       return self.time_input_dim * 4



 @property
 def       lowerCAmelCase_  (							self  :		Any						):
       return 100



 @property
 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       torch.manual_seed(0						)

       SCREAMING_SNAKE_CASE_							=			{
           'in_channels': 8,
           # Out channels is double in channels because predicts mean and variance
           'out_channels': 8,
           'addition_embed_type': 'image_hint',
           '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(**_lowerCAmelCase						)
       return model



 @property
 def       lowerCAmelCase_  (							self  :		str						):
       return {
           "block_out_channels": [32, 32, 64, 64],
           "down_block_types": [
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
           "vq_embed_dim": 4,
       }



 @property
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       torch.manual_seed(0						)
       SCREAMING_SNAKE_CASE_							=			VQModel(**self.dummy_movq_kwargs						)
       return model



 def       lowerCAmelCase_  (							self  :		List[str]						):
       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_0085				,				beta_end=0.012				,				clip_sample=_lowerCAmelCase				,				set_alpha_to_one=_lowerCAmelCase				,				steps_offset=1				,				prediction_type='epsilon'				,				thresholding=_lowerCAmelCase				,				)

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



 def       lowerCAmelCase_  (							self  :		Dict				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		List[str]=0						):
       SCREAMING_SNAKE_CASE_							=			floats_tensor((1, self.text_embedder_hidden_size)				,				rng=random.Random(_lowerCAmelCase						)						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			floats_tensor((1, self.text_embedder_hidden_size)				,				rng=random.Random(seed + 1						)						).to(
           _lowerCAmelCase						)

       # create hint
       SCREAMING_SNAKE_CASE_							=			floats_tensor((1, 3, 64, 64)				,				rng=random.Random(_lowerCAmelCase						)						).to(_lowerCAmelCase						)

       if str(_lowerCAmelCase						).startswith('mps'						):
             SCREAMING_SNAKE_CASE_							=			torch.manual_seed(_lowerCAmelCase						)
       else:
             SCREAMING_SNAKE_CASE_							=			torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			{
           'image_embeds': image_embeds,
           'negative_image_embeds': negative_image_embeds,
           'hint': hint,
           'generator': generator,
           'height': 64,
           'width': 64,
           'guidance_scale': 4.0,
           'num_inference_steps': 2,
           'output_type': 'np',
       }
       return inputs




 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			'cpu'

       SCREAMING_SNAKE_CASE_							=			self.get_dummy_components()

       SCREAMING_SNAKE_CASE_							=			self.pipeline_class(**_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			pipe.to(_lowerCAmelCase						)

       pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			pipe(**self.get_dummy_inputs(_lowerCAmelCase						)						)
       SCREAMING_SNAKE_CASE_							=			output.images

       SCREAMING_SNAKE_CASE_							=			pipe(
           **self.get_dummy_inputs(_lowerCAmelCase						)				,				return_dict=_lowerCAmelCase				,				)[0]

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

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

       SCREAMING_SNAKE_CASE_							=			np.array(
           [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]						)

       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        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 def       lowerCAmelCase_  (							self  :		List[Any]						):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def       lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_							=			load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'						)

       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinskyv22/hint_image_cat.png'						)
       SCREAMING_SNAKE_CASE_							=			torch.from_numpy(np.array(_lowerCAmelCase						)						).float() / 255.0
       SCREAMING_SNAKE_CASE_							=			hint.permute(2				,				0				,				1						).unsqueeze(0						)

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

       SCREAMING_SNAKE_CASE_							=			KandinskyVaaControlnetPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-controlnet-depth'				,				torch_dtype=torch.floataa						)
       SCREAMING_SNAKE_CASE_							=			pipeline.to(_lowerCAmelCase						)
       pipeline.set_progress_bar_config(disable=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			'A robot, 4k photo'

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

       SCREAMING_SNAKE_CASE_							=			torch.Generator(device='cuda'						).manual_seed(0						)
       SCREAMING_SNAKE_CASE_							=			pipeline(
           image_embeds=_lowerCAmelCase				,				negative_image_embeds=_lowerCAmelCase				,				hint=_lowerCAmelCase				,				generator=_lowerCAmelCase				,				num_inference_steps=100				,				output_type='np'				,				)

       SCREAMING_SNAKE_CASE_							=			output.images[0]

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

       assert_mean_pixel_difference(_lowerCAmelCase				,				_lowerCAmelCase						)





import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
   import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 @property
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			ort.SessionOptions()
       SCREAMING_SNAKE_CASE_							=			False
       return options




 def       lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo.png'						)
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'						)
       SCREAMING_SNAKE_CASE_							=			load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'						)

       # using the PNDM scheduler by default
       SCREAMING_SNAKE_CASE_							=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           'CompVis/stable-diffusion-v1-4'				,				revision='onnx'				,				safety_checker=_lowerCAmelCase				,				feature_extractor=_lowerCAmelCase				,				provider=self.gpu_provider				,				sess_options=self.gpu_options				,				)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			'A red cat sitting on a park bench'

       SCREAMING_SNAKE_CASE_							=			np.random.RandomState(0						)
       SCREAMING_SNAKE_CASE_							=			pipe(
           prompt=_lowerCAmelCase				,				image=_lowerCAmelCase				,				mask_image=_lowerCAmelCase				,				strength=0.75				,				guidance_scale=7.5				,				num_inference_steps=15				,				generator=_lowerCAmelCase				,				output_type='np'				,				)

       SCREAMING_SNAKE_CASE_							=			output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image						).max() < 1E-2