



"""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 SCREAMING_SNAKE_CASE__					(		__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					KandinskyVaaInpaintPipeline
						_lowerCAmelCase							=					["image_embeds", "negative_image_embeds", "image", "mask_image"]
						_lowerCAmelCase							=					[
						    "image_embeds",
						    "negative_image_embeds",
						    "image",
						    "mask_image",
						]
						_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     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 32



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 32



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.time_input_dim



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.time_input_dim * 4



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 100



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)

													__a		:		List[str]									=       {
													    """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,
													}

													__a		:		List[str]									=       UNetaDConditionModel(**_lowercase							)
													return model



						@property
						def     lowerCAmelCase__(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     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)
													__a		:		Any									=       VQModel(**self.dummy_movq_kwargs							)
													return model



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       self.dummy_unet
													__a		:		str									=       self.dummy_movq

													__a		:		Optional[int]									=       DDIMScheduler(
													    num_train_timesteps=1000						,     beta_schedule="""linear"""						,     beta_start=0.0_0085						,     beta_end=0.012						,     clip_sample=_lowercase						,     set_alpha_to_one=_lowercase						,     steps_offset=1						,     prediction_type="""epsilon"""						,     thresholding=_lowercase						,     )

													__a		:		int									=       {
													    """unet""": unet,
													    """scheduler""": scheduler,
													    """movq""": movq,
													}

													return components



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase=0							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       floats_tensor((1, self.text_embedder_hidden_size)						,     rng=random.Random(_lowercase							)							).to(_lowercase							)
													__a		:		Optional[Any]									=       floats_tensor((1, self.text_embedder_hidden_size)						,     rng=random.Random(seed + 1							)							).to(
													    _lowercase							)
													# create init_image
													__a		:		Optional[Any]									=       floats_tensor((1, 3, 64, 64)						,     rng=random.Random(_lowercase							)							).to(_lowercase							)
													__a		:		Optional[Any]									=       image.cpu().permute(0						,     2						,     3						,     1							)[0]
													__a		:		Union[str, Any]									=       Image.fromarray(np.uinta(_lowercase							)							).convert("""RGB"""							).resize((256, 256)							)
													# create mask
													__a		:		Tuple									=       np.ones((64, 64)						,     dtype=np.floataa							)
													__a		:		int									=       0

													if str(_lowercase							).startswith("""mps"""							):
																				__a		:		List[str]									=       torch.manual_seed(_lowercase							)
													else:
																				__a		:		Tuple									=       torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
													__a		:		Optional[Any]									=       {
													    """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     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[Any]									=       """cpu"""

													__a		:		Union[str, Any]									=       self.get_dummy_components()

													__a		:		Tuple									=       self.pipeline_class(**_lowercase							)
													__a		:		str									=       pipe.to(_lowercase							)

													pipe.set_progress_bar_config(disable=_lowercase							)

													__a		:		Any									=       pipe(**self.get_dummy_inputs(_lowercase							)							)
													__a		:		str									=       output.images

													__a		:		Optional[Any]									=       pipe(
													    **self.get_dummy_inputs(_lowercase							)						,     return_dict=_lowercase						,     )[0]

													__a		:		Optional[int]									=       image[0, -3:, -3:, -1]
													__a		:		Optional[int]									=       image_from_tuple[0, -3:, -3:, -1]

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

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

													__a		:		Tuple									=       np.array(
													    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]							)

													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     lowerCAmelCase__(self							):





													'''simple docstring'''

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







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



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

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





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       load_numpy(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""							)

													__a		:		Optional[int]									=       load_image(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
													__a		:		Tuple									=       np.ones((768, 768)						,     dtype=np.floataa							)
													__a		:		str									=       0

													__a		:		List[Any]									=       """a hat"""

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

													__a		:		int									=       KandinskyVaaInpaintPipeline.from_pretrained(
													    """kandinsky-community/kandinsky-2-2-decoder-inpaint"""						,     torch_dtype=torch.floataa							)
													__a		:		List[Any]									=       pipeline.to(_lowercase							)
													pipeline.set_progress_bar_config(disable=_lowercase							)

													__a		:		Union[str, Any]									=       torch.Generator(device="""cpu"""							).manual_seed(0							)
													__a							,     __a		:		Union[str, Any]									=       pipe_prior(
													    _lowercase						,     generator=_lowercase						,     num_inference_steps=5						,     negative_prompt=""""""						,     ).to_tuple()

													__a		:		Dict									=       pipeline(
													    image=_lowercase						,     mask_image=_lowercase						,     image_embeds=_lowercase						,     negative_image_embeds=_lowercase						,     generator=_lowercase						,     num_inference_steps=100						,     height=768						,     width=768						,     output_type="""np"""						,     )

													__a		:		Tuple									=       output.images[0]

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

													assert_mean_pixel_difference(_lowercase						,     _lowercase							)






"""simple docstring"""

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=False					):
							__a		:		Dict									=       OmegaConf.load(_lowerCamelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
							return config





def       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: Union[str, Any]=None  ,       _lowerCamelCase				: int=None					):
							if conf_path is None:
														__a		:		str									=       """./model_checkpoints/vqgan_only.yaml"""
							__a		:		List[Any]									=       load_config(_lowerCamelCase  ,       display=_lowerCamelCase					)
							__a		:		Dict									=       VQModel(**config.model.params					)
							if ckpt_path is None:
														__a		:		List[Any]									=       """./model_checkpoints/vqgan_only.pt"""
							__a		:		Tuple									=       torch.load(_lowerCamelCase  ,       map_location=_lowerCamelCase					)
							if ".ckpt" in ckpt_path:
														__a		:		List[str]									=       sd["""state_dict"""]
							model.load_state_dict(_lowerCamelCase  ,       strict=_lowerCamelCase					)
							model.to(_lowerCamelCase					)
							del sd
							return model





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]					):
							__a							,     __a							,     __a		:		Tuple									=       model.encode(_lowerCamelCase					)
							print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
							__a		:		Union[str, Any]									=       model.decode(_lowerCamelCase					)
							return xrec





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]=False					):
							__a							,     __a		:		Optional[Any]									=       string.rsplit("""."""  ,       1					)
							if reload:
														__a		:		Optional[Any]									=       importlib.import_module(_lowerCamelCase					)
														importlib.reload(_lowerCamelCase					)
							return getattr(importlib.import_module(_lowerCamelCase  ,       package=_lowerCamelCase					)  ,       cls					)





def       __magic_name__    (   _lowerCamelCase				: Any					):
							if "target" not in config:
														raise KeyError("""Expected key `target` to instantiate."""					)
							return get_obj_from_str(config["""target"""]					)(**config.get("""params"""  ,       {}					)					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int=True  ,       _lowerCamelCase				: int=True					):
							__a		:		Union[str, Any]									=       instantiate_from_config(_lowerCamelCase					)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase					)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}







def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							# load the specified checkpoint
							if ckpt:
														__a		:		List[str]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
														__a		:		Any									=       pl_sd["""global_step"""]
														print(F'''loaded model from global step {global_step}.'''					)
							else:
														__a		:		List[Any]									=       {"""state_dict""": None}
														__a		:		Any									=       None
							__a		:		Union[str, Any]									=       load_model_from_config(config.model  ,       pl_sd["""state_dict"""]  ,       gpu=_lowerCamelCase  ,       eval_mode=_lowerCamelCase					)["""model"""]
							return model, global_step

