



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  lowerCamelCase__    (							lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=KandinskyVaaInpaintPipeline
		snake_case_			=["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
		snake_case_			=[
		    """image_embeds""",
		    """negative_image_embeds""",
		    """image""",
		    """mask_image""",
		]
		snake_case_			=[
		    """generator""",
		    """height""",
		    """width""",
		    """latents""",
		    """guidance_scale""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		snake_case_			=False



		@property
		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						return 32



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

						"""simple docstring"""






						return 32



		@property
		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						return self.time_input_dim



		@property
		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						return self.time_input_dim * 4



		@property
		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						return 1_00



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

						"""simple docstring"""






						torch.manual_seed(0				)

						lowerCAmelCase__  :       int				=  {
						    '''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__  :       List[Any]				=  UNetaDConditionModel(**__lowerCamelCase				)
						return model



		@property
		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""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				)  ->    int:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.dummy_unet
						lowerCAmelCase__  :       List[str]				=  self.dummy_movq

						lowerCAmelCase__  :       List[str]				=  DDIMScheduler(
						    num_train_timesteps=10_00	,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	,)

						lowerCAmelCase__  :       Optional[Any]				=  {
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''movq''': movq,
						}

						return components



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase=0				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(__lowerCamelCase				)				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(seed + 1				)				).to(
						    __lowerCamelCase				)
						# create init_image
						lowerCAmelCase__  :       Dict				=  floats_tensor((1, 3, 64, 64)	,rng=random.Random(__lowerCamelCase				)				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  image.cpu().permute(0	,2	,3	,1				)[0]
						lowerCAmelCase__  :       Optional[int]				=  Image.fromarray(np.uinta(__lowerCamelCase				)				).convert('''RGB'''				).resize((2_56, 2_56)				)
						# create mask
						lowerCAmelCase__  :       int				=  np.ones((64, 64)	,dtype=np.floataa				)
						lowerCAmelCase__  :       List[Any]				=  0

						if str(__lowerCamelCase				).startswith('''mps'''				):
										lowerCAmelCase__  :       Any				=  torch.manual_seed(__lowerCamelCase				)
						else:
										lowerCAmelCase__  :       Optional[int]				=  torch.Generator(device=__lowerCamelCase				).manual_seed(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  {
						    '''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				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  '''cpu'''

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

						lowerCAmelCase__  :       Optional[Any]				=  self.pipeline_class(**__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  pipe.to(__lowerCamelCase				)

						pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[int]				=  pipe(**self.get_dummy_inputs(__lowerCamelCase				)				)
						lowerCAmelCase__  :       Optional[Any]				=  output.images

						lowerCAmelCase__  :       Dict				=  pipe(
						    **self.get_dummy_inputs(__lowerCamelCase				)	,return_dict=__lowerCamelCase	,)[0]

						lowerCAmelCase__  :       List[str]				=  image[0, -3:, -3:, -1]
						lowerCAmelCase__  :       List[str]				=  image_from_tuple[0, -3:, -3:, -1]

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

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

						lowerCAmelCase__  :       Dict				=  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				)  ->    Optional[int]:

						"""simple docstring"""






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



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




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






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

						lowerCAmelCase__  :       List[str]				=  load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''				)
						lowerCAmelCase__  :       Optional[int]				=  np.ones((7_68, 7_68)	,dtype=np.floataa				)
						lowerCAmelCase__  :       List[Any]				=  0

						lowerCAmelCase__  :       Any				=  '''a hat'''

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

						lowerCAmelCase__  :       str				=  KandinskyVaaInpaintPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''	,torch_dtype=torch.floataa				)
						lowerCAmelCase__  :       Union[str, Any]				=  pipeline.to(__lowerCamelCase				)
						pipeline.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[Any]				=  torch.Generator(device='''cpu'''				).manual_seed(0				)
						lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[Any]				=  pipe_prior(
						    __lowerCamelCase	,generator=__lowerCamelCase	,num_inference_steps=5	,negative_prompt=''''''	,).to_tuple()

						lowerCAmelCase__  :       Union[str, Any]				=  pipeline(
						    image=__lowerCamelCase	,mask_image=__lowerCamelCase	,image_embeds=__lowerCamelCase	,negative_image_embeds=__lowerCamelCase	,generator=__lowerCamelCase	,num_inference_steps=1_00	,height=7_68	,width=7_68	,output_type='''np'''	,)

						lowerCAmelCase__  :       Optional[int]				=  output.images[0]

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

						assert_mean_pixel_difference(__lowerCamelCase	,__lowerCamelCase				)






from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict[str, torch.Tensor]):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  []
				lowerCAmelCase__  :       int				=  []
				lowerCAmelCase__  :       Optional[int]				=  []

				for rt in rc.restypes:
								lowerCAmelCase__  :       int				=  rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
								restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names])
								lowerCAmelCase__  :       Any				=  {name: i for i, name in enumerate(lowerCamelCase_)}
								restype_atomaa_to_atomaa_list.append(
								    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types])

								restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names])

				# Add dummy mapping for restype 'UNK'
				restype_atomaa_to_atomaa_list.append([0] * 14)
				restype_atomaa_to_atomaa_list.append([0] * 37)
				restype_atomaa_mask_list.append([0.0] * 14)

				lowerCAmelCase__  :       Optional[Any]				=  torch.tensor(
				    lowerCamelCase_ ,dtype=torch.intaa ,device=protein['''aatype'''].device ,)
				lowerCAmelCase__  :       Optional[int]				=  torch.tensor(
				    lowerCamelCase_ ,dtype=torch.intaa ,device=protein['''aatype'''].device ,)
				lowerCAmelCase__  :       List[str]				=  torch.tensor(
				    lowerCamelCase_ ,dtype=torch.floataa ,device=protein['''aatype'''].device ,)
				lowerCAmelCase__  :       Optional[Any]				=  protein['''aatype'''].to(torch.long)

				# create the mapping for (residx, atom14) --> atom37, i.e. an array
				# with shape (num_res, 14) containing the atom37 indices for this protein
				lowerCAmelCase__  :       Optional[int]				=  restype_atomaa_to_atomaa[protein_aatype]
				lowerCAmelCase__  :       List[str]				=  restype_atomaa_mask[protein_aatype]

				lowerCAmelCase__  :       Tuple				=  residx_atomaa_mask
				lowerCAmelCase__  :       Dict				=  residx_atomaa_to_atomaa.long()

				# create the gather indices for mapping back
				lowerCAmelCase__  :       Union[str, Any]				=  restype_atomaa_to_atomaa[protein_aatype]
				lowerCAmelCase__  :       int				=  residx_atomaa_to_atomaa.long()

				# create the corresponding mask
				lowerCAmelCase__  :       List[str]				=  torch.zeros([21, 37] ,dtype=torch.floataa ,device=protein['''aatype'''].device)
				for restype, restype_letter in enumerate(rc.restypes):
								lowerCAmelCase__  :       List[str]				=  rc.restype_atoa[restype_letter]
								lowerCAmelCase__  :       List[str]				=  rc.residue_atoms[restype_name]
								for atom_name in atom_names:
												lowerCAmelCase__  :       List[Any]				=  rc.atom_order[atom_name]
												lowerCAmelCase__  :       Any				=  1

				lowerCAmelCase__  :       int				=  restype_atomaa_mask[protein_aatype]
				lowerCAmelCase__  :       int				=  residx_atomaa_mask

				return protein






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict[str, torch.Tensor]):





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[Any]				=  tree_map(lambda lowerCamelCase_: torch.tensor(lowerCamelCase_ ,device=batch['''aatype'''].device) ,lowerCamelCase_ ,np.ndarray)
				lowerCAmelCase__  :       Union[str, Any]				=  tensor_tree_map(lambda lowerCamelCase_: np.array(lowerCamelCase_) ,make_atomaa_masks(lowerCamelCase_))
				return out

