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       			=  KandinskyVaaInpaintPipeline
						lowercase       			=  ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
						lowercase       			=  [
						    'image_embeds',
						    'negative_image_embeds',
						    'image',
						    'mask_image',
						]
						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 							__a		(						self					:   Optional[Any] )				->		Any:


									'''simple docstring'''

									return 32

						@property
						def 							__a		(						self					:   int )				->		int:


									'''simple docstring'''

									return 32

						@property
						def 							__a		(						self					:   Optional[int] )				->		List[str]:


									'''simple docstring'''

									return self.time_input_dim

						@property
						def 							__a		(						self					:   str )				->		Dict:


									'''simple docstring'''

									return self.time_input_dim * 4

						@property
						def 							__a		(						self					:   Optional[Any] )				->		str:


									'''simple docstring'''

									return 1_00

						@property
						def 							__a		(						self					:   str )				->		Dict:


									'''simple docstring'''

									torch.manual_seed(0 )

									lowercase       		=		{
									    '''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,
									}

									lowercase       		=		UNetaDConditionModel(**snake_case_ )
									return model

						@property
						def 							__a		(						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 							__a		(						self					:   List[str] )				->		Union[str, Any]:


									'''simple docstring'''

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

						def 							__a		(						self					:   str )				->		Dict:


									'''simple docstring'''

									lowercase       		=		self.dummy_unet
									lowercase       		=		self.dummy_movq

									lowercase       		=		DDIMScheduler(
									    num_train_timesteps=10_00				,				beta_schedule='''linear'''				,				beta_start=0.0_0085				,				beta_end=0.012				,				clip_sample=snake_case_				,				set_alpha_to_one=snake_case_				,				steps_offset=1				,				prediction_type='''epsilon'''				,				thresholding=snake_case_				,				)

									lowercase       		=		{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''movq''': movq,
									}

									return components

						def 							__a		(						self					:   Any				,				__lowerCamelCase					:   List[Any]				,				__lowerCamelCase					:   Optional[Any]=0 )				->		Dict:


									'''simple docstring'''

									lowercase       		=		floats_tensor((1, self.text_embedder_hidden_size)				,				rng=random.Random(snake_case_ ) ).to(snake_case_ )
									lowercase       		=		floats_tensor((1, self.text_embedder_hidden_size)				,				rng=random.Random(seed + 1 ) ).to(
									    snake_case_ )
									# create init_image
									lowercase       		=		floats_tensor((1, 3, 64, 64)				,				rng=random.Random(snake_case_ ) ).to(snake_case_ )
									lowercase       		=		image.cpu().permute(0				,				2				,				3				,				1 )[0]
									lowercase       		=		Image.fromarray(np.uinta(snake_case_ ) ).convert('''RGB''' ).resize((2_56, 2_56) )
									# create mask
									lowercase       		=		np.ones((64, 64)				,				dtype=np.floataa )
									lowercase       		=		0

									if str(snake_case_ ).startswith('''mps''' ):
												lowercase       		=		torch.manual_seed(snake_case_ )
									else:
												lowercase       		=		torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
									lowercase       		=		{
									    '''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 							__a		(						self					:   str )				->		List[str]:


									'''simple docstring'''

									lowercase       		=		'''cpu'''

									lowercase       		=		self.get_dummy_components()

									lowercase       		=		self.pipeline_class(**snake_case_ )
									lowercase       		=		pipe.to(snake_case_ )

									pipe.set_progress_bar_config(disable=snake_case_ )

									lowercase       		=		pipe(**self.get_dummy_inputs(snake_case_ ) )
									lowercase       		=		output.images

									lowercase       		=		pipe(
									    **self.get_dummy_inputs(snake_case_ )				,				return_dict=snake_case_				,				)[0]

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

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

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

									lowercase       		=		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 							__a		(						self					:   List[str] )				->		Union[str, Any]:


									'''simple docstring'''

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






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

						def 							__a		(						self					:   List[str] )				->		List[str]:


									'''simple docstring'''

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



						def 							__a		(						self					:   Union[str, Any] )				->		List[Any]:


									'''simple docstring'''

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

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

									lowercase       		=		'''a hat'''

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

									lowercase       		=		KandinskyVaaInpaintPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''				,				torch_dtype=torch.floataa )
									lowercase       		=		pipeline.to(snake_case_ )
									pipeline.set_progress_bar_config(disable=snake_case_ )

									lowercase       		=		torch.Generator(device='''cpu''' ).manual_seed(0 )
									lowercase		,lowercase       		=		pipe_prior(
									    snake_case_				,				generator=snake_case_				,				num_inference_steps=5				,				negative_prompt=''''''				,				).to_tuple()

									lowercase       		=		pipeline(
									    image=snake_case_				,				mask_image=snake_case_				,				image_embeds=snake_case_				,				negative_image_embeds=snake_case_				,				generator=snake_case_				,				num_inference_steps=1_00				,				height=7_68				,				width=7_68				,				output_type='''np'''				,				)

									lowercase       		=		output.images[0]

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

									assert_mean_pixel_difference(snake_case_				,				snake_case_ )







"""simple docstring"""
from typing import List

from .keymap import KEYMAP, get_character

def  lowercase     (_lowerCAmelCase						):

						def decorator(_lowerCAmelCase						):
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    """handle_key""" ,    []						)
												handle += [key]
												setattr(_lowerCAmelCase ,    """handle_key""" ,    _lowerCAmelCase						)
												return func

						return decorator

def  lowercase     (*_lowerCAmelCase						):

						def decorator(_lowerCAmelCase						):
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    """handle_key""" ,    []						)
												handle += keys
												setattr(_lowerCAmelCase ,    """handle_key""" ,    _lowerCAmelCase						)
												return func

						return decorator
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __new__( cls    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Any:
													__lowerCAmelCase     =  super().__new__(cls    ,				snake_case_    ,				snake_case_    ,				snake_case_				)
													if not hasattr(snake_case_    ,				"""key_handler"""				):
																			setattr(snake_case_    ,				"""key_handler"""    ,				{}				)
													setattr(snake_case_    ,				"""handle_input"""    ,				KeyHandler.handle_input				)

													for value in attrs.values():
																			__lowerCAmelCase     =  getattr(snake_case_    ,				"""handle_key"""    ,				[]				)
																			for key in handled_keys:
																									__lowerCAmelCase     =  value
													return new_cls




							@staticmethod
							def    A__						( cls				)       ->  Tuple:
													__lowerCAmelCase     =  get_character()
													if char != KEYMAP["undefined"]:
																			__lowerCAmelCase     =  ord(snake_case_				)
													__lowerCAmelCase     =  cls.key_handler.get(snake_case_				)
													if handler:
																			__lowerCAmelCase     =  char
																			return handler(cls				)
													else:
																			return None

def  lowercase     (cls						):
						return KeyHandler(cls.__name__ ,    cls.__bases__ ,    cls.__dict__.copy()						)
