
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 				UpperCAmelCase (    __UpperCamelCase     ,     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 							UpperCamelCase_    (			self   :					Any					):
					return 32






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






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






	@property
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					return self.time_input_dim * 4






	@property
	def 							UpperCamelCase_    (			self   :					Tuple					):
					return 1_00






	@property
	def 							UpperCamelCase_    (			self   :					int					):
					torch.manual_seed(0					)

					__A       =					{
					    "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       =					UNetaDConditionModel(**__UpperCAmelCase					)
					return model






	@property
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					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 							UpperCamelCase_    (			self   :					str					):
					torch.manual_seed(0					)
					__A       =					VQModel(**self.dummy_movq_kwargs					)
					return model






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					self.dummy_unet
					__A       =					self.dummy_movq

					__A       =					DDIMScheduler(
					    num_train_timesteps=10_00				,beta_schedule="linear"				,beta_start=0.0_00_85				,beta_end=0.0_12				,clip_sample=__UpperCAmelCase				,set_alpha_to_one=__UpperCAmelCase				,steps_offset=1				,prediction_type="epsilon"				,thresholding=__UpperCAmelCase				,)

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

					return components






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

					if str(__UpperCAmelCase					).startswith("mps"					):
									__A       =					torch.manual_seed(__UpperCAmelCase					)
					else:
									__A       =					torch.Generator(device=__UpperCAmelCase					).manual_seed(__UpperCAmelCase					)
					__A       =					{
					    "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 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					"cpu"

					__A       =					self.get_dummy_components()

					__A       =					self.pipeline_class(**__UpperCAmelCase					)
					__A       =					pipe.to(__UpperCAmelCase					)

					pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

					__A       =					pipe(**self.get_dummy_inputs(__UpperCAmelCase					)					)
					__A       =					output.images

					__A       =					pipe(
					    **self.get_dummy_inputs(__UpperCAmelCase					)				,return_dict=__UpperCAmelCase				,)[0]

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

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

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

					__A       =					np.array(
					    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]					)

					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 							UpperCamelCase_    (			self   :					Any					):
					super().test_inference_batch_single_identical(expected_max_diff=3E-3					)





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




	'''simple docstring'''





	def 							UpperCamelCase_    (			self   :					Any					):
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"					)

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

					__A       =					"a hat"

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

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

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

					__A       =					pipeline(
					    image=__UpperCAmelCase				,mask_image=__UpperCAmelCase				,image_embeds=__UpperCAmelCase				,negative_image_embeds=__UpperCAmelCase				,generator=__UpperCAmelCase				,num_inference_steps=1_00				,height=7_68				,width=7_68				,output_type="np"				,)

					__A       =					output.images[0]

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

					assert_mean_pixel_difference(__UpperCAmelCase				,__UpperCAmelCase					)








UpperCAmelCase__      			=						'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''







def     a_   ()				->						None:



					"""simple docstring"""
					__a					:						Dict      =					input("Enter message: "      )
					__a					:						Dict      =					input("Enter key [alphanumeric]: "      )
					__a					:						str      =					input("Encrypt/Decrypt [e/d]: "      )

					if mode.lower().startswith("e"      ):
										__a					:						List[str]      =					"encrypt"
										__a					:						Union[str, Any]      =					encrypt_message(__A				,       __A      )
					elif mode.lower().startswith("d"      ):
										__a					:						int      =					"decrypt"
										__a					:						List[str]      =					decrypt_message(__A				,       __A      )

					print(f'\n{mode.title()}ed message:'      )
					print(__A      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "encrypt"      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "decrypt"      )







def     a_   (__A				,       __A				,       __A      )				->						str:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					[]
					__a					:						Tuple      =					0
					__a					:						Dict      =					key.upper()

					for symbol in message:
										__a					:						Dict      =					LETTERS.find(symbol.upper()      )
										if num != -1:
															if mode == "encrypt":
																				num += LETTERS.find(key[key_index]      )
															elif mode == "decrypt":
																				num -= LETTERS.find(key[key_index]      )

															num %= len(__A      )

															if symbol.isupper():
																				translated.append(LETTERS[num]      )
															elif symbol.islower():
																				translated.append(LETTERS[num].lower()      )

															key_index += 1
															if key_index == len(__A      ):
																				__a					:						List[str]      =					0
										else:
															translated.append(__A      )
					return "".join(__A      )


if __name__ == "__main__":
			main()





