


'''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   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Any					=     KandinskyVaaInpaintPipeline
    lowerCamelCase    :   Dict					=     ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
    lowerCamelCase    :   List[Any]					=     [
        'image_embeds',
        'negative_image_embeds',
        'image',
        'mask_image',
    ]
    lowerCamelCase    :   Union[str, Any]					=     [
        'generator',
        'height',
        'width',
        'latents',
        'guidance_scale',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    lowerCamelCase    :   str					=     False







    @property
    def  lowercase_							(				self      )							->	Union[str, Any]:
         return 32







    @property
    def  lowercase_							(				self      )							->	str:
         return 32







    @property
    def  lowercase_							(				self      )							->	Tuple:
         return self.time_input_dim







    @property
    def  lowercase_							(				self      )							->	Optional[Any]:
         return self.time_input_dim * 4







    @property
    def  lowercase_							(				self      )							->	Optional[Any]:
         return 1_00







    @property
    def  lowercase_							(				self      )							->	Any:
         torch.manual_seed(0      )

         __lowerCamelCase  :      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,
         }

         __lowerCamelCase  :      Union[str, Any]														=						UNetaDConditionModel(**SCREAMING_SNAKE_CASE_      )
         return model







    @property
    def  lowercase_							(				self      )							->	List[Any]:
         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  lowercase_							(				self      )							->	Optional[int]:
         torch.manual_seed(0      )
         __lowerCamelCase  :      List[Any]														=						VQModel(**self.dummy_movq_kwargs      )
         return model







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      List[str]														=						self.dummy_unet
         __lowerCamelCase  :      str														=						self.dummy_movq

         __lowerCamelCase  :      List[str]														=						DDIMScheduler(
             num_train_timesteps=10_00     ,	beta_schedule='linear'     ,	beta_start=0.0_0_0_8_5     ,	beta_end=0.0_1_2     ,	clip_sample=SCREAMING_SNAKE_CASE_     ,	set_alpha_to_one=SCREAMING_SNAKE_CASE_     ,	steps_offset=1     ,	prediction_type='epsilon'     ,	thresholding=SCREAMING_SNAKE_CASE_     ,	)

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

         return components







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

         if str(SCREAMING_SNAKE_CASE_      ).startswith('mps'      ):
              __lowerCamelCase  :      Any														=						torch.manual_seed(SCREAMING_SNAKE_CASE_      )
         else:
              __lowerCamelCase  :      Optional[int]														=						torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(SCREAMING_SNAKE_CASE_      )
         __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  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      List[str]														=						'cpu'

         __lowerCamelCase  :      int														=						self.get_dummy_components()

         __lowerCamelCase  :      Optional[int]														=						self.pipeline_class(**SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						pipe.to(SCREAMING_SNAKE_CASE_      )

         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      str														=						pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_      )      )
         __lowerCamelCase  :      List[str]														=						output.images

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

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

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

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

         __lowerCamelCase  :      Dict														=						np.array(
             [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]      )

         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  lowercase_							(				self      )							->	List[Any]:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







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





    """simple docstring"""






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







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase  :      str														=						load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'      )

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

         __lowerCamelCase  :      Tuple														=						'a hat'

         __lowerCamelCase  :      Union[str, Any]														=						KandinskyVaaPriorPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-prior'     ,	torch_dtype=torch.floataa      )
         pipe_prior.to(SCREAMING_SNAKE_CASE_      )

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

         __lowerCamelCase  :      Dict														=						torch.Generator(device='cpu'      ).manual_seed(0      )
         __lowerCamelCase							,			__lowerCamelCase  :      str														=						pipe_prior(
             SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	num_inference_steps=5     ,	negative_prompt=''     ,	).to_tuple()

         __lowerCamelCase  :      Optional[Any]														=						pipeline(
             image=SCREAMING_SNAKE_CASE_     ,	mask_image=SCREAMING_SNAKE_CASE_     ,	image_embeds=SCREAMING_SNAKE_CASE_     ,	negative_image_embeds=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	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(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )








"""simple docstring"""



def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Optional[int]     							=					''
    for i in table:
        res += inp[i - 1]
    return res
def 					lowerCamelCase   (     _snake_case ):
    return data[1:] + data[0]
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      str     							=					''
    for i in range(len(_snake_case ) ):
        if a[i] == b[i]:
            res += "0"
        else:
            res += "1"
    return res
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      str     							=					int('0b' + data[0] + data[-1]    ,2 )
    UpperCAmelCase__	:      Optional[Any]     							=					int('0b' + data[1:3]    ,2 )
    return bin(s[row][col] )[2:]
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Any     							=					message[:4]
    UpperCAmelCase__	:      Optional[Any]     							=					message[4:]
    UpperCAmelCase__	:      Dict     							=					apply_table(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					xor(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Dict     							=					apply_sbox(_snake_case    ,temp[:4] )  # noqa: E741
    UpperCAmelCase__	:      Tuple     							=					apply_sbox(_snake_case    ,temp[4:] )
    UpperCAmelCase__	:      List[str]     							=					'0' * (2 - len(_snake_case )) + l  # noqa: E741
    UpperCAmelCase__	:      Optional[Any]     							=					'0' * (2 - len(_snake_case )) + r
    UpperCAmelCase__	:      Optional[int]     							=					apply_table(l + r    ,_snake_case )
    UpperCAmelCase__	:      Any     							=					xor(_snake_case    ,_snake_case )
    return temp + right


if __name__ == "__main__":
    UpperCamelCase__          =	input('Enter 10 bit key: ')
    UpperCamelCase__          =	input('Enter 8 bit message: ')

    UpperCamelCase__          =	[6, 3, 7, 4, 8, 5, 10, 9]
    UpperCamelCase__          =	[3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
    UpperCamelCase__          =	[2, 4, 3, 1]
    UpperCamelCase__          =	[2, 6, 3, 1, 4, 8, 5, 7]
    UpperCamelCase__          =	[4, 1, 3, 5, 7, 2, 8, 6]
    UpperCamelCase__          =	[4, 1, 2, 3, 2, 3, 4, 1]
    UpperCamelCase__          =	[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
    UpperCamelCase__          =	[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

    # key generation
    UpperCamelCase__          =	apply_table(key, paa_table)
    UpperCamelCase__          =	temp[:5]
    UpperCamelCase__          =	temp[5:]
    UpperCamelCase__          =	left_shift(left)
    UpperCamelCase__          =	left_shift(right)
    UpperCamelCase__          =	apply_table(left + right, pa_table)
    UpperCamelCase__          =	left_shift(left)
    UpperCamelCase__          =	left_shift(right)
    UpperCamelCase__          =	left_shift(left)
    UpperCamelCase__          =	left_shift(right)
    UpperCamelCase__          =	apply_table(left + right, pa_table)

    # encryption
    UpperCamelCase__          =	apply_table(message, IP)
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	temp[4:] + temp[:4]
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	apply_table(temp, IP_inv)
    print('Cipher text is:', CT)

    # decryption
    UpperCamelCase__          =	apply_table(CT, IP)
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	temp[4:] + temp[:4]
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	apply_table(temp, IP_inv)
    print('Plain text after decypting is:', PT)

