"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					List[str]							=					KandinskyVaaControlnetImgaImgPipeline
    UpperCamelCase_	:					List[str]							=					['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
    UpperCamelCase_	:					Optional[Any]							=					['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
    UpperCamelCase_	:					Tuple							=					[
        '''generator''',
        '''height''',
        '''width''',
        '''strength''',
        '''guidance_scale''',
        '''num_inference_steps''',
        '''return_dict''',
        '''guidance_scale''',
        '''num_images_per_prompt''',
        '''output_type''',
        '''return_dict''',
    ]
    UpperCamelCase_	:					Dict							=					False
    @property
    def  lowercase					(		self							:     List[str]    )    ->       int:
         return 32
    @property
    def  lowercase					(		self							:     Tuple    )    ->       int:
         return 32
    @property
    def  lowercase					(		self							:     List[str]    )    ->       Union[str, Any]:
         return self.time_input_dim
    @property
    def  lowercase					(		self							:     Dict    )    ->       Any:
         return self.time_input_dim * 4
    @property
    def  lowercase					(		self							:     Tuple    )    ->       Optional[Any]:
         return 100
    @property
    def  lowercase					(		self							:     List[Any]    )    ->       int:
         torch.manual_seed(0    )

         __snake_case				=							{
             '''in_channels''': 8,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''image_hint''',
             '''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,
         }

         __snake_case				=							UNetaDConditionModel(**A_    )
         return model
    @property
    def  lowercase					(		self							:     int    )    ->       Optional[int]:
         return {
             "block_out_channels": [32, 32, 64, 64],
             "down_block_types": [
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
             "vq_embed_dim": 4,
         }
    @property
    def  lowercase					(		self							:     Optional[Any]    )    ->       Optional[Any]:
         torch.manual_seed(0    )
         __snake_case				=							VQModel(**self.dummy_movq_kwargs    )
         return model
    def  lowercase					(		self							:     int    )    ->       Any:
         __snake_case				=							self.dummy_unet
         __snake_case				=							self.dummy_movq

         __snake_case				=							{
             '''num_train_timesteps''': 1_000,
             '''beta_schedule''': '''linear''',
             '''beta_start''': 0.0_00_85,
             '''beta_end''': 0.0_12,
             '''clip_sample''': False,
             '''set_alpha_to_one''': False,
             '''steps_offset''': 0,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
         }

         __snake_case				=							DDIMScheduler(**A_    )

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

         return components
    def  lowercase					(		self							:     List[Any]	,      A_							:     Tuple	,      A_							:     List[Any]=0    )    ->       str:
         __snake_case				=							floats_tensor((1, self.text_embedder_hidden_size)	,      rng=random.Random(A_    )    ).to(A_    )
         __snake_case				=							floats_tensor((1, self.text_embedder_hidden_size)	,      rng=random.Random(seed + 1    )    ).to(
             A_    )
         # create init_image
         __snake_case				=							floats_tensor((1, 3, 64, 64)	,      rng=random.Random(A_    )    ).to(A_    )
         __snake_case				=							image.cpu().permute(0	,      2	,      3	,      1    )[0]
         __snake_case				=							Image.fromarray(np.uinta(A_    )    ).convert('''RGB'''    ).resize((256, 256)    )
         # create hint
         __snake_case				=							floats_tensor((1, 3, 64, 64)	,      rng=random.Random(A_    )    ).to(A_    )

         if str(A_    ).startswith('''mps'''    ):
              __snake_case				=							torch.manual_seed(A_    )
         else:
              __snake_case				=							torch.Generator(device=A_    ).manual_seed(A_    )
         __snake_case				=							{
             '''image''': init_image,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''hint''': hint,
             '''generator''': generator,
             '''height''': 64,
             '''width''': 64,
             '''num_inference_steps''': 10,
             '''guidance_scale''': 7.0,
             '''strength''': 0.2,
             '''output_type''': '''np''',
         }
         return inputs







    def  lowercase					(		self							:     Union[str, Any]    )    ->       int:
         __snake_case				=							'''cpu'''

         __snake_case				=							self.get_dummy_components()

         __snake_case				=							self.pipeline_class(**A_    )
         __snake_case				=							pipe.to(A_    )

         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							pipe(**self.get_dummy_inputs(A_    )    )
         __snake_case				=							output.images

         __snake_case				=							pipe(
             **self.get_dummy_inputs(A_    )	,      return_dict=A_	,      )[0]

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

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

         __snake_case				=							np.array(
             [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]    )
         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()}"






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




    """simple docstring"""




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







    def  lowercase					(		self							:     Optional[int]    )    ->       int:
         __snake_case				=							load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''    )

         __snake_case				=							load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''    )
         __snake_case				=							init_image.resize((512, 512)    )

         __snake_case				=							load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinskyv22/hint_image_cat.png'''    )
         __snake_case				=							torch.from_numpy(np.array(A_    )    ).float() / 2_55.0
         __snake_case				=							hint.permute(2	,      0	,      1    ).unsqueeze(0    )

         __snake_case				=							'''A robot, 4k photo'''

         __snake_case				=							KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-prior'''	,      torch_dtype=torch.floataa    )
         pipe_prior.to(A_    )

         __snake_case				=							KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-controlnet-depth'''	,      torch_dtype=torch.floataa    )
         __snake_case				=							pipeline.to(A_    )

         pipeline.set_progress_bar_config(disable=A_    )

         __snake_case				=							torch.Generator(device='''cpu'''    ).manual_seed(0    )

         __snake_case		,	__snake_case				=							pipe_prior(
             A_	,      image=A_	,      strength=0.85	,      generator=A_	,      negative_prompt=''''''	,      ).to_tuple()

         __snake_case				=							pipeline(
             image=A_	,      image_embeds=A_	,      negative_image_embeds=A_	,      hint=A_	,      generator=A_	,      num_inference_steps=100	,      height=512	,      width=512	,      strength=0.5	,      output_type='''np'''	,      )

         __snake_case				=							output.images[0]

         assert image.shape == (512, 512, 3)

         assert_mean_pixel_difference(A_	,      A_    )
"""simple docstring"""



def    SCREAMING_SNAKE_CASE       (     snake_case):
     return 1 if digit in (0, 1) else (digit * factorial(digit - 1))





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							0
     __snake_case				=							number
     while duplicate > 0:
          __snake_case		,	__snake_case				=							divmod(snake_case,      10)
          fact_sum += factorial(snake_case)
     return fact_sum == number


if __name__ == "__main__":
       print("Program to check whether a number is a Krisnamurthy Number or not.")
       __lowercase		:		Optional[int]														=							int(input("Enter number: ").strip())
       print(
           F"""{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."""
       )