
'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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  A				(							__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		KandinskyVaaControlnetPipeline
     __magic_name__						=		['''image_embeds''', '''negative_image_embeds''', '''hint''']
     __magic_name__						=		['''image_embeds''', '''negative_image_embeds''', '''hint''']
     __magic_name__						=		[
         '''generator''',
         '''height''',
         '''width''',
         '''latents''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     __magic_name__						=		False





     @property
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         return 32





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






         """simple docstring"""






         return 32





     @property
     def 				__lowerCAmelCase							(							self	) ->				Optional[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 100





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






         """simple docstring"""






         torch.manual_seed(0	)

         A       :   Union[str, Any]					=						{
             '''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,
         }

         A       :   Any					=						UNetaDConditionModel(**SCREAMING_SNAKE_CASE	)
         return model





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






         """simple docstring"""






         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 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   str					=						self.dummy_unet
         A       :   Optional[int]					=						self.dummy_movq

         A       :   List[Any]					=						DDIMScheduler(
             num_train_timesteps=1000						,			beta_schedule='''linear'''						,			beta_start=0.00_085						,			beta_end=0.012						,			clip_sample=SCREAMING_SNAKE_CASE						,			set_alpha_to_one=SCREAMING_SNAKE_CASE						,			steps_offset=1						,			prediction_type='''epsilon'''						,			thresholding=SCREAMING_SNAKE_CASE						,			)

         A       :   int					=						{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }
         return components





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=0	) ->				Tuple:






         """simple docstring"""






         A       :   Union[str, Any]					=						floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(SCREAMING_SNAKE_CASE	)	).to(SCREAMING_SNAKE_CASE	)
         A       :   Optional[Any]					=						floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(seed + 1	)	).to(
             SCREAMING_SNAKE_CASE	)

         # create hint
         A       :   List[Any]					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(SCREAMING_SNAKE_CASE	)	).to(SCREAMING_SNAKE_CASE	)

         if str(SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
             A       :   List[str]					=						torch.manual_seed(SCREAMING_SNAKE_CASE	)
         else:
             A       :   Any					=						torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						{
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''hint''': hint,
             '''generator''': generator,
             '''height''': 64,
             '''width''': 64,
             '''guidance_scale''': 4.0,
             '''num_inference_steps''': 2,
             '''output_type''': '''np''',
         }
         return inputs





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






         """simple docstring"""






         A       :   int					=						'''cpu'''

         A       :   Dict					=						self.get_dummy_components()

         A       :   Optional[Any]					=						self.pipeline_class(**SCREAMING_SNAKE_CASE	)
         A       :   Any					=						pipe.to(SCREAMING_SNAKE_CASE	)

         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

         A       :   Any					=						pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)	)
         A       :   Any					=						output.images

         A       :   Tuple					=						pipe(
             **self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)						,			return_dict=SCREAMING_SNAKE_CASE						,			)[0]

         A       :   Tuple					=						image[0, -3:, -3:, -1]
         A       :   Optional[int]					=						image_from_tuple[0, -3:, -3:, -1]

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

         A       :   List[Any]					=						np.array(
             [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]	)

         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						):





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         A       :   Dict					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''	)

         A       :   Optional[int]					=						load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinskyv22/hint_image_cat.png'''	)
         A       :   Optional[Any]					=						torch.from_numpy(np.array(SCREAMING_SNAKE_CASE	)	).float() / 255.0
         A       :   List[Any]					=						hint.permute(2						,			0						,			1	).unsqueeze(0	)

         A       :   str					=						KandinskyVaaPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-prior'''						,			torch_dtype=torch.floataa	)
         pipe_prior.to(SCREAMING_SNAKE_CASE	)

         A       :   List[str]					=						KandinskyVaaControlnetPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-controlnet-depth'''						,			torch_dtype=torch.floataa	)
         A       :   Any					=						pipeline.to(SCREAMING_SNAKE_CASE	)
         pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

         A       :   Union[str, Any]					=						'''A robot, 4k photo'''

         A       :   Optional[int]					=						torch.Generator(device='''cuda'''	).manual_seed(0	)
         A,  A       :   Tuple					=						pipe_prior(
             SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=5						,			negative_prompt=''''''						,			).to_tuple()

         A       :   List[Any]					=						torch.Generator(device='''cuda'''	).manual_seed(0	)
         A       :   Union[str, Any]					=						pipeline(
             image_embeds=SCREAMING_SNAKE_CASE						,			negative_image_embeds=SCREAMING_SNAKE_CASE						,			hint=SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=100						,			output_type='''np'''						,			)

         A       :   int					=						output.images[0]

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

         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)






'''simple docstring'''




def 		lowerCAmelCase_				(   snake_case__ = 10						):




    '''simple docstring'''



    if not isinstance(snake_case__		,				snake_case__						) or n < 0:
        raise ValueError('''Invalid input'''						)
    A       :   List[str]					=						10**n
    A       :   Tuple					=						2_8433 * (pow(2		,				783_0457		,				snake_case__						)) + 1
    return str(number % modulus						)


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      print(f'''{solution(10) = }''')




