






import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		List[str]								=						KandinskyVaaPipeline
     UpperCamelCase_				:		List[Any]								=						[
         '''image_embeds''',
         '''negative_image_embeds''',
     ]
     UpperCamelCase_				:		Tuple								=						['''image_embeds''', '''negative_image_embeds''']
     UpperCamelCase_				:		Any								=						[
         '''generator''',
         '''height''',
         '''width''',
         '''latents''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     UpperCamelCase_				:		List[str]								=						False


     @property
     def 					_A							(					self							:						List[Any]   ):
       return 32


     @property
     def 					_A							(					self							:						List[Any]   ):
       return 32


     @property
     def 					_A							(					self							:						Any   ):
       return self.time_input_dim


     @property
     def 					_A							(					self							:						Union[str, Any]   ):
       return self.time_input_dim * 4


     @property
     def 					_A							(					self							:						Tuple   ):
       return 100


     @property
     def 					_A							(					self							:						Optional[int]   ):
       torch.manual_seed(0   )

       SCREAMING_SNAKE_CASE							:   int						    =     {
           "in_channels": 4,
           # 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,
       }

       SCREAMING_SNAKE_CASE							:   str						    =     UNetaDConditionModel(**UpperCAmelCase_   )
       return model


     @property
     def 					_A							(					self							:						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 					_A							(					self							:						Any   ):
       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     VQModel(**self.dummy_movq_kwargs   )
       return model


     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.dummy_unet
       SCREAMING_SNAKE_CASE							:   str						    =     self.dummy_movq

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     DDIMScheduler(
           num_train_timesteps=1000       ,			beta_schedule="linear"       ,			beta_start=0.00_085       ,			beta_end=0.012       ,			clip_sample=UpperCAmelCase_       ,			set_alpha_to_one=UpperCAmelCase_       ,			steps_offset=1       ,			prediction_type="epsilon"       ,			thresholding=UpperCAmelCase_       ,			)

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     {
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }
       return components


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						Optional[Any]=0   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     floats_tensor((1, self.text_embedder_hidden_size)       ,			rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     floats_tensor((1, self.text_embedder_hidden_size)       ,			rng=random.Random(seed + 1   )   ).to(
           UpperCAmelCase_   )
       if str(UpperCAmelCase_   ).startswith("mps"   ):
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     torch.manual_seed(UpperCAmelCase_   )
       else:
         SCREAMING_SNAKE_CASE							:   Dict						    =     torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     {
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "generator": generator,
           "height": 64,
           "width": 64,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs






     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     "cpu"

       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.get_dummy_components()

       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.pipeline_class(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     pipe.to(UpperCAmelCase_   )

       pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     pipe(**self.get_dummy_inputs(UpperCAmelCase_   )   )
       SCREAMING_SNAKE_CASE							:   str						    =     output.images

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

       SCREAMING_SNAKE_CASE							:   str						    =     image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE							:   Any						    =     image_from_tuple[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     np.array(
           [0.6_237_976, 1.0, 0.36_441_332, 1.0, 0.70_639_634, 0.29_877_186, 0.85_652_125, 0.5_216_843, 0.54_454_046]   )

       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 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


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






     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"   )

       SCREAMING_SNAKE_CASE							:   int						    =     KandinskyVaaPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-prior"       ,			torch_dtype=torch.floataa   )
       pipe_prior.to(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   int						    =     KandinskyVaaPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-decoder"       ,			torch_dtype=torch.floataa   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     pipeline.to(UpperCAmelCase_   )
       pipeline.set_progress_bar_config(disable=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     "red cat, 4k photo"

       SCREAMING_SNAKE_CASE							:   Tuple						    =     torch.Generator(device="cuda"   ).manual_seed(0   )
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Tuple						    =     pipe_prior(
           UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			num_inference_steps=5       ,			negative_prompt=""       ,			).to_tuple()

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     torch.Generator(device="cuda"   ).manual_seed(0   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     pipeline(
           image_embeds=UpperCAmelCase_       ,			negative_image_embeds=UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			num_inference_steps=100       ,			output_type="np"       ,			)

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     output.images[0]

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

       assert_mean_pixel_difference(UpperCAmelCase_       ,			UpperCAmelCase_   )












'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> int:




     '''simple docstring'''





     if divisor % 5 == 0 or divisor % 2 == 0:
          return 0
     __snake_case            =	1
     __snake_case            =	1
     while repunit:
          __snake_case            =	(10 * repunit + 1) % divisor
          repunit_index += 1
     return repunit_index







def       _UpperCamelCase			(_lowerCamelCase				:  int = 1_00_00_00						)-> int:




     '''simple docstring'''





     __snake_case            =	limit - 1
     if divisor % 2 == 0:
          divisor += 1
     while least_divisible_repunit(_lowerCamelCase						) <= limit:
          divisor += 2
     return divisor


if __name__ == "__main__":
     print(F"""{solution() = }""")






