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       snake_case					(      lowerCAmelCase__   ,						unittest.TestCase						):
  lowercase_			=     KandinskyVaaControlnetImgaImgPipeline
  lowercase_			=     ["image_embeds", "negative_image_embeds", "image", "hint"]
  lowercase_			=     ["image_embeds", "negative_image_embeds", "image", "hint"]
  lowercase_			=     [
      "generator",
      "height",
      "width",
      "strength",
      "guidance_scale",
      "num_inference_steps",
      "return_dict",
      "guidance_scale",
      "num_images_per_prompt",
      "output_type",
      "return_dict",
  ]
  lowercase_			=     False




  @property
  def 	__lowercase(      self     :	List[Any]				)->       Dict:




       """simple docstring"""
       return 32




  @property
  def 	__lowercase(      self     :	Optional[Any]				)->       List[str]:




       """simple docstring"""
       return 32




  @property
  def 	__lowercase(      self     :	Any				)->       str:




       """simple docstring"""
       return self.time_input_dim




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




       """simple docstring"""
       return self.time_input_dim * 4




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




       """simple docstring"""
       return 100




  @property
  def 	__lowercase(      self     :	Dict				)->       Dict:




       """simple docstring"""
       torch.manual_seed(0				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{
           '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,
       }

       SCREAMING_SNAKE_CASE__				:				Any			=		UNetaDConditionModel(**_lowerCamelCase				)
       return model




  @property
  def 	__lowercase(      self     :	Any				)->       List[Any]:




       """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 	__lowercase(      self     :	List[str]				)->       Optional[Any]:




       """simple docstring"""
       torch.manual_seed(0				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		VQModel(**self.dummy_movq_kwargs				)
       return model




  def 	__lowercase(      self     :	Dict				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.dummy_unet
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.dummy_movq

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{
           'num_train_timesteps': 1000,
           'beta_schedule': 'linear',
           'beta_start': 0.0_0085,
           'beta_end': 0.012,
           'clip_sample': False,
           'set_alpha_to_one': False,
           'steps_offset': 0,
           'prediction_type': 'epsilon',
           'thresholding': False,
       }

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		DDIMScheduler(**_lowerCamelCase				)

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

       return components




  def 	__lowercase(      self     :	Optional[int]     , a_     :	List[str]     , a_     :	Union[str, Any]=0				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		floats_tensor((1, self.text_embedder_hidden_size)     , rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		floats_tensor((1, self.text_embedder_hidden_size)     , rng=random.Random(seed + 1				)				).to(
           _lowerCamelCase				)
       # create init_image
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		floats_tensor((1, 3, 64, 64)     , rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		image.cpu().permute(0     , 2     , 3     , 1				)[0]
       SCREAMING_SNAKE_CASE__				:				str			=		Image.fromarray(np.uinta(_lowerCamelCase				)				).convert('RGB'				).resize((256, 256)				)
       # create hint
       SCREAMING_SNAKE_CASE__				:				str			=		floats_tensor((1, 3, 64, 64)     , rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)

       if str(_lowerCamelCase				).startswith('mps'				):
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.manual_seed(_lowerCamelCase				)
       else:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.Generator(device=_lowerCamelCase				).manual_seed(_lowerCamelCase				)
       SCREAMING_SNAKE_CASE__				:				str			=		{
           '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]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'cpu'

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.get_dummy_components()

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.pipeline_class(**_lowerCamelCase				)
       SCREAMING_SNAKE_CASE__				:				int			=		pipe.to(_lowerCamelCase				)

       pipe.set_progress_bar_config(disable=_lowerCamelCase				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		pipe(**self.get_dummy_inputs(_lowerCamelCase				)				)
       SCREAMING_SNAKE_CASE__				:				Any			=		output.images

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		pipe(
           **self.get_dummy_inputs(_lowerCamelCase				)     , return_dict=_lowerCamelCase     , )[0]

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

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

       SCREAMING_SNAKE_CASE__				:				Any			=		np.array(
           [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]				)
       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       snake_case					(      unittest.TestCase						):




  def 	__lowercase(      self     :	int				)->       Optional[Any]:




       """simple docstring"""
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




  def 	__lowercase(      self     :	Any				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
       SCREAMING_SNAKE_CASE__				:				str			=		init_image.resize((512, 512)				)

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

       SCREAMING_SNAKE_CASE__				:				Any			=		'A robot, 4k photo'

       SCREAMING_SNAKE_CASE__				:				Any			=		KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-prior'     , torch_dtype=torch.floataa				)
       pipe_prior.to(_lowerCamelCase				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-controlnet-depth'     , torch_dtype=torch.floataa				)
       SCREAMING_SNAKE_CASE__				:				str			=		pipeline.to(_lowerCamelCase				)

       pipeline.set_progress_bar_config(disable=_lowerCamelCase				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.Generator(device='cpu'				).manual_seed(0				)

       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		pipe_prior(
           _lowerCamelCase     , image=_lowerCamelCase     , strength=0.85     , generator=_lowerCamelCase     , negative_prompt=''     , ).to_tuple()

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		pipeline(
           image=_lowerCamelCase     , image_embeds=_lowerCamelCase     , negative_image_embeds=_lowerCamelCase     , hint=_lowerCamelCase     , generator=_lowerCamelCase     , num_inference_steps=100     , height=512     , width=512     , strength=0.5     , output_type='np'     , )

       SCREAMING_SNAKE_CASE__				:				List[str]			=		output.images[0]

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

       assert_mean_pixel_difference(_lowerCamelCase     , _lowerCamelCase				)




def     _a				(      lowercase__  :			int = 1_00_00_00 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		[i - 1 for i in range(limit + 1 )]

     for i in range(2     ,  limit + 1 ):
          if phi[i] == i - 1:
               for j in range(2 * i     ,  limit + 1     ,  lowercase__ ):
                    phi[j] -= phi[j] // i

     return sum(phi[2 : limit + 1] )


if __name__ == "__main__":
      print(solution())



