


"""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      (      SCREAMING_SNAKE_CASE_		,		unittest.TestCase ):
    a_    :				int             =    KandinskyVaaControlnetImgaImgPipeline
    a_    :				List[str]             =    ['image_embeds', 'negative_image_embeds', 'image', 'hint']
    a_    :				Dict             =    ['image_embeds', 'negative_image_embeds', 'image', 'hint']
    a_    :				Optional[int]             =    [
        'generator',
        'height',
        'width',
        'strength',
        'guidance_scale',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    a_    :				Union[str, Any]             =    False
    @property
    def      _UpperCamelCase       ( self							:					str				):
          return 32
    @property
    def      _UpperCamelCase       ( self							:					int				):
          return 32
    @property
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          return self.time_input_dim
    @property
    def      _UpperCamelCase       ( self							:					Tuple				):
          return self.time_input_dim * 4
    @property
    def      _UpperCamelCase       ( self							:					Tuple				):
          return 1_00
    @property
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          torch.manual_seed(0				)

          lowerCamelCase__           =		{
              '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,
          }

          lowerCamelCase__           =		UNetaDConditionModel(**SCREAMING_SNAKE_CASE__				)
          return model
    @property
    def      _UpperCamelCase       ( self							:					Tuple				):
          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      _UpperCamelCase       ( self							:					Tuple				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		VQModel(**self.dummy_movq_kwargs				)
          return model
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		self.dummy_unet
          lowerCamelCase__           =		self.dummy_movq

          lowerCamelCase__           =		{
              'num_train_timesteps': 10_00,
              '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,
          }

          lowerCamelCase__           =		DDIMScheduler(**SCREAMING_SNAKE_CASE__				)

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

          return components
    def      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=0				):
          lowerCamelCase__           =		floats_tensor((1, self.text_embedder_hidden_size)							,					rng=random.Random(SCREAMING_SNAKE_CASE__				)				).to(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		floats_tensor((1, self.text_embedder_hidden_size)							,					rng=random.Random(seed + 1				)				).to(
              SCREAMING_SNAKE_CASE__				)
          # create init_image
          lowerCamelCase__           =		floats_tensor((1, 3, 64, 64)							,					rng=random.Random(SCREAMING_SNAKE_CASE__				)				).to(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		image.cpu().permute(0							,					2							,					3							,					1				)[0]
          lowerCamelCase__           =		Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__				)				).convert('RGB'				).resize((2_56, 2_56)				)
          # create hint
          lowerCamelCase__           =		floats_tensor((1, 3, 64, 64)							,					rng=random.Random(SCREAMING_SNAKE_CASE__				)				).to(SCREAMING_SNAKE_CASE__				)

          if str(SCREAMING_SNAKE_CASE__				).startswith('mps'				):
                lowerCamelCase__           =		torch.manual_seed(SCREAMING_SNAKE_CASE__				)
          else:
                lowerCamelCase__           =		torch.Generator(device=SCREAMING_SNAKE_CASE__				).manual_seed(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              '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      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		'cpu'

          lowerCamelCase__           =		self.get_dummy_components()

          lowerCamelCase__           =		self.pipeline_class(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		pipe.to(SCREAMING_SNAKE_CASE__				)

          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)				)
          lowerCamelCase__           =		output.images

          lowerCamelCase__           =		pipe(
              **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)							,					return_dict=SCREAMING_SNAKE_CASE__							,					)[0]

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

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

          lowerCamelCase__           =		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 ):
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'				)

          lowerCamelCase__           =		load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
          lowerCamelCase__           =		init_image.resize((5_12, 5_12)				)

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

          lowerCamelCase__           =		'A robot, 4k photo'

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

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

          pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

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

          lowerCamelCase__    ,  lowerCamelCase__           =		pipe_prior(
              SCREAMING_SNAKE_CASE__							,					image=SCREAMING_SNAKE_CASE__							,					strength=0.85							,					generator=SCREAMING_SNAKE_CASE__							,					negative_prompt=''							,					).to_tuple()

          lowerCamelCase__           =		pipeline(
              image=SCREAMING_SNAKE_CASE__							,					image_embeds=SCREAMING_SNAKE_CASE__							,					negative_image_embeds=SCREAMING_SNAKE_CASE__							,					hint=SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					num_inference_steps=1_00							,					height=5_12							,					width=5_12							,					strength=0.5							,					output_type='np'							,					)

          lowerCamelCase__           =		output.images[0]

          assert image.shape == (5_12, 5_12, 3)

          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)







"""simple docstring"""


def   snake_case						(				_a:     int = 4000000							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		[0, 1]
      lowerCamelCase__           =		0
      while fib[i] <= n:
            fib.append(fib[i] + fib[i + 1]							)
            if fib[i + 2] > n:
                  break
            i += 1
      lowerCamelCase__           =		0
      for j in range(len(_a							) - 1							):
            if fib[j] % 2 == 0:
                  total += fib[j]

      return total


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



