






"""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 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase       ,		unittest.TestCase		):
							snake_case_     =							KandinskyVaaControlnetImgaImgPipeline
							snake_case_     =							['image_embeds', 'negative_image_embeds', 'image', 'hint']
							snake_case_     =							['image_embeds', 'negative_image_embeds', 'image', 'hint']
							snake_case_     =							[
							    'generator',
							    'height',
							    'width',
							    'strength',
							    'guidance_scale',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							snake_case_     =							False


							@property
							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													return 32


							@property
							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													return 32


							@property
							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													return self.time_input_dim


							@property
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													return self.time_input_dim * 4


							@property
							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													return 100


							@property
							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													torch.manual_seed(0							)

													A__						:  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,
													}

													A__						:  int		  =  UNetaDConditionModel(**snake_case							)
													return model


							@property
							def 						_UpperCamelCase       (			self      :						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 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													torch.manual_seed(0							)
													A__						:  Union[str, Any]		  =  VQModel(**self.dummy_movq_kwargs							)
													return model


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													A__						:  Dict		  =  self.dummy_unet
													A__						:  Dict		  =  self.dummy_movq

													A__						:  List[str]		  =  {
													    """num_train_timesteps""": 1000,
													    """beta_schedule""": """linear""",
													    """beta_start""": 0.00085,
													    """beta_end""": 0.012,
													    """clip_sample""": False,
													    """set_alpha_to_one""": False,
													    """steps_offset""": 0,
													    """prediction_type""": """epsilon""",
													    """thresholding""": False,
													}

													A__						:  Any		  =  DDIMScheduler(**snake_case							)

													A__						:  List[str]		  =  {
													    """unet""": unet,
													    """scheduler""": scheduler,
													    """movq""": movq,
													}

													return components


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Tuple			,    snake_case      :						List[Any]=0							):
													'''simple docstring'''





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

													if str(snake_case							).startswith("""mps"""							):
																			A__						:  Union[str, Any]		  =  torch.manual_seed(snake_case							)
													else:
																			A__						:  Dict		  =  torch.Generator(device=snake_case							).manual_seed(snake_case							)
													A__						:  int		  =  {
													    """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      :						Any							):
													'''simple docstring'''





													A__						:  List[Any]		  =  """cpu"""

													A__						:  Optional[Any]		  =  self.get_dummy_components()

													A__						:  List[Any]		  =  self.pipeline_class(**snake_case							)
													A__						:  Tuple		  =  pipe.to(snake_case							)

													pipe.set_progress_bar_config(disable=snake_case							)

													A__						:  Optional[int]		  =  pipe(**self.get_dummy_inputs(snake_case							)							)
													A__						:  List[Any]		  =  output.images

													A__						:  Dict		  =  pipe(
													    **self.get_dummy_inputs(snake_case							)			,    return_dict=snake_case			,    )[0]

													A__						:  Dict		  =  image[0, -3:, -3:, -1]
													A__						:  List[str]		  =  image_from_tuple[0, -3:, -3:, -1]

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

													A__						:  List[str]		  =  np.array(
													    [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]							)
													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		):


							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													A__						:  Dict		  =  load_numpy(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""							)

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

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

													A__						:  int		  =  """A robot, 4k photo"""

													A__						:  int		  =  KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
													    """kandinsky-community/kandinsky-2-2-prior"""			,    torch_dtype=torch.floataa							)
													pipe_prior.to(snake_case							)

													A__						:  Optional[Any]		  =  KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
													    """kandinsky-community/kandinsky-2-2-controlnet-depth"""			,    torch_dtype=torch.floataa							)
													A__						:  Any		  =  pipeline.to(snake_case							)

													pipeline.set_progress_bar_config(disable=snake_case							)

													A__						:  List[str]		  =  torch.Generator(device="""cpu"""							).manual_seed(0							)

													A__  ,  A__						:  Dict		  =  pipe_prior(
													    snake_case			,    image=snake_case			,    strength=0.85			,    generator=snake_case			,    negative_prompt=""""""			,    ).to_tuple()

													A__						:  Any		  =  pipeline(
													    image=snake_case			,    image_embeds=snake_case			,    negative_image_embeds=snake_case			,    hint=snake_case			,    generator=snake_case			,    num_inference_steps=100			,    height=512			,    width=512			,    strength=0.5			,    output_type="""np"""			,    )

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

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

													assert_mean_pixel_difference(snake_case			,    snake_case							)














"""simple docstring"""



from __future__ import annotations

A_    =							10




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[int]      )		->list[int]:
						A__						:  Any		  =  1
						A__						:  Optional[int]		  =  max(UpperCAmelCase__      )
						while placement <= max_digit:
												# declare and initialize empty buckets
												A__						:  list[list]		  =  [[] for _ in range(UpperCAmelCase__      )]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		A__						:  List[Any]		  =  int((i / placement) % RADIX      )
																		buckets[tmp].append(UpperCAmelCase__      )
												# put each buckets' contents into list_of_ints
												A__						:  Union[str, Any]		  =  0
												for b in range(UpperCAmelCase__      ):
																		for i in buckets[b]:
																								A__						:  str		  =  i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
						import doctest

						doctest.testmod()






