






"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()






class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


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





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


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





													A__						:  Any		  =  1
													A__						:  int		  =  3
													A__						:  Tuple		  =  (32, 32)

													A__						:  List[Any]		  =  floats_tensor((batch_size, num_channels) + sizes			,    rng=random.Random(0							)							).to(snake_case							)
													return image


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





													torch.manual_seed(0							)
													A__						:  int		  =  UNetaDConditionModel(
													    block_out_channels=(32, 32, 64)			,    layers_per_block=2			,    sample_size=32			,    in_channels=7			,    out_channels=4			,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""")			,    up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""")			,    cross_attention_dim=32			,    attention_head_dim=8			,    use_linear_projection=snake_case			,    only_cross_attention=(True, True, False)			,    num_class_embeds=100			,    )
													return model


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





													torch.manual_seed(0							)
													A__						:  List[Any]		  =  AutoencoderKL(
													    block_out_channels=[32, 32, 64]			,    in_channels=3			,    out_channels=3			,    down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,    up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,    latent_channels=4			,    )
													return model


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





													torch.manual_seed(0							)
													A__						:  Optional[int]		  =  CLIPTextConfig(
													    bos_token_id=0			,    eos_token_id=2			,    hidden_size=32			,    intermediate_size=37			,    layer_norm_eps=1e-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    pad_token_id=1			,    vocab_size=1000			,    hidden_act="""gelu"""			,    projection_dim=512			,    )
													return CLIPTextModel(snake_case							)


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





													A__						:  Optional[Any]		  =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
													A__						:  Tuple		  =  self.dummy_cond_unet_upscale
													A__						:  Union[str, Any]		  =  DDPMScheduler()
													A__						:  str		  =  DDIMScheduler(prediction_type="""v_prediction"""							)
													A__						:  List[str]		  =  self.dummy_vae
													A__						:  str		  =  self.dummy_text_encoder
													A__						:  str		  =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

													A__						:  Optional[Any]		  =  self.dummy_image.cpu().permute(0			,    2			,    3			,    1							)[0]
													A__						:  List[str]		  =  Image.fromarray(np.uinta(snake_case							)							).convert("""RGB"""							).resize((64, 64)							)

													# make sure here that pndm scheduler skips prk
													A__						:  Any		  =  StableDiffusionUpscalePipeline(
													    unet=snake_case			,    low_res_scheduler=snake_case			,    scheduler=snake_case			,    vae=snake_case			,    text_encoder=snake_case			,    tokenizer=snake_case			,    max_noise_level=350			,    )
													A__						:  Optional[int]		  =  sd_pipe.to(snake_case							)
													sd_pipe.set_progress_bar_config(disable=snake_case							)

													A__						:  str		  =  """A painting of a squirrel eating a burger"""
													A__						:  Union[str, Any]		  =  torch.Generator(device=snake_case							).manual_seed(0							)
													A__						:  int		  =  sd_pipe(
													    [prompt]			,    image=snake_case			,    generator=snake_case			,    guidance_scale=6.0			,    noise_level=20			,    num_inference_steps=2			,    output_type="""np"""			,    )

													A__						:  Dict		  =  output.images

													A__						:  Union[str, Any]		  =  torch.Generator(device=snake_case							).manual_seed(0							)
													A__						:  int		  =  sd_pipe(
													    [prompt]			,    image=snake_case			,    generator=snake_case			,    guidance_scale=6.0			,    noise_level=20			,    num_inference_steps=2			,    output_type="""np"""			,    return_dict=snake_case			,    )[0]

													A__						:  Optional[int]		  =  image[0, -3:, -3:, -1]
													A__						:  List[Any]		  =  image_from_tuple[0, -3:, -3:, -1]

													A__						:  List[Any]		  =  low_res_image.size[0] * 4
													assert image.shape == (1, expected_height_width, expected_height_width, 3)
													A__						:  Tuple		  =  np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]							)

													assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2


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





													A__						:  Dict		  =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
													A__						:  List[Any]		  =  self.dummy_cond_unet_upscale
													A__						:  List[str]		  =  DDPMScheduler()
													A__						:  Tuple		  =  DDIMScheduler(prediction_type="""v_prediction"""							)
													A__						:  List[str]		  =  self.dummy_vae
													A__						:  List[Any]		  =  self.dummy_text_encoder
													A__						:  Union[str, Any]		  =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

													A__						:  Any		  =  self.dummy_image.cpu().permute(0			,    2			,    3			,    1							)[0]
													A__						:  int		  =  Image.fromarray(np.uinta(snake_case							)							).convert("""RGB"""							).resize((64, 64)							)

													# make sure here that pndm scheduler skips prk
													A__						:  List[Any]		  =  StableDiffusionUpscalePipeline(
													    unet=snake_case			,    low_res_scheduler=snake_case			,    scheduler=snake_case			,    vae=snake_case			,    text_encoder=snake_case			,    tokenizer=snake_case			,    max_noise_level=350			,    )
													A__						:  Dict		  =  sd_pipe.to(snake_case							)
													sd_pipe.set_progress_bar_config(disable=snake_case							)

													A__						:  str		  =  """A painting of a squirrel eating a burger"""
													A__						:  int		  =  sd_pipe(
													    2 * [prompt]			,    image=2 * [low_res_image]			,    guidance_scale=6.0			,    noise_level=20			,    num_inference_steps=2			,    output_type="""np"""			,    )
													A__						:  int		  =  output.images
													assert image.shape[0] == 2

													A__						:  Dict		  =  torch.Generator(device=snake_case							).manual_seed(0							)
													A__						:  Tuple		  =  sd_pipe(
													    [prompt]			,    image=snake_case			,    generator=snake_case			,    num_images_per_prompt=2			,    guidance_scale=6.0			,    noise_level=20			,    num_inference_steps=2			,    output_type="""np"""			,    )
													A__						:  Any		  =  output.images
													assert image.shape[0] == 2


							@unittest.skipIf(torch_device != """cuda"""			,    """This test requires a GPU"""							)
							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.dummy_cond_unet_upscale
													A__						:  Tuple		  =  DDPMScheduler()
													A__						:  List[Any]		  =  DDIMScheduler(prediction_type="""v_prediction"""							)
													A__						:  int		  =  self.dummy_vae
													A__						:  str		  =  self.dummy_text_encoder
													A__						:  Optional[Any]		  =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

													A__						:  Any		  =  self.dummy_image.cpu().permute(0			,    2			,    3			,    1							)[0]
													A__						:  Optional[int]		  =  Image.fromarray(np.uinta(snake_case							)							).convert("""RGB"""							).resize((64, 64)							)

													# put models in fp16, except vae as it overflows in fp16
													A__						:  List[Any]		  =  unet.half()
													A__						:  Optional[int]		  =  text_encoder.half()

													# make sure here that pndm scheduler skips prk
													A__						:  Tuple		  =  StableDiffusionUpscalePipeline(
													    unet=snake_case			,    low_res_scheduler=snake_case			,    scheduler=snake_case			,    vae=snake_case			,    text_encoder=snake_case			,    tokenizer=snake_case			,    max_noise_level=350			,    )
													A__						:  Tuple		  =  sd_pipe.to(snake_case							)
													sd_pipe.set_progress_bar_config(disable=snake_case							)

													A__						:  List[str]		  =  """A painting of a squirrel eating a burger"""
													A__						:  Optional[int]		  =  torch.manual_seed(0							)
													A__						:  str		  =  sd_pipe(
													    [prompt]			,    image=snake_case			,    generator=snake_case			,    num_inference_steps=2			,    output_type="""np"""			,    ).images

													A__						:  Any		  =  low_res_image.size[0] * 4
													assert image.shape == (1, expected_height_width, expected_height_width, 3)






@slow
@require_torch_gpu
class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





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


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





													A__						:  str		  =  load_image(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/sd2-upscale/low_res_cat.png"""							)
													A__						:  Optional[int]		  =  load_numpy(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
													    """/upsampled_cat.npy"""							)

													A__						:  Dict		  =  """stabilityai/stable-diffusion-x4-upscaler"""
													A__						:  List[Any]		  =  StableDiffusionUpscalePipeline.from_pretrained(snake_case							)
													pipe.to(snake_case							)
													pipe.set_progress_bar_config(disable=snake_case							)
													pipe.enable_attention_slicing()

													A__						:  str		  =  """a cat sitting on a park bench"""

													A__						:  Optional[Any]		  =  torch.manual_seed(0							)
													A__						:  Dict		  =  pipe(
													    prompt=snake_case			,    image=snake_case			,    generator=snake_case			,    output_type="""np"""			,    )
													A__						:  Any		  =  output.images[0]

													assert image.shape == (512, 512, 3)
													assert np.abs(expected_image - image							).max() < 1e-3


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





													A__						:  Dict		  =  load_image(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/sd2-upscale/low_res_cat.png"""							)
													A__						:  Optional[Any]		  =  load_numpy(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
													    """/upsampled_cat_fp16.npy"""							)

													A__						:  List[Any]		  =  """stabilityai/stable-diffusion-x4-upscaler"""
													A__						:  Optional[Any]		  =  StableDiffusionUpscalePipeline.from_pretrained(
													    snake_case			,    torch_dtype=torch.floataa			,    )
													pipe.to(snake_case							)
													pipe.set_progress_bar_config(disable=snake_case							)
													pipe.enable_attention_slicing()

													A__						:  Dict		  =  """a cat sitting on a park bench"""

													A__						:  Tuple		  =  torch.manual_seed(0							)
													A__						:  Optional[Any]		  =  pipe(
													    prompt=snake_case			,    image=snake_case			,    generator=snake_case			,    output_type="""np"""			,    )
													A__						:  Optional[int]		  =  output.images[0]

													assert image.shape == (512, 512, 3)
													assert np.abs(expected_image - image							).max() < 5e-1


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





													torch.cuda.empty_cache()
													torch.cuda.reset_max_memory_allocated()
													torch.cuda.reset_peak_memory_stats()

													A__						:  Tuple		  =  load_image(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/sd2-upscale/low_res_cat.png"""							)

													A__						:  Dict		  =  """stabilityai/stable-diffusion-x4-upscaler"""
													A__						:  List[str]		  =  StableDiffusionUpscalePipeline.from_pretrained(
													    snake_case			,    torch_dtype=torch.floataa			,    )
													pipe.to(snake_case							)
													pipe.set_progress_bar_config(disable=snake_case							)
													pipe.enable_attention_slicing(1							)
													pipe.enable_sequential_cpu_offload()

													A__						:  Optional[Any]		  =  """a cat sitting on a park bench"""

													A__						:  Union[str, Any]		  =  torch.manual_seed(0							)
													A__						:  Dict		  =  pipe(
													    prompt=snake_case			,    image=snake_case			,    generator=snake_case			,    num_inference_steps=5			,    output_type="""np"""			,    )

													A__						:  Tuple		  =  torch.cuda.max_memory_allocated()
													# make sure that less than 2.9 GB is allocated
													assert mem_bytes < 2.9 * 10**9














"""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()






