



"""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 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



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

					@property
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				1
									_lowerCamelCase        =				3
									_lowerCamelCase        =				(3_2, 3_2)

									_lowerCamelCase        =				floats_tensor((batch_size, num_channels) + sizes				,       rng=random.Random(0   )   ).to(lowerCamelCase__   )
									return image

					@property
					def 			snake_case__      (    self   ):
									torch.manual_seed(0   )
									_lowerCamelCase        =				UNetaDConditionModel(
									    block_out_channels=(3_2, 3_2, 6_4)				,       layers_per_block=2				,       sample_size=3_2				,       in_channels=7				,       out_channels=4				,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')				,       up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')				,       cross_attention_dim=3_2				,       attention_head_dim=8				,       use_linear_projection=lowerCamelCase__				,       only_cross_attention=(True, True, False)				,       num_class_embeds=1_0_0				,       )
									return model

					@property
					def 			snake_case__      (    self   ):
									torch.manual_seed(0   )
									_lowerCamelCase        =				AutoencoderKL(
									    block_out_channels=[3_2, 3_2, 6_4]				,       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 			snake_case__      (    self   ):
									torch.manual_seed(0   )
									_lowerCamelCase        =				CLIPTextConfig(
									    bos_token_id=0				,       eos_token_id=2				,       hidden_size=3_2				,       intermediate_size=3_7				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_0_0_0				,       hidden_act='''gelu'''				,       projection_dim=5_1_2				,       )
									return CLIPTextModel(lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									_lowerCamelCase        =				self.dummy_cond_unet_upscale
									_lowerCamelCase        =				DDPMScheduler()
									_lowerCamelCase        =				DDIMScheduler(prediction_type='''v_prediction'''   )
									_lowerCamelCase        =				self.dummy_vae
									_lowerCamelCase        =				self.dummy_text_encoder
									_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

									_lowerCamelCase        =				self.dummy_image.cpu().permute(0				,       2				,       3				,       1   )[0]
									_lowerCamelCase        =				Image.fromarray(np.uinta(lowerCamelCase__   )   ).convert('''RGB'''   ).resize((6_4, 6_4)   )

									# make sure here that pndm scheduler skips prk
									_lowerCamelCase        =				StableDiffusionUpscalePipeline(
									    unet=lowerCamelCase__				,       low_res_scheduler=lowerCamelCase__				,       scheduler=lowerCamelCase__				,       vae=lowerCamelCase__				,       text_encoder=lowerCamelCase__				,       tokenizer=lowerCamelCase__				,       max_noise_level=3_5_0				,       )
									_lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				'''A painting of a squirrel eating a burger'''
									_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(0   )
									_lowerCamelCase        =				sd_pipe(
									    [prompt]				,       image=lowerCamelCase__				,       generator=lowerCamelCase__				,       guidance_scale=6.0				,       noise_level=2_0				,       num_inference_steps=2				,       output_type='''np'''				,       )

									_lowerCamelCase        =				output.images

									_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(0   )
									_lowerCamelCase        =				sd_pipe(
									    [prompt]				,       image=lowerCamelCase__				,       generator=lowerCamelCase__				,       guidance_scale=6.0				,       noise_level=2_0				,       num_inference_steps=2				,       output_type='''np'''				,       return_dict=lowerCamelCase__				,       )[0]

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

									_lowerCamelCase        =				low_res_image.size[0] * 4
									assert image.shape == (1, expected_height_width, expected_height_width, 3)
									_lowerCamelCase        =				np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]   )

									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 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									_lowerCamelCase        =				self.dummy_cond_unet_upscale
									_lowerCamelCase        =				DDPMScheduler()
									_lowerCamelCase        =				DDIMScheduler(prediction_type='''v_prediction'''   )
									_lowerCamelCase        =				self.dummy_vae
									_lowerCamelCase        =				self.dummy_text_encoder
									_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

									_lowerCamelCase        =				self.dummy_image.cpu().permute(0				,       2				,       3				,       1   )[0]
									_lowerCamelCase        =				Image.fromarray(np.uinta(lowerCamelCase__   )   ).convert('''RGB'''   ).resize((6_4, 6_4)   )

									# make sure here that pndm scheduler skips prk
									_lowerCamelCase        =				StableDiffusionUpscalePipeline(
									    unet=lowerCamelCase__				,       low_res_scheduler=lowerCamelCase__				,       scheduler=lowerCamelCase__				,       vae=lowerCamelCase__				,       text_encoder=lowerCamelCase__				,       tokenizer=lowerCamelCase__				,       max_noise_level=3_5_0				,       )
									_lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				'''A painting of a squirrel eating a burger'''
									_lowerCamelCase        =				sd_pipe(
									    2 * [prompt]				,       image=2 * [low_res_image]				,       guidance_scale=6.0				,       noise_level=2_0				,       num_inference_steps=2				,       output_type='''np'''				,       )
									_lowerCamelCase        =				output.images
									assert image.shape[0] == 2

									_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(0   )
									_lowerCamelCase        =				sd_pipe(
									    [prompt]				,       image=lowerCamelCase__				,       generator=lowerCamelCase__				,       num_images_per_prompt=2				,       guidance_scale=6.0				,       noise_level=2_0				,       num_inference_steps=2				,       output_type='''np'''				,       )
									_lowerCamelCase        =				output.images
									assert image.shape[0] == 2





					@unittest.skipIf(torch_device != '''cuda'''				,       '''This test requires a GPU'''   )
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.dummy_cond_unet_upscale
									_lowerCamelCase        =				DDPMScheduler()
									_lowerCamelCase        =				DDIMScheduler(prediction_type='''v_prediction'''   )
									_lowerCamelCase        =				self.dummy_vae
									_lowerCamelCase        =				self.dummy_text_encoder
									_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

									_lowerCamelCase        =				self.dummy_image.cpu().permute(0				,       2				,       3				,       1   )[0]
									_lowerCamelCase        =				Image.fromarray(np.uinta(lowerCamelCase__   )   ).convert('''RGB'''   ).resize((6_4, 6_4)   )

									# put models in fp16, except vae as it overflows in fp16
									_lowerCamelCase        =				unet.half()
									_lowerCamelCase        =				text_encoder.half()

									# make sure here that pndm scheduler skips prk
									_lowerCamelCase        =				StableDiffusionUpscalePipeline(
									    unet=lowerCamelCase__				,       low_res_scheduler=lowerCamelCase__				,       scheduler=lowerCamelCase__				,       vae=lowerCamelCase__				,       text_encoder=lowerCamelCase__				,       tokenizer=lowerCamelCase__				,       max_noise_level=3_5_0				,       )
									_lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				'''A painting of a squirrel eating a burger'''
									_lowerCamelCase        =				torch.manual_seed(0   )
									_lowerCamelCase        =				sd_pipe(
									    [prompt]				,       image=lowerCamelCase__				,       generator=lowerCamelCase__				,       num_inference_steps=2				,       output_type='''np'''				,       ).images

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


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





					'''simple docstring'''



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

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-upscale/low_res_cat.png'''   )
									_lowerCamelCase        =				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
									    '''/upsampled_cat.npy'''   )

									_lowerCamelCase        =				'''stabilityai/stable-diffusion-x4-upscaler'''
									_lowerCamelCase        =				StableDiffusionUpscalePipeline.from_pretrained(lowerCamelCase__   )
									pipe.to(lowerCamelCase__   )
									pipe.set_progress_bar_config(disable=lowerCamelCase__   )
									pipe.enable_attention_slicing()

									_lowerCamelCase        =				'''a cat sitting on a park bench'''

									_lowerCamelCase        =				torch.manual_seed(0   )
									_lowerCamelCase        =				pipe(
									    prompt=lowerCamelCase__				,       image=lowerCamelCase__				,       generator=lowerCamelCase__				,       output_type='''np'''				,       )
									_lowerCamelCase        =				output.images[0]

									assert image.shape == (5_1_2, 5_1_2, 3)
									assert np.abs(expected_image - image   ).max() < 1e-3

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-upscale/low_res_cat.png'''   )
									_lowerCamelCase        =				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
									    '''/upsampled_cat_fp16.npy'''   )

									_lowerCamelCase        =				'''stabilityai/stable-diffusion-x4-upscaler'''
									_lowerCamelCase        =				StableDiffusionUpscalePipeline.from_pretrained(
									    lowerCamelCase__				,       torch_dtype=torch.floataa				,       )
									pipe.to(lowerCamelCase__   )
									pipe.set_progress_bar_config(disable=lowerCamelCase__   )
									pipe.enable_attention_slicing()

									_lowerCamelCase        =				'''a cat sitting on a park bench'''

									_lowerCamelCase        =				torch.manual_seed(0   )
									_lowerCamelCase        =				pipe(
									    prompt=lowerCamelCase__				,       image=lowerCamelCase__				,       generator=lowerCamelCase__				,       output_type='''np'''				,       )
									_lowerCamelCase        =				output.images[0]

									assert image.shape == (5_1_2, 5_1_2, 3)
									assert np.abs(expected_image - image   ).max() < 5e-1





					def 			snake_case__      (    self   ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

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

									_lowerCamelCase        =				'''stabilityai/stable-diffusion-x4-upscaler'''
									_lowerCamelCase        =				StableDiffusionUpscalePipeline.from_pretrained(
									    lowerCamelCase__				,       torch_dtype=torch.floataa				,       )
									pipe.to(lowerCamelCase__   )
									pipe.set_progress_bar_config(disable=lowerCamelCase__   )
									pipe.enable_attention_slicing(1   )
									pipe.enable_sequential_cpu_offload()

									_lowerCamelCase        =				'''a cat sitting on a park bench'''

									_lowerCamelCase        =				torch.manual_seed(0   )
									_lowerCamelCase        =				pipe(
									    prompt=lowerCamelCase__				,       image=lowerCamelCase__				,       generator=lowerCamelCase__				,       num_inference_steps=5				,       output_type='''np'''				,       )

									_lowerCamelCase        =				torch.cuda.max_memory_allocated()
									# make sure that less than 2.9 GB is allocated
									assert mem_bytes < 2.9 * 1_0**9





"""simple docstring"""
from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
__SCREAMING_SNAKE_CASE  :						List[str]    							=      numpy.array([0, 0])
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      numpy.array([0.5, 0.866_0254])
__SCREAMING_SNAKE_CASE  :						Tuple    							=      numpy.array([1, 0])
__SCREAMING_SNAKE_CASE  :						List[Any]    							=      [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]						,   lowercase_     :							int							)	->       list[numpy.ndarray]:
				_lowerCamelCase        =				initial_vectors
				for _ in range(lowercase_							):
								_lowerCamelCase        =				iteration_step(lowercase_							)
				return vectors



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]							)	->       list[numpy.ndarray]:
				_lowerCamelCase        =				[]
				for i, start_vector in enumerate(vectors[:-1]							):
								_lowerCamelCase        =				vectors[i + 1]
								new_vectors.append(lowercase_							)
								_lowerCamelCase        =				end_vector - start_vector
								new_vectors.append(start_vector + difference_vector / 3							)
								new_vectors.append(
								    start_vector + difference_vector / 3 + rotate(difference_vector / 3						,   60							)							)
								new_vectors.append(start_vector + difference_vector * 2 / 3							)
				new_vectors.append(vectors[-1]							)
				return new_vectors



def        lowerCAmelCase_(   lowercase_     :							numpy.ndarray						,   lowercase_     :							float							)	->       numpy.ndarray:
				_lowerCamelCase        =				numpy.radians(lowercase_							)
				_lowerCamelCase  ,     _lowerCamelCase        =				numpy.cos(lowercase_							), numpy.sin(lowercase_							)
				_lowerCamelCase        =				numpy.array(((c, -s), (s, c))							)
				return numpy.dot(lowercase_						,   lowercase_							)



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]							)	->       None:
				_lowerCamelCase        =				plt.gca()
				axes.set_aspect('''equal'''							)

				# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
				# y-coordinates as inputs, which are constructed from the vector-list using
				# zip()
				_lowerCamelCase  ,     _lowerCamelCase        =				zip(*lowercase_							)
				plt.plot(lowercase_						,   lowercase_							)
				plt.show()


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__SCREAMING_SNAKE_CASE  :						str    							=      iterate(INITIAL_VECTORS, 5)
			plot(processed_vectors)
