
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    _lowerCamelCase				(					self		:Tuple						)		-> Optional[int]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	@property
	def    _lowerCamelCase				(					self		:List[Any]						)		-> Dict:
								__UpperCamelCase	:   Union[str, Any]			   =					1
								__UpperCamelCase	:   int			   =					3
								__UpperCamelCase	:   Dict			   =					(3_2, 3_2)

								__UpperCamelCase	:   Optional[int]			   =					floats_tensor((batch_size, num_channels) + sizes     ,   rng=random.Random(0						)						).to(a						)
								return image







	@property
	def    _lowerCamelCase				(					self		:int						)		-> Union[str, Any]:
								torch.manual_seed(0						)
								__UpperCamelCase	:   int			   =					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=a     ,   only_cross_attention=(True, True, False)     ,   num_class_embeds=1_0_0     ,   )
								return model







	@property
	def    _lowerCamelCase				(					self		:Tuple						)		-> Dict:
								torch.manual_seed(0						)
								__UpperCamelCase	:   Any			   =					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    _lowerCamelCase				(					self		:Tuple						)		-> Optional[Any]:
								torch.manual_seed(0						)
								__UpperCamelCase	:   Tuple			   =					CLIPTextConfig(
								    bos_token_id=0     ,   eos_token_id=2     ,   hidden_size=3_2     ,   intermediate_size=3_7     ,   layer_norm_eps=1E-0_5     ,   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(a						)







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

								__UpperCamelCase	:   int			   =					self.dummy_image.cpu().permute(0     ,   2     ,   3     ,   1						)[0]
								__UpperCamelCase	:   Optional[int]			   =					Image.fromarray(np.uinta(a						)						).convert("RGB"						).resize((6_4, 6_4)						)

								# make sure here that pndm scheduler skips prk
								__UpperCamelCase	:   Tuple			   =					StableDiffusionUpscalePipeline(
								    unet=a     ,   low_res_scheduler=a     ,   scheduler=a     ,   vae=a     ,   text_encoder=a     ,   tokenizer=a     ,   max_noise_level=3_5_0     ,   )
								__UpperCamelCase	:   int			   =					sd_pipe.to(a						)
								sd_pipe.set_progress_bar_config(disable=a						)

								__UpperCamelCase	:   Any			   =					"A painting of a squirrel eating a burger"
								__UpperCamelCase	:   Tuple			   =					torch.Generator(device=a						).manual_seed(0						)
								__UpperCamelCase	:   List[str]			   =					sd_pipe(
								    [prompt]     ,   image=a     ,   generator=a     ,   guidance_scale=6.0     ,   noise_level=2_0     ,   num_inference_steps=2     ,   output_type="np"     ,   )

								__UpperCamelCase	:   Optional[Any]			   =					output.images

								__UpperCamelCase	:   str			   =					torch.Generator(device=a						).manual_seed(0						)
								__UpperCamelCase	:   Tuple			   =					sd_pipe(
								    [prompt]     ,   image=a     ,   generator=a     ,   guidance_scale=6.0     ,   noise_level=2_0     ,   num_inference_steps=2     ,   output_type="np"     ,   return_dict=a     ,   )[0]

								__UpperCamelCase	:   int			   =					image[0, -3:, -3:, -1]
								__UpperCamelCase	:   Dict			   =					image_from_tuple[0, -3:, -3:, -1]

								__UpperCamelCase	:   str			   =					low_res_image.size[0] * 4
								assert image.shape == (1, expected_height_width, expected_height_width, 3)
								__UpperCamelCase	:   int			   =					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    _lowerCamelCase				(					self		:str						)		-> Union[str, Any]:
								__UpperCamelCase	:   List[str]			   =					"cpu"  # ensure determinism for the device-dependent torch.Generator
								__UpperCamelCase	:   Union[str, Any]			   =					self.dummy_cond_unet_upscale
								__UpperCamelCase	:   Optional[Any]			   =					DDPMScheduler()
								__UpperCamelCase	:   Dict			   =					DDIMScheduler(prediction_type="v_prediction"						)
								__UpperCamelCase	:   Union[str, Any]			   =					self.dummy_vae
								__UpperCamelCase	:   Union[str, Any]			   =					self.dummy_text_encoder
								__UpperCamelCase	:   Union[str, Any]			   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

								__UpperCamelCase	:   Any			   =					self.dummy_image.cpu().permute(0     ,   2     ,   3     ,   1						)[0]
								__UpperCamelCase	:   List[Any]			   =					Image.fromarray(np.uinta(a						)						).convert("RGB"						).resize((6_4, 6_4)						)

								# make sure here that pndm scheduler skips prk
								__UpperCamelCase	:   List[Any]			   =					StableDiffusionUpscalePipeline(
								    unet=a     ,   low_res_scheduler=a     ,   scheduler=a     ,   vae=a     ,   text_encoder=a     ,   tokenizer=a     ,   max_noise_level=3_5_0     ,   )
								__UpperCamelCase	:   Dict			   =					sd_pipe.to(a						)
								sd_pipe.set_progress_bar_config(disable=a						)

								__UpperCamelCase	:   Optional[int]			   =					"A painting of a squirrel eating a burger"
								__UpperCamelCase	:   List[str]			   =					sd_pipe(
								    2 * [prompt]     ,   image=2 * [low_res_image]     ,   guidance_scale=6.0     ,   noise_level=2_0     ,   num_inference_steps=2     ,   output_type="np"     ,   )
								__UpperCamelCase	:   List[Any]			   =					output.images
								assert image.shape[0] == 2

								__UpperCamelCase	:   int			   =					torch.Generator(device=a						).manual_seed(0						)
								__UpperCamelCase	:   Tuple			   =					sd_pipe(
								    [prompt]     ,   image=a     ,   generator=a     ,   num_images_per_prompt=2     ,   guidance_scale=6.0     ,   noise_level=2_0     ,   num_inference_steps=2     ,   output_type="np"     ,   )
								__UpperCamelCase	:   List[str]			   =					output.images
								assert image.shape[0] == 2







	@unittest.skipIf(torch_device != "cuda"     ,   "This test requires a GPU"						)
	def    _lowerCamelCase				(					self		:str						)		-> Optional[Any]:
								__UpperCamelCase	:   Optional[int]			   =					self.dummy_cond_unet_upscale
								__UpperCamelCase	:   Union[str, Any]			   =					DDPMScheduler()
								__UpperCamelCase	:   List[str]			   =					DDIMScheduler(prediction_type="v_prediction"						)
								__UpperCamelCase	:   Any			   =					self.dummy_vae
								__UpperCamelCase	:   Optional[Any]			   =					self.dummy_text_encoder
								__UpperCamelCase	:   List[str]			   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

								__UpperCamelCase	:   Tuple			   =					self.dummy_image.cpu().permute(0     ,   2     ,   3     ,   1						)[0]
								__UpperCamelCase	:   Dict			   =					Image.fromarray(np.uinta(a						)						).convert("RGB"						).resize((6_4, 6_4)						)

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

								# make sure here that pndm scheduler skips prk
								__UpperCamelCase	:   Optional[Any]			   =					StableDiffusionUpscalePipeline(
								    unet=a     ,   low_res_scheduler=a     ,   scheduler=a     ,   vae=a     ,   text_encoder=a     ,   tokenizer=a     ,   max_noise_level=3_5_0     ,   )
								__UpperCamelCase	:   int			   =					sd_pipe.to(a						)
								sd_pipe.set_progress_bar_config(disable=a						)

								__UpperCamelCase	:   int			   =					"A painting of a squirrel eating a burger"
								__UpperCamelCase	:   Tuple			   =					torch.manual_seed(0						)
								__UpperCamelCase	:   Union[str, Any]			   =					sd_pipe(
								    [prompt]     ,   image=a     ,   generator=a     ,   num_inference_steps=2     ,   output_type="np"     ,   ).images

								__UpperCamelCase	:   Optional[int]			   =					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    _lowerCamelCase				(					self		:List[str]						)		-> Dict:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	def    _lowerCamelCase				(					self		:List[Any]						)		-> Optional[Any]:
								__UpperCamelCase	:   Union[str, Any]			   =					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/sd2-upscale/low_res_cat.png"						)
								__UpperCamelCase	:   Optional[int]			   =					load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
								    "/upsampled_cat.npy"						)

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

								__UpperCamelCase	:   Union[str, Any]			   =					"a cat sitting on a park bench"

								__UpperCamelCase	:   str			   =					torch.manual_seed(0						)
								__UpperCamelCase	:   List[Any]			   =					pipe(
								    prompt=a     ,   image=a     ,   generator=a     ,   output_type="np"     ,   )
								__UpperCamelCase	:   Union[str, Any]			   =					output.images[0]

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







	def    _lowerCamelCase				(					self		:List[str]						)		-> int:
								__UpperCamelCase	:   Tuple			   =					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/sd2-upscale/low_res_cat.png"						)
								__UpperCamelCase	:   List[str]			   =					load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
								    "/upsampled_cat_fp16.npy"						)

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

								__UpperCamelCase	:   Tuple			   =					"a cat sitting on a park bench"

								__UpperCamelCase	:   Optional[Any]			   =					torch.manual_seed(0						)
								__UpperCamelCase	:   List[str]			   =					pipe(
								    prompt=a     ,   image=a     ,   generator=a     ,   output_type="np"     ,   )
								__UpperCamelCase	:   List[str]			   =					output.images[0]

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







	def    _lowerCamelCase				(					self		:List[str]						)		-> Tuple:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								__UpperCamelCase	:   List[Any]			   =					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/sd2-upscale/low_res_cat.png"						)

								__UpperCamelCase	:   Union[str, Any]			   =					"stabilityai/stable-diffusion-x4-upscaler"
								__UpperCamelCase	:   Union[str, Any]			   =					StableDiffusionUpscalePipeline.from_pretrained(
								    a     ,   torch_dtype=torch.floataa     ,   )
								pipe.to(a						)
								pipe.set_progress_bar_config(disable=a						)
								pipe.enable_attention_slicing(1						)
								pipe.enable_sequential_cpu_offload()

								__UpperCamelCase	:   Union[str, Any]			   =					"a cat sitting on a park bench"

								__UpperCamelCase	:   List[str]			   =					torch.manual_seed(0						)
								__UpperCamelCase	:   Optional[int]			   =					pipe(
								    prompt=a     ,   image=a     ,   generator=a     ,   num_inference_steps=5     ,   output_type="np"     ,   )

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

from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowercase  :					Optional[Any]													=       TypeVar('T')




class  lowerCamelCase__							(				Generic[T]):




	'''simple docstring'''




	_A								=     42  # Cache store of keys
	_A								=     42  # References of the keys in cache
	_A								=     1_0  # Maximum capacity of cache







	def __init__(					self		:Optional[Any]     ,   a		:int						)		-> None:
								__UpperCamelCase	:   Union[str, Any]			   =					deque()
								__UpperCamelCase	:   str			   =					set()
								if not n:
															__UpperCamelCase	:   Union[str, Any]			   =					sys.maxsize
								elif n < 0:
															raise ValueError("n should be an integer greater than 0."						)
								else:
															__UpperCamelCase	:   Any			   =					n







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:T						)		-> None:
								if x not in self.key_reference:
															if len(self.dq_store						) == LRUCache._MAX_CAPACITY:
																						__UpperCamelCase	:   int			   =					self.dq_store.pop()
																						self.key_reference.remove(a						)
								else:
															self.dq_store.remove(a						)

								self.dq_store.appendleft(a						)
								self.key_reference.add(a						)







	def    _lowerCamelCase				(					self		:Any						)		-> None:
								for k in self.dq_store:
															print(a						)







	def __repr__(					self		:Tuple						)		-> str:
								return f'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store						)}'


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowercase  :					LRUCache[str | int]													=       LRUCache(4)
		lru_cache.refer('A')
		lru_cache.refer(2)
		lru_cache.refer(3)
		lru_cache.refer('A')
		lru_cache.refer(4)
		lru_cache.refer(5)
		lru_cache.display()

		print(lru_cache)
		assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"