




'''simple docstring'''


import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, 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				:							List[str]	)			->		Optional[Any]:







       '''simple docstring'''



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



  @property
  def 					_snake_case     ( self				:							Optional[int]	)			->		str:







       '''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(SCREAMING_SNAKE_CASE_	)
       return image



  @property
  def 					_snake_case     ( self				:							List[str]	)			->		Optional[int]:







       '''simple docstring'''



       torch.manual_seed(0	)
       A:		Any          =		UNetaDConditionModel(
           block_out_channels=(32, 64)					,  layers_per_block=2					,  sample_size=32					,  in_channels=4					,  out_channels=4					,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,  cross_attention_dim=32					,  )
       return model



  @property
  def 					_snake_case     ( self				:							str	)			->		Optional[int]:







       '''simple docstring'''



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



  @property
  def 					_snake_case     ( self				:							List[str]	)			->		int:







       '''simple docstring'''



       torch.manual_seed(0	)
       A:		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=10_00					,  )
       return CLIPTextModel(SCREAMING_SNAKE_CASE_	)



  @property
  def 					_snake_case     ( self				:							str	)			->		Dict:







       '''simple docstring'''



       def extract(*SCREAMING_SNAKE_CASE_				:							int					,  **SCREAMING_SNAKE_CASE_				:							Any	):






            class 						lowerCAmelCase_	:







              '''simple docstring'''

              def __init__( self				:							Dict	)			->		int:







                   '''simple docstring'''



                   A:		List[Any]          =		torch.ones([0]	)




              def 					_snake_case     ( self				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							Optional[int]	)			->		str:







                   '''simple docstring'''



                   self.pixel_values.to(SCREAMING_SNAKE_CASE_	)
                   return self






            return Out()

       return extract



  def 					_snake_case     ( self				:							Optional[int]	)			->		int:







       '''simple docstring'''



       A:		Optional[Any]          =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       A:		Union[str, Any]          =		self.dummy_cond_unet
       A:		Tuple          =		DDIMScheduler(
           beta_start=0.0_0085					,  beta_end=0.012					,  beta_schedule='''scaled_linear'''					,  clip_sample=SCREAMING_SNAKE_CASE_					,  set_alpha_to_one=SCREAMING_SNAKE_CASE_					,  )

       A:		Union[str, Any]          =		self.dummy_vae
       A:		List[Any]          =		self.dummy_text_encoder
       A:		Dict          =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

       # make sure here that pndm scheduler skips prk
       A:		Dict          =		StableDiffusionPipeline(
           unet=SCREAMING_SNAKE_CASE_					,  scheduler=SCREAMING_SNAKE_CASE_					,  vae=SCREAMING_SNAKE_CASE_					,  text_encoder=SCREAMING_SNAKE_CASE_					,  tokenizer=SCREAMING_SNAKE_CASE_					,  safety_checker=SCREAMING_SNAKE_CASE_					,  feature_extractor=self.dummy_extractor					,  )
       A:		str          =		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
       sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		str          =		'''A painting of a squirrel eating a burger'''

       A:		Dict          =		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(0	)
       A:		int          =		sd_pipe([prompt]					,  generator=SCREAMING_SNAKE_CASE_					,  guidance_scale=6.0					,  num_inference_steps=2					,  output_type='''np'''	)
       A:		List[Any]          =		output.images

       A:		int          =		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(0	)
       A:		str          =		sd_pipe(
           [prompt]					,  generator=SCREAMING_SNAKE_CASE_					,  guidance_scale=6.0					,  num_inference_steps=2					,  output_type='''np'''					,  return_dict=SCREAMING_SNAKE_CASE_					,  )[0]

       A:		str          =		image[0, -3:, -3:, -1]
       A:		Optional[int]          =		image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       A:		List[str]          =		np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]	)

       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				:							Optional[Any]	)			->		int:







       '''simple docstring'''



       A:		List[str]          =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       A:		str          =		self.dummy_cond_unet
       A:		int          =		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_	)
       A:		Dict          =		self.dummy_vae
       A:		List[str]          =		self.dummy_text_encoder
       A:		int          =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

       # make sure here that pndm scheduler skips prk
       A:		Any          =		StableDiffusionPipeline(
           unet=SCREAMING_SNAKE_CASE_					,  scheduler=SCREAMING_SNAKE_CASE_					,  vae=SCREAMING_SNAKE_CASE_					,  text_encoder=SCREAMING_SNAKE_CASE_					,  tokenizer=SCREAMING_SNAKE_CASE_					,  safety_checker=SCREAMING_SNAKE_CASE_					,  feature_extractor=self.dummy_extractor					,  )
       A:		List[Any]          =		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
       sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		Optional[Any]          =		'''A painting of a squirrel eating a burger'''
       A:		Optional[int]          =		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(0	)
       A:		Tuple          =		sd_pipe([prompt]					,  generator=SCREAMING_SNAKE_CASE_					,  guidance_scale=6.0					,  num_inference_steps=2					,  output_type='''np'''	)

       A:		List[str]          =		output.images

       A:		int          =		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(0	)
       A:		Union[str, Any]          =		sd_pipe(
           [prompt]					,  generator=SCREAMING_SNAKE_CASE_					,  guidance_scale=6.0					,  num_inference_steps=2					,  output_type='''np'''					,  return_dict=SCREAMING_SNAKE_CASE_					,  )[0]

       A:		Union[str, Any]          =		image[0, -3:, -3:, -1]
       A:		Tuple          =		image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       A:		List[Any]          =		np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]	)

       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				:							Optional[Any]	)			->		Dict:







       '''simple docstring'''



       A:		Any          =		StableDiffusionPipeline.from_pretrained(
           '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''					,  safety_checker=SCREAMING_SNAKE_CASE_	)
       assert isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       assert isinstance(pipe.scheduler					,  SCREAMING_SNAKE_CASE_	)
       assert pipe.safety_checker is None

       A:		int          =		pipe('''example prompt'''					,  num_inference_steps=2	).images[0]
       assert image is not None

       # check that there's no error when saving a pipeline with one of the models being None
       with tempfile.TemporaryDirectory() as tmpdirname:
            pipe.save_pretrained(SCREAMING_SNAKE_CASE_	)
            A:		int          =		StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_	)

       # sanity check that the pipeline still works
       assert pipe.safety_checker is None
       A:		Optional[Any]          =		pipe('''example prompt'''					,  num_inference_steps=2	).images[0]
       assert image is not None




  @unittest.skipIf(torch_device != '''cuda'''					,  '''This test requires a GPU'''	)
  def 					_snake_case     ( self				:							Union[str, Any]	)			->		Optional[Any]:







       '''simple docstring'''



       A:		Dict          =		self.dummy_cond_unet
       A:		Union[str, Any]          =		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_	)
       A:		List[str]          =		self.dummy_vae
       A:		str          =		self.dummy_text_encoder
       A:		Union[str, Any]          =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

       # put models in fp16
       A:		int          =		unet.half()
       A:		Dict          =		vae.half()
       A:		Tuple          =		bert.half()

       # make sure here that pndm scheduler skips prk
       A:		List[str]          =		StableDiffusionPipeline(
           unet=SCREAMING_SNAKE_CASE_					,  scheduler=SCREAMING_SNAKE_CASE_					,  vae=SCREAMING_SNAKE_CASE_					,  text_encoder=SCREAMING_SNAKE_CASE_					,  tokenizer=SCREAMING_SNAKE_CASE_					,  safety_checker=SCREAMING_SNAKE_CASE_					,  feature_extractor=self.dummy_extractor					,  )
       A:		int          =		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
       sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		Optional[int]          =		'''A painting of a squirrel eating a burger'''
       A:		Optional[Any]          =		sd_pipe([prompt]					,  num_inference_steps=2					,  output_type='''np'''	).images

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






@nightly
@require_torch_gpu
class 						lowerCAmelCase_	(       unittest.TestCase							):







  '''simple docstring'''

  def 					_snake_case     ( self				:							List[Any]	)			->		int:







       '''simple docstring'''



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



  def 					_snake_case     ( self				:							Dict	)			->		List[Any]:







       '''simple docstring'''



       A:		Dict          =		'''cuda'''
       A:		Union[str, Any]          =		StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''	)
       A:		str          =		pipe.to(SCREAMING_SNAKE_CASE_	)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		List[Any]          =		'''a photo of a cat'''
       A:		Dict          =		{
           '''editing_prompt''': ['''sunglasses'''],
           '''reverse_editing_direction''': [False],
           '''edit_warmup_steps''': 10,
           '''edit_guidance_scale''': 6,
           '''edit_threshold''': 0.95,
           '''edit_momentum_scale''': 0.5,
           '''edit_mom_beta''': 0.6,
       }

       A:		int          =		3
       A:		Optional[Any]          =		7

       # no sega enabled
       A:		List[Any]          =		torch.Generator(SCREAMING_SNAKE_CASE_	)
       generator.manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		Union[str, Any]          =		pipe(
           [prompt]					,  generator=SCREAMING_SNAKE_CASE_					,  guidance_scale=SCREAMING_SNAKE_CASE_					,  num_inference_steps=50					,  output_type='''np'''					,  width=5_12					,  height=5_12					,  )

       A:		Tuple          =		output.images
       A:		str          =		image[0, -3:, -3:, -1]
       A:		Optional[Any]          =		[
           0.3467_3113,
           0.3849_2733,
           0.3759_7352,
           0.3408_6335,
           0.3565_0748,
           0.3557_9205,
           0.338_4763,
           0.3434_0236,
           0.357_3271,
       ]

       assert image.shape == (1, 5_12, 5_12, 3)

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

       # with sega enabled
       # generator = torch.manual_seed(seed)
       generator.manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		Tuple          =		pipe(
           [prompt]					,  generator=SCREAMING_SNAKE_CASE_					,  guidance_scale=SCREAMING_SNAKE_CASE_					,  num_inference_steps=50					,  output_type='''np'''					,  width=5_12					,  height=5_12					,  **SCREAMING_SNAKE_CASE_					,  )

       A:		str          =		output.images
       A:		Union[str, Any]          =		image[0, -3:, -3:, -1]
       A:		Any          =		[
           0.4188_7826,
           0.3772_8766,
           0.3013_8272,
           0.4141_6335,
           0.4166_4985,
           0.3628_3392,
           0.3619_1246,
           0.4336_4465,
           0.4300_1732,
       ]

       assert image.shape == (1, 5_12, 5_12, 3)

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



  def 					_snake_case     ( self				:							Any	)			->		Any:







       '''simple docstring'''



       A:		str          =		'''cuda'''
       A:		Optional[int]          =		StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''	)
       A:		Tuple          =		pipe.to(SCREAMING_SNAKE_CASE_	)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		Optional[int]          =		'''an image of a crowded boulevard, realistic, 4k'''
       A:		Optional[int]          =		{
           '''editing_prompt''': '''crowd, crowded, people''',
           '''reverse_editing_direction''': True,
           '''edit_warmup_steps''': 10,
           '''edit_guidance_scale''': 8.3,
           '''edit_threshold''': 0.9,
           '''edit_momentum_scale''': 0.5,
           '''edit_mom_beta''': 0.6,
       }

       A:		Union[str, Any]          =		9
       A:		Tuple          =		7

       # no sega enabled
       A:		Optional[int]          =		torch.Generator(SCREAMING_SNAKE_CASE_	)
       generator.manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		Optional[Any]          =		pipe(
           [prompt]					,  generator=SCREAMING_SNAKE_CASE_					,  guidance_scale=SCREAMING_SNAKE_CASE_					,  num_inference_steps=50					,  output_type='''np'''					,  width=5_12					,  height=5_12					,  )

       A:		int          =		output.images
       A:		Dict          =		image[0, -3:, -3:, -1]
       A:		List[str]          =		[
           0.4349_7998,
           0.9181_4065,
           0.754_0739,
           0.5558_0205,
           0.846_7265,
           0.538_9691,
           0.6257_4506,
           0.5889_7763,
           0.5092_6757,
       ]

       assert image.shape == (1, 5_12, 5_12, 3)

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

       # with sega enabled
       # generator = torch.manual_seed(seed)
       generator.manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		Union[str, Any]          =		pipe(
           [prompt]					,  generator=SCREAMING_SNAKE_CASE_					,  guidance_scale=SCREAMING_SNAKE_CASE_					,  num_inference_steps=50					,  output_type='''np'''					,  width=5_12					,  height=5_12					,  **SCREAMING_SNAKE_CASE_					,  )

       A:		Union[str, Any]          =		output.images
       A:		str          =		image[0, -3:, -3:, -1]
       A:		Any          =		[
           0.308_9719,
           0.3050_0144,
           0.2901_6042,
           0.3063_0964,
           0.32_5687,
           0.2941_9225,
           0.290_8091,
           0.2872_3598,
           0.2769_6294,
       ]

       assert image.shape == (1, 5_12, 5_12, 3)

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



  def 					_snake_case     ( self				:							Union[str, Any]	)			->		Tuple:







       '''simple docstring'''



       A:		List[str]          =		'''cuda'''
       A:		Optional[Any]          =		StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''	)
       A:		str          =		pipe.to(SCREAMING_SNAKE_CASE_	)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		Union[str, Any]          =		'''a castle next to a river'''
       A:		int          =		{
           '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
           '''reverse_editing_direction''': False,
           '''edit_warmup_steps''': [15, 18],
           '''edit_guidance_scale''': 6,
           '''edit_threshold''': [0.9, 0.8],
           '''edit_momentum_scale''': 0.5,
           '''edit_mom_beta''': 0.6,
       }

       A:		List[str]          =		48
       A:		Union[str, Any]          =		7

       # no sega enabled
       A:		str          =		torch.Generator(SCREAMING_SNAKE_CASE_	)
       generator.manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		pipe(
           [prompt]					,  generator=SCREAMING_SNAKE_CASE_					,  guidance_scale=SCREAMING_SNAKE_CASE_					,  num_inference_steps=50					,  output_type='''np'''					,  width=5_12					,  height=5_12					,  )

       A:		List[str]          =		output.images
       A:		Tuple          =		image[0, -3:, -3:, -1]
       A:		Optional[Any]          =		[
           0.7516_3555,
           0.7603_7145,
           0.6_1785,
           0.918_9673,
           0.862_7701,
           0.8518_9694,
           0.851_2813,
           0.8701_2076,
           0.831_2857,
       ]

       assert image.shape == (1, 5_12, 5_12, 3)

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

       # with sega enabled
       # generator = torch.manual_seed(seed)
       generator.manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		Optional[int]          =		pipe(
           [prompt]					,  generator=SCREAMING_SNAKE_CASE_					,  guidance_scale=SCREAMING_SNAKE_CASE_					,  num_inference_steps=50					,  output_type='''np'''					,  width=5_12					,  height=5_12					,  **SCREAMING_SNAKE_CASE_					,  )

       A:		int          =		output.images
       A:		List[Any]          =		image[0, -3:, -3:, -1]
       A:		Any          =		[
           0.7355_3365,
           0.753_7271,
           0.7434_1905,
           0.6648_0356,
           0.647_2925,
           0.6303_9416,
           0.6481_2905,
           0.674_9717,
           0.651_7102,
       ]

       assert image.shape == (1, 5_12, 5_12, 3)

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




  def 					_snake_case     ( self				:							Tuple	)			->		List[Any]:







       '''simple docstring'''



       A:		Optional[int]          =		'''cuda'''
       A:		Union[str, Any]          =		StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''					,  torch_dtype=torch.floataa	)
       A:		Optional[Any]          =		pipe.to(SCREAMING_SNAKE_CASE_	)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		Optional[int]          =		'''a photo of a cat'''
       A:		List[str]          =		{
           '''editing_prompt''': ['''sunglasses'''],
           '''reverse_editing_direction''': [False],
           '''edit_warmup_steps''': 10,
           '''edit_guidance_scale''': 6,
           '''edit_threshold''': 0.95,
           '''edit_momentum_scale''': 0.5,
           '''edit_mom_beta''': 0.6,
       }

       A:		List[str]          =		3
       A:		Dict          =		7

       # no sega enabled
       A:		List[str]          =		torch.Generator(SCREAMING_SNAKE_CASE_	)
       generator.manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		Union[str, Any]          =		pipe(
           [prompt]					,  generator=SCREAMING_SNAKE_CASE_					,  guidance_scale=SCREAMING_SNAKE_CASE_					,  num_inference_steps=50					,  output_type='''np'''					,  width=5_12					,  height=5_12					,  )

       A:		int          =		output.images
       A:		Any          =		image[0, -3:, -3:, -1]
       A:		Optional[Any]          =		[
           0.3488_7695,
           0.387_6953,
           0.375,
           0.3442_3828,
           0.358_1543,
           0.3571_7773,
           0.338_3789,
           0.3457_0312,
           0.35_9375,
       ]

       assert image.shape == (1, 5_12, 5_12, 3)

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

       # with sega enabled
       # generator = torch.manual_seed(seed)
       generator.manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		Union[str, Any]          =		pipe(
           [prompt]					,  generator=SCREAMING_SNAKE_CASE_					,  guidance_scale=SCREAMING_SNAKE_CASE_					,  num_inference_steps=50					,  output_type='''np'''					,  width=5_12					,  height=5_12					,  **SCREAMING_SNAKE_CASE_					,  )

       A:		Tuple          =		output.images
       A:		Dict          =		image[0, -3:, -3:, -1]
       A:		Dict          =		[
           0.4228_5156,
           0.3691_4062,
           0.2907_7148,
           0.4204_1016,
           0.4191_8945,
           0.3549_8047,
           0.361_8164,
           0.442_3828,
           0.4311_5234,
       ]

       assert image.shape == (1, 5_12, 5_12, 3)

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












'''simple docstring'''


from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCamelCase			     =					TypeVar('''T''')
UpperCamelCase			     =					TypeVar('''U''')






class 						lowerCAmelCase_	(       Generic[T, U]							):







  '''simple docstring'''

  def __init__( self				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							T | None					,  SCREAMING_SNAKE_CASE_				:							U | None	)			->		Any:







       '''simple docstring'''



       A:		Union[str, Any]          =		key
       A:		Union[str, Any]          =		val
       A:		int          =		0
       A:		DoubleLinkedListNode[T, U] | None          =		None
       A:		DoubleLinkedListNode[T, U] | None          =		None




  def __repr__( self				:							Any	)			->		str:







       '''simple docstring'''



       return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
           self.key					,  self.val					,  self.freq					,  self.next is not None					,  self.prev is not None	)






class 						lowerCAmelCase_	(       Generic[T, U]							):







  '''simple docstring'''

  def __init__( self				:							int	)			->		None:







       '''simple docstring'''



       A:		DoubleLinkedListNode[T, U]          =		DoubleLinkedListNode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A:		DoubleLinkedListNode[T, U]          =		DoubleLinkedListNode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A     ,    A:		Any          =		self.rear, self.head



  def __repr__( self				:							Optional[int]	)			->		str:







       '''simple docstring'''



       A:		int          =		['''DoubleLinkedList''']
       A:		Dict          =		self.head
       while node.next is not None:
            rep.append(str(SCREAMING_SNAKE_CASE_	)	)
            A:		int          =		node.next
       rep.append(str(self.rear	)	)
       return ",\n    ".join(SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Any					,  SCREAMING_SNAKE_CASE_				:							DoubleLinkedListNode[T, U]	)			->		None:







       '''simple docstring'''



       A:		Union[str, Any]          =		self.rear.prev

       # All nodes other than self.head are guaranteed to have non-None previous
       assert previous is not None

       A:		Optional[int]          =		node
       A:		int          =		previous
       A:		Dict          =		node
       A:		int          =		self.rear
       node.freq += 1
       self._position_node(SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							int					,  SCREAMING_SNAKE_CASE_				:							DoubleLinkedListNode[T, U]	)			->		None:







       '''simple docstring'''



       while node.prev is not None and node.prev.freq > node.freq:
            # swap node with previous node
            A:		Any          =		node.prev

            A:		Optional[Any]          =		previous_node.prev
            A:		int          =		node.prev
            A:		int          =		previous_node
            A:		Optional[int]          =		node




  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							DoubleLinkedListNode[T, U]	)			->		DoubleLinkedListNode[T, U] | None:







       '''simple docstring'''



       if node.prev is None or node.next is None:
            return None

       A:		List[Any]          =		node.next
       A:		Dict          =		node.prev
       A:		str          =		None
       A:		Union[str, Any]          =		None
       return node






class 						lowerCAmelCase_	(       Generic[T, U]							):







  '''simple docstring'''

  UpperCamelCase_			:      dict[Callable[[T], U], LFUCache[T, U]]										=				{}



  def __init__( self				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							int	)			->		int:







       '''simple docstring'''



       A:		DoubleLinkedList[T, U]          =		DoubleLinkedList()
       A:		Any          =		capacity
       A:		List[Any]          =		0
       A:		Dict          =		0
       A:		Dict          =		0
       A:		dict[T, DoubleLinkedListNode[T, U]]          =		{}



  def __repr__( self				:							Optional[int]	)			->		str:







       '''simple docstring'''



       return (
           f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
           f"""capacity={self.capacity}, current_size={self.num_keys})"""
       )



  def __contains__( self				:							str					,  SCREAMING_SNAKE_CASE_				:							T	)			->		bool:







       '''simple docstring'''



       return key in self.cache



  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							T	)			->		U | None:







       '''simple docstring'''



       if key in self.cache:
            self.hits += 1
            A:		DoubleLinkedListNode[T, U]          =		self.cache[key]
            A:		List[str]          =		self.list.remove(self.cache[key]	)
            assert node == value_node

            # node is guaranteed not None because it is in self.cache
            assert node is not None
            self.list.add(SCREAMING_SNAKE_CASE_	)
            return node.val
       self.miss += 1
       return None



  def 					_snake_case     ( self				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							T					,  SCREAMING_SNAKE_CASE_				:							U	)			->		None:







       '''simple docstring'''



       if key not in self.cache:
            if self.num_keys >= self.capacity:
                 # delete first node when over capacity
                 A:		List[Any]          =		self.list.head.next

                 # guaranteed to have a non-None first node when num_keys > 0
                 # explain to type checker via assertions
                 assert first_node is not None
                 assert first_node.key is not None
                 assert self.list.remove(SCREAMING_SNAKE_CASE_	) is not None
                 # first_node guaranteed to be in list

                 del self.cache[first_node.key]
                 self.num_keys -= 1
            A:		Tuple          =		DoubleLinkedListNode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
            self.list.add(self.cache[key]	)
            self.num_keys += 1

       else:
            A:		Optional[Any]          =		self.list.remove(self.cache[key]	)
            assert node is not None  # node guaranteed to be in list
            A:		List[str]          =		value
            self.list.add(SCREAMING_SNAKE_CASE_	)




  @classmethod
  def 					_snake_case     ( cls				:							type[LFUCache[T, U]]					,  SCREAMING_SNAKE_CASE_				:							int = 1_28	)			->		Callable[[Callable[[T], U]], Callable[..., U]]:







       '''simple docstring'''



       def cache_decorator_inner(SCREAMING_SNAKE_CASE_				:							Callable[[T], U]	) -> Callable[..., U]:
            def cache_decorator_wrapper(*SCREAMING_SNAKE_CASE_				:							T	) -> U:
                 if func not in cls.decorator_function_to_instance_map:
                      A:		Tuple          =		LFUCache(SCREAMING_SNAKE_CASE_	)

                 A:		List[Any]          =		cls.decorator_function_to_instance_map[func].get(args[0]	)
                 if result is None:
                      A:		List[str]          =		func(*SCREAMING_SNAKE_CASE_	)
                      cls.decorator_function_to_instance_map[func].put(args[0]					,  SCREAMING_SNAKE_CASE_	)
                 return result

            def cache_info() -> LFUCache[T, U]:
                 return cls.decorator_function_to_instance_map[func]

            setattr(SCREAMING_SNAKE_CASE_					,  '''cache_info'''					,  SCREAMING_SNAKE_CASE_	)  # noqa: B010

            return cache_decorator_wrapper

       return cache_decorator_inner


if __name__ == "__main__":
     import doctest

     doctest.testmod()






