
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.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			str       )       ->     Optional[int]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()
	@property
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Optional[Any]:
					lowercase_       =					1
					lowercase_       =					3
					lowercase_       =					(3_2, 3_2)

					lowercase_       =					floats_tensor((batch_size, num_channels) + sizes       ,							rng=random.Random(0       )       ).to(SCREAMING_SNAKE_CASE_       )
					return image
	@property
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Tuple:
					torch.manual_seed(0       )
					lowercase_       =					UNetaDConditionModel(
					    block_out_channels=(3_2, 6_4)       ,							layers_per_block=2       ,							sample_size=3_2       ,							in_channels=4       ,							out_channels=4       ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,							cross_attention_dim=3_2       ,							)
					return model
	@property
	def 						_lowercase  (       self  :			Optional[int]       )       ->     int:
					torch.manual_seed(0       )
					lowercase_       =					AutoencoderKL(
					    block_out_channels=[3_2, 6_4]       ,							in_channels=3       ,							out_channels=3       ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,							latent_channels=4       ,							)
					return model
	@property
	def 						_lowercase  (       self  :			Dict       )       ->     Union[str, Any]:
					torch.manual_seed(0       )
					lowercase_       =					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       ,							)
					return CLIPTextModel(SCREAMING_SNAKE_CASE_       )
	@property
	def 						_lowercase  (       self  :			int       )       ->     Optional[int]:
					def extract(*SCREAMING_SNAKE_CASE_  :			List[Any]       ,							**SCREAMING_SNAKE_CASE_  :			str       ):
									class  lowercase__:



										"""simple docstring"""




										def __init__(       self  :			Tuple       )       ->     Optional[int]:
														lowercase_       =					torch.ones([0]       )




										def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     Any:
														self.pixel_values.to(SCREAMING_SNAKE_CASE_       )
														return self

									return Out()

					return extract
	def 						_lowercase  (       self  :			Dict       )       ->     List[Any]:
					lowercase_       =					"cpu"  # ensure determinism for the device-dependent torch.Generator
					lowercase_       =					self.dummy_cond_unet
					lowercase_       =					DDIMScheduler(
					    beta_start=0.0_00_85       ,							beta_end=0.0_12       ,							beta_schedule='''scaled_linear'''       ,							clip_sample=SCREAMING_SNAKE_CASE_       ,							set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,							)

					lowercase_       =					self.dummy_vae
					lowercase_       =					self.dummy_text_encoder
					lowercase_       =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

					# make sure here that pndm scheduler skips prk
					lowercase_       =					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       ,							)
					lowercase_       =					sd_pipe.to(SCREAMING_SNAKE_CASE_       )
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					"A painting of a squirrel eating a burger"

					lowercase_       =					torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(0       )
					lowercase_       =					sd_pipe([prompt]       ,							generator=SCREAMING_SNAKE_CASE_       ,							guidance_scale=6.0       ,							num_inference_steps=2       ,							output_type='''np'''       )
					lowercase_       =					output.images

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

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

					assert image.shape == (1, 6_4, 6_4, 3)
					lowercase_       =					np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64]       )

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

					# make sure here that pndm scheduler skips prk
					lowercase_       =					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       ,							)
					lowercase_       =					sd_pipe.to(SCREAMING_SNAKE_CASE_       )
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

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

					lowercase_       =					output.images

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

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

					assert image.shape == (1, 6_4, 6_4, 3)
					lowercase_       =					np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93]       )

					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 						_lowercase  (       self  :			Optional[Any]       )       ->     Any:
					lowercase_       =					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

					lowercase_       =					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_       )
									lowercase_       =					StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_       )

					# sanity check that the pipeline still works
					assert pipe.safety_checker is None
					lowercase_       =					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 						_lowercase  (       self  :			Any       )       ->     List[Any]:
					lowercase_       =					self.dummy_cond_unet
					lowercase_       =					PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					self.dummy_vae
					lowercase_       =					self.dummy_text_encoder
					lowercase_       =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

					# put models in fp16
					lowercase_       =					unet.half()
					lowercase_       =					vae.half()
					lowercase_       =					bert.half()

					# make sure here that pndm scheduler skips prk
					lowercase_       =					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       ,							)
					lowercase_       =					sd_pipe.to(SCREAMING_SNAKE_CASE_       )
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					"A painting of a squirrel eating a burger"
					lowercase_       =					sd_pipe([prompt]       ,							num_inference_steps=2       ,							output_type='''np'''       ).images

					assert image.shape == (1, 6_4, 6_4, 3)

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



	"""simple docstring"""




	def 						_lowercase  (       self  :			int       )       ->     Dict:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()
	def 						_lowercase  (       self  :			str       )       ->     Tuple:
					lowercase_       =					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''       ,							safety_checker=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
					lowercase_       =					sd_pipe.to(SCREAMING_SNAKE_CASE_       )
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					(
					    "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
					    " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
					    " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
					    " children from bahnhof zoo, detailed "
					)
					lowercase_       =					4_0_0_3_6_6_0_3_4_6
					lowercase_       =					7

					# without safety guidance (sld_guidance_scale = 0)
					lowercase_       =					torch.manual_seed(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					sd_pipe(
					    [prompt]       ,							generator=SCREAMING_SNAKE_CASE_       ,							guidance_scale=SCREAMING_SNAKE_CASE_       ,							num_inference_steps=5_0       ,							output_type='''np'''       ,							width=5_1_2       ,							height=5_1_2       ,							sld_guidance_scale=0       ,							)

					lowercase_       =					output.images
					lowercase_       =					image[0, -3:, -3:, -1]
					lowercase_       =					[0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

					assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

					# without safety guidance (strong configuration)
					lowercase_       =					torch.manual_seed(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					sd_pipe(
					    [prompt]       ,							generator=SCREAMING_SNAKE_CASE_       ,							guidance_scale=SCREAMING_SNAKE_CASE_       ,							num_inference_steps=5_0       ,							output_type='''np'''       ,							width=5_1_2       ,							height=5_1_2       ,							sld_guidance_scale=2_0_0_0       ,							sld_warmup_steps=7       ,							sld_threshold=0.0_25       ,							sld_momentum_scale=0.5       ,							sld_mom_beta=0.7       ,							)

					lowercase_       =					output.images
					lowercase_       =					image[0, -3:, -3:, -1]
					lowercase_       =					[0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

					assert image.shape == (1, 5_1_2, 5_1_2, 3)

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Union[str, Any]:
					lowercase_       =					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''       ,							safety_checker=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
					lowercase_       =					sd_pipe.to(SCREAMING_SNAKE_CASE_       )
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					"padme amidala taking a bath artwork, safe for work, no nudity"
					lowercase_       =					2_7_3_4_9_7_1_7_5_5
					lowercase_       =					7

					lowercase_       =					torch.manual_seed(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					sd_pipe(
					    [prompt]       ,							generator=SCREAMING_SNAKE_CASE_       ,							guidance_scale=SCREAMING_SNAKE_CASE_       ,							num_inference_steps=5_0       ,							output_type='''np'''       ,							width=5_1_2       ,							height=5_1_2       ,							sld_guidance_scale=0       ,							)

					lowercase_       =					output.images
					lowercase_       =					image[0, -3:, -3:, -1]
					lowercase_       =					[0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

					assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

					lowercase_       =					torch.manual_seed(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					sd_pipe(
					    [prompt]       ,							generator=SCREAMING_SNAKE_CASE_       ,							guidance_scale=SCREAMING_SNAKE_CASE_       ,							num_inference_steps=5_0       ,							output_type='''np'''       ,							width=5_1_2       ,							height=5_1_2       ,							sld_guidance_scale=2_0_0_0       ,							sld_warmup_steps=7       ,							sld_threshold=0.0_25       ,							sld_momentum_scale=0.5       ,							sld_mom_beta=0.7       ,							)

					lowercase_       =					output.images
					lowercase_       =					image[0, -3:, -3:, -1]
					lowercase_       =					[0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

					assert image.shape == (1, 5_1_2, 5_1_2, 3)

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




	def 						_lowercase  (       self  :			Optional[Any]       )       ->     List[str]:
					lowercase_       =					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''       )
					lowercase_       =					sd_pipe.to(SCREAMING_SNAKE_CASE_       )
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					(
					    "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
					    " leyendecker"
					)
					lowercase_       =					1_0_4_4_3_5_5_2_3_4
					lowercase_       =					1_2

					lowercase_       =					torch.manual_seed(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					sd_pipe(
					    [prompt]       ,							generator=SCREAMING_SNAKE_CASE_       ,							guidance_scale=SCREAMING_SNAKE_CASE_       ,							num_inference_steps=5_0       ,							output_type='''np'''       ,							width=5_1_2       ,							height=5_1_2       ,							sld_guidance_scale=0       ,							)

					lowercase_       =					output.images
					lowercase_       =					image[0, -3:, -3:, -1]
					lowercase_       =					np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]       )

					assert image.shape == (1, 5_1_2, 5_1_2, 3)

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-7

					lowercase_       =					torch.manual_seed(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					sd_pipe(
					    [prompt]       ,							generator=SCREAMING_SNAKE_CASE_       ,							guidance_scale=SCREAMING_SNAKE_CASE_       ,							num_inference_steps=5_0       ,							output_type='''np'''       ,							width=5_1_2       ,							height=5_1_2       ,							sld_guidance_scale=2_0_0_0       ,							sld_warmup_steps=7       ,							sld_threshold=0.0_25       ,							sld_momentum_scale=0.5       ,							sld_mom_beta=0.7       ,							)

					lowercase_       =					output.images
					lowercase_       =					image[0, -3:, -3:, -1]
					lowercase_       =					np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61]       )
					assert image.shape == (1, 5_1_2, 5_1_2, 3)

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






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]"