
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        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

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

			@property
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							List[str]	=						1
							UpperCamelCase_:							Dict	=						3
							UpperCamelCase_:							Any	=						(32, 32)

							UpperCamelCase_:							Dict	=						floats_tensor((batch_size, num_channels) + sizes						,	rng=random.Random(0      )      ).to(__lowercase      )
							return image

			@property
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							List[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       lowerCAmelCase__	(       self :       List[str]      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							str	=						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       lowerCAmelCase__	(       self :       Tuple      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							Dict	=						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						,	)
							return CLIPTextModel(__lowercase      )

			@property
			def       lowerCAmelCase__	(       self :       Dict      ):
							def extract(*snake_case_ :       Optional[Any]						,	**snake_case_ :       Union[str, Any]      ):

											class        _UpperCamelCase				:






														'''simple docstring'''

														def __init__(       self :       Optional[int]      ):
																		UpperCamelCase_:							Any	=						torch.ones([0]      )




														def       lowerCAmelCase__	(       self :       Any						,	snake_case_ :       Dict      ):
																		self.pixel_values.to(__lowercase      )
																		return self




											return Out()

							return extract

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Union[str, Any]	=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase_:							List[Any]	=						self.dummy_cond_unet
							UpperCamelCase_:							Optional[int]	=						DDIMScheduler(
							    beta_start=0.0_0085						,	beta_end=0.012						,	beta_schedule="""scaled_linear"""						,	clip_sample=__lowercase						,	set_alpha_to_one=__lowercase						,	)

							UpperCamelCase_:							Dict	=						self.dummy_vae
							UpperCamelCase_:							Dict	=						self.dummy_text_encoder
							UpperCamelCase_:							int	=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

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

							UpperCamelCase_:							Tuple	=						"""A painting of a squirrel eating a burger"""

							UpperCamelCase_:							Optional[int]	=						torch.Generator(device=__lowercase      ).manual_seed(0      )
							UpperCamelCase_:							Optional[Any]	=						sd_pipe([prompt]						,	generator=__lowercase						,	guidance_scale=6.0						,	num_inference_steps=2						,	output_type="""np"""      )
							UpperCamelCase_:							Optional[Any]	=						output.images

							UpperCamelCase_:							str	=						torch.Generator(device=__lowercase      ).manual_seed(0      )
							UpperCamelCase_:							Optional[int]	=						sd_pipe(
							    [prompt]						,	generator=__lowercase						,	guidance_scale=6.0						,	num_inference_steps=2						,	output_type="""np"""						,	return_dict=__lowercase						,	)[0]

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

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase_:							Tuple	=						np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]      )

							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 :       int      ):
							UpperCamelCase_:							List[Any]	=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase_:							Union[str, Any]	=						self.dummy_cond_unet
							UpperCamelCase_:							List[str]	=						PNDMScheduler(skip_prk_steps=__lowercase      )
							UpperCamelCase_:							Optional[int]	=						self.dummy_vae
							UpperCamelCase_:							Tuple	=						self.dummy_text_encoder
							UpperCamelCase_:							Any	=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

							# make sure here that pndm scheduler skips prk
							UpperCamelCase_:							Union[str, Any]	=						StableDiffusionPipeline(
							    unet=__lowercase						,	scheduler=__lowercase						,	vae=__lowercase						,	text_encoder=__lowercase						,	tokenizer=__lowercase						,	safety_checker=__lowercase						,	feature_extractor=self.dummy_extractor						,	)
							UpperCamelCase_:							Tuple	=						sd_pipe.to(__lowercase      )
							sd_pipe.set_progress_bar_config(disable=__lowercase      )

							UpperCamelCase_:							Tuple	=						"""A painting of a squirrel eating a burger"""
							UpperCamelCase_:							Dict	=						torch.Generator(device=__lowercase      ).manual_seed(0      )
							UpperCamelCase_:							Dict	=						sd_pipe([prompt]						,	generator=__lowercase						,	guidance_scale=6.0						,	num_inference_steps=2						,	output_type="""np"""      )

							UpperCamelCase_:							Optional[Any]	=						output.images

							UpperCamelCase_:							Union[str, Any]	=						torch.Generator(device=__lowercase      ).manual_seed(0      )
							UpperCamelCase_:							Any	=						sd_pipe(
							    [prompt]						,	generator=__lowercase						,	guidance_scale=6.0						,	num_inference_steps=2						,	output_type="""np"""						,	return_dict=__lowercase						,	)[0]

							UpperCamelCase_:							List[Any]	=						image[0, -3:, -3:, -1]
							UpperCamelCase_:							Union[str, Any]	=						image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase_:							Optional[int]	=						np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]      )

							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 :       Union[str, Any]      ):
							UpperCamelCase_:							str	=						StableDiffusionPipeline.from_pretrained(
							    """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""						,	safety_checker=__lowercase      )
							assert isinstance(__lowercase						,	__lowercase      )
							assert isinstance(pipe.scheduler						,	__lowercase      )
							assert pipe.safety_checker is None

							UpperCamelCase_:							str	=						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(__lowercase      )
											UpperCamelCase_:							Tuple	=						StableDiffusionPipeline.from_pretrained(__lowercase      )

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							UpperCamelCase_:							str	=						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       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Optional[Any]	=						self.dummy_cond_unet
							UpperCamelCase_:							str	=						PNDMScheduler(skip_prk_steps=__lowercase      )
							UpperCamelCase_:							Optional[int]	=						self.dummy_vae
							UpperCamelCase_:							Any	=						self.dummy_text_encoder
							UpperCamelCase_:							Tuple	=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

							# put models in fp16
							UpperCamelCase_:							Union[str, Any]	=						unet.half()
							UpperCamelCase_:							int	=						vae.half()
							UpperCamelCase_:							Optional[int]	=						bert.half()

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

							UpperCamelCase_:							Tuple	=						"""A painting of a squirrel eating a burger"""
							UpperCamelCase_:							Union[str, Any]	=						sd_pipe([prompt]						,	num_inference_steps=2						,	output_type="""np"""      ).images

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




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






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Dict	=						StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""						,	safety_checker=__lowercase      )
							UpperCamelCase_:							Union[str, Any]	=						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
							UpperCamelCase_:							Tuple	=						sd_pipe.to(__lowercase      )
							sd_pipe.set_progress_bar_config(disable=__lowercase      )

							UpperCamelCase_:							str	=						(
							    """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 """
							)
							UpperCamelCase_:							Optional[int]	=						40_0366_0346
							UpperCamelCase_:							Any	=						7

							# without safety guidance (sld_guidance_scale = 0)
							UpperCamelCase_:							List[Any]	=						torch.manual_seed(__lowercase      )
							UpperCamelCase_:							int	=						sd_pipe(
							    [prompt]						,	generator=__lowercase						,	guidance_scale=__lowercase						,	num_inference_steps=50						,	output_type="""np"""						,	width=512						,	height=512						,	sld_guidance_scale=0						,	)

							UpperCamelCase_:							str	=						output.images
							UpperCamelCase_:							Any	=						image[0, -3:, -3:, -1]
							UpperCamelCase_:							List[str]	=						[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

							assert image.shape == (1, 512, 512, 3)

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

							# without safety guidance (strong configuration)
							UpperCamelCase_:							str	=						torch.manual_seed(__lowercase      )
							UpperCamelCase_:							Optional[int]	=						sd_pipe(
							    [prompt]						,	generator=__lowercase						,	guidance_scale=__lowercase						,	num_inference_steps=50						,	output_type="""np"""						,	width=512						,	height=512						,	sld_guidance_scale=2000						,	sld_warmup_steps=7						,	sld_threshold=0.025						,	sld_momentum_scale=0.5						,	sld_mom_beta=0.7						,	)

							UpperCamelCase_:							Tuple	=						output.images
							UpperCamelCase_:							str	=						image[0, -3:, -3:, -1]
							UpperCamelCase_:							Union[str, Any]	=						[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

							assert image.shape == (1, 512, 512, 3)

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

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Dict	=						StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""						,	safety_checker=__lowercase      )
							UpperCamelCase_:							Optional[Any]	=						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
							UpperCamelCase_:							Optional[int]	=						sd_pipe.to(__lowercase      )
							sd_pipe.set_progress_bar_config(disable=__lowercase      )

							UpperCamelCase_:							str	=						"""padme amidala taking a bath artwork, safe for work, no nudity"""
							UpperCamelCase_:							Union[str, Any]	=						27_3497_1755
							UpperCamelCase_:							List[str]	=						7

							UpperCamelCase_:							List[str]	=						torch.manual_seed(__lowercase      )
							UpperCamelCase_:							Tuple	=						sd_pipe(
							    [prompt]						,	generator=__lowercase						,	guidance_scale=__lowercase						,	num_inference_steps=50						,	output_type="""np"""						,	width=512						,	height=512						,	sld_guidance_scale=0						,	)

							UpperCamelCase_:							Dict	=						output.images
							UpperCamelCase_:							Tuple	=						image[0, -3:, -3:, -1]
							UpperCamelCase_:							Any	=						[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

							assert image.shape == (1, 512, 512, 3)

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

							UpperCamelCase_:							int	=						torch.manual_seed(__lowercase      )
							UpperCamelCase_:							int	=						sd_pipe(
							    [prompt]						,	generator=__lowercase						,	guidance_scale=__lowercase						,	num_inference_steps=50						,	output_type="""np"""						,	width=512						,	height=512						,	sld_guidance_scale=2000						,	sld_warmup_steps=7						,	sld_threshold=0.025						,	sld_momentum_scale=0.5						,	sld_mom_beta=0.7						,	)

							UpperCamelCase_:							List[str]	=						output.images
							UpperCamelCase_:							str	=						image[0, -3:, -3:, -1]
							UpperCamelCase_:							Any	=						[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

							assert image.shape == (1, 512, 512, 3)

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




			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Optional[Any]	=						StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""      )
							UpperCamelCase_:							int	=						sd_pipe.to(__lowercase      )
							sd_pipe.set_progress_bar_config(disable=__lowercase      )

							UpperCamelCase_:							Optional[int]	=						(
							    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
							    """ leyendecker"""
							)
							UpperCamelCase_:							Tuple	=						10_4435_5234
							UpperCamelCase_:							Optional[Any]	=						12

							UpperCamelCase_:							Dict	=						torch.manual_seed(__lowercase      )
							UpperCamelCase_:							Dict	=						sd_pipe(
							    [prompt]						,	generator=__lowercase						,	guidance_scale=__lowercase						,	num_inference_steps=50						,	output_type="""np"""						,	width=512						,	height=512						,	sld_guidance_scale=0						,	)

							UpperCamelCase_:							Tuple	=						output.images
							UpperCamelCase_:							List[Any]	=						image[0, -3:, -3:, -1]
							UpperCamelCase_:							Dict	=						np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]      )

							assert image.shape == (1, 512, 512, 3)

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

							UpperCamelCase_:							List[str]	=						torch.manual_seed(__lowercase      )
							UpperCamelCase_:							int	=						sd_pipe(
							    [prompt]						,	generator=__lowercase						,	guidance_scale=__lowercase						,	num_inference_steps=50						,	output_type="""np"""						,	width=512						,	height=512						,	sld_guidance_scale=2000						,	sld_warmup_steps=7						,	sld_threshold=0.025						,	sld_momentum_scale=0.5						,	sld_mom_beta=0.7						,	)

							UpperCamelCase_:							Optional[int]	=						output.images
							UpperCamelCase_:							Optional[int]	=						image[0, -3:, -3:, -1]
							UpperCamelCase_:							Optional[Any]	=						np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]      )
							assert image.shape == (1, 512, 512, 3)

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








def        A__					(						lowerCamelCase = 50						)  ->	int:
				UpperCamelCase_:							List[Any]	=						[[0] * 3 for _ in range(length + 1						)]

				for row_length in range(length + 1						):
								for tile_length in range(2     ,    5						):
												for tile_start in range(row_length - tile_length + 1						):
																different_colour_ways_number[row_length][tile_length - 2] += (
																    different_colour_ways_number[row_length - tile_start - tile_length][
																        tile_length - 2
																    ]
																    + 1
																)

				return sum(different_colour_ways_number[length]						)


if __name__ == "__main__":
							print(F"""{solution() = }""")






