


"""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.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 	lowerCamelCase__  (	unittest.TestCase							):







					"""simple docstring"""


					def lowerCamelCase__				(			self       :					int    ):
												'''simple docstring'''






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






					@property
					def lowerCamelCase__				(			self       :					List[str]    ):
												'''simple docstring'''






												__UpperCAmelCase  :		List[Any] 				=   1
												__UpperCAmelCase  :		Optional[Any] 				=   3
												__UpperCAmelCase  :		Dict 				=   (32, 32)

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






					@property
					def lowerCamelCase__				(			self       :					Optional[int]    ):
												'''simple docstring'''






												torch.manual_seed(0    )
												__UpperCAmelCase  :		Dict 				=   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       :					Dict    ):
												'''simple docstring'''






												torch.manual_seed(0    )
												__UpperCAmelCase  :		Optional[int] 				=   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       :					int    ):
												'''simple docstring'''






												torch.manual_seed(0    )
												__UpperCAmelCase  :		str 				=   CLIPTextConfig(
												    bos_token_id=0					,			eos_token_id=2					,			hidden_size=32					,			intermediate_size=37					,			layer_norm_eps=1e-0_5					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=1_000					,			)
												return CLIPTextModel(a__    )






					@property
					def lowerCamelCase__				(			self       :					Optional[int]    ):
												'''simple docstring'''






												def extract(*UpperCamelCase       :					Optional[Any]					,			**UpperCamelCase       :					List[str]    ):





																			class 	lowerCamelCase__  :







																								"""simple docstring"""


																								def __init__(			self       :					Dict    ):
																															'''simple docstring'''






																															__UpperCAmelCase  :		Any 				=   torch.ones([0]    )






																								def lowerCamelCase__				(			self       :					Union[str, Any]					,			UpperCamelCase       :					Tuple    ):
																															'''simple docstring'''






																															self.pixel_values.to(a__    )
																															return self






																			return Out()

												return extract






					def lowerCamelCase__				(			self       :					Dict    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Tuple 				=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
												__UpperCAmelCase  :		Tuple 				=   self.dummy_cond_unet
												__UpperCAmelCase  :		List[Any] 				=   DDIMScheduler(
												    beta_start=0.00085					,			beta_end=0.012					,			beta_schedule="""scaled_linear"""					,			clip_sample=a__					,			set_alpha_to_one=a__					,			)

												__UpperCAmelCase  :		Optional[Any] 				=   self.dummy_vae
												__UpperCAmelCase  :		List[str] 				=   self.dummy_text_encoder
												__UpperCAmelCase  :		Any 				=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

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

												__UpperCAmelCase  :		str 				=   """A painting of a squirrel eating a burger"""

												__UpperCAmelCase  :		str 				=   torch.Generator(device=a__    ).manual_seed(0    )
												__UpperCAmelCase  :		int 				=   sd_pipe([prompt]					,			generator=a__					,			guidance_scale=6.0					,			num_inference_steps=2					,			output_type="""np"""    )
												__UpperCAmelCase  :		str 				=   output.images

												__UpperCAmelCase  :		Optional[int] 				=   torch.Generator(device=a__    ).manual_seed(0    )
												__UpperCAmelCase  :		List[Any] 				=   sd_pipe(
												    [prompt]					,			generator=a__					,			guidance_scale=6.0					,			num_inference_steps=2					,			output_type="""np"""					,			return_dict=a__					,			)[0]

												__UpperCAmelCase  :		Any 				=   image[0, -3:, -3:, -1]
												__UpperCAmelCase  :		str 				=   image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												__UpperCAmelCase  :		List[str] 				=   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       :					List[Any]    ):
												'''simple docstring'''






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

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

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

												__UpperCAmelCase  :		Union[str, Any] 				=   output.images

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

												__UpperCAmelCase  :		Dict 				=   image[0, -3:, -3:, -1]
												__UpperCAmelCase  :		List[str] 				=   image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												__UpperCAmelCase  :		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       :					str    ):
												'''simple docstring'''






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

												__UpperCAmelCase  :		Optional[Any] 				=   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(a__    )
																			__UpperCAmelCase  :		List[str] 				=   StableDiffusionPipeline.from_pretrained(a__    )

												# sanity check that the pipeline still works
												assert pipe.safety_checker is None
												__UpperCAmelCase  :		List[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       :					Tuple    ):
												'''simple docstring'''






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

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

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

												__UpperCAmelCase  :		str 				=   """A painting of a squirrel eating a burger"""
												__UpperCAmelCase  :		Dict 				=   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 lowerCamelCase__				(			self       :					List[str]    ):
												'''simple docstring'''






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






					def lowerCamelCase__				(			self       :					Tuple    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Dict 				=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""					,			safety_checker=a__    )
												__UpperCAmelCase  :		Dict 				=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config    )
												__UpperCAmelCase  :		List[Any] 				=   sd_pipe.to(a__    )
												sd_pipe.set_progress_bar_config(disable=a__    )

												__UpperCAmelCase  :		int 				=   (
												    """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[Any] 				=   4_003_660_346
												__UpperCAmelCase  :		int 				=   7

												# without safety guidance (sld_guidance_scale = 0)
												__UpperCAmelCase  :		Optional[Any] 				=   torch.manual_seed(a__    )
												__UpperCAmelCase  :		Union[str, Any] 				=   sd_pipe(
												    [prompt]					,			generator=a__					,			guidance_scale=a__					,			num_inference_steps=50					,			output_type="""np"""					,			width=512					,			height=512					,			sld_guidance_scale=0					,			)

												__UpperCAmelCase  :		str 				=   output.images
												__UpperCAmelCase  :		int 				=   image[0, -3:, -3:, -1]
												__UpperCAmelCase  :		Union[str, Any] 				=   [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  :		int 				=   torch.manual_seed(a__    )
												__UpperCAmelCase  :		int 				=   sd_pipe(
												    [prompt]					,			generator=a__					,			guidance_scale=a__					,			num_inference_steps=50					,			output_type="""np"""					,			width=512					,			height=512					,			sld_guidance_scale=2_000					,			sld_warmup_steps=7					,			sld_threshold=0.025					,			sld_momentum_scale=0.5					,			sld_mom_beta=0.7					,			)

												__UpperCAmelCase  :		List[str] 				=   output.images
												__UpperCAmelCase  :		Optional[int] 				=   image[0, -3:, -3:, -1]
												__UpperCAmelCase  :		int 				=   [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       :					Any    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Union[str, Any] 				=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""					,			safety_checker=a__    )
												__UpperCAmelCase  :		Dict 				=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config    )
												__UpperCAmelCase  :		Any 				=   sd_pipe.to(a__    )
												sd_pipe.set_progress_bar_config(disable=a__    )

												__UpperCAmelCase  :		Union[str, Any] 				=   """padme amidala taking a bath artwork, safe for work, no nudity"""
												__UpperCAmelCase  :		str 				=   2_734_971_755
												__UpperCAmelCase  :		Optional[int] 				=   7

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

												__UpperCAmelCase  :		List[str] 				=   output.images
												__UpperCAmelCase  :		Tuple 				=   image[0, -3:, -3:, -1]
												__UpperCAmelCase  :		Dict 				=   [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  :		Optional[int] 				=   torch.manual_seed(a__    )
												__UpperCAmelCase  :		str 				=   sd_pipe(
												    [prompt]					,			generator=a__					,			guidance_scale=a__					,			num_inference_steps=50					,			output_type="""np"""					,			width=512					,			height=512					,			sld_guidance_scale=2_000					,			sld_warmup_steps=7					,			sld_threshold=0.025					,			sld_momentum_scale=0.5					,			sld_mom_beta=0.7					,			)

												__UpperCAmelCase  :		Any 				=   output.images
												__UpperCAmelCase  :		List[Any] 				=   image[0, -3:, -3:, -1]
												__UpperCAmelCase  :		int 				=   [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       :					Dict    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Any 				=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""    )
												__UpperCAmelCase  :		Optional[Any] 				=   sd_pipe.to(a__    )
												sd_pipe.set_progress_bar_config(disable=a__    )

												__UpperCAmelCase  :		int 				=   (
												    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
												    """ leyendecker"""
												)
												__UpperCAmelCase  :		Union[str, Any] 				=   1_044_355_234
												__UpperCAmelCase  :		List[str] 				=   12

												__UpperCAmelCase  :		str 				=   torch.manual_seed(a__    )
												__UpperCAmelCase  :		Union[str, Any] 				=   sd_pipe(
												    [prompt]					,			generator=a__					,			guidance_scale=a__					,			num_inference_steps=50					,			output_type="""np"""					,			width=512					,			height=512					,			sld_guidance_scale=0					,			)

												__UpperCAmelCase  :		List[str] 				=   output.images
												__UpperCAmelCase  :		List[str] 				=   image[0, -3:, -3:, -1]
												__UpperCAmelCase  :		List[str] 				=   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  :		Dict 				=   torch.manual_seed(a__    )
												__UpperCAmelCase  :		Optional[int] 				=   sd_pipe(
												    [prompt]					,			generator=a__					,			guidance_scale=a__					,			num_inference_steps=50					,			output_type="""np"""					,			width=512					,			height=512					,			sld_guidance_scale=2_000					,			sld_warmup_steps=7					,			sld_threshold=0.025					,			sld_momentum_scale=0.5					,			sld_mom_beta=0.7					,			)

												__UpperCAmelCase  :		Any 				=   output.images
												__UpperCAmelCase  :		int 				=   image[0, -3:, -3:, -1]
												__UpperCAmelCase  :		int 				=   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




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def 	_lowerCAmelCase					( )			-> str:





	"""simple docstring"""







	A             =					HfArgumentParser(UpperCamelCase__    )
	A             =					parser.parse_args_into_dataclasses()[0]
	A             =					TensorFlowBenchmark(args=UpperCamelCase__    )
	try:
		A             =					parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		A             =					"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
		A             =					""" """.join(str(UpperCamelCase__    ).split(""" """    )[:-1]    )
		A             =					""""""
		A             =					eval(str(UpperCamelCase__    ).split(""" """    )[-1]    )
		A             =					[]
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]    )
			else:
				wrong_args.append(UpperCamelCase__    )
		if len(UpperCamelCase__    ) > 0:
			A             =					full_error_msg + begin_error_msg + str(UpperCamelCase__    )
		raise ValueError(UpperCamelCase__    )
	benchmark.run()


if __name__ == "__main__":
				main()



