

"""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    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    int)->       Union[str, Any]:



						'''simple docstring'''


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

				@property
				def 							lowercase_					(     self							:    List[str])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      1
						__lowerCAmelCase:	Union[str, Any]   				=      3
						__lowerCAmelCase:	Union[str, Any]   				=      (3_2, 3_2)

						__lowerCAmelCase:	Tuple   				=      floats_tensor((batch_size, num_channels) + sizes					,  rng=random.Random(0)).to(UpperCamelCase__)
						return image

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



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:	Any   				=      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							:    List[Any])->       List[str]:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:	Union[str, Any]   				=      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							:    Optional[Any])->       List[str]:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:	Dict   				=      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(UpperCamelCase__)

				@property
				def 							lowercase_					(     self							:    Any)->       int:



						'''simple docstring'''


						def extract(*UpperCamelCase__							:    str					,  **UpperCamelCase__							:    Union[str, Any]):





								class    snake_case     :

												def __init__(     self							:    Dict)->       Dict:



														'''simple docstring'''


														__lowerCAmelCase:	Union[str, Any]   				=      torch.ones([0])




												def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    int)->       Any:



														'''simple docstring'''


														self.pixel_values.to(UpperCamelCase__)
														return self


								return Out()

						return extract

				def 							lowercase_					(     self							:    Dict)->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      "cpu"  # ensure determinism for the device-dependent torch.Generator
						__lowerCAmelCase:	str   				=      self.dummy_cond_unet
						__lowerCAmelCase:	Any   				=      DDIMScheduler(
						    beta_start=0.00085					,  beta_end=0.012					,  beta_schedule="scaled_linear"					,  clip_sample=UpperCamelCase__					,  set_alpha_to_one=UpperCamelCase__					,  )

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

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

						__lowerCAmelCase:	Any   				=      "A painting of a squirrel eating a burger"

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

						__lowerCAmelCase:	Optional[Any]   				=      torch.Generator(device=UpperCamelCase__).manual_seed(0)
						__lowerCAmelCase:	Tuple   				=      sd_pipe(
						    [prompt]					,  generator=UpperCamelCase__					,  guidance_scale=6.0					,  num_inference_steps=2					,  output_type="np"					,  return_dict=UpperCamelCase__					,  )[0]

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

						assert image.shape == (1, 6_4, 6_4, 3)
						__lowerCAmelCase:	int   				=      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 							lowercase_					(     self							:    List[Any])->       List[str]:



						'''simple docstring'''


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

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

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

						__lowerCAmelCase:	Optional[Any]   				=      output.images

						__lowerCAmelCase:	int   				=      torch.Generator(device=UpperCamelCase__).manual_seed(0)
						__lowerCAmelCase:	Dict   				=      sd_pipe(
						    [prompt]					,  generator=UpperCamelCase__					,  guidance_scale=6.0					,  num_inference_steps=2					,  output_type="np"					,  return_dict=UpperCamelCase__					,  )[0]

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

						assert image.shape == (1, 6_4, 6_4, 3)
						__lowerCAmelCase:	Optional[Any]   				=      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 							lowercase_					(     self							:    List[Any])->       int:



						'''simple docstring'''


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

						__lowerCAmelCase:	Optional[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(UpperCamelCase__)
								__lowerCAmelCase:	List[Any]   				=      StableDiffusionPipeline.from_pretrained(UpperCamelCase__)

						# sanity check that the pipeline still works
						assert pipe.safety_checker is None
						__lowerCAmelCase:	Optional[int]   				=      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							:    List[str])->       List[str]:



						'''simple docstring'''


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

						# put models in fp16
						__lowerCAmelCase:	str   				=      unet.half()
						__lowerCAmelCase:	Dict   				=      vae.half()
						__lowerCAmelCase:	str   				=      bert.half()

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

						__lowerCAmelCase:	Tuple   				=      "A painting of a squirrel eating a burger"
						__lowerCAmelCase:	Union[str, Any]   				=      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    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    Dict)->       List[str]:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Dict)->       Union[str, Any]:



						'''simple docstring'''


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

						__lowerCAmelCase:	Dict   				=      (
						    "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 "
						)
						__lowerCAmelCase:	int   				=      4_0_0_3_6_6_0_3_4_6
						__lowerCAmelCase:	List[Any]   				=      7

						# without safety guidance (sld_guidance_scale = 0)
						__lowerCAmelCase:	int   				=      torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      sd_pipe(
						    [prompt]					,  generator=UpperCamelCase__					,  guidance_scale=UpperCamelCase__					,  num_inference_steps=5_0					,  output_type="np"					,  width=5_1_2					,  height=5_1_2					,  sld_guidance_scale=0					,  )

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

						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)
						__lowerCAmelCase:	Tuple   				=      torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      sd_pipe(
						    [prompt]					,  generator=UpperCamelCase__					,  guidance_scale=UpperCamelCase__					,  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=0.025					,  sld_momentum_scale=0.5					,  sld_mom_beta=0.7					,  )

						__lowerCAmelCase:	str   				=      output.images
						__lowerCAmelCase:	int   				=      image[0, -3:, -3:, -1]
						__lowerCAmelCase:	Optional[int]   				=      [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

						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							:    str)->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"					,  safety_checker=UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
						__lowerCAmelCase:	Optional[int]   				=      sd_pipe.to(UpperCamelCase__)
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:	Any   				=      "padme amidala taking a bath artwork, safe for work, no nudity"
						__lowerCAmelCase:	Optional[int]   				=      2_7_3_4_9_7_1_7_5_5
						__lowerCAmelCase:	Optional[int]   				=      7

						__lowerCAmelCase:	int   				=      torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	int   				=      sd_pipe(
						    [prompt]					,  generator=UpperCamelCase__					,  guidance_scale=UpperCamelCase__					,  num_inference_steps=5_0					,  output_type="np"					,  width=5_1_2					,  height=5_1_2					,  sld_guidance_scale=0					,  )

						__lowerCAmelCase:	Any   				=      output.images
						__lowerCAmelCase:	List[str]   				=      image[0, -3:, -3:, -1]
						__lowerCAmelCase:	str   				=      [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

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

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

						__lowerCAmelCase:	Tuple   				=      torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      sd_pipe(
						    [prompt]					,  generator=UpperCamelCase__					,  guidance_scale=UpperCamelCase__					,  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=0.025					,  sld_momentum_scale=0.5					,  sld_mom_beta=0.7					,  )

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

						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[int])->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
						__lowerCAmelCase:	List[str]   				=      sd_pipe.to(UpperCamelCase__)
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:	Dict   				=      (
						    "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
						    " leyendecker"
						)
						__lowerCAmelCase:	List[Any]   				=      1_0_4_4_3_5_5_2_3_4
						__lowerCAmelCase:	Union[str, Any]   				=      1_2

						__lowerCAmelCase:	Tuple   				=      torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      sd_pipe(
						    [prompt]					,  generator=UpperCamelCase__					,  guidance_scale=UpperCamelCase__					,  num_inference_steps=5_0					,  output_type="np"					,  width=5_1_2					,  height=5_1_2					,  sld_guidance_scale=0					,  )

						__lowerCAmelCase:	int   				=      output.images
						__lowerCAmelCase:	Optional[int]   				=      image[0, -3:, -3:, -1]
						__lowerCAmelCase:	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, 5_1_2, 5_1_2, 3)

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

						__lowerCAmelCase:	List[Any]   				=      torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	int   				=      sd_pipe(
						    [prompt]					,  generator=UpperCamelCase__					,  guidance_scale=UpperCamelCase__					,  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=0.025					,  sld_momentum_scale=0.5					,  sld_mom_beta=0.7					,  )

						__lowerCAmelCase:	int   				=      output.images
						__lowerCAmelCase:	Optional[int]   				=      image[0, -3:, -3:, -1]
						__lowerCAmelCase:	List[str]   				=      np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561])
						assert image.shape == (1, 5_1_2, 5_1_2, 3)

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





"""simple docstring"""


from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     np.ndarray:
		__lowerCAmelCase:	List[Any]   				=      cva.getAffineTransform(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		return cva.warpAffine(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			(rows, cols)			)


if __name__ == "__main__":
			# read original image
			__A						       =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
			)
			# turn image in gray scale value
			__A						       =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			__A       ,    __A						       =      gray_img.shape

			# set different points to rotate image
			__A						       =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			__A						       =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			__A						       =      [
			    gray_img,
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			]

			# plot different image rotations
			__A						       =      plt.figure(1)
			__A						       =      ["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
			for i, image in enumerate(images):
						plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
						plt.title(titles[i])
						plt.axis("off")
						plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
			plt.show()


