





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class        _a(    unittest.TestCase    ):
		lowerCamelCase__     :int   				=		StableDiffusionLDMaDPipeline
		lowerCamelCase__     :List[str]   				=		TEXT_TO_IMAGE_PARAMS
		lowerCamelCase__     :int   				=		TEXT_TO_IMAGE_BATCH_PARAMS
		lowerCamelCase__     :List[str]   				=		TEXT_TO_IMAGE_IMAGE_PARAMS







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							torch.manual_seed(0				)
							_snake_case       :					Optional[int]				       =   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 ,						)
							_snake_case       :					int				       =   DDIMScheduler(
							    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule="scaled_linear" ,						clip_sample=__snake_case ,						set_alpha_to_one=__snake_case ,						)
							torch.manual_seed(0				)
							_snake_case       :					List[str]				       =   AutoencoderKL(
							    block_out_channels=[3_2, 6_4] ,						in_channels=6 ,						out_channels=6 ,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,						latent_channels=4 ,						)
							torch.manual_seed(0				)
							_snake_case       :					List[Any]				       =   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 ,						)
							_snake_case       :					Optional[Any]				       =   CLIPTextModel(__snake_case				)
							_snake_case       :					str				       =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

							_snake_case       :					Union[str, Any]				       =   {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}
							return components







		def        lowercase       (    self ,						__snake_case ,						__snake_case=0				)      ->      Any:


							'''simple docstring'''

							if str(__snake_case				).startswith("mps"				):
												_snake_case       :					List[Any]				       =   torch.manual_seed(__snake_case				)
							else:
												_snake_case       :					int				       =   torch.Generator(device=__snake_case				).manual_seed(__snake_case				)
							_snake_case       :					str				       =   {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							}
							return inputs







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


							'''simple docstring'''

							_snake_case       :					str				       =   'cpu'  # ensure determinism for the device-dependent torch.Generator

							_snake_case       :					Dict				       =   self.get_dummy_components()
							_snake_case       :					Union[str, Any]				       =   StableDiffusionLDMaDPipeline(**__snake_case				)
							_snake_case       :					str				       =   ldmad_pipe.to(__snake_case				)
							ldmad_pipe.set_progress_bar_config(disable=__snake_case				)

							_snake_case       :					int				       =   self.get_dummy_inputs(__snake_case				)
							_snake_case       :					List[str]				       =   ldmad_pipe(**__snake_case				)
							_snake_case       :					List[Any]				       =   output.rgb, output.depth

							_snake_case       :					int				       =   rgb[0, -3:, -3:, -1]
							_snake_case       :					Dict				       =   depth[0, -3:, -1]

							assert rgb.shape == (1, 6_4, 6_4, 3)
							assert depth.shape == (1, 6_4, 6_4)

							_snake_case       :					Any				       =   np.array(
							    [0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62]				)
							_snake_case       :					Dict				       =   np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36]				)

							assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb				).max() < 1E-2
							assert np.abs(image_slice_depth.flatten() - expected_slice_depth				).max() < 1E-2







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


							'''simple docstring'''

							_snake_case       :					str				       =   self.get_dummy_components()
							_snake_case       :					Any				       =   StableDiffusionLDMaDPipeline(**__snake_case				)
							_snake_case       :					Optional[int]				       =   ldmad_pipe.to(__snake_case				)
							ldmad_pipe.set_progress_bar_config(disable=__snake_case				)

							_snake_case       :					Optional[Any]				       =   self.get_dummy_inputs(__snake_case				)
							_snake_case       :					Optional[int]				       =   3 * [inputs['prompt']]

							# forward
							_snake_case       :					int				       =   ldmad_pipe(**__snake_case				)
							_snake_case       :					List[str]				       =   output.rgb, output.depth
							_snake_case       :					str				       =   rgb_slice_a[0, -3:, -3:, -1]
							_snake_case       :					Union[str, Any]				       =   depth_slice_a[0, -3:, -1]

							_snake_case       :					Union[str, Any]				       =   self.get_dummy_inputs(__snake_case				)
							_snake_case       :					List[Any]				       =   3 * [inputs.pop("prompt"				)]

							_snake_case       :					int				       =   ldmad_pipe.tokenizer(
							    __snake_case ,						padding="max_length" ,						max_length=ldmad_pipe.tokenizer.model_max_length ,						truncation=__snake_case ,						return_tensors="pt" ,						)
							_snake_case       :					int				       =   text_inputs['input_ids'].to(__snake_case				)

							_snake_case       :					Optional[Any]				       =   ldmad_pipe.text_encoder(__snake_case				)[0]

							_snake_case       :					int				       =   prompt_embeds

							# forward
							_snake_case       :					str				       =   ldmad_pipe(**__snake_case				)
							_snake_case       :					List[str]				       =   output.rgb, output.depth
							_snake_case       :					Optional[int]				       =   rgb_slice_a[0, -3:, -3:, -1]
							_snake_case       :					List[str]				       =   depth_slice_a[0, -3:, -1]

							assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()				).max() < 1E-4
							assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()				).max() < 1E-4







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


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
							_snake_case       :					Optional[int]				       =   self.get_dummy_components()
							_snake_case       :					Optional[Any]				       =   PNDMScheduler(skip_prk_steps=__snake_case				)
							_snake_case       :					Union[str, Any]				       =   StableDiffusionLDMaDPipeline(**__snake_case				)
							_snake_case       :					Optional[Any]				       =   ldmad_pipe.to(__snake_case				)
							ldmad_pipe.set_progress_bar_config(disable=__snake_case				)

							_snake_case       :					Dict				       =   self.get_dummy_inputs(__snake_case				)
							_snake_case       :					Any				       =   'french fries'
							_snake_case       :					Optional[int]				       =   ldmad_pipe(**__snake_case ,						negative_prompt=__snake_case				)

							_snake_case       :					Any				       =   output.rgb, output.depth
							_snake_case       :					str				       =   rgb[0, -3:, -3:, -1]
							_snake_case       :					List[str]				       =   depth[0, -3:, -1]

							assert rgb.shape == (1, 6_4, 6_4, 3)
							assert depth.shape == (1, 6_4, 6_4)

							_snake_case       :					List[str]				       =   np.array(
							    [0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17]				)
							_snake_case       :					str				       =   np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35]				)
							assert np.abs(rgb_slice.flatten() - expected_slice_rgb				).max() < 1E-2
							assert np.abs(depth_slice.flatten() - expected_slice_depth				).max() < 1E-2






@slow
@require_torch_gpu
class        _a(    unittest.TestCase    ):







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

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







		def        lowercase       (    self ,						__snake_case ,						__snake_case="cpu" ,						__snake_case=torch.floataa ,						__snake_case=0				)      ->      Optional[int]:


							'''simple docstring'''

							_snake_case       :					int				       =   torch.Generator(device=__snake_case				).manual_seed(__snake_case				)
							_snake_case       :					Union[str, Any]				       =   np.random.RandomState(__snake_case				).standard_normal((1, 4, 6_4, 6_4)				)
							_snake_case       :					int				       =   torch.from_numpy(__snake_case				).to(device=__snake_case ,						dtype=__snake_case				)
							_snake_case       :					Tuple				       =   {
							    'prompt': 'a photograph of an astronaut riding a horse',
							    'latents': latents,
							    'generator': generator,
							    'num_inference_steps': 3,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"				)
							_snake_case       :					List[Any]				       =   ldmad_pipe.to(__snake_case				)
							ldmad_pipe.set_progress_bar_config(disable=__snake_case				)

							_snake_case       :					Tuple				       =   self.get_inputs(__snake_case				)
							_snake_case       :					int				       =   ldmad_pipe(**__snake_case				)
							_snake_case       :					Union[str, Any]				       =   output.rgb, output.depth
							_snake_case       :					Dict				       =   rgb[0, -3:, -3:, -1].flatten()
							_snake_case       :					int				       =   rgb[0, -3:, -1].flatten()

							assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
							assert depth.shape == (1, 5_1_2, 5_1_2)

							_snake_case       :					List[str]				       =   np.array(
							    [0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06]				)
							_snake_case       :					Any				       =   np.array(
							    [0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06]				)
							assert np.abs(rgb_slice - expected_slice_rgb				).max() < 3E-3
							assert np.abs(depth_slice - expected_slice_depth				).max() < 3E-3






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







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

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







		def        lowercase       (    self ,						__snake_case ,						__snake_case="cpu" ,						__snake_case=torch.floataa ,						__snake_case=0				)      ->      int:


							'''simple docstring'''

							_snake_case       :					str				       =   torch.Generator(device=__snake_case				).manual_seed(__snake_case				)
							_snake_case       :					Optional[Any]				       =   np.random.RandomState(__snake_case				).standard_normal((1, 4, 6_4, 6_4)				)
							_snake_case       :					Optional[int]				       =   torch.from_numpy(__snake_case				).to(device=__snake_case ,						dtype=__snake_case				)
							_snake_case       :					Tuple				       =   {
							    'prompt': 'a photograph of an astronaut riding a horse',
							    'latents': latents,
							    'generator': generator,
							    'num_inference_steps': 5_0,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"				).to(__snake_case				)
							ldmad_pipe.set_progress_bar_config(disable=__snake_case				)

							_snake_case       :					List[Any]				       =   self.get_inputs(__snake_case				)
							_snake_case       :					int				       =   ldmad_pipe(**__snake_case				)
							_snake_case       :					Any				       =   output.rgb, output.depth

							_snake_case       :					str				       =   0.49_55_86
							_snake_case       :					List[Any]				       =   0.33_79_55_15
							_snake_case       :					Optional[Any]				       =   1_12.4_85_18
							_snake_case       :					int				       =   98.48_97_46
							assert np.abs(expected_rgb_mean - rgb.mean()				) < 1E-3
							assert np.abs(expected_rgb_std - rgb.std()				) < 1E-3
							assert np.abs(expected_depth_mean - depth.mean()				) < 1E-3
							assert np.abs(expected_depth_std - depth.std()				) < 1E-3







		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c"				).to(__snake_case				)
							ldmad_pipe.set_progress_bar_config(disable=__snake_case				)

							_snake_case       :					List[Any]				       =   self.get_inputs(__snake_case				)
							_snake_case       :					int				       =   ldmad_pipe(**__snake_case				)
							_snake_case       :					Tuple				       =   output.rgb, output.depth

							_snake_case       :					List[str]				       =   0.4_19_41_27
							_snake_case       :					int				       =   0.35_37_55_86
							_snake_case       :					int				       =   0.5_63_85_02
							_snake_case       :					Optional[Any]				       =   0.34_68_61_03

							assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
							assert depth.shape == (1, 5_1_2, 5_1_2, 1)
							assert np.abs(expected_rgb_mean - rgb.mean()				) < 1E-3
							assert np.abs(expected_rgb_std - rgb.std()				) < 1E-3
							assert np.abs(expected_depth_mean - depth.mean()				) < 1E-3
							assert np.abs(expected_depth_std - depth.std()				) < 1E-3






import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[str]				       =   1.5
					_snake_case       :					Union[str, Any]				       =   int(factor * num_class_images				)
					_snake_case       :					Dict				       =   ClipClient(
					    url="https://knn.laion.ai/knn-service"  ,  indice_name="laion_400m"  ,  num_images=UpperCAmelCase  ,  aesthetic_weight=0.1				)

					os.makedirs(F"""{class_data_dir}/images"""  ,  exist_ok=UpperCAmelCase				)
					if len(list(Path(F"""{class_data_dir}/images"""				).iterdir()				)				) >= num_class_images:
										return

					while True:
										_snake_case       :					Optional[int]				       =   client.query(text=UpperCAmelCase				)
										if len(UpperCAmelCase				) >= factor * num_class_images or num_images > 1e4:
															break
										else:
															_snake_case       :					str				       =   int(factor * num_images				)
															_snake_case       :					Union[str, Any]				       =   ClipClient(
															    url="https://knn.laion.ai/knn-service"  ,  indice_name="laion_400m"  ,  num_images=UpperCAmelCase  ,  aesthetic_weight=0.1  ,  )

					_snake_case       :					List[Any]				       =   0
					_snake_case       :					Any				       =   0
					_snake_case       :					List[Any]				       =   tqdm(desc="downloading real regularization images"  ,  total=UpperCAmelCase				)

					with open(F"""{class_data_dir}/caption.txt"""  ,  "w"				) as fa, open(F"""{class_data_dir}/urls.txt"""  ,  "w"				) as fa, open(
					    F"""{class_data_dir}/images.txt"""  ,  "w"				) as fa:
										while total < num_class_images:
															_snake_case       :					Optional[Any]				       =   class_images[count]
															count += 1
															try:
																				_snake_case       :					Tuple				       =   requests.get(images["url"]				)
																				if img.status_code == 200:
																									_snake_case       :					int				       =   Image.open(BytesIO(img.content				)				)
																									with open(F"""{class_data_dir}/images/{total}.jpg"""  ,  "wb"				) as f:
																														f.write(img.content				)
																									fa.write(images["caption"] + "\n"				)
																									fa.write(images["url"] + "\n"				)
																									fa.write(F"""{class_data_dir}/images/{total}.jpg""" + "\n"				)
																									total += 1
																									pbar.update(1				)
																				else:
																									continue
															except Exception:
																				continue
					return







def    A				(							):
					_snake_case       :					Any				       =   argparse.ArgumentParser(""  ,  add_help=UpperCAmelCase				)
					parser.add_argument("--class_prompt"  ,  help="text prompt to retrieve images"  ,  required=UpperCAmelCase  ,  type=UpperCAmelCase				)
					parser.add_argument("--class_data_dir"  ,  help="path to save images"  ,  required=UpperCAmelCase  ,  type=UpperCAmelCase				)
					parser.add_argument("--num_class_images"  ,  help="number of images to download"  ,  default=200  ,  type=UpperCAmelCase				)
					return parser.parse_args()


if __name__ == "__main__":
							__lowerCAmelCase :List[Any]     =    parse_args()
							retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)