


"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()




class  UpperCamelCase_	( UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  unittest.TestCase):

	"""simple docstring"""





	snake_case__				:  Dict    					=			StableUnCLIPImgaImgPipeline
	snake_case__				:  Dict    					=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS
	snake_case__				:  str    					=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	snake_case__				:  Union[str, Any]    					=			frozenset(
	    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	snake_case__				:  Union[str, Any]    					=			frozenset([])




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				3_2
						__SCREAMING_SNAKE_CASE						   =				embedder_hidden_size

						# image encoding components

						__SCREAMING_SNAKE_CASE						   =				CLIPImageProcessor(crop_size=3_2			,			size=3_2   )

						torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				CLIPVisionModelWithProjection(
						    CLIPVisionConfig(
						        hidden_size=UpperCAmelCase__			,			projection_dim=UpperCAmelCase__			,			num_hidden_layers=5			,			num_attention_heads=4			,			image_size=3_2			,			intermediate_size=3_7			,			patch_size=1			,			)   )

						# regular denoising components

						torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				StableUnCLIPImageNormalizer(embedding_dim=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				DDPMScheduler(beta_schedule="squaredcos_cap_v2"   )

						torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

						torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				CLIPTextModel(
						    CLIPTextConfig(
						        bos_token_id=0			,			eos_token_id=2			,			hidden_size=UpperCAmelCase__			,			projection_dim=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			,			)   )

						torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				UNetaDConditionModel(
						    sample_size=3_2			,			in_channels=4			,			out_channels=4			,			down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")			,			up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")			,			block_out_channels=(3_2, 6_4)			,			attention_head_dim=(2, 4)			,			class_embed_type="projection"			,			projection_class_embeddings_input_dim=embedder_projection_dim * 2			,			cross_attention_dim=UpperCAmelCase__			,			layers_per_block=1			,			upcast_attention=UpperCAmelCase__			,			use_linear_projection=UpperCAmelCase__			,			)

						torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				DDIMScheduler(
						    beta_schedule="scaled_linear"			,			beta_start=0.00_085			,			beta_end=0.012			,			prediction_type="v_prediction"			,			set_alpha_to_one=UpperCAmelCase__			,			steps_offset=1			,			)

						torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				AutoencoderKL()

						__SCREAMING_SNAKE_CASE						   =				{
						    # image encoding components
						    "feature_extractor": feature_extractor,
						    "image_encoder": image_encoder.eval(),
						    # image noising components
						    "image_normalizer": image_normalizer.eval(),
						    "image_noising_scheduler": image_noising_scheduler,
						    # regular denoising components
						    "tokenizer": tokenizer,
						    "text_encoder": text_encoder.eval(),
						    "unet": unet.eval(),
						    "scheduler": scheduler,
						    "vae": vae.eval(),
						}

						return components




	def   UpperCAmelCase_      ( self			:				str			,			UpperCAmelCase__			:				Any			,			UpperCAmelCase__			:				int=0			,			UpperCAmelCase__			:				Union[str, Any]=True   )       ->	Optional[int]:
						if str(UpperCAmelCase__   ).startswith("mps"   ):
											__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(UpperCAmelCase__   )
						else:
											__SCREAMING_SNAKE_CASE						   =				torch.Generator(device=UpperCAmelCase__   ).manual_seed(UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				floats_tensor((1, 3, 3_2, 3_2)			,			rng=random.Random(UpperCAmelCase__   )   ).to(UpperCAmelCase__   )

						if pil_image:
											__SCREAMING_SNAKE_CASE						   =				input_image * 0.5 + 0.5
											__SCREAMING_SNAKE_CASE						   =				input_image.clamp(0			,			1   )
											__SCREAMING_SNAKE_CASE						   =				input_image.cpu().permute(0			,			2			,			3			,			1   ).float().numpy()
											__SCREAMING_SNAKE_CASE						   =				DiffusionPipeline.numpy_to_pil(UpperCAmelCase__   )[0]

						return {
						    "prompt": "An anime racoon running a marathon",
						    "image": input_image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "output_type": "np",
						}




	@skip_mps
	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				"cpu"  # ensure determinism for the device-dependent torch.Generator
						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_components()
						__SCREAMING_SNAKE_CASE						   =				StableUnCLIPImgaImgPipeline(**UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				sd_pipe.to(UpperCAmelCase__   )
						sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_inputs(UpperCAmelCase__   )
						inputs.update({"image_embeds": None}   )
						__SCREAMING_SNAKE_CASE						   =				sd_pipe(**UpperCAmelCase__   ).images
						__SCREAMING_SNAKE_CASE						   =				image[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						__SCREAMING_SNAKE_CASE						   =				np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]   )

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				torch_device in ["cpu", "mps"]

						self._test_attention_slicing_forward_pass(test_max_difference=UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				torch_device in ["cpu", "mps"]

						self._test_inference_batch_single_identical(test_max_difference=UpperCAmelCase__   )




	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()			,			reason="XFormers attention is only available with CUDA and `xformers` installed"			,			)
	def   UpperCAmelCase_      ( self			:				Any   )       ->	Any:
						self._test_xformers_attention_forwardGenerator_pass(test_max_difference=UpperCAmelCase__   )




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

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	int:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	def   UpperCAmelCase_      ( self			:				Any   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"   )

						__SCREAMING_SNAKE_CASE						   =				load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"   )

						__SCREAMING_SNAKE_CASE						   =				StableUnCLIPImgaImgPipeline.from_pretrained(
						    "fusing/stable-unclip-2-1-l-img2img"			,			torch_dtype=torch.floataa   )
						pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
						# stable unclip will oom when integration tests are run on a V100,
						# so turn on memory savings
						pipe.enable_attention_slicing()
						pipe.enable_sequential_cpu_offload()

						__SCREAMING_SNAKE_CASE						   =				torch.Generator(device="cpu"   ).manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				pipe(UpperCAmelCase__			,			"anime turle"			,			generator=UpperCAmelCase__			,			output_type="np"   )

						__SCREAMING_SNAKE_CASE						   =				output.images[0]

						assert image.shape == (7_6_8, 7_6_8, 3)

						assert_mean_pixel_difference(UpperCAmelCase__			,			UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Dict   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"   )

						__SCREAMING_SNAKE_CASE						   =				load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"   )

						__SCREAMING_SNAKE_CASE						   =				StableUnCLIPImgaImgPipeline.from_pretrained(
						    "fusing/stable-unclip-2-1-h-img2img"			,			torch_dtype=torch.floataa   )
						pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
						# stable unclip will oom when integration tests are run on a V100,
						# so turn on memory savings
						pipe.enable_attention_slicing()
						pipe.enable_sequential_cpu_offload()

						__SCREAMING_SNAKE_CASE						   =				torch.Generator(device="cpu"   ).manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				pipe(UpperCAmelCase__			,			"anime turle"			,			generator=UpperCAmelCase__			,			output_type="np"   )

						__SCREAMING_SNAKE_CASE						   =				output.images[0]

						assert image.shape == (7_6_8, 7_6_8, 3)

						assert_mean_pixel_difference(UpperCAmelCase__			,			UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				int   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"   )

						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						__SCREAMING_SNAKE_CASE						   =				StableUnCLIPImgaImgPipeline.from_pretrained(
						    "fusing/stable-unclip-2-1-h-img2img"			,			torch_dtype=torch.floataa   )
						__SCREAMING_SNAKE_CASE						   =				pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
						pipe.enable_attention_slicing()
						pipe.enable_sequential_cpu_offload()

						__SCREAMING_SNAKE_CASE						   =				pipe(
						    UpperCAmelCase__			,			"anime turtle"			,			num_inference_steps=2			,			output_type="np"			,			)

						__SCREAMING_SNAKE_CASE						   =				torch.cuda.max_memory_allocated()
						# make sure that less than 7 GB is allocated
						assert mem_bytes < 7 * 1_0**9




"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					if number > 0:
										raise ValueError("input must be a negative integer"   )
					__SCREAMING_SNAKE_CASE						   =				len(bin(lowerCAmelCase_   )[3:]   )
					__SCREAMING_SNAKE_CASE						   =				bin(abs(lowerCAmelCase_   ) - (1 << binary_number_length)   )[3:]
					__SCREAMING_SNAKE_CASE						   =				(
					    (
					        "1"
					        + "0" * (binary_number_length - len(lowerCAmelCase_   ))
					        + twos_complement_number
					    )
					    if number < 0
					    else "0"
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
		import doctest

		doctest.testmod()
