






from __future__ import annotations

from math import pow, sqrt

def    UpperCamelCase						(	_A,   _A,   _A			):






						"""simple docstring"""







						if (resistance, reactance, impedance).count(0			) != 1:
												raise ValueError("""One and only one argument must be 0"""			)
						if resistance == 0:
												return {"resistance": sqrt(pow(__lowerCAmelCase,   2			) - pow(__lowerCAmelCase,   2			)			)}
						elif reactance == 0:
												return {"reactance": sqrt(pow(__lowerCAmelCase,   2			) - pow(__lowerCAmelCase,   2			)			)}
						elif impedance == 0:
												return {"impedance": sqrt(pow(__lowerCAmelCase,   2			) + pow(__lowerCAmelCase,   2			)			)}
						else:
												raise ValueError("""Exactly one argument must be 0"""			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()








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 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					str											=   StableUnCLIPImgaImgPipeline
	lowercase__						:					Any											=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
	lowercase__						:					Tuple											=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	lowercase__						:					Union[str, Any]											=   frozenset(
	    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowercase__						:					Union[str, Any]											=   frozenset([]				)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Any       						= 32
							__magic_name__      :       Union[str, Any]       						= embedder_hidden_size

							# image encoding components

							__magic_name__      :       Optional[int]       						= CLIPImageProcessor(crop_size=32      ,       size=32			)

							torch.manual_seed(0			)
							__magic_name__      :       Optional[Any]       						= CLIPVisionModelWithProjection(
							    CLIPVisionConfig(
							        hidden_size=lowerCAmelCase__      ,       projection_dim=lowerCAmelCase__      ,       num_hidden_layers=5      ,       num_attention_heads=4      ,       image_size=32      ,       intermediate_size=37      ,       patch_size=1      ,       )			)

							# regular denoising components

							torch.manual_seed(0			)
							__magic_name__      :       Any       						= StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase__			)
							__magic_name__      :       int       						= DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""			)

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

							torch.manual_seed(0			)
							__magic_name__      :       Optional[Any]       						= CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=lowerCAmelCase__      ,       projection_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1e-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=10_00      ,       )			)

							torch.manual_seed(0			)
							__magic_name__      :       str       						= UNetaDConditionModel(
							    sample_size=32      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")      ,       up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")      ,       block_out_channels=(32, 64)      ,       attention_head_dim=(2, 4)      ,       class_embed_type="""projection"""      ,       projection_class_embeddings_input_dim=embedder_projection_dim * 2      ,       cross_attention_dim=lowerCAmelCase__      ,       layers_per_block=1      ,       upcast_attention=lowerCAmelCase__      ,       use_linear_projection=lowerCAmelCase__      ,       )

							torch.manual_seed(0			)
							__magic_name__      :       Optional[Any]       						= DDIMScheduler(
							    beta_schedule="""scaled_linear"""      ,       beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       prediction_type="""v_prediction"""      ,       set_alpha_to_one=lowerCAmelCase__      ,       steps_offset=1      ,       )

							torch.manual_seed(0			)
							__magic_name__      :       List[str]       						= AutoencoderKL()

							__magic_name__      :       List[str]       						= {
							    # 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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=0      ,       lowerCAmelCase__=True			)							->				List[Any]:
							if str(lowerCAmelCase__			).startswith("""mps"""			):
													__magic_name__      :       Optional[int]       						= torch.manual_seed(lowerCAmelCase__			)
							else:
													__magic_name__      :       Optional[int]       						= torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)

							__magic_name__      :       Any       						= floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(lowerCAmelCase__			)			).to(lowerCAmelCase__			)

							if pil_image:
													__magic_name__      :       Optional[Any]       						= input_image * 0.5 + 0.5
													__magic_name__      :       int       						= input_image.clamp(0      ,       1			)
													__magic_name__      :       Union[str, Any]       						= input_image.cpu().permute(0      ,       2      ,       3      ,       1			).float().numpy()
													__magic_name__      :       Optional[int]       						= DiffusionPipeline.numpy_to_pil(lowerCAmelCase__			)[0]

							return {
							    "prompt": "An anime racoon running a marathon",
							    "image": input_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "np",
							}
	@skip_mps
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Optional[Any]       						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
							__magic_name__      :       List[str]       						= self.get_dummy_components()
							__magic_name__      :       int       						= StableUnCLIPImgaImgPipeline(**lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= sd_pipe.to(lowerCAmelCase__			)
							sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

							__magic_name__      :       Any       						= self.get_dummy_inputs(lowerCAmelCase__			)
							inputs.update({"""image_embeds""": None}			)
							__magic_name__      :       List[str]       						= sd_pipe(**lowerCAmelCase__			).images
							__magic_name__      :       Dict       						= image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							__magic_name__      :       int       						= np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3
	def       __magic_name__    (    self			)							->				Dict:
							__magic_name__      :       int       						= torch_device in ["""cpu""", """mps"""]

							self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Tuple       						= torch_device in ["""cpu""", """mps"""]

							self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase__			)







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





@slow
@require_torch_gpu
class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				int:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Optional[Any]       						= load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""			)

							__magic_name__      :       int       						= 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"""			)

							__magic_name__      :       List[Any]       						= StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-l-img2img"""      ,       torch_dtype=torch.floataa			)
							pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
							# 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()

							__magic_name__      :       Union[str, Any]       						= torch.Generator(device="""cpu"""			).manual_seed(0			)
							__magic_name__      :       Union[str, Any]       						= pipe(lowerCAmelCase__      ,       """anime turle"""      ,       generator=lowerCAmelCase__      ,       output_type="""np"""			)

							__magic_name__      :       Optional[Any]       						= output.images[0]

							assert image.shape == (7_68, 7_68, 3)

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

							__magic_name__      :       Dict       						= 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"""			)

							__magic_name__      :       Union[str, Any]       						= StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-h-img2img"""      ,       torch_dtype=torch.floataa			)
							pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
							# 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()

							__magic_name__      :       List[str]       						= torch.Generator(device="""cpu"""			).manual_seed(0			)
							__magic_name__      :       Any       						= pipe(lowerCAmelCase__      ,       """anime turle"""      ,       generator=lowerCAmelCase__      ,       output_type="""np"""			)

							__magic_name__      :       Any       						= output.images[0]

							assert image.shape == (7_68, 7_68, 3)

							assert_mean_pixel_difference(lowerCAmelCase__      ,       lowerCAmelCase__			)







	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Any       						= 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()

							__magic_name__      :       Union[str, Any]       						= StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-h-img2img"""      ,       torch_dtype=torch.floataa			)
							__magic_name__      :       int       						= pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__magic_name__      :       List[Any]       						= pipe(
							    lowerCAmelCase__      ,       """anime turtle"""      ,       num_inference_steps=2      ,       output_type="""np"""      ,       )

							__magic_name__      :       List[str]       						= torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9
