
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 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     StableDiffusionLDMaDPipeline
	snake_case_					    =     TEXT_TO_IMAGE_PARAMS
	snake_case_					    =     TEXT_TO_IMAGE_BATCH_PARAMS
	snake_case_					    =     TEXT_TO_IMAGE_IMAGE_PARAMS






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					torch.manual_seed(0 )
					__A   			= 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							,)
					__A   			= DDIMScheduler(
					    beta_start=0.0_00_85							,beta_end=0.0_12							,beta_schedule="scaled_linear"							,clip_sample=A							,set_alpha_to_one=A							,)
					torch.manual_seed(0 )
					__A   			= AutoencoderKL(
					    block_out_channels=[32, 64]							,in_channels=6							,out_channels=6							,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,latent_channels=4							,)
					torch.manual_seed(0 )
					__A   			= CLIPTextConfig(
					    bos_token_id=0							,eos_token_id=2							,hidden_size=32							,intermediate_size=37							,layer_norm_eps=1E-05							,num_attention_heads=4							,num_hidden_layers=5							,pad_token_id=1							,vocab_size=10_00							,)
					__A   			= CLIPTextModel(A )
					__A   			= CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

					__A   			= {
					    "unet": unet,
					    "scheduler": scheduler,
					    "vae": vae,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "safety_checker": None,
					    "feature_extractor": None,
					}
					return components






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: Any							,A					: List[Any]=0 ):
					if str(A ).startswith("mps" ):
									__A   			= torch.manual_seed(A )
					else:
									__A   			= torch.Generator(device=A ).manual_seed(A )
					__A   			= {
					    "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 				UpperCamelCase_  (       self					: List[str] ):
					__A   			= "cpu"  # ensure determinism for the device-dependent torch.Generator

					__A   			= self.get_dummy_components()
					__A   			= StableDiffusionLDMaDPipeline(**A )
					__A   			= ldmad_pipe.to(A )
					ldmad_pipe.set_progress_bar_config(disable=A )

					__A   			= self.get_dummy_inputs(A )
					__A   			= ldmad_pipe(**A )
					__A			,							__A   			= output.rgb, output.depth

					__A   			= rgb[0, -3:, -3:, -1]
					__A   			= depth[0, -3:, -1]

					assert rgb.shape == (1, 64, 64, 3)
					assert depth.shape == (1, 64, 64)

					__A   			= 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] )
					__A   			= 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 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.get_dummy_components()
					__A   			= StableDiffusionLDMaDPipeline(**A )
					__A   			= ldmad_pipe.to(A )
					ldmad_pipe.set_progress_bar_config(disable=A )

					__A   			= self.get_dummy_inputs(A )
					__A   			= 3 * [inputs["prompt"]]

					# forward
					__A   			= ldmad_pipe(**A )
					__A			,							__A   			= output.rgb, output.depth
					__A   			= rgb_slice_a[0, -3:, -3:, -1]
					__A   			= depth_slice_a[0, -3:, -1]

					__A   			= self.get_dummy_inputs(A )
					__A   			= 3 * [inputs.pop("prompt" )]

					__A   			= ldmad_pipe.tokenizer(
					    A							,padding="max_length"							,max_length=ldmad_pipe.tokenizer.model_max_length							,truncation=A							,return_tensors="pt"							,)
					__A   			= text_inputs["input_ids"].to(A )

					__A   			= ldmad_pipe.text_encoder(A )[0]

					__A   			= prompt_embeds

					# forward
					__A   			= ldmad_pipe(**A )
					__A			,							__A   			= output.rgb, output.depth
					__A   			= rgb_slice_a[0, -3:, -3:, -1]
					__A   			= 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 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= "cpu"  # ensure determinism for the device-dependent torch.Generator
					__A   			= self.get_dummy_components()
					__A   			= PNDMScheduler(skip_prk_steps=A )
					__A   			= StableDiffusionLDMaDPipeline(**A )
					__A   			= ldmad_pipe.to(A )
					ldmad_pipe.set_progress_bar_config(disable=A )

					__A   			= self.get_dummy_inputs(A )
					__A   			= "french fries"
					__A   			= ldmad_pipe(**A							,negative_prompt=A )

					__A			,							__A   			= output.rgb, output.depth
					__A   			= rgb[0, -3:, -3:, -1]
					__A   			= depth[0, -3:, -1]

					assert rgb.shape == (1, 64, 64, 3)
					assert depth.shape == (1, 64, 64)

					__A   			= 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] )
					__A   			= 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 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Any ):
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






	def 				UpperCamelCase_  (       self					: Tuple							,A					: Dict							,A					: str="cpu"							,A					: Dict=torch.floataa							,A					: Optional[Any]=0 ):
					__A   			= torch.Generator(device=A ).manual_seed(A )
					__A   			= np.random.RandomState(A ).standard_normal((1, 4, 64, 64) )
					__A   			= torch.from_numpy(A ).to(device=A							,dtype=A )
					__A   			= {
					    "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 				UpperCamelCase_  (       self					: List[str] ):
					__A   			= StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
					__A   			= ldmad_pipe.to(A )
					ldmad_pipe.set_progress_bar_config(disable=A )

					__A   			= self.get_inputs(A )
					__A   			= ldmad_pipe(**A )
					__A			,							__A   			= output.rgb, output.depth
					__A   			= rgb[0, -3:, -3:, -1].flatten()
					__A   			= rgb[0, -3:, -1].flatten()

					assert rgb.shape == (1, 5_12, 5_12, 3)
					assert depth.shape == (1, 5_12, 5_12)

					__A   			= 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] )
					__A   			= 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 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: List[str] ):
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: Any							,A					: Optional[int]="cpu"							,A					: List[str]=torch.floataa							,A					: Optional[int]=0 ):
					__A   			= torch.Generator(device=A ).manual_seed(A )
					__A   			= np.random.RandomState(A ).standard_normal((1, 4, 64, 64) )
					__A   			= torch.from_numpy(A ).to(device=A							,dtype=A )
					__A   			= {
					    "prompt": "a photograph of an astronaut riding a horse",
					    "latents": latents,
					    "generator": generator,
					    "num_inference_steps": 50,
					    "guidance_scale": 7.5,
					    "output_type": "numpy",
					}
					return inputs






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(A )
					ldmad_pipe.set_progress_bar_config(disable=A )

					__A   			= self.get_inputs(A )
					__A   			= ldmad_pipe(**A )
					__A			,							__A   			= output.rgb, output.depth

					__A   			= 0.49_55_86
					__A   			= 0.33_79_55_15
					__A   			= 1_12.4_85_18
					__A   			= 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 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(A )
					ldmad_pipe.set_progress_bar_config(disable=A )

					__A   			= self.get_inputs(A )
					__A   			= ldmad_pipe(**A )
					__A			,							__A   			= output.rgb, output.depth

					__A   			= 0.4_19_41_27
					__A   			= 0.35_37_55_86
					__A   			= 0.5_63_85_02
					__A   			= 0.34_68_61_03

					assert rgb.shape == (1, 5_12, 5_12, 3)
					assert depth.shape == (1, 5_12, 5_12, 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 numpy as np


def 						UpperCAmelCase			(	a_		,   a_		,   a_ = 1E-12		,   a_ = 1_0_0		,   )		->	tuple[float, np.ndarray]:







				"""simple docstring"""



				assert np.shape(a_						)[0] == np.shape(a_						)[1]
				# Ensure proper dimensionality.
				assert np.shape(a_						)[0] == np.shape(a_						)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(a_						) == np.iscomplexobj(a_						)
				__A   			= np.iscomplexobj(a_						)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(a_		,   input_matrix.conj().T						)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				__A   			= False
				__A   			= 0
				__A   			= 0
				__A   			= 1E12

				while not convergence:
								# Multiple matrix by the vector.
								__A   			= np.dot(a_		,   a_						)
								# Normalize the resulting output vector.
								__A   			= w / np.linalg.norm(a_						)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								__A   			= vector.conj().T if is_complex else vector.T
								__A   			= np.dot(a_		,   np.dot(a_		,   a_						)						)

								# Check convergence.
								__A   			= np.abs(lambda_ - lambda_previous						) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												__A   			= True

								__A   			= lambda_

				if is_complex:
								__A   			= np.real(lambda_						)

				return lambda_, vector


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]						)
				__A   			= np.array([4_1, 4, 2_0]						)
				__A   			= real_input_matrix.astype(np.complexaaa						)
				__A   			= np.triu(1J * complex_input_matrix		,   1						)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__A   			= np.array([4_1, 4, 2_0]						).astype(np.complexaaa						)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												__A   			= real_input_matrix
												__A   			= real_vector
								elif problem_type == "complex":
												__A   			= complex_input_matrix
												__A   			= complex_vector

								# Our implementation.
								__A			,							__A   			= power_iteration(a_		,   a_						)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								__A			,							__A   			= np.linalg.eigh(a_						)
								# Last eigenvalue is the maximum one.
								__A   			= eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								__A   			= eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max						) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(a_						) - np.abs(a_						)						) <= 1E-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_power_iteration()
