
import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


def 						UpperCAmelCase      (     a_						)		->     Tuple:







				"""simple docstring"""



				__A             =			[tensor.shape for tensor in tensor_list]
				return all(shape == shapes[0] for shape in shapes[1:]						)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




			'''simple docstring'''





			snake_case_    =					StableDiffusionLatentUpscalePipeline
			snake_case_    =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
			    "height",
			    "width",
			    "cross_attention_kwargs",
			    "negative_prompt_embeds",
			    "prompt_embeds",
			}
			snake_case_    =					PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
			snake_case_    =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			snake_case_    =					frozenset(
			    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			snake_case_    =					frozenset([]  )

			snake_case_    =					True






			@property
			def     UpperCamelCase_  (	self :			str			):
							__A             =			1
							__A             =			4
							__A             =			(16, 16)

							__A             =			floats_tensor((batch_size, num_channels) + sizes							,rng=random.Random(0			)			).to(_lowerCAmelCase			)
							return image






			def     UpperCamelCase_  (	self :			List[str]			):
							torch.manual_seed(0			)
							__A             =			UNetaDConditionModel(
							    act_fn="gelu"							,attention_head_dim=8							,norm_num_groups=_lowerCAmelCase							,block_out_channels=[32, 32, 64, 64]							,time_cond_proj_dim=1_60							,conv_in_kernel=1							,conv_out_kernel=1							,cross_attention_dim=32							,down_block_types=(
							        "KDownBlock2D",
							        "KCrossAttnDownBlock2D",
							        "KCrossAttnDownBlock2D",
							        "KCrossAttnDownBlock2D",
							    )							,in_channels=8							,mid_block_type=_lowerCAmelCase							,only_cross_attention=_lowerCAmelCase							,out_channels=5							,resnet_time_scale_shift="scale_shift"							,time_embedding_type="fourier"							,timestep_post_act="gelu"							,up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")							,)
							__A             =			AutoencoderKL(
							    block_out_channels=[32, 32, 64, 64]							,in_channels=3							,out_channels=3							,down_block_types=[
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							    ]							,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]							,latent_channels=4							,)
							__A             =			EulerDiscreteScheduler(prediction_type="sample"			)
							__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							,hidden_act="quick_gelu"							,projection_dim=5_12							,)
							__A             =			CLIPTextModel(_lowerCAmelCase			)
							__A             =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

							__A             =			{
							    "unet": model.eval(),
							    "vae": vae.eval(),
							    "scheduler": scheduler,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							}

							return components






			def     UpperCamelCase_  (	self :			List[str]							,A :			Optional[Any]							,A :			str=0			):

							if str(_lowerCAmelCase			).startswith("mps"			):
											__A             =			torch.manual_seed(_lowerCAmelCase			)
							else:
											__A             =			torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)
							__A             =			{
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": self.dummy_image.cpu(),
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "numpy",
							}
							return inputs






			def     UpperCamelCase_  (	self :			Union[str, Any]			):
							__A             =			"cpu"

							__A             =			self.get_dummy_components()
							__A             =			self.pipeline_class(**_lowerCAmelCase			)
							pipe.to(_lowerCAmelCase			)
							pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

							__A             =			self.get_dummy_inputs(_lowerCAmelCase			)
							__A             =			pipe(**_lowerCAmelCase			).images
							__A             =			image[0, -3:, -3:, -1]

							self.assertEqual(image.shape							,(1, 2_56, 2_56, 3)			)
							__A             =			np.array(
							    [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]			)
							__A             =			np.abs(image_slice.flatten() - expected_slice			).max()
							self.assertLessEqual(_lowerCAmelCase							,1E-3			)






			def     UpperCamelCase_  (	self :			Dict			):
							super().test_attention_slicing_forward_pass(expected_max_diff=7E-3			)






			def     UpperCamelCase_  (	self :			Optional[Any]			):
							super().test_cpu_offload_forward_pass(expected_max_diff=3E-3			)






			def     UpperCamelCase_  (	self :			List[Any]			):
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3			)






			def     UpperCamelCase_  (	self :			Dict			):
							super().test_inference_batch_single_identical(expected_max_diff=7E-3			)






			def     UpperCamelCase_  (	self :			int			):
							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3			)






			def     UpperCamelCase_  (	self :			List[Any]			):
							super().test_save_load_local(expected_max_difference=3E-3			)






			def     UpperCamelCase_  (	self :			int			):
							super().test_save_load_optional_components(expected_max_difference=3E-3			)







			def     UpperCamelCase_  (	self :			Optional[int]			):
							__A             =			[
							    "DDIMScheduler",
							    "DDPMScheduler",
							    "PNDMScheduler",
							    "HeunDiscreteScheduler",
							    "EulerAncestralDiscreteScheduler",
							    "KDPM2DiscreteScheduler",
							    "KDPM2AncestralDiscreteScheduler",
							    "DPMSolverSDEScheduler",
							]
							__A             =			self.get_dummy_components()
							__A             =			self.pipeline_class(**_lowerCAmelCase			)

							# make sure that PNDM does not need warm-up
							pipe.scheduler.register_to_config(skip_prk_steps=_lowerCAmelCase			)

							pipe.to(_lowerCAmelCase			)
							pipe.set_progress_bar_config(disable=_lowerCAmelCase			)
							__A             =			self.get_dummy_inputs(_lowerCAmelCase			)
							__A             =			2

							__A             =			[]
							for scheduler_enum in KarrasDiffusionSchedulers:
											if scheduler_enum.name in skip_schedulers:
															# no sigma schedulers are not supported
															# no schedulers
															continue

											__A             =			getattr(_lowerCAmelCase							,scheduler_enum.name			)
											__A             =			scheduler_cls.from_config(pipe.scheduler.config			)
											__A             =			pipe(**_lowerCAmelCase			)[0]
											outputs.append(_lowerCAmelCase			)

							assert check_same_shape(_lowerCAmelCase			)





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




			'''simple docstring'''





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






			def     UpperCamelCase_  (	self :			Tuple			):
							__A             =			torch.manual_seed(33			)

							__A             =			StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"							,torch_dtype=torch.floataa			)
							pipe.to("cuda"			)

							__A             =			StableDiffusionLatentUpscalePipeline.from_pretrained(
							    "stabilityai/sd-x2-latent-upscaler"							,torch_dtype=torch.floataa			)
							upscaler.to("cuda"			)

							__A             =			"a photo of an astronaut high resolution, unreal engine, ultra realistic"

							__A             =			pipe(_lowerCAmelCase							,generator=_lowerCAmelCase							,output_type="latent"			).images

							__A             =			upscaler(
							    prompt=_lowerCAmelCase							,image=_lowerCAmelCase							,num_inference_steps=20							,guidance_scale=0							,generator=_lowerCAmelCase							,output_type="np"							,).images[0]

							__A             =			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"			)
							assert np.abs((expected_image - image).mean()			) < 5E-2







			def     UpperCamelCase_  (	self :			Union[str, Any]			):
							__A             =			torch.manual_seed(33			)

							__A             =			StableDiffusionLatentUpscalePipeline.from_pretrained(
							    "stabilityai/sd-x2-latent-upscaler"							,torch_dtype=torch.floataa			)
							upscaler.to("cuda"			)

							__A             =			"the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

							__A             =			load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"			)

							__A             =			upscaler(
							    prompt=_lowerCAmelCase							,image=_lowerCAmelCase							,num_inference_steps=20							,guidance_scale=0							,generator=_lowerCAmelCase							,output_type="np"							,).images[0]

							__A             =			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"			)
							assert np.abs((expected_image - image).max()			) < 5E-2


from math import log

from scipy.constants import Boltzmann, physical_constants

SCREAMING_SNAKE_CASE							:Dict										=    300  # TEMPERATURE (unit = K)


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       )		->     float:







				"""simple docstring"""



				if donor_conc <= 0:
								raise ValueError("Donor concentration should be positive"						)
				elif acceptor_conc <= 0:
								raise ValueError("Acceptor concentration should be positive"						)
				elif intrinsic_conc <= 0:
								raise ValueError("Intrinsic concentration should be positive"						)
				elif donor_conc <= intrinsic_conc:
								raise ValueError(
								    "Donor concentration should be greater than intrinsic concentration"						)
				elif acceptor_conc <= intrinsic_conc:
								raise ValueError(
								    "Acceptor concentration should be greater than intrinsic concentration"						)
				else:
								return (
								    Boltzmann
								    * T
								    * log((donor_conc * acceptor_conc) / intrinsic_conc**2						)
								    / physical_constants["electron volt"][0]
								)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
