"""simple docstring"""




import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class       a_      (     __UpperCamelCase ,		__UpperCamelCase ,		__UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Dict			=    StableDiffusionInstructPixaPixPipeline
					UpperCamelCase_ :	List[Any]			=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
					UpperCamelCase_ :	List[Any]			=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					UpperCamelCase_ :	Tuple			=    IMAGE_TO_IMAGE_IMAGE_PARAMS
					UpperCamelCase_ :	Optional[Any]			=    IMAGE_TO_IMAGE_IMAGE_PARAMS




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							torch.manual_seed(0     )
							lowerCAmelCase__        =   UNetaDConditionModel(
							    block_out_channels=(32, 64)  ,						layers_per_block=2  ,						sample_size=32  ,						in_channels=8  ,						out_channels=4  ,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,						up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,						cross_attention_dim=32  ,						)
							lowerCAmelCase__        =   PNDMScheduler(skip_prk_steps=snake_case__     )
							torch.manual_seed(0     )
							lowerCAmelCase__        =   AutoencoderKL(
							    block_out_channels=[32, 64]  ,						in_channels=3  ,						out_channels=3  ,						down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  ,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  ,						latent_channels=4  ,						)
							torch.manual_seed(0     )
							lowerCAmelCase__        =   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=1000  ,						)
							lowerCAmelCase__        =   CLIPTextModel(snake_case__     )
							lowerCAmelCase__        =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     List[str]=0     ):
							lowerCAmelCase__        =   floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(snake_case__     )     ).to(snake_case__     )
							lowerCAmelCase__        =   image.cpu().permute(0  ,						2  ,						3  ,						1     )[0]
							lowerCAmelCase__        =   Image.fromarray(np.uinta(snake_case__     )     ).convert("""RGB"""     )
							if str(snake_case__     ).startswith("""mps"""     ):
									lowerCAmelCase__        =   torch.manual_seed(snake_case__     )
							else:
									lowerCAmelCase__        =   torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )
							lowerCAmelCase__        =   {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 6.0,
							    """image_guidance_scale""": 1,
							    """output_type""": """numpy""",
							}
							return inputs




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__        =   self.get_dummy_components()
							lowerCAmelCase__        =   StableDiffusionInstructPixaPixPipeline(**snake_case__     )
							lowerCAmelCase__        =   sd_pipe.to(snake_case__     )
							sd_pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   self.get_dummy_inputs(snake_case__     )
							lowerCAmelCase__        =   sd_pipe(**snake_case__     ).images
							lowerCAmelCase__        =   image[0, -3:, -3:, -1]
							assert image.shape == (1, 32, 32, 3)
							lowerCAmelCase__        =   np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__        =   self.get_dummy_components()
							lowerCAmelCase__        =   StableDiffusionInstructPixaPixPipeline(**snake_case__     )
							lowerCAmelCase__        =   sd_pipe.to(snake_case__     )
							sd_pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   self.get_dummy_inputs(snake_case__     )
							lowerCAmelCase__        =   """french fries"""
							lowerCAmelCase__        =   sd_pipe(**snake_case__  ,						negative_prompt=snake_case__     )
							lowerCAmelCase__        =   output.images
							lowerCAmelCase__        =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							lowerCAmelCase__        =   np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__        =   self.get_dummy_components()
							lowerCAmelCase__        =   StableDiffusionInstructPixaPixPipeline(**snake_case__     )
							lowerCAmelCase__        =   sd_pipe.to(snake_case__     )
							sd_pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   self.get_dummy_inputs(snake_case__     )
							lowerCAmelCase__        =   [inputs["""prompt"""]] * 2

							lowerCAmelCase__        =   np.array(inputs["""image"""]     ).astype(np.floataa     ) / 255.0
							lowerCAmelCase__        =   torch.from_numpy(snake_case__     ).unsqueeze(0     ).to(snake_case__     )
							lowerCAmelCase__        =   image / 2 + 0.5
							lowerCAmelCase__        =   image.permute(0  ,						3  ,						1  ,						2     )
							lowerCAmelCase__        =   image.repeat(2  ,						1  ,						1  ,						1     )

							lowerCAmelCase__        =   sd_pipe(**snake_case__     ).images
							lowerCAmelCase__        =   image[-1, -3:, -3:, -1]

							assert image.shape == (2, 32, 32, 3)
							lowerCAmelCase__        =   np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__        =   self.get_dummy_components()
							lowerCAmelCase__        =   EulerAncestralDiscreteScheduler(
							    beta_start=0.0_0085  ,						beta_end=0.012  ,						beta_schedule="""scaled_linear"""     )
							lowerCAmelCase__        =   StableDiffusionInstructPixaPixPipeline(**snake_case__     )
							lowerCAmelCase__        =   sd_pipe.to(snake_case__     )
							sd_pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   self.get_dummy_inputs(snake_case__     )
							lowerCAmelCase__        =   sd_pipe(**snake_case__     ).images
							lowerCAmelCase__        =   image[0, -3:, -3:, -1]

							lowerCAmelCase__        =   [round(snake_case__  ,						4     ) for x in image_slice.flatten().tolist()]
							print(""",""".join([str(snake_case__     ) for x in slice]     )     )

							assert image.shape == (1, 32, 32, 3)
							lowerCAmelCase__        =   np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]     )

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




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   self.get_dummy_components()
							lowerCAmelCase__        =   StableDiffusionInstructPixaPixPipeline(**snake_case__     )
							lowerCAmelCase__        =   VaeImageProcessor(do_resize=snake_case__  ,						do_normalize=snake_case__     )
							lowerCAmelCase__        =   pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   pipe(**self.get_dummy_inputs_by_type(snake_case__  ,						input_image_type="""pt"""     )     )[0]

							lowerCAmelCase__        =   components["""vae"""]
							lowerCAmelCase__        =   self.get_dummy_inputs_by_type(snake_case__  ,						input_image_type="""pt"""     )

							for image_param in self.image_latents_params:
									if image_param in inputs.keys():
											lowerCAmelCase__        =   vae.encode(inputs[image_param]     ).latent_dist.mode()

							lowerCAmelCase__        =   pipe(**snake_case__     )[0]

							lowerCAmelCase__        =   np.abs(out - out_latents_inputs     ).max()
							self.assertLess(snake_case__  ,						1E-4  ,						"""passing latents as image input generate different result from passing image"""     )

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




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     Any=0     ):
							lowerCAmelCase__        =   torch.manual_seed(snake_case__     )
							lowerCAmelCase__        =   load_image(
							    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""     )
							lowerCAmelCase__        =   {
							    """prompt""": """turn him into a cyborg""",
							    """image""": image,
							    """generator""": generator,
							    """num_inference_steps""": 3,
							    """guidance_scale""": 7.5,
							    """image_guidance_scale""": 1.0,
							    """output_type""": """numpy""",
							}
							return inputs




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""  ,						safety_checker=snake_case__     )
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )
							pipe.enable_attention_slicing()

							lowerCAmelCase__        =   self.get_inputs()
							lowerCAmelCase__        =   pipe(**snake_case__     ).images
							lowerCAmelCase__        =   image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__        =   np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""  ,						safety_checker=snake_case__     )
							lowerCAmelCase__        =   LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )
							pipe.enable_attention_slicing()

							lowerCAmelCase__        =   self.get_inputs()
							lowerCAmelCase__        =   pipe(**snake_case__     ).images
							lowerCAmelCase__        =   image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__        =   np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""  ,						safety_checker=snake_case__     )
							lowerCAmelCase__        =   DDIMScheduler.from_config(pipe.scheduler.config     )
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )
							pipe.enable_attention_slicing()

							lowerCAmelCase__        =   self.get_inputs()
							lowerCAmelCase__        =   pipe(**snake_case__     ).images
							lowerCAmelCase__        =   image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__        =   np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   0

							def callback_fn(snake_case__						:     int  ,						snake_case__						:     int  ,						snake_case__						:     torch.FloatTensor     ) -> None:
									lowerCAmelCase__        =   True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 1:
											lowerCAmelCase__        =   latents.detach().cpu().numpy()
											assert latents.shape == (1, 4, 64, 64)
											lowerCAmelCase__        =   latents[0, -3:, -3:, -1]
											lowerCAmelCase__        =   np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]     )

											assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2
									elif step == 2:
											lowerCAmelCase__        =   latents.detach().cpu().numpy()
											assert latents.shape == (1, 4, 64, 64)
											lowerCAmelCase__        =   latents[0, -3:, -3:, -1]
											lowerCAmelCase__        =   np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]     )

											assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2

							lowerCAmelCase__        =   False

							lowerCAmelCase__        =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""  ,						safety_checker=snake_case__  ,						torch_dtype=torch.floataa     )
							lowerCAmelCase__        =   pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )
							pipe.enable_attention_slicing()

							lowerCAmelCase__        =   self.get_inputs()
							pipe(**snake_case__  ,						callback=snake_case__  ,						callback_steps=1     )
							assert callback_fn.has_been_called
							assert number_of_steps == 3




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase__        =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""  ,						safety_checker=snake_case__  ,						torch_dtype=torch.floataa     )
							lowerCAmelCase__        =   pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )
							pipe.enable_attention_slicing(1     )
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase__        =   self.get_inputs()
							lowerCAmelCase__        =   pipe(**snake_case__     )

							lowerCAmelCase__        =   torch.cuda.max_memory_allocated()
							# make sure that less than 2.2 GB is allocated
							assert mem_bytes < 2.2 * 10**9




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self.get_inputs()
							# resize to resolution that is divisible by 8 but not 16 or 32
							lowerCAmelCase__        =   inputs["""image"""].resize((504, 504)     )

							lowerCAmelCase__        =   """timbrooks/instruct-pix2pix"""
							lowerCAmelCase__        =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    snake_case__  ,						safety_checker=snake_case__  ,						)
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )
							pipe.enable_attention_slicing()

							lowerCAmelCase__        =   pipe(**snake_case__     )
							lowerCAmelCase__        =   output.images[0]

							lowerCAmelCase__        =   image[255:258, 383:386, -1]

							assert image.shape == (504, 504, 3)
							lowerCAmelCase__        =   np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]     )

							assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5E-3





"""simple docstring"""




def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		assert isinstance(lowerCamelCase__			,  lowerCamelCase__     ), f"""The input value of [n={number}] is not an integer"""

		if number == 1:
				return 2
		elif number < 1:
				lowerCAmelCase__        =   f"""The input value of [n={number}] has to be > 0"""
				raise ValueError(lowerCamelCase__     )
		else:
				lowerCAmelCase__        =   sylvester(number - 1     )
				lowerCAmelCase__        =   num - 1
				lowerCAmelCase__        =   num
				return lower * upper + 1


if __name__ == "__main__":
			print(F"The 8th number in Sylvester's sequence: {sylvester(8)}")




