

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 _UpperCamelCase				(				__A				,						__A				,						__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =StableDiffusionInstructPixaPixPipeline
			lowerCamelCase__							     =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
			lowerCamelCase__							     =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase__							     =IMAGE_TO_IMAGE_IMAGE_PARAMS
			lowerCamelCase__							     =IMAGE_TO_IMAGE_IMAGE_PARAMS
			def 				__UpperCamelCase			(      self				:			int						)				->							str:

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							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   ,   )
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							PNDMScheduler(skip_prk_steps=a						)
				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   int					  =							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						)
				SCREAMING_SNAKE_CASE :   Dict					  =							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   ,   )
				SCREAMING_SNAKE_CASE :   Tuple					  =							CLIPTextModel(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

				SCREAMING_SNAKE_CASE :   str					  =							{
				    "unet": unet,
				    "scheduler": scheduler,
				    "vae": vae,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
				return components
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			str   ,   a				:			Dict=0						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							floats_tensor((1, 3, 32, 32)   ,   rng=random.Random(a						)						).to(a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							image.cpu().permute(0   ,   2   ,   3   ,   1						)[0]
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							Image.fromarray(np.uinta(a						)						).convert("RGB"						)
				if str(a						).startswith("mps"						):
					SCREAMING_SNAKE_CASE :   Tuple					  =							torch.manual_seed(a						)
				else:
					SCREAMING_SNAKE_CASE :   int					  =							torch.Generator(device=a						).manual_seed(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							{
				    "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 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE :   List[str]					  =							self.get_dummy_components()
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							StableDiffusionInstructPixaPixPipeline(**a						)
				SCREAMING_SNAKE_CASE :   Any					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.get_dummy_inputs(a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							sd_pipe(**a						).images
				SCREAMING_SNAKE_CASE :   Any					  =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE :   str					  =							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 				__UpperCamelCase			(      self				:			int						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.get_dummy_components()
				SCREAMING_SNAKE_CASE :   int					  =							StableDiffusionInstructPixaPixPipeline(**a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.get_dummy_inputs(a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							"french fries"
				SCREAMING_SNAKE_CASE :   Dict					  =							sd_pipe(**a   ,   negative_prompt=a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							output.images
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE :   Dict					  =							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 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.get_dummy_components()
				SCREAMING_SNAKE_CASE :   List[str]					  =							StableDiffusionInstructPixaPixPipeline(**a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							self.get_dummy_inputs(a						)
				SCREAMING_SNAKE_CASE :   int					  =							[inputs["prompt"]] * 2

				SCREAMING_SNAKE_CASE :   List[str]					  =							np.array(inputs["image"]						).astype(np.floataa						) / 255.0
				SCREAMING_SNAKE_CASE :   str					  =							torch.from_numpy(a						).unsqueeze(0						).to(a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							image / 2 + 0.5
				SCREAMING_SNAKE_CASE :   str					  =							image.permute(0   ,   3   ,   1   ,   2						)
				SCREAMING_SNAKE_CASE :   int					  =							image.repeat(2   ,   1   ,   1   ,   1						)

				SCREAMING_SNAKE_CASE :   Dict					  =							sd_pipe(**a						).images
				SCREAMING_SNAKE_CASE :   str					  =							image[-1, -3:, -3:, -1]

				assert image.shape == (2, 32, 32, 3)
				SCREAMING_SNAKE_CASE :   List[str]					  =							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 				__UpperCamelCase			(      self				:			str						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE :   Tuple					  =							self.get_dummy_components()
				SCREAMING_SNAKE_CASE :   int					  =							EulerAncestralDiscreteScheduler(
				    beta_start=0.0_0085   ,   beta_end=0.012   ,   beta_schedule="scaled_linear"						)
				SCREAMING_SNAKE_CASE :   str					  =							StableDiffusionInstructPixaPixPipeline(**a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.get_dummy_inputs(a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe(**a						).images
				SCREAMING_SNAKE_CASE :   Dict					  =							image[0, -3:, -3:, -1]

				SCREAMING_SNAKE_CASE :   Any					  =							[round(a   ,   4						) for x in image_slice.flatten().tolist()]
				print(",".join([str(a						) for x in slice]						)						)

				assert image.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE :   Any					  =							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 				__UpperCamelCase			(      self				:			str						)				->							Any:

				"""simple docstring"""

				super().test_inference_batch_single_identical(expected_max_diff=3e-3						)






			def 				__UpperCamelCase			(      self				:			Any						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.get_dummy_components()
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							StableDiffusionInstructPixaPixPipeline(**a						)
				SCREAMING_SNAKE_CASE :   str					  =							VaeImageProcessor(do_resize=a   ,   do_normalize=a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							pipe(**self.get_dummy_inputs_by_type(a   ,   input_image_type="pt"						)						)[0]

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							components["vae"]
				SCREAMING_SNAKE_CASE :   Any					  =							self.get_dummy_inputs_by_type(a   ,   input_image_type="pt"						)

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

				SCREAMING_SNAKE_CASE :   List[Any]					  =							pipe(**a						)[0]

				SCREAMING_SNAKE_CASE :   List[Any]					  =							np.abs(out - out_latents_inputs						).max()
				self.assertLess(a   ,   1e-4   ,   "passing latents as image input generate different result from passing image"						)






@slow
@require_torch_gpu
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			str						)				->							List[Any]:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			Union[str, Any]=0						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.manual_seed(a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_image(
				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							{
				    "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 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix"   ,   safety_checker=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)
				pipe.enable_attention_slicing()

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.get_inputs()
				SCREAMING_SNAKE_CASE :   List[Any]					  =							pipe(**a						).images
				SCREAMING_SNAKE_CASE :   Any					  =							image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							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 				__UpperCamelCase			(      self				:			List[str]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix"   ,   safety_checker=a						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)
				pipe.enable_attention_slicing()

				SCREAMING_SNAKE_CASE :   int					  =							self.get_inputs()
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pipe(**a						).images
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							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 				__UpperCamelCase			(      self				:			Tuple						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix"   ,   safety_checker=a						)
				SCREAMING_SNAKE_CASE :   Any					  =							DDIMScheduler.from_config(pipe.scheduler.config						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)
				pipe.enable_attention_slicing()

				SCREAMING_SNAKE_CASE :   int					  =							self.get_inputs()
				SCREAMING_SNAKE_CASE :   Any					  =							pipe(**a						).images
				SCREAMING_SNAKE_CASE :   Any					  =							image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Dict					  =							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 				__UpperCamelCase			(      self				:			Dict						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							0

				def callback_fn(a				:			int   ,   a				:			int   ,   a				:			torch.FloatTensor						) -> None:
					SCREAMING_SNAKE_CASE :   str					  =							True
					nonlocal number_of_steps
					number_of_steps += 1
					if step == 1:
						SCREAMING_SNAKE_CASE :   Any					  =							latents.detach().cpu().numpy()
						assert latents.shape == (1, 4, 64, 64)
						SCREAMING_SNAKE_CASE :   int					  =							latents[0, -3:, -3:, -1]
						SCREAMING_SNAKE_CASE :   List[str]					  =							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:
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							latents.detach().cpu().numpy()
						assert latents.shape == (1, 4, 64, 64)
						SCREAMING_SNAKE_CASE :   str					  =							latents[0, -3:, -3:, -1]
						SCREAMING_SNAKE_CASE :   List[str]					  =							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

				SCREAMING_SNAKE_CASE :   List[Any]					  =							False

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix"   ,   safety_checker=a   ,   torch_dtype=torch.floataa						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)
				pipe.enable_attention_slicing()

				SCREAMING_SNAKE_CASE :   int					  =							self.get_inputs()
				pipe(**a   ,   callback=a   ,   callback_steps=1						)
				assert callback_fn.has_been_called
				assert number_of_steps == 3
			def 				__UpperCamelCase			(      self				:			Dict						)				->							str:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE :   int					  =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix"   ,   safety_checker=a   ,   torch_dtype=torch.floataa						)
				SCREAMING_SNAKE_CASE :   Any					  =							pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)
				pipe.enable_attention_slicing(1						)
				pipe.enable_sequential_cpu_offload()

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.get_inputs()
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							pipe(**a						)

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






			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							List[str]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE :   List[Any]					  =							"timbrooks/instruct-pix2pix"
				SCREAMING_SNAKE_CASE :   Any					  =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    a   ,   safety_checker=a   ,   )
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)
				pipe.enable_attention_slicing()

				SCREAMING_SNAKE_CASE :   List[Any]					  =							pipe(**a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							output.images[0]

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

				assert image.shape == (504, 504, 3)
				SCREAMING_SNAKE_CASE :   int					  =							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'''



import functools


def A (__lowerCamelCase :list[int]					,      __lowerCamelCase :list[int]						):

						# Validation
						if not isinstance(__lowerCamelCase					,      __lowerCamelCase						) or not all(isinstance(__lowerCamelCase					,      __lowerCamelCase						) for day in days						):
												raise ValueError("""The parameter days should be a list of integers"""						)

						if len(__lowerCamelCase						) != 3 or not all(isinstance(__lowerCamelCase					,      __lowerCamelCase						) for cost in costs						):
												raise ValueError("""The parameter costs should be a list of three integers"""						)

						if len(__lowerCamelCase						) == 0:
												return 0

						if min(__lowerCamelCase						) <= 0:
												raise ValueError("""All days elements should be greater than 0"""						)

						if max(__lowerCamelCase						) >= 366:
												raise ValueError("""All days elements should be less than 366"""						)

						_lowerCAmelCase    		=					set(__lowerCamelCase						)

						@functools.cache
						def dynamic_programming(__lowerCamelCase :int						) -> int:
												if index > 365:
																		return 0

												if index not in days_set:
																		return dynamic_programming(index + 1						)

												return min(
												    costs[0] + dynamic_programming(index + 1						)					,      costs[1] + dynamic_programming(index + 7						)					,      costs[2] + dynamic_programming(index + 30						)					,      )

						return dynamic_programming(1						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
