



import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 						__a (     __lowerCamelCase	,    __lowerCamelCase	,    unittest.TestCase				):
	SCREAMING_SNAKE_CASE       =       StableDiffusionDiffEditPipeline
	SCREAMING_SNAKE_CASE       =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
	SCREAMING_SNAKE_CASE       =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
	SCREAMING_SNAKE_CASE       =       frozenset(
	    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	SCREAMING_SNAKE_CASE       =       frozenset([]				)



	def 		UpperCamelCase							(						self							:    int)->							List[Any]:
						torch.manual_seed(0)
						__lowerCAmelCase	   =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				,	attention_head_dim=(2, 4)				,	use_linear_projection=a_				,	)
						__lowerCAmelCase	   =DDIMScheduler(
						    beta_start=0.0_0_0_8_5				,	beta_end=0.0_1_2				,	beta_schedule="""scaled_linear"""				,	clip_sample=a_				,	set_alpha_to_one=a_				,	)
						__lowerCAmelCase	   =DDIMInverseScheduler(
						    beta_start=0.0_0_0_8_5				,	beta_end=0.0_1_2				,	beta_schedule="""scaled_linear"""				,	clip_sample=a_				,	set_alpha_to_zero=a_				,	)
						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				,	sample_size=1_28				,	)
						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=10_00				,	hidden_act="""gelu"""				,	projection_dim=5_12				,	)
						__lowerCAmelCase	   =CLIPTextModel(a_)
						__lowerCAmelCase	   =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

						__lowerCAmelCase	   ={
						    "unet": unet,
						    "scheduler": scheduler,
						    "inverse_scheduler": inverse_scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}

						return components



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    List[str]				,	snake_case_							:    int=0)->							List[Any]:
						__lowerCAmelCase	   =floats_tensor((1, 16, 16)				,	rng=random.Random(a_)).to(a_)
						__lowerCAmelCase	   =floats_tensor((1, 2, 4, 16, 16)				,	rng=random.Random(a_)).to(a_)
						if str(a_).startswith("""mps"""):
											__lowerCAmelCase	   =torch.manual_seed(a_)
						else:
											__lowerCAmelCase	   =torch.Generator(device=a_).manual_seed(a_)
						__lowerCAmelCase	   ={
						    "prompt": "a dog and a newt",
						    "mask_image": mask,
						    "image_latents": latents,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "inpaint_strength": 1.0,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}

						return inputs



	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    str				,	snake_case_							:    Optional[int]=0)->							Tuple:
						__lowerCAmelCase	   =floats_tensor((1, 3, 32, 32)				,	rng=random.Random(a_)).to(a_)
						__lowerCAmelCase	   =image.cpu().permute(0				,	2				,	3				,	1)[0]
						__lowerCAmelCase	   =Image.fromarray(np.uinta(a_)).convert("""RGB""")
						if str(a_).startswith("""mps"""):
											__lowerCAmelCase	   =torch.manual_seed(a_)
						else:
											__lowerCAmelCase	   =torch.Generator(device=a_).manual_seed(a_)
						__lowerCAmelCase	   ={
						    "image": image,
						    "source_prompt": "a cat and a frog",
						    "target_prompt": "a dog and a newt",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "num_maps_per_mask": 2,
						    "mask_encode_strength": 1.0,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}

						return inputs



	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    Any				,	snake_case_							:    Tuple=0)->							int:
						__lowerCAmelCase	   =floats_tensor((1, 3, 32, 32)				,	rng=random.Random(a_)).to(a_)
						__lowerCAmelCase	   =image.cpu().permute(0				,	2				,	3				,	1)[0]
						__lowerCAmelCase	   =Image.fromarray(np.uinta(a_)).convert("""RGB""")
						if str(a_).startswith("""mps"""):
											__lowerCAmelCase	   =torch.manual_seed(a_)
						else:
											__lowerCAmelCase	   =torch.Generator(device=a_).manual_seed(a_)
						__lowerCAmelCase	   ={
						    "image": image,
						    "prompt": "a cat and a frog",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "inpaint_strength": 1.0,
						    "guidance_scale": 6.0,
						    "decode_latents": True,
						    "output_type": "numpy",
						}
						return inputs



	def 		UpperCamelCase							(						self							:    Tuple)->							Tuple:
						if not hasattr(self.pipeline_class				,	"""_optional_components"""):
											return

						__lowerCAmelCase	   =self.get_dummy_components()
						__lowerCAmelCase	   =self.pipeline_class(**a_)
						pipe.to(a_)
						pipe.set_progress_bar_config(disable=a_)

						# set all optional components to None and update pipeline config accordingly
						for optional_component in pipe._optional_components:
											setattr(a_				,	a_				,	a_)
						pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components})

						__lowerCAmelCase	   =self.get_dummy_inputs(a_)
						__lowerCAmelCase	   =pipe(**a_)[0]

						with tempfile.TemporaryDirectory() as tmpdir:
											pipe.save_pretrained(a_)
											__lowerCAmelCase	   =self.pipeline_class.from_pretrained(a_)
											pipe_loaded.to(a_)
											pipe_loaded.set_progress_bar_config(disable=a_)

						for optional_component in pipe._optional_components:
											self.assertTrue(
											    getattr(a_				,	a_) is None				,	F"""`{optional_component}` did not stay set to None after loading."""				,	)

						__lowerCAmelCase	   =self.get_dummy_inputs(a_)
						__lowerCAmelCase	   =pipe_loaded(**a_)[0]

						__lowerCAmelCase	   =np.abs(output - output_loaded).max()
						self.assertLess(a_				,	1e-4)



	def 		UpperCamelCase							(						self							:    int)->							str:
						__lowerCAmelCase	   ="cpu"

						__lowerCAmelCase	   =self.get_dummy_components()
						__lowerCAmelCase	   =self.pipeline_class(**a_)
						pipe.to(a_)
						pipe.set_progress_bar_config(disable=a_)

						__lowerCAmelCase	   =self.get_dummy_mask_inputs(a_)
						__lowerCAmelCase	   =pipe.generate_mask(**a_)
						__lowerCAmelCase	   =mask[0, -3:, -3:]

						self.assertEqual(mask.shape				,	(1, 16, 16))
						__lowerCAmelCase	   =np.array([0] * 9)
						__lowerCAmelCase	   =np.abs(mask_slice.flatten() - expected_slice).max()
						self.assertLessEqual(a_				,	1e-3)
						self.assertEqual(mask[0, -3, -4]				,	0)



	def 		UpperCamelCase							(						self							:    int)->							int:
						__lowerCAmelCase	   ="cpu"

						__lowerCAmelCase	   =self.get_dummy_components()
						__lowerCAmelCase	   =self.pipeline_class(**a_)
						pipe.to(a_)
						pipe.set_progress_bar_config(disable=a_)

						__lowerCAmelCase	   =self.get_dummy_inversion_inputs(a_)
						__lowerCAmelCase	   =pipe.invert(**a_).images
						__lowerCAmelCase	   =image[0, -1, -3:, -3:]

						self.assertEqual(image.shape				,	(2, 32, 32, 3))
						__lowerCAmelCase	   =np.array(
						    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]				,	)
						__lowerCAmelCase	   =np.abs(image_slice.flatten() - expected_slice).max()
						self.assertLessEqual(a_				,	1e-3)



	def 		UpperCamelCase							(						self							:    Any)->							Optional[Any]:
						super().test_inference_batch_single_identical(expected_max_diff=5e-3)



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							List[str]:
						__lowerCAmelCase	   ="cpu"

						__lowerCAmelCase	   =self.get_dummy_components()

						__lowerCAmelCase	   ={"beta_start": 0.0_0_0_8_5, "beta_end": 0.0_1_2, "beta_schedule": "scaled_linear"}
						__lowerCAmelCase	   =DPMSolverMultistepScheduler(**a_)
						__lowerCAmelCase	   =DPMSolverMultistepInverseScheduler(**a_)

						__lowerCAmelCase	   =self.pipeline_class(**a_)
						pipe.to(a_)
						pipe.set_progress_bar_config(disable=a_)

						__lowerCAmelCase	   =self.get_dummy_inversion_inputs(a_)
						__lowerCAmelCase	   =pipe.invert(**a_).images
						__lowerCAmelCase	   =image[0, -1, -3:, -3:]

						self.assertEqual(image.shape				,	(2, 32, 32, 3))
						__lowerCAmelCase	   =np.array(
						    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]				,	)
						__lowerCAmelCase	   =np.abs(image_slice.flatten() - expected_slice).max()
						self.assertLessEqual(a_				,	1e-3)






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



	def 		UpperCamelCase							(						self							:    int)->							str:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



	@classmethod
	def 		UpperCamelCase							(						cls							:    List[Any])->							Any:
						__lowerCAmelCase	   =load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png""")

						__lowerCAmelCase	   =raw_image.convert("""RGB""").resize((7_68, 7_68))

						__lowerCAmelCase	   =raw_image



	def 		UpperCamelCase							(						self							:    Optional[int])->							Optional[Any]:
						__lowerCAmelCase	   =torch.manual_seed(0)

						__lowerCAmelCase	   =StableDiffusionDiffEditPipeline.from_pretrained(
						    """stabilityai/stable-diffusion-2-1"""				,	safety_checker=a_				,	torch_dtype=torch.floataa)
						__lowerCAmelCase	   =DDIMScheduler.from_config(pipe.scheduler.config)
						__lowerCAmelCase	   =DDIMInverseScheduler.from_config(pipe.scheduler.config)
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=a_)

						__lowerCAmelCase	   ="a bowl of fruit"
						__lowerCAmelCase	   ="a bowl of pears"

						__lowerCAmelCase	   =pipe.generate_mask(
						    image=self.raw_image				,	source_prompt=a_				,	target_prompt=a_				,	generator=a_				,	)

						__lowerCAmelCase	   =pipe.invert(
						    prompt=a_				,	image=self.raw_image				,	inpaint_strength=0.7				,	generator=a_).latents

						__lowerCAmelCase	   =pipe(
						    prompt=a_				,	mask_image=a_				,	image_latents=a_				,	generator=a_				,	negative_prompt=a_				,	inpaint_strength=0.7				,	output_type="""numpy"""				,	).images[0]

						__lowerCAmelCase	   =(
						    np.array(
						        load_image(
						            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						            """/diffedit/pears.png""").resize((7_68, 7_68)))
						    / 2_55
						)
						assert np.abs((expected_image - image).max()) < 5e-1



	def 		UpperCamelCase							(						self							:    Dict)->							List[Any]:
						__lowerCAmelCase	   =torch.manual_seed(0)

						__lowerCAmelCase	   =StableDiffusionDiffEditPipeline.from_pretrained(
						    """stabilityai/stable-diffusion-2-1"""				,	safety_checker=a_				,	torch_dtype=torch.floataa)
						__lowerCAmelCase	   =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
						__lowerCAmelCase	   =DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config)
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=a_)

						__lowerCAmelCase	   ="a bowl of fruit"
						__lowerCAmelCase	   ="a bowl of pears"

						__lowerCAmelCase	   =pipe.generate_mask(
						    image=self.raw_image				,	source_prompt=a_				,	target_prompt=a_				,	generator=a_				,	)

						__lowerCAmelCase	   =pipe.invert(
						    prompt=a_				,	image=self.raw_image				,	inpaint_strength=0.7				,	generator=a_				,	num_inference_steps=25				,	).latents

						__lowerCAmelCase	   =pipe(
						    prompt=a_				,	mask_image=a_				,	image_latents=a_				,	generator=a_				,	negative_prompt=a_				,	inpaint_strength=0.7				,	num_inference_steps=25				,	output_type="""numpy"""				,	).images[0]

						__lowerCAmelCase	   =(
						    np.array(
						        load_image(
						            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						            """/diffedit/pears.png""").resize((7_68, 7_68)))
						    / 2_55
						)
						assert np.abs((expected_image - image).max()) < 5e-1








def 					__lowerCAmelCase		(  __lowerCamelCase	:    int	)  ->						list:
					__lowerCAmelCase	   =int(__lowerCamelCase	)
					if n_element < 1:
										__lowerCAmelCase	   =ValueError("""a should be a positive number"""	)
										raise my_error

					__lowerCAmelCase	   =[1]
					__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =(0, 0, 0)
					__lowerCAmelCase	   =1
					while index < n_element:
										while hamming_list[i] * 2 <= hamming_list[-1]:
															i += 1
										while hamming_list[j] * 3 <= hamming_list[-1]:
															j += 1
										while hamming_list[k] * 5 <= hamming_list[-1]:
															k += 1
										hamming_list.append(
										    min(hamming_list[i] * 2				,   hamming_list[j] * 3				,   hamming_list[k] * 5	)	)
										index += 1
					return hamming_list


if __name__ == "__main__":
				lowercase_	       =			input('''Enter the last number (nth term) of the Hamming Number Series: ''')
				print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
				lowercase_	       =			hamming(int(n))
				print('''-----------------------------------------------------''')
				print(F"The list with nth numbers is: {hamming_numbers}")
				print('''-----------------------------------------------------''')




