"""simple docstring"""







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 	UpperCamelCase    (					lowercase   ,			lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Union[str, Any]					=	StableDiffusionDiffEditPipeline
				UpperCAmelCase       :   List[str]					=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
				UpperCAmelCase       :   Tuple					=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
				UpperCAmelCase       :   str					=	frozenset(
				    []		)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				UpperCAmelCase       :   Dict					=	frozenset([]		)



				def       _lowercase  (self       :   List[Any])					->			Union[str, Any]:
					torch.manual_seed(0)
					__snake_case    :							Union[str, Any]   	=				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			,						)
					__snake_case    :							List[Any]   	=				DDIMScheduler(
					    beta_start=0.00_085			,						beta_end=0.012			,						beta_schedule='scaled_linear'			,						clip_sample=_A			,						set_alpha_to_one=_A			,						)
					__snake_case    :							Dict   	=				DDIMInverseScheduler(
					    beta_start=0.00_085			,						beta_end=0.012			,						beta_schedule='scaled_linear'			,						clip_sample=_A			,						set_alpha_to_zero=_A			,						)
					torch.manual_seed(0)
					__snake_case    :							List[Any]   	=				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)
					__snake_case    :							Any   	=				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			,						)
					__snake_case    :							Optional[Any]   	=				CLIPTextModel(_A)
					__snake_case    :							Dict   	=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

					__snake_case    :							Union[str, Any]   	=				{
					    '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       _lowercase  (self       :   List[str]			,						_A       :   Union[str, Any]			,						_A       :   List[str]=0)					->			Optional[int]:
					__snake_case    :							Tuple   	=				floats_tensor((1, 16, 16)			,						rng=random.Random(_A)).to(_A)
					__snake_case    :							Dict   	=				floats_tensor((1, 2, 4, 16, 16)			,						rng=random.Random(_A)).to(_A)
					if str(_A).startswith('mps'):
						__snake_case    :							Any   	=				torch.manual_seed(_A)
					else:
						__snake_case    :							Tuple   	=				torch.Generator(device=_A).manual_seed(_A)
					__snake_case    :							Dict   	=				{
					    '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       _lowercase  (self       :   Any			,						_A       :   Union[str, Any]			,						_A       :   Dict=0)					->			Union[str, Any]:
					__snake_case    :							Tuple   	=				floats_tensor((1, 3, 32, 32)			,						rng=random.Random(_A)).to(_A)
					__snake_case    :							Dict   	=				image.cpu().permute(0			,						2			,						3			,						1)[0]
					__snake_case    :							str   	=				Image.fromarray(np.uinta(_A)).convert('RGB')
					if str(_A).startswith('mps'):
						__snake_case    :							int   	=				torch.manual_seed(_A)
					else:
						__snake_case    :							List[str]   	=				torch.Generator(device=_A).manual_seed(_A)
					__snake_case    :							Any   	=				{
					    '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       _lowercase  (self       :   Any			,						_A       :   Dict			,						_A       :   List[Any]=0)					->			List[str]:
					__snake_case    :							str   	=				floats_tensor((1, 3, 32, 32)			,						rng=random.Random(_A)).to(_A)
					__snake_case    :							int   	=				image.cpu().permute(0			,						2			,						3			,						1)[0]
					__snake_case    :							Any   	=				Image.fromarray(np.uinta(_A)).convert('RGB')
					if str(_A).startswith('mps'):
						__snake_case    :							List[Any]   	=				torch.manual_seed(_A)
					else:
						__snake_case    :							Tuple   	=				torch.Generator(device=_A).manual_seed(_A)
					__snake_case    :							List[Any]   	=				{
					    '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       _lowercase  (self       :   Any)					->			Optional[int]:
					if not hasattr(self.pipeline_class			,						'_optional_components'):
						return

					__snake_case    :							List[str]   	=				self.get_dummy_components()
					__snake_case    :							Union[str, Any]   	=				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})

					__snake_case    :							Optional[Any]   	=				self.get_dummy_inputs(_A)
					__snake_case    :							int   	=				pipe(**_A)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
						pipe.save_pretrained(_A)
						__snake_case    :							Tuple   	=				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."			,						)

					__snake_case    :							Optional[Any]   	=				self.get_dummy_inputs(_A)
					__snake_case    :							List[str]   	=				pipe_loaded(**_A)[0]

					__snake_case    :							Optional[int]   	=				np.abs(output - output_loaded).max()
					self.assertLess(_A			,						1E-4)



				def       _lowercase  (self       :   Optional[Any])					->			Optional[Any]:
					__snake_case    :							Union[str, Any]   	=				'cpu'

					__snake_case    :							Dict   	=				self.get_dummy_components()
					__snake_case    :							Union[str, Any]   	=				self.pipeline_class(**_A)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case    :							Dict   	=				self.get_dummy_mask_inputs(_A)
					__snake_case    :							int   	=				pipe.generate_mask(**_A)
					__snake_case    :							Union[str, Any]   	=				mask[0, -3:, -3:]

					self.assertEqual(mask.shape			,						(1, 16, 16))
					__snake_case    :							str   	=				np.array([0] * 9)
					__snake_case    :							Optional[int]   	=				np.abs(mask_slice.flatten() - expected_slice).max()
					self.assertLessEqual(_A			,						1E-3)
					self.assertEqual(mask[0, -3, -4]			,						0)



				def       _lowercase  (self       :   List[str])					->			Any:
					__snake_case    :							List[str]   	=				'cpu'

					__snake_case    :							List[str]   	=				self.get_dummy_components()
					__snake_case    :							Union[str, Any]   	=				self.pipeline_class(**_A)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case    :							int   	=				self.get_dummy_inversion_inputs(_A)
					__snake_case    :							Optional[Any]   	=				pipe.invert(**_A).images
					__snake_case    :							Any   	=				image[0, -1, -3:, -3:]

					self.assertEqual(image.shape			,						(2, 32, 32, 3))
					__snake_case    :							Optional[int]   	=				np.array(
					    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]			,						)
					__snake_case    :							Tuple   	=				np.abs(image_slice.flatten() - expected_slice).max()
					self.assertLessEqual(_A			,						1E-3)



				def       _lowercase  (self       :   Dict)					->			int:
					super().test_inference_batch_single_identical(expected_max_diff=5E-3)






				def       _lowercase  (self       :   Union[str, Any])					->			Any:
					__snake_case    :							Union[str, Any]   	=				'cpu'

					__snake_case    :							List[Any]   	=				self.get_dummy_components()

					__snake_case    :							str   	=				{'beta_start': 0.00_085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
					__snake_case    :							Union[str, Any]   	=				DPMSolverMultistepScheduler(**_A)
					__snake_case    :							int   	=				DPMSolverMultistepInverseScheduler(**_A)

					__snake_case    :							Union[str, Any]   	=				self.pipeline_class(**_A)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case    :							List[str]   	=				self.get_dummy_inversion_inputs(_A)
					__snake_case    :							Dict   	=				pipe.invert(**_A).images
					__snake_case    :							List[str]   	=				image[0, -1, -3:, -3:]

					self.assertEqual(image.shape			,						(2, 32, 32, 3))
					__snake_case    :							Optional[int]   	=				np.array(
					    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]			,						)
					__snake_case    :							Tuple   	=				np.abs(image_slice.flatten() - expected_slice).max()
					self.assertLessEqual(_A			,						1E-3)







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



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



				@classmethod
				def       _lowercase  (cls       :   int)					->			int:
					__snake_case    :							List[str]   	=				load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png')

					__snake_case    :							Any   	=				raw_image.convert('RGB').resize((7_68, 7_68))

					__snake_case    :							Any   	=				raw_image



				def       _lowercase  (self       :   str)					->			List[Any]:
					__snake_case    :							Tuple   	=				torch.manual_seed(0)

					__snake_case    :							Union[str, Any]   	=				StableDiffusionDiffEditPipeline.from_pretrained(
					    'stabilityai/stable-diffusion-2-1'			,						safety_checker=_A			,						torch_dtype=torch.floataa)
					__snake_case    :							List[Any]   	=				DDIMScheduler.from_config(pipe.scheduler.config)
					__snake_case    :							List[str]   	=				DDIMInverseScheduler.from_config(pipe.scheduler.config)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=_A)

					__snake_case    :							Optional[int]   	=				'a bowl of fruit'
					__snake_case    :							Optional[Any]   	=				'a bowl of pears'

					__snake_case    :							Dict   	=				pipe.generate_mask(
					    image=self.raw_image			,						source_prompt=_A			,						target_prompt=_A			,						generator=_A			,						)

					__snake_case    :							Tuple   	=				pipe.invert(
					    prompt=_A			,						image=self.raw_image			,						inpaint_strength=0.7			,						generator=_A).latents

					__snake_case    :							Dict   	=				pipe(
					    prompt=_A			,						mask_image=_A			,						image_latents=_A			,						generator=_A			,						negative_prompt=_A			,						inpaint_strength=0.7			,						output_type='numpy'			,						).images[0]

					__snake_case    :							Optional[int]   	=				(
					    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       _lowercase  (self       :   str)					->			Dict:
					__snake_case    :							Tuple   	=				torch.manual_seed(0)

					__snake_case    :							str   	=				StableDiffusionDiffEditPipeline.from_pretrained(
					    'stabilityai/stable-diffusion-2-1'			,						safety_checker=_A			,						torch_dtype=torch.floataa)
					__snake_case    :							Tuple   	=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
					__snake_case    :							Tuple   	=				DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=_A)

					__snake_case    :							Optional[int]   	=				'a bowl of fruit'
					__snake_case    :							int   	=				'a bowl of pears'

					__snake_case    :							Dict   	=				pipe.generate_mask(
					    image=self.raw_image			,						source_prompt=_A			,						target_prompt=_A			,						generator=_A			,						)

					__snake_case    :							List[Any]   	=				pipe.invert(
					    prompt=_A			,						image=self.raw_image			,						inpaint_strength=0.7			,						generator=_A			,						num_inference_steps=25			,						).latents

					__snake_case    :							Optional[int]   	=				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]

					__snake_case    :							Any   	=				(
					    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






"""simple docstring"""







from __future__ import annotations

_a  :  str=				"#"

class 	UpperCamelCase    :



				def __init__(self       :   str)					->			None:
					__snake_case    :							dict   	=				{}



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			None:
					__snake_case    :							Optional[Any]   	=				self._trie
					for char in text:
						if char not in trie:
							__snake_case    :							Optional[int]   	=				{}
						__snake_case    :							Optional[int]   	=				trie[char]
					__snake_case    :							Optional[int]   	=				True



				def       _lowercase  (self       :   List[str]			,						_A       :   str)					->			tuple | list:
					__snake_case    :							str   	=				self._trie
					for char in prefix:
						if char in trie:
							__snake_case    :							Optional[int]   	=				trie[char]
						else:
							return []
					return self._elements(_A)






				def       _lowercase  (self       :   List[Any]			,						_A       :   dict)					->			tuple:
					__snake_case    :							str   	=				[]
					for c, v in d.items():
						__snake_case    :							Optional[Any]   	=				[' '] if c == END else [(c + s) for s in self._elements(_A)]
						result.extend(_A)
					return tuple(_A)







_a  :  str=				Trie()
_a  :  Any=				("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
	trie.insert_word(word)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    tuple:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				trie.find_word(UpperCAmelCase_						)
	return tuple(string + word for word in suffixes						)
def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	print(autocomplete_using_trie('de'						)						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





