



"""simple docstring"""







import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class       _a  ( SCREAMING_SNAKE_CASE__     ,	unittest.TestCase):
			__magic_name__     					=     DiTPipeline
			__magic_name__     					=     CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
			__magic_name__     					=     PipelineTesterMixin.required_optional_params - {
			    """latents""",
			    """num_images_per_prompt""",
			    """callback""",
			    """callback_steps""",
			}
			__magic_name__     					=     CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
			__magic_name__     					=     False



			def 				__lowercase					(			self							:				int		)							->					Dict:
									torch.manual_seed(0		)
									snake_case   :			List[Any]						    = TransformeraDModel(
									    sample_size=16   ,	num_layers=2   ,	patch_size=4   ,	attention_head_dim=8   ,	num_attention_heads=2   ,	in_channels=4   ,	out_channels=8   ,	attention_bias=_lowercase   ,	activation_fn="gelu-approximate"   ,	num_embeds_ada_norm=1000   ,	norm_type="ada_norm_zero"   ,	norm_elementwise_affine=_lowercase   ,	)
									snake_case   :			Tuple						    = AutoencoderKL()
									snake_case   :			List[str]						    = DDIMScheduler()
									snake_case   :			Union[str, Any]						    = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
									return components



			def 				__lowercase					(			self							:				Tuple   ,	_lowercase							:				str   ,	_lowercase							:				Dict=0		)							->					Any:
									if str(_lowercase		).startswith("mps"		):
															snake_case   :			Any						    = torch.manual_seed(_lowercase		)
									else:
															snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(_lowercase		)
									snake_case   :			Optional[int]						    = {
									    "class_labels": [1],
									    "generator": generator,
									    "num_inference_steps": 2,
									    "output_type": "numpy",
									}
									return inputs



			def 				__lowercase					(			self							:				int		)							->					Any:
									snake_case   :			int						    = "cpu"

									snake_case   :			str						    = self.get_dummy_components()
									snake_case   :			List[Any]						    = self.pipeline_class(**_lowercase		)
									pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			int						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			Optional[int]						    = pipe(**_lowercase		).images
									snake_case   :			Union[str, Any]						    = image[0, -3:, -3:, -1]

									self.assertEqual(image.shape   ,	(1, 16, 16, 3)		)
									snake_case   :			Optional[Any]						    = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]		)
									snake_case   :			Tuple						    = np.abs(image_slice.flatten() - expected_slice		).max()
									self.assertLessEqual(_lowercase   ,	1E-3		)



			def 				__lowercase					(			self							:				List[Any]		)							->					List[Any]:
									self._test_inference_batch_single_identical(relax_max_difference=_lowercase   ,	expected_max_diff=1E-3		)



			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()   ,	reason="XFormers attention is only available with CUDA and `xformers` installed"   ,	)
			def 				__lowercase					(			self							:				Optional[int]		)							->					str:
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)



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



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



			def 				__lowercase					(			self							:				Tuple		)							->					Optional[int]:
									snake_case   :			int						    = torch.manual_seed(0		)

									snake_case   :			Tuple						    = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256"		)
									pipe.to("cuda"		)

									snake_case   :			Any						    = ["vase", "umbrella", "white shark", "white wolf"]
									snake_case   :			Any						    = pipe.get_label_ids(_lowercase		)

									snake_case   :			Optional[Any]						    = pipe(_lowercase   ,	generator=_lowercase   ,	num_inference_steps=40   ,	output_type="np"		).images

									for word, image in zip(_lowercase   ,	_lowercase		):
															snake_case   :			List[str]						    = load_numpy(
															    F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''		)
															assert np.abs((expected_image - image).max()		) < 1E-2



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Dict:
									snake_case   :			str						    = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"		)
									snake_case   :			Optional[int]						    = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
									pipe.to("cuda"		)

									snake_case   :			Optional[Any]						    = ["vase", "umbrella"]
									snake_case   :			Optional[Any]						    = pipe.get_label_ids(_lowercase		)

									snake_case   :			str						    = torch.manual_seed(0		)
									snake_case   :			Optional[Any]						    = pipe(_lowercase   ,	generator=_lowercase   ,	num_inference_steps=25   ,	output_type="np"		).images

									for word, image in zip(_lowercase   ,	_lowercase		):
															snake_case   :			str						    = load_numpy(
															    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
															    F'''/dit/{word}_512.npy'''		)

															assert np.abs((expected_image - image).max()		) < 1E-1








"""simple docstring"""







from __future__ import annotations

A		=						'#'

class       _a  :



			def __init__(			self							:				List[Any]		)							->					None:
									snake_case   :			dict						    = {}



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



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				str		)							->					tuple | list:
									snake_case   :			Dict						    = self._trie
									for char in prefix:
															if char in trie:
																					snake_case   :			Tuple						    = trie[char]
															else:
																					return []
									return self._elements(_lowercase		)



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				dict		)							->					tuple:
									snake_case   :			int						    = []
									for c, v in d.items():
															snake_case   :			int						    = [" "] if c == END else [(c + s) for s in self._elements(_lowercase		)]
															result.extend(_lowercase		)
									return tuple(_lowercase		)



A		=						Trie()
A		=						('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
			trie.insert_word(word)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = trie.find_word(lowerCamelCase_ )
						return tuple(string + word for word in suffixes )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						print(autocomplete_using_trie("de" ) )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



