"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 	UpperCamelCase    (					__SCREAMING_SNAKE_CASE   ,			unittest.TestCase		):
				UpperCAmelCase       :   int					=	KandinskyInpaintPipeline
				UpperCAmelCase       :   Any					=	["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
				UpperCAmelCase       :   List[str]					=	[
				    """prompt""",
				    """negative_prompt""",
				    """image_embeds""",
				    """negative_image_embeds""",
				    """image""",
				    """mask_image""",
				]
				UpperCAmelCase       :   int					=	[
				    """generator""",
				    """height""",
				    """width""",
				    """latents""",
				    """guidance_scale""",
				    """negative_prompt""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				UpperCAmelCase       :   Optional[int]					=	False



				@property
				def       _lowercase  (self       :   Tuple)					->			int:
					return 32



				@property
				def       _lowercase  (self       :   str)					->			Tuple:
					return 32



				@property
				def       _lowercase  (self       :   List[Any])					->			List[Any]:
					return self.time_input_dim



				@property
				def       _lowercase  (self       :   str)					->			Tuple:
					return self.time_input_dim * 4



				@property
				def       _lowercase  (self       :   Dict)					->			List[str]:
					return 1_00



				@property
				def       _lowercase  (self       :   List[Any])					->			List[Any]:
					__snake_case    :							str   	=				XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
					return tokenizer



				@property
				def       _lowercase  (self       :   Union[str, Any])					->			Tuple:
					torch.manual_seed(0)
					__snake_case    :							Any   	=				MCLIPConfig(
					    numDims=self.cross_attention_dim			,						transformerDimensions=self.text_embedder_hidden_size			,						hidden_size=self.text_embedder_hidden_size			,						intermediate_size=37			,						num_attention_heads=4			,						num_hidden_layers=5			,						vocab_size=10_05			,						)

					__snake_case    :							List[str]   	=				MultilingualCLIP(__snake_case)
					__snake_case    :							List[Any]   	=				text_encoder.eval()

					return text_encoder



				@property
				def       _lowercase  (self       :   str)					->			List[str]:
					torch.manual_seed(0)

					__snake_case    :							List[Any]   	=				{
					    '''in_channels''': 9,
					    # Out channels is double in channels because predicts mean and variance
					    '''out_channels''': 8,
					    '''addition_embed_type''': '''text_image''',
					    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
					    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
					    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
					    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
					    '''layers_per_block''': 1,
					    '''encoder_hid_dim''': self.text_embedder_hidden_size,
					    '''encoder_hid_dim_type''': '''text_image_proj''',
					    '''cross_attention_dim''': self.cross_attention_dim,
					    '''attention_head_dim''': 4,
					    '''resnet_time_scale_shift''': '''scale_shift''',
					    '''class_embed_type''': None,
					}

					__snake_case    :							Dict   	=				UNetaDConditionModel(**__snake_case)
					return model



				@property
				def       _lowercase  (self       :   List[Any])					->			str:
					return {
					    "block_out_channels": [32, 64],
					    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
					    "in_channels": 3,
					    "latent_channels": 4,
					    "layers_per_block": 1,
					    "norm_num_groups": 8,
					    "norm_type": "spatial",
					    "num_vq_embeddings": 12,
					    "out_channels": 3,
					    "up_block_types": [
					        "AttnUpDecoderBlock2D",
					        "UpDecoderBlock2D",
					    ],
					    "vq_embed_dim": 4,
					}



				@property
				def       _lowercase  (self       :   Union[str, Any])					->			str:
					torch.manual_seed(0)
					__snake_case    :							str   	=				VQModel(**self.dummy_movq_kwargs)
					return model



				def       _lowercase  (self       :   Optional[Any])					->			str:
					__snake_case    :							Union[str, Any]   	=				self.dummy_text_encoder
					__snake_case    :							int   	=				self.dummy_tokenizer
					__snake_case    :							Optional[Any]   	=				self.dummy_unet
					__snake_case    :							str   	=				self.dummy_movq

					__snake_case    :							Optional[Any]   	=				DDIMScheduler(
					    num_train_timesteps=10_00			,						beta_schedule='linear'			,						beta_start=0.00_085			,						beta_end=0.012			,						clip_sample=__snake_case			,						set_alpha_to_one=__snake_case			,						steps_offset=1			,						prediction_type='epsilon'			,						thresholding=__snake_case			,						)

					__snake_case    :							Optional[int]   	=				{
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''movq''': movq,
					}

					return components



				def       _lowercase  (self       :   List[Any]			,						_A       :   List[Any]			,						_A       :   str=0)					->			List[str]:
					__snake_case    :							Union[str, Any]   	=				floats_tensor((1, self.cross_attention_dim)			,						rng=random.Random(__snake_case)).to(__snake_case)
					__snake_case    :							Tuple   	=				floats_tensor((1, self.cross_attention_dim)			,						rng=random.Random(seed + 1)).to(__snake_case)
					# create init_image
					__snake_case    :							Tuple   	=				floats_tensor((1, 3, 64, 64)			,						rng=random.Random(__snake_case)).to(__snake_case)
					__snake_case    :							str   	=				image.cpu().permute(0			,						2			,						3			,						1)[0]
					__snake_case    :							Tuple   	=				Image.fromarray(np.uinta(__snake_case)).convert('RGB').resize((2_56, 2_56))
					# create mask
					__snake_case    :							Tuple   	=				np.ones((64, 64)			,						dtype=np.floataa)
					__snake_case    :							Tuple   	=				0

					if str(__snake_case).startswith('mps'):
						__snake_case    :							Tuple   	=				torch.manual_seed(__snake_case)
					else:
						__snake_case    :							Dict   	=				torch.Generator(device=__snake_case).manual_seed(__snake_case)
					__snake_case    :							Optional[int]   	=				{
					    '''prompt''': '''horse''',
					    '''image''': init_image,
					    '''mask_image''': mask,
					    '''image_embeds''': image_embeds,
					    '''negative_image_embeds''': negative_image_embeds,
					    '''generator''': generator,
					    '''height''': 64,
					    '''width''': 64,
					    '''num_inference_steps''': 2,
					    '''guidance_scale''': 4.0,
					    '''output_type''': '''np''',
					}
					return inputs



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

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

					__snake_case    :							Tuple   	=				self.pipeline_class(**__snake_case)
					__snake_case    :							str   	=				pipe.to(__snake_case)

					pipe.set_progress_bar_config(disable=__snake_case)

					__snake_case    :							Optional[int]   	=				pipe(**self.get_dummy_inputs(__snake_case))
					__snake_case    :							Optional[Any]   	=				output.images

					__snake_case    :							Tuple   	=				pipe(
					    **self.get_dummy_inputs(__snake_case)			,						return_dict=__snake_case			,						)[0]

					__snake_case    :							Optional[Any]   	=				image[0, -3:, -3:, -1]
					__snake_case    :							List[Any]   	=				image_from_tuple[0, -3:, -3:, -1]

					print(f"image.shape {image.shape}")

					assert image.shape == (1, 64, 64, 3)

					__snake_case    :							Optional[Any]   	=				np.array(
					    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786])

					assert (
					    np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
					), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
					assert (
					    np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
					), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






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







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



				def       _lowercase  (self       :   Dict)					->			List[Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






				def       _lowercase  (self       :   Optional[Any])					->			int:
					__snake_case    :							Optional[int]   	=				load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')

					__snake_case    :							List[Any]   	=				load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
					__snake_case    :							str   	=				np.ones((7_68, 7_68)			,						dtype=np.floataa)
					__snake_case    :							Optional[int]   	=				0

					__snake_case    :							Dict   	=				'''a hat'''

					__snake_case    :							str   	=				KandinskyPriorPipeline.from_pretrained(
					    'kandinsky-community/kandinsky-2-1-prior'			,						torch_dtype=torch.floataa)
					pipe_prior.to(__snake_case)

					__snake_case    :							Optional[int]   	=				KandinskyInpaintPipeline.from_pretrained(
					    'kandinsky-community/kandinsky-2-1-inpaint'			,						torch_dtype=torch.floataa)
					__snake_case    :							List[str]   	=				pipeline.to(__snake_case)
					pipeline.set_progress_bar_config(disable=__snake_case)

					__snake_case    :							Any   	=				torch.Generator(device='cpu').manual_seed(0)
					__snake_case    :							Dict   	=				pipe_prior(
					    __snake_case			,						generator=__snake_case			,						num_inference_steps=5			,						negative_prompt=''			,						).to_tuple()

					__snake_case    :							Optional[int]   	=				pipeline(
					    __snake_case			,						image=__snake_case			,						mask_image=__snake_case			,						image_embeds=__snake_case			,						negative_image_embeds=__snake_case			,						generator=__snake_case			,						num_inference_steps=1_00			,						height=7_68			,						width=7_68			,						output_type='np'			,						)

					__snake_case    :							List[Any]   	=				output.images[0]

					assert image.shape == (7_68, 7_68, 3)

					assert_mean_pixel_difference(__snake_case			,						__snake_case)






"""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()





