


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        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  str						      =	StableDiffusionInstructPixaPixPipeline
     UpperCAmelCase__ :  int						      =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
     UpperCAmelCase__ :  str						      =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     UpperCAmelCase__ :  List[Any]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase__ :  Optional[Any]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        torch.manual_seed(0							)
        __lowerCamelCase:				Optional[int]     =	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    ,	)
        __lowerCamelCase:				Optional[int]     =	PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_							)
        torch.manual_seed(0							)
        __lowerCamelCase:				Optional[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    ,	)
        torch.manual_seed(0							)
        __lowerCamelCase:				Optional[int]     =	CLIPTextConfig(
            bos_token_id=0    ,	eos_token_id=2    ,	hidden_size=32    ,	intermediate_size=37    ,	layer_norm_eps=1E-0_5    ,	num_attention_heads=4    ,	num_hidden_layers=5    ,	pad_token_id=1    ,	vocab_size=1000    ,	)
        __lowerCamelCase:				List[Any]     =	CLIPTextModel(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

        __lowerCamelCase:				Dict     =	{
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]=0							):
        __lowerCamelCase:				Union[str, Any]     =	floats_tensor((1, 3, 32, 32)    ,	rng=random.Random(SCREAMING_SNAKE_CASE_							)							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	image.cpu().permute(0    ,	2    ,	3    ,	1							)[0]
        __lowerCamelCase:				Union[str, Any]     =	Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_							)							).convert("""RGB"""							)
        if str(SCREAMING_SNAKE_CASE_							).startswith("""mps"""							):
           __lowerCamelCase:				int     =	torch.manual_seed(SCREAMING_SNAKE_CASE_							)
        else:
           __lowerCamelCase:				Optional[Any]     =	torch.Generator(device=SCREAMING_SNAKE_CASE_							).manual_seed(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[int]     =	{
            """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  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Optional[Any]     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        __lowerCamelCase:				Dict     =	self.get_dummy_components()
        __lowerCamelCase:				str     =	StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	sd_pipe.to(SCREAMING_SNAKE_CASE_							)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	sd_pipe(**SCREAMING_SNAKE_CASE_							).images
        __lowerCamelCase:				Optional[Any]     =	image[0, -3:, -3:, -1]
        assert image.shape == (1, 32, 32, 3)
        __lowerCamelCase:				Union[str, Any]     =	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  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Optional[Any]     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        __lowerCamelCase:				Optional[Any]     =	self.get_dummy_components()
        __lowerCamelCase:				Any     =	StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	sd_pipe.to(SCREAMING_SNAKE_CASE_							)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[int]     =	"""french fries"""
        __lowerCamelCase:				Optional[int]     =	sd_pipe(**SCREAMING_SNAKE_CASE_    ,	negative_prompt=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	output.images
        __lowerCamelCase:				Tuple     =	image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __lowerCamelCase:				str     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        __lowerCamelCase:				Any     =	self.get_dummy_components()
        __lowerCamelCase:				int     =	StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	sd_pipe.to(SCREAMING_SNAKE_CASE_							)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	[inputs["""prompt"""]] * 2

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

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

        assert image.shape == (2, 32, 32, 3)
        __lowerCamelCase:				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  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Union[str, Any]     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        __lowerCamelCase:				Optional[Any]     =	self.get_dummy_components()
        __lowerCamelCase:				Union[str, Any]     =	EulerAncestralDiscreteScheduler(
            beta_start=0.0_0085    ,	beta_end=0.012    ,	beta_schedule="""scaled_linear"""							)
        __lowerCamelCase:				Optional[int]     =	StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	sd_pipe.to(SCREAMING_SNAKE_CASE_							)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Dict     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	sd_pipe(**SCREAMING_SNAKE_CASE_							).images
        __lowerCamelCase:				Dict     =	image[0, -3:, -3:, -1]

        __lowerCamelCase:				List[str]     =	[round(SCREAMING_SNAKE_CASE_    ,	4							) for x in image_slice.flatten().tolist()]
        print(""",""".join([str(SCREAMING_SNAKE_CASE_							) for x in slice]							)							)

        assert image.shape == (1, 32, 32, 3)
        __lowerCamelCase:				Dict     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        super().test_inference_batch_single_identical(expected_max_diff=3E-3							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Dict     =	self.get_dummy_components()
        __lowerCamelCase:				Optional[Any]     =	StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE_    ,	do_normalize=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				int     =	pipe(**self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_    ,	input_image_type="""pt"""							)							)[0]

        __lowerCamelCase:				List[str]     =	components["""vae"""]
        __lowerCamelCase:				Tuple     =	self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_    ,	input_image_type="""pt"""							)

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

        __lowerCamelCase:				Dict     =	pipe(**SCREAMING_SNAKE_CASE_							)[0]

        __lowerCamelCase:				Tuple     =	np.abs(out - out_latents_inputs							).max()
        self.assertLess(SCREAMING_SNAKE_CASE_    ,	1E-4    ,	"""passing latents as image input generate different result from passing image"""							)



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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Any=0							):
        __lowerCamelCase:				Any     =	torch.manual_seed(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[int]     =	load_image(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""							)
        __lowerCamelCase:				List[str]     =	{
            """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  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				List[Any]     =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""    ,	safety_checker=SCREAMING_SNAKE_CASE_							)
        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				Dict     =	self.get_inputs()
        __lowerCamelCase:				Optional[Any]     =	pipe(**SCREAMING_SNAKE_CASE_							).images
        __lowerCamelCase:				Optional[Any]     =	image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        __lowerCamelCase:				str     =	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  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				Any     =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""    ,	safety_checker=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				str     =	self.get_inputs()
        __lowerCamelCase:				Optional[int]     =	pipe(**SCREAMING_SNAKE_CASE_							).images
        __lowerCamelCase:				List[str]     =	image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        __lowerCamelCase:				int     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				List[Any]     =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""    ,	safety_checker=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	DDIMScheduler.from_config(pipe.scheduler.config							)
        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				List[Any]     =	self.get_inputs()
        __lowerCamelCase:				Any     =	pipe(**SCREAMING_SNAKE_CASE_							).images
        __lowerCamelCase:				List[str]     =	image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        __lowerCamelCase:				Optional[int]     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				Any     =	0

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

        __lowerCamelCase:				List[Any]     =	False

        __lowerCamelCase:				Any     =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""    ,	safety_checker=SCREAMING_SNAKE_CASE_    ,	torch_dtype=torch.floataa							)
        __lowerCamelCase:				str     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				Optional[Any]     =	self.get_inputs()
        pipe(**SCREAMING_SNAKE_CASE_    ,	callback=SCREAMING_SNAKE_CASE_    ,	callback_steps=1							)
        assert callback_fn.has_been_called
        assert number_of_steps == 3





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __lowerCamelCase:				str     =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""    ,	safety_checker=SCREAMING_SNAKE_CASE_    ,	torch_dtype=torch.floataa							)
        __lowerCamelCase:				Tuple     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing(1							)
        pipe.enable_sequential_cpu_offload()

        __lowerCamelCase:				List[Any]     =	self.get_inputs()
        __lowerCamelCase:				List[Any]     =	pipe(**SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[int]     =	torch.cuda.max_memory_allocated()
        # make sure that less than 2.2 GB is allocated
        assert mem_bytes < 2.2 * 10**9







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

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

        __lowerCamelCase:				Optional[int]     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	output.images[0]

        __lowerCamelCase:				Optional[Any]     =	image[255:258, 383:386, -1]

        assert image.shape == (504, 504, 3)
        __lowerCamelCase:				List[Any]     =	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






class        a					:





     def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str = ""    ,	SCREAMING_SNAKE_CASE_					:				bool = False							):
        # Mapping from the first character of the prefix of the node
        __lowerCamelCase:				dict[str, RadixNode]     =	{}

        # A node will be a leaf if the tree contains its word
        __lowerCamelCase:				str     =	is_leaf

        __lowerCamelCase:				Optional[int]     =	prefix





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				Optional[Any]     =	0
        for q, w in zip(self.prefix    ,	SCREAMING_SNAKE_CASE_							):
           if q != w:
              break

           x += 1

        return self.prefix[:x], self.prefix[x:], word[x:]





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				list[str]							):
        for word in words:
           self.insert(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        # Case 1: If the word is the prefix of the node
        # Solution: We set the current node as leaf
        if self.prefix == word:
           __lowerCamelCase:				Union[str, Any]     =	True

        # Case 2: The node has no edges that have a prefix to the word
        # Solution: We create an edge from the current node to a new one
        # containing the word
        elif word[0] not in self.nodes:
           __lowerCamelCase:				Any     =	RadixNode(prefix=SCREAMING_SNAKE_CASE_    ,	is_leaf=SCREAMING_SNAKE_CASE_							)

        else:
           __lowerCamelCase:				Union[str, Any]     =	self.nodes[word[0]]
           __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				List[str]     =	incoming_node.match(
               SCREAMING_SNAKE_CASE_							)

           # Case 3: The node prefix is equal to the matching
           # Solution: We insert remaining word on the next node
           if remaining_prefix == "":
              self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE_							)

           # Case 4: The word is greater equal to the matching
           # Solution: Create a node in between both nodes, change
           # prefixes and add the new node for the remaining word
           else:
              __lowerCamelCase:				List[Any]     =	remaining_prefix

              __lowerCamelCase:				Optional[Any]     =	self.nodes[matching_string[0]]
              __lowerCamelCase:				Optional[int]     =	RadixNode(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase:				Union[str, Any]     =	aux_node

              if remaining_word == "":
                 __lowerCamelCase:				Optional[int]     =	True
              else:
                 self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				int     =	self.nodes.get(word[0]    ,	SCREAMING_SNAKE_CASE_							)
        if not incoming_node:
           return False
        else:
           __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Any     =	incoming_node.match(
               SCREAMING_SNAKE_CASE_							)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
              return False
           # This applies when the word and the prefix are equal
           elif remaining_word == "":
              return incoming_node.is_leaf
           # We have word remaining so we check the next node
           else:
              return incoming_node.find(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				str     =	self.nodes.get(word[0]    ,	SCREAMING_SNAKE_CASE_							)
        if not incoming_node:
           return False
        else:
           __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Dict     =	incoming_node.match(
               SCREAMING_SNAKE_CASE_							)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
              return False
           # We have word remaining so we check the next node
           elif remaining_word != "":
              return incoming_node.delete(SCREAMING_SNAKE_CASE_							)
           else:
              # If it is not a leaf, we don't have to delete
              if not incoming_node.is_leaf:
                 return False
              else:
                 # We delete the nodes if no edges go from it
                 if len(incoming_node.nodes							) == 0:
                    del self.nodes[word[0]]
                    # We merge the current node with its only child
                    if len(self.nodes							) == 1 and not self.is_leaf:
                       __lowerCamelCase:				List[Any]     =	list(self.nodes.values()							)[0]
                       __lowerCamelCase:				Any     =	merging_node.is_leaf
                       self.prefix += merging_node.prefix
                       __lowerCamelCase:				Tuple     =	merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                 elif len(incoming_node.nodes							) > 1:
                    __lowerCamelCase:				int     =	False
                 # If there is 1 edge, we merge it with its child
                 else:
                    __lowerCamelCase:				Union[str, Any]     =	list(incoming_node.nodes.values()							)[0]
                    __lowerCamelCase:				List[str]     =	merging_node.is_leaf
                    incoming_node.prefix += merging_node.prefix
                    __lowerCamelCase:				Union[str, Any]     =	merging_node.nodes

                 return True







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				int = 0							):
        if self.prefix != "":
           print("""-""" * height    ,	self.prefix    ,	"""  (leaf)""" if self.is_leaf else """"""							)

        for value in self.nodes.values():
           value.print_tree(height + 1							)



def     __lowerCAmelCase						(							)       ->							bool:
   __lowerCamelCase:				Optional[int]     =	"""banana bananas bandana band apple all beast""".split()
   __lowerCamelCase:				Optional[Any]     =	RadixNode()
   root.insert_many(snake_case )

   assert all(root.find(snake_case ) for word in words )
   assert not root.find("""bandanas""" )
   assert not root.find("""apps""" )
   root.delete("""all""" )
   assert not root.find("""all""" )
   root.delete("""banana""" )
   assert not root.find("""banana""" )
   assert root.find("""bananas""" )

   return True



def     __lowerCAmelCase						(							)       ->							None:
   assert test_trie()



def     __lowerCAmelCase						(							)       ->							None:
   __lowerCamelCase:				int     =	RadixNode()
   __lowerCamelCase:				str     =	"""banana bananas bandanas bandana band apple all beast""".split()
   root.insert_many(snake_case )

   print("""Words:"""						,      snake_case )
   print("""Tree:""" )
   root.print_tree()


if __name__ == "__main__":
   main()


