


'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case          =  logging.get_logger(__name__)


def     a   (    __a     )						->			List[Any]:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				SwinConfig.from_pretrained(
					    '''microsoft/swin-tiny-patch4-window7-224'''				,      out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']     )
					UpperCamelCase__     :Tuple							      =				MaskFormerConfig(backbone_config=__lowerCAmelCase     )

					UpperCamelCase__     :List[Any]							      =				'''huggingface/label-files'''
					if "ade20k-full" in model_name:
										# this should be ok
										UpperCamelCase__     :Optional[int]							      =				847
										UpperCamelCase__     :Any							      =				'''maskformer-ade20k-full-id2label.json'''
					elif "ade" in model_name:
										# this should be ok
										UpperCamelCase__     :List[Any]							      =				150
										UpperCamelCase__     :Optional[int]							      =				'''ade20k-id2label.json'''
					elif "coco-stuff" in model_name:
										# this should be ok
										UpperCamelCase__     :int							      =				171
										UpperCamelCase__     :Optional[int]							      =				'''maskformer-coco-stuff-id2label.json'''
					elif "coco" in model_name:
										# TODO
										UpperCamelCase__     :Tuple							      =				133
										UpperCamelCase__     :str							      =				'''coco-panoptic-id2label.json'''
					elif "cityscapes" in model_name:
										# this should be ok
										UpperCamelCase__     :Optional[int]							      =				19
										UpperCamelCase__     :int							      =				'''cityscapes-id2label.json'''
					elif "vistas" in model_name:
										# this should be ok
										UpperCamelCase__     :int							      =				65
										UpperCamelCase__     :Dict							      =				'''mapillary-vistas-id2label.json'''

					UpperCamelCase__     :List[Any]							      =				json.load(open(hf_hub_download(__lowerCAmelCase				,      __lowerCAmelCase				,      repo_type='''dataset'''     )				,      '''r'''     )     )
					UpperCamelCase__     :Dict							      =				{int(__lowerCAmelCase     ): v for k, v in idalabel.items()}

					return config


def     a   (    __a     )						->			Any:



					'''simple docstring'''






					UpperCamelCase__     :Dict							      =				[]
					# stem
					# fmt: off
					rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')     )
					rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')     )
					rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')     )
					rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')     )
					# stages
					for i in range(len(config.backbone_config.depths     )     ):
										for j in range(config.backbone_config.depths[i]     ):
															rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')     )
															rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')     )
															rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')     )
															rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')     )
															rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')     )
															rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')     )
															rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')     )
															rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')     )
															rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')     )
															rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')     )
															rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')     )
															rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')     )

										if i < 3:
															rename_keys.append((f'''backbone.layers.{i}.downsample.reduction.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')     )
															rename_keys.append((f'''backbone.layers.{i}.downsample.norm.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')     )
															rename_keys.append((f'''backbone.layers.{i}.downsample.norm.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')     )
										rename_keys.append((f'''backbone.norm{i}.weight''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')     )
										rename_keys.append((f'''backbone.norm{i}.bias''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')     )

					# FPN
					rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')     )
					rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')     )
					rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')     )
					for source_index, target_index in zip(range(3				,      0				,      -1     )				,      range(0				,      3     )     ):
										rename_keys.append((f'''sem_seg_head.adapter_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')     )
										rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')     )
										rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')     )
										rename_keys.append((f'''sem_seg_head.layer_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')     )
										rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')     )
										rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')     )
					rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')     )
					rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')     )

					# Transformer decoder
					for idx in range(config.decoder_config.decoder_layers     ):
										# self-attention out projection
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')     )
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')     )
										# cross-attention out projection
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')     )
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')     )
										# MLP 1
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')     )
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')     )
										# MLP 2
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')     )
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')     )
										# layernorm 1 (self-attention layernorm)
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')     )
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')     )
										# layernorm 2 (cross-attention layernorm)
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')     )
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')     )
										# layernorm 3 (final layernorm)
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')     )
										rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')     )

					rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')     )
					rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')     )

					# heads on top
					rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')     )

					rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')     )
					rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')     )

					rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')     )
					rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')     )

					for i in range(3     ):
										rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', f'''mask_embedder.{i}.0.weight''')     )
										rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', f'''mask_embedder.{i}.0.bias''')     )
					# fmt: on

					return rename_keys


def     a   (    __a				,      __a				,      __a     )						->			List[str]:



					'''simple docstring'''






					UpperCamelCase__     :Dict							      =				dct.pop(__lowerCAmelCase     )
					UpperCamelCase__     :str							      =				val


def     a   (    __a				,      __a     )						->			Optional[int]:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				[int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
					for i in range(len(backbone_config.depths     )     ):
										UpperCamelCase__     :List[Any]							      =				num_features[i]
										for j in range(backbone_config.depths[i]     ):
															# fmt: off
															# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
															UpperCamelCase__     :Optional[int]							      =				state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''     )
															UpperCamelCase__     :Any							      =				state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''     )
															# next, add query, keys and values (in that order) to the state dict
															UpperCamelCase__     :List[str]							      =				in_proj_weight[:dim, :]
															UpperCamelCase__     :Union[str, Any]							      =				in_proj_bias[: dim]
															UpperCamelCase__     :List[Any]							      =				in_proj_weight[
															    dim : dim * 2, :
															]
															UpperCamelCase__     :List[Any]							      =				in_proj_bias[
															    dim : dim * 2
															]
															UpperCamelCase__     :Union[str, Any]							      =				in_proj_weight[
															    -dim :, :
															]
															UpperCamelCase__     :Union[str, Any]							      =				in_proj_bias[-dim :]
															# fmt: on


def     a   (    __a				,      __a     )						->			Optional[int]:



					'''simple docstring'''






					UpperCamelCase__     :List[str]							      =				config.decoder_config.hidden_size
					for idx in range(config.decoder_config.decoder_layers     ):
										# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
										UpperCamelCase__     :Dict							      =				state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''     )
										UpperCamelCase__     :List[Any]							      =				state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''     )
										# next, add query, keys and values (in that order) to the state dict
										UpperCamelCase__     :List[str]							      =				in_proj_weight[: hidden_size, :]
										UpperCamelCase__     :str							      =				in_proj_bias[:config.hidden_size]
										UpperCamelCase__     :List[str]							      =				in_proj_weight[hidden_size : hidden_size * 2, :]
										UpperCamelCase__     :Optional[Any]							      =				in_proj_bias[hidden_size : hidden_size * 2]
										UpperCamelCase__     :Optional[int]							      =				in_proj_weight[-hidden_size :, :]
										UpperCamelCase__     :Dict							      =				in_proj_bias[-hidden_size :]
										# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
										UpperCamelCase__     :str							      =				state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''     )
										UpperCamelCase__     :List[Any]							      =				state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''     )
										# next, add query, keys and values (in that order) to the state dict
										UpperCamelCase__     :Optional[Any]							      =				in_proj_weight[: hidden_size, :]
										UpperCamelCase__     :str							      =				in_proj_bias[:config.hidden_size]
										UpperCamelCase__     :Optional[int]							      =				in_proj_weight[hidden_size : hidden_size * 2, :]
										UpperCamelCase__     :Optional[int]							      =				in_proj_bias[hidden_size : hidden_size * 2]
										UpperCamelCase__     :Any							      =				in_proj_weight[-hidden_size :, :]
										UpperCamelCase__     :Union[str, Any]							      =				in_proj_bias[-hidden_size :]
					# fmt: on


def     a   (    )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :Any							      =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					UpperCamelCase__     :Any							      =				Image.open(requests.get(__lowerCAmelCase				,      stream=__lowerCAmelCase     ).raw     )
					return im


@torch.no_grad()
def     a   (    __a				,      __a				,      __a				,      __a = False     )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :Optional[int]							      =				get_maskformer_config(__lowerCAmelCase     )

					# load original state_dict
					with open(__lowerCAmelCase				,      '''rb'''     ) as f:
										UpperCamelCase__     :Any							      =				pickle.load(__lowerCAmelCase     )
					UpperCamelCase__     :str							      =				data['''model''']

					# for name, param in state_dict.items():
					#     print(name, param.shape)

					# rename keys
					UpperCamelCase__     :str							      =				create_rename_keys(__lowerCAmelCase     )
					for src, dest in rename_keys:
										rename_key(__lowerCAmelCase				,      __lowerCAmelCase				,      __lowerCAmelCase     )
					read_in_swin_q_k_v(__lowerCAmelCase				,      config.backbone_config     )
					read_in_decoder_q_k_v(__lowerCAmelCase				,      __lowerCAmelCase     )

					# update to torch tensors
					for key, value in state_dict.items():
										UpperCamelCase__     :Dict							      =				torch.from_numpy(__lowerCAmelCase     )

					# load 🤗 model
					UpperCamelCase__     :int							      =				MaskFormerForInstanceSegmentation(__lowerCAmelCase     )
					model.eval()

					for name, param in model.named_parameters():
										print(__lowerCAmelCase				,      param.shape     )

					UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				model.load_state_dict(__lowerCAmelCase				,      strict=__lowerCAmelCase     )
					assert missing_keys == [
					    "model.pixel_level_module.encoder.model.layernorm.weight",
					    "model.pixel_level_module.encoder.model.layernorm.bias",
					]
					assert len(__lowerCAmelCase     ) == 0, f'''Unexpected keys: {unexpected_keys}'''

					# verify results
					UpperCamelCase__     :Dict							      =				prepare_img()
					if "vistas" in model_name:
										UpperCamelCase__     :int							      =				65
					elif "cityscapes" in model_name:
										UpperCamelCase__     :Tuple							      =				65535
					else:
										UpperCamelCase__     :List[Any]							      =				255
					UpperCamelCase__     :List[Any]							      =				True if '''ade''' in model_name else False
					UpperCamelCase__     :Any							      =				MaskFormerImageProcessor(ignore_index=__lowerCAmelCase				,      reduce_labels=__lowerCAmelCase     )

					UpperCamelCase__     :List[str]							      =				image_processor(__lowerCAmelCase				,      return_tensors='''pt'''     )

					UpperCamelCase__     :int							      =				model(**__lowerCAmelCase     )

					print('''Logits:'''				,      outputs.class_queries_logits[0, :3, :3]     )

					if model_name == "maskformer-swin-tiny-ade":
										UpperCamelCase__     :Optional[Any]							      =				torch.tensor(
										    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]     )
					assert torch.allclose(outputs.class_queries_logits[0, :3, :3]				,      __lowerCAmelCase				,      atol=1e-4     )
					print('''Looks ok!'''     )

					if pytorch_dump_folder_path is not None:
										print(f'''Saving model and image processor to {pytorch_dump_folder_path}'''     )
										Path(__lowerCAmelCase     ).mkdir(exist_ok=__lowerCAmelCase     )
										model.save_pretrained(__lowerCAmelCase     )
										image_processor.save_pretrained(__lowerCAmelCase     )

					if push_to_hub:
										print('''Pushing model and image processor to the hub...'''     )
										model.push_to_hub(f'''nielsr/{model_name}'''     )
										image_processor.push_to_hub(f'''nielsr/{model_name}'''     )


if __name__ == "__main__":
		__snake_case          =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''maskformer-swin-tiny-ade''',
		    type=str,
		    help=('''Name of the MaskFormer model you\'d like to convert''',),
		)
		parser.add_argument(
		    '''--checkpoint_path''',
		    default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
		    type=str,
		    help='''Path to the original state dict (.pth file).''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)

		__snake_case          =  parser.parse_args()
		convert_maskformer_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)



'''simple docstring'''
class      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_ = ""					,     UpperCamelCase_ = False							):

							'''simple docstring'''


							UpperCamelCase__     :dict[str, RadixNode]							      =				{}

							# A node will be a leaf if the tree contains its word
							UpperCamelCase__     :List[Any]							      =				is_leaf

							UpperCamelCase__     :Dict							      =				prefix




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				0
							for q, w in zip(self.prefix					,     UpperCamelCase_							):
												if q != w:
																	break

												x += 1

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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							for word in words:
												self.insert(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.prefix == word:
												UpperCamelCase__     :Tuple							      =				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:
												UpperCamelCase__     :Optional[Any]							      =				RadixNode(prefix=UpperCamelCase_					,     is_leaf=UpperCamelCase_							)

							else:
												UpperCamelCase__     :Optional[Any]							      =				self.nodes[word[0]]
												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				incoming_node.match(
												    UpperCamelCase_							)

												# 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(UpperCamelCase_							)

												# 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:
																	UpperCamelCase__     :str							      =				remaining_prefix

																	UpperCamelCase__     :Optional[Any]							      =				self.nodes[matching_string[0]]
																	UpperCamelCase__     :Dict							      =				RadixNode(UpperCamelCase_					,     UpperCamelCase_							)
																	UpperCamelCase__     :List[Any]							      =				aux_node

																	if remaining_word == "":
																						UpperCamelCase__     :List[Any]							      =				True
																	else:
																						self.nodes[matching_string[0]].insert(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.nodes.get(word[0]					,     UpperCamelCase_							)
							if not incoming_node:
												return False
							else:
												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				incoming_node.match(
												    UpperCamelCase_							)
												# 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(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.nodes.get(word[0]					,     UpperCamelCase_							)
							if not incoming_node:
												return False
							else:
												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				incoming_node.match(
												    UpperCamelCase_							)
												# 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(UpperCamelCase_							)
												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:
																																UpperCamelCase__     :Tuple							      =				list(self.nodes.values()							)[0]
																																UpperCamelCase__     :Union[str, Any]							      =				merging_node.is_leaf
																																self.prefix += merging_node.prefix
																																UpperCamelCase__     :Dict							      =				merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																						elif len(incoming_node.nodes							) > 1:
																											UpperCamelCase__     :List[str]							      =				False
																						# If there is 1 edge, we merge it with its child
																						else:
																											UpperCamelCase__     :Optional[int]							      =				list(incoming_node.nodes.values()							)[0]
																											UpperCamelCase__     :int							      =				merging_node.is_leaf
																											incoming_node.prefix += merging_node.prefix
																											UpperCamelCase__     :Optional[int]							      =				merging_node.nodes

																						return True




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_ = 0							):

							'''simple docstring'''


							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     a   (    )						->			bool:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				'''banana bananas bandana band apple all beast'''.split()
					UpperCamelCase__     :Tuple							      =				RadixNode()
					root.insert_many(__a     )

					assert all(root.find(__a     ) 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     a   (    )						->			None:



					'''simple docstring'''






					assert test_trie()


def     a   (    )						->			None:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				RadixNode()
					UpperCamelCase__     :List[Any]							      =				'''banana bananas bandanas bandana band apple all beast'''.split()
					root.insert_many(__a     )

					print('''Words:'''				,      __a     )
					print('''Tree:'''     )
					root.print_tree()


if __name__ == "__main__":
		main()