






'''simple docstring'''




import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[Any]:
		UpperCamelCase__				:					int					=					384
		if "tiny" in model_name:
				UpperCamelCase__				:					List[str]					=					[3, 3, 9, 3]
				UpperCamelCase__				:					int					=					[96, 192, 384, 768]
		if "small" in model_name:
				UpperCamelCase__				:					Tuple					=					[3, 3, 27, 3]
				UpperCamelCase__				:					List[Any]					=					[96, 192, 384, 768]
		if "base" in model_name:
				UpperCamelCase__				:					List[str]					=					[3, 3, 27, 3]
				UpperCamelCase__				:					Any					=					[128, 256, 512, 1_024]
				UpperCamelCase__				:					Optional[int]					=					512
		if "large" in model_name:
				UpperCamelCase__				:					str					=					[3, 3, 27, 3]
				UpperCamelCase__				:					str					=					[192, 384, 768, 1_536]
				UpperCamelCase__				:					str					=					768
		if "xlarge" in model_name:
				UpperCamelCase__				:					Optional[int]					=					[3, 3, 27, 3]
				UpperCamelCase__				:					Optional[int]					=					[256, 512, 1_024, 2_048]
				UpperCamelCase__				:					List[str]					=					1_024

		# set label information
		UpperCamelCase__				:					Optional[Any]					=					150
		UpperCamelCase__				:					Union[str, Any]					=					'huggingface/label-files'
		UpperCamelCase__				:					Tuple					=					'ade20k-id2label.json'
		UpperCamelCase__				:					List[Any]					=					json.load(open(hf_hub_download(lowerCamelCase_     ,      lowerCamelCase_     ,      repo_type='dataset')     ,      'r'))
		UpperCamelCase__				:					Union[str, Any]					=					{int(lowerCamelCase_): v for k, v in idalabel.items()}
		UpperCamelCase__				:					int					=					{v: k for k, v in idalabel.items()}

		UpperCamelCase__				:					int					=					ConvNextConfig(
		    depths=lowerCamelCase_     ,      hidden_sizes=lowerCamelCase_     ,      out_features=['stage1', 'stage2', 'stage3', 'stage4'])
		UpperCamelCase__				:					List[Any]					=					UperNetConfig(
		    backbone_config=lowerCamelCase_     ,      auxiliary_in_channels=lowerCamelCase_     ,      num_labels=lowerCamelCase_     ,      idalabel=lowerCamelCase_     ,      labelaid=lowerCamelCase_     ,      )

		return config







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Union[str, Any]:
		UpperCamelCase__				:					str					=					[]

		# fmt: off
		# stem
		rename_keys.append(('backbone.downsample_layers.0.0.weight', 'backbone.embeddings.patch_embeddings.weight'))
		rename_keys.append(('backbone.downsample_layers.0.0.bias', 'backbone.embeddings.patch_embeddings.bias'))
		rename_keys.append(('backbone.downsample_layers.0.1.weight', 'backbone.embeddings.layernorm.weight'))
		rename_keys.append(('backbone.downsample_layers.0.1.bias', 'backbone.embeddings.layernorm.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.stages.{i}.{j}.gamma', f'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter'))
						rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.weight', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight'))
						rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.bias', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias'))
						rename_keys.append((f'backbone.stages.{i}.{j}.norm.weight', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight'))
						rename_keys.append((f'backbone.stages.{i}.{j}.norm.bias', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias'))
						rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight'))
						rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias'))
						rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight'))
						rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias'))
				if i > 0:
						rename_keys.append((f'backbone.downsample_layers.{i}.0.weight', f'backbone.encoder.stages.{i}.downsampling_layer.0.weight'))
						rename_keys.append((f'backbone.downsample_layers.{i}.0.bias', f'backbone.encoder.stages.{i}.downsampling_layer.0.bias'))
						rename_keys.append((f'backbone.downsample_layers.{i}.1.weight', f'backbone.encoder.stages.{i}.downsampling_layer.1.weight'))
						rename_keys.append((f'backbone.downsample_layers.{i}.1.bias', f'backbone.encoder.stages.{i}.downsampling_layer.1.bias'))

				rename_keys.append((f'backbone.norm{i}.weight', f'backbone.hidden_states_norms.stage{i+1}.weight'))
				rename_keys.append((f'backbone.norm{i}.bias', f'backbone.hidden_states_norms.stage{i+1}.bias'))

		# decode head
		rename_keys.extend(
		    [
		        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
		        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
		        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
		        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
		    ])
		# fmt: on

		return rename_keys







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Tuple:
		UpperCamelCase__				:					Tuple					=					dct.pop(lowerCamelCase_)
		UpperCamelCase__				:					Optional[Any]					=					val







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Union[str, Any]:
		UpperCamelCase__				:					Optional[int]					=					{
		    'upernet-convnext-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth',
		    'upernet-convnext-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth',
		    'upernet-convnext-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth',
		    'upernet-convnext-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth',
		    'upernet-convnext-xlarge': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth',
		}
		UpperCamelCase__				:					Optional[int]					=					model_name_to_url[model_name]
		UpperCamelCase__				:					List[Any]					=					torch.hub.load_state_dict_from_url(lowerCamelCase_     ,      map_location='cpu')['state_dict']

		UpperCamelCase__				:					Tuple					=					get_upernet_config(lowerCamelCase_)
		UpperCamelCase__				:					List[Any]					=					UperNetForSemanticSegmentation(lowerCamelCase_)
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				UpperCamelCase__				:					Optional[Any]					=					state_dict.pop(lowerCamelCase_)
				if "bn" in key:
						UpperCamelCase__				:					Tuple					=					key.replace('bn'     ,      'batch_norm')
				UpperCamelCase__				:					List[Any]					=					val

		# rename keys
		UpperCamelCase__				:					Union[str, Any]					=					create_rename_keys(lowerCamelCase_)
		for src, dest in rename_keys:
				rename_key(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)

		model.load_state_dict(lowerCamelCase_)

		# verify on image
		UpperCamelCase__				:					Union[str, Any]					=					'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
		UpperCamelCase__				:					Dict					=					Image.open(requests.get(lowerCamelCase_     ,      stream=lowerCamelCase_).raw).convert('RGB')

		UpperCamelCase__				:					List[Any]					=					SegformerImageProcessor()
		UpperCamelCase__				:					Tuple					=					processor(lowerCamelCase_     ,      return_tensors='pt').pixel_values

		with torch.no_grad():
				UpperCamelCase__				:					Union[str, Any]					=					model(lowerCamelCase_)

		if model_name == "upernet-convnext-tiny":
				UpperCamelCase__				:					List[Any]					=					torch.tensor(
				    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]])
		elif model_name == "upernet-convnext-small":
				UpperCamelCase__				:					str					=					torch.tensor(
				    [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]])
		elif model_name == "upernet-convnext-base":
				UpperCamelCase__				:					Optional[int]					=					torch.tensor(
				    [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]])
		elif model_name == "upernet-convnext-large":
				UpperCamelCase__				:					Union[str, Any]					=					torch.tensor(
				    [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]])
		elif model_name == "upernet-convnext-xlarge":
				UpperCamelCase__				:					Optional[int]					=					torch.tensor(
				    [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]])
		print('Logits:'     ,      outputs.logits[0, 0, :3, :3])
		assert torch.allclose(outputs.logits[0, 0, :3, :3]     ,      lowerCamelCase_     ,      atol=1e-4)
		print('Looks ok!')

		if pytorch_dump_folder_path is not None:
				print(f'Saving model {model_name} to {pytorch_dump_folder_path}')
				model.save_pretrained(lowerCamelCase_)
				print(f'Saving processor to {pytorch_dump_folder_path}')
				processor.save_pretrained(lowerCamelCase_)

		if push_to_hub:
				print(f'Pushing model and processor for {model_name} to hub')
				model.push_to_hub(f'openmmlab/{model_name}')
				processor.push_to_hub(f'openmmlab/{model_name}')


if __name__ == "__main__":
		lowerCAmelCase__          =						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='upernet-convnext-tiny',
		    type=str,
		    choices=[f'''upernet-convnext-{size}''' for size in ['tiny', 'small', 'base', 'large', 'xlarge']],
		    help='Name of the ConvNext UperNet model you\'d like to convert.',
		)
		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.'
		)

		lowerCAmelCase__          =						parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''




class       __lowercase					:

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

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

			UpperCamelCase__				:					Optional[Any]					=					prefix

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Optional[int]					=					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      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						list[str]):
			for word in words:
					self.insert(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						str):
			# Case 1: If the word is the prefix of the node
			# Solution: We set the current node as leaf
			if self.prefix == word:
					UpperCamelCase__				:					Optional[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:
					UpperCamelCase__				:					Optional[Any]					=					RadixNode(prefix=UpperCAmelCase_  ,					is_leaf=UpperCAmelCase_)

			else:
					UpperCamelCase__				:					int					=					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__				:					Tuple					=					remaining_prefix

							UpperCamelCase__				:					str					=					self.nodes[matching_string[0]]
							UpperCamelCase__				:					Optional[Any]					=					RadixNode(UpperCAmelCase_  ,					UpperCAmelCase_)
							UpperCamelCase__				:					str					=					aux_node

							if remaining_word == "":
									UpperCamelCase__				:					int					=					True
							else:
									self.nodes[matching_string[0]].insert(UpperCAmelCase_)

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

									return True





	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						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       __UpperCAmelCase						(  )							->     bool:
		UpperCamelCase__				:					Union[str, Any]					=					'banana bananas bandana band apple all beast'.split()
		UpperCamelCase__				:					List[Any]					=					RadixNode()
		root.insert_many(lowerCamelCase_)

		assert all(root.find(lowerCamelCase_) 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       __UpperCAmelCase						(  )							->     None:
		assert test_trie()







def       __UpperCAmelCase						(  )							->     None:
		UpperCamelCase__				:					List[Any]					=					RadixNode()
		UpperCamelCase__				:					List[str]					=					'banana bananas bandanas bandana band apple all beast'.split()
		root.insert_many(lowerCamelCase_)

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


if __name__ == "__main__":
		main()




