


'''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 lowerCamelCase      (      lowerCAmelCase    :     Dict		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					384
	if "tiny" in model_name:
		__magic_name__	:   List[Any]					     =					[3, 3, 9, 3]
		__magic_name__	:   Optional[Any]					     =					[96, 192, 384, 768]
	if "small" in model_name:
		__magic_name__	:   List[Any]					     =					[3, 3, 27, 3]
		__magic_name__	:   Any					     =					[96, 192, 384, 768]
	if "base" in model_name:
		__magic_name__	:   List[str]					     =					[3, 3, 27, 3]
		__magic_name__	:   Dict					     =					[128, 256, 512, 1024]
		__magic_name__	:   int					     =					512
	if "large" in model_name:
		__magic_name__	:   Optional[Any]					     =					[3, 3, 27, 3]
		__magic_name__	:   int					     =					[192, 384, 768, 1536]
		__magic_name__	:   Tuple					     =					768
	if "xlarge" in model_name:
		__magic_name__	:   List[str]					     =					[3, 3, 27, 3]
		__magic_name__	:   Union[str, Any]					     =					[256, 512, 1024, 2048]
		__magic_name__	:   Dict					     =					1024

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

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

	return config





def lowerCamelCase      (      lowerCAmelCase    :     Union[str, Any]		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					[]

	# 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 lowerCamelCase      (      lowerCAmelCase    :     int		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     List[str]		):





	"""simple docstring"""







	__magic_name__	:   List[Any]					     =					dct.pop(lowerCAmelCase		)
	__magic_name__	:   str					     =					val





def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Union[str, Any]		):





	"""simple docstring"""







	__magic_name__	:   str					     =					{
	    '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',
	}
	__magic_name__	:   Dict					     =					model_name_to_url[model_name]
	__magic_name__	:   Dict					     =					torch.hub.load_state_dict_from_url(lowerCAmelCase		,		map_location='cpu'		)['state_dict']

	__magic_name__	:   Optional[int]					     =					get_upernet_config(lowerCAmelCase		)
	__magic_name__	:   Dict					     =					UperNetForSemanticSegmentation(lowerCAmelCase		)
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		__magic_name__	:   int					     =					state_dict.pop(lowerCAmelCase		)
		if "bn" in key:
			__magic_name__	:   Any					     =					key.replace('bn'		,		'batch_norm'		)
		__magic_name__	:   str					     =					val

	# rename keys
	__magic_name__	:   str					     =					create_rename_keys(lowerCAmelCase		)
	for src, dest in rename_keys:
		rename_key(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)

	model.load_state_dict(lowerCAmelCase		)

	# verify on image
	__magic_name__	:   Any					     =					'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
	__magic_name__	:   Tuple					     =					Image.open(requests.get(lowerCAmelCase		,		stream=lowerCAmelCase		).raw		).convert('RGB'		)

	__magic_name__	:   Any					     =					SegformerImageProcessor()
	__magic_name__	:   Tuple					     =					processor(lowerCAmelCase		,		return_tensors='pt'		).pixel_values

	with torch.no_grad():
		__magic_name__	:   int					     =					model(lowerCAmelCase		)

	if model_name == "upernet-convnext-tiny":
		__magic_name__	:   Any					     =					torch.tensor(
		    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]		)
	elif model_name == "upernet-convnext-small":
		__magic_name__	:   Any					     =					torch.tensor(
		    [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]		)
	elif model_name == "upernet-convnext-base":
		__magic_name__	:   Tuple					     =					torch.tensor(
		    [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]		)
	elif model_name == "upernet-convnext-large":
		__magic_name__	:   Union[str, Any]					     =					torch.tensor(
		    [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]		)
	elif model_name == "upernet-convnext-xlarge":
		__magic_name__	:   Optional[int]					     =					torch.tensor(
		    [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]		)
	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				:Union[str, Any]        =      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				:int        =      parser.parse_args()
							convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase				:Any        =      TypeVar('''T''')





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (position - 1) // 2





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (2 * position) + 1





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (2 * position) + 2
class _lowerCamelCase	(						Generic[T]							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]      )  ->			None:
			__magic_name__	:   list[tuple[T, int]]					     =					[]
			__magic_name__	:   dict[T, int]					     =					{}
			__magic_name__	:   int					     =					0







		def __len__( self		:						Optional[int]      )  ->			int:
			return self.elements







		def __repr__( self		:						Optional[Any]      )  ->			str:
			return str(self.heap      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			bool:
			# Check if the priority queue is empty
			return self.elements == 0







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Add an element with given priority to the queue
			self.heap.append((elem, weight)      )
			__magic_name__	:   List[str]					     =					self.elements
			self.elements += 1
			self._bubble_up(_A      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			T:
			# Remove and return the element with lowest weight (highest priority)
			if self.elements > 1:
				self._swap_nodes(0  ,					self.elements - 1      )
			__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					self.heap.pop()
			del self.position_map[elem]
			self.elements -= 1
			if self.elements > 0:
				__magic_name__     ,    __magic_name__	:   Tuple					     =					self.heap[0]
				self._bubble_down(_A      )
			return elem







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Update the weight of the given key
			__magic_name__	:   Optional[Any]					     =					self.position_map[elem]
			__magic_name__	:   Any					     =					(elem, weight)
			if position > 0:
				__magic_name__	:   Optional[int]					     =					get_parent_position(_A      )
				__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[parent_position]
				if parent_weight > weight:
					self._bubble_up(_A      )
				else:
					self._bubble_down(_A      )
			else:
				self._bubble_down(_A      )







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						T      )  ->			None:
			# Place a node at the proper position (upward movement) [to be used internally
			# only]
			__magic_name__	:   Union[str, Any]					     =					self.position_map[elem]
			if curr_pos == 0:
				return None
			__magic_name__	:   Optional[Any]					     =					get_parent_position(_A      )
			__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[curr_pos]
			__magic_name__     ,    __magic_name__	:   int					     =					self.heap[parent_position]
			if parent_weight > weight:
				self._swap_nodes(_A  ,					_A      )
				return self._bubble_up(_A      )
			return None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						T      )  ->			None:
			# Place a node at the proper position (downward movement) [to be used
			# internally only]
			__magic_name__	:   Dict					     =					self.position_map[elem]
			__magic_name__     ,    __magic_name__	:   List[str]					     =					self.heap[curr_pos]
			__magic_name__	:   str					     =					get_child_left_position(_A      )
			__magic_name__	:   List[str]					     =					get_child_right_position(_A      )
			if child_left_position < self.elements and child_right_position < self.elements:
				__magic_name__     ,    __magic_name__	:   int					     =					self.heap[child_left_position]
				__magic_name__     ,    __magic_name__	:   int					     =					self.heap[child_right_position]
				if child_right_weight < child_left_weight and child_right_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			if child_left_position < self.elements:
				__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[child_left_position]
				if child_left_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			else:
				return None
			if child_right_position < self.elements:
				__magic_name__     ,    __magic_name__	:   str					     =					self.heap[child_right_position]
				if child_right_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			return None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						int  ,					_A		:						int      )  ->			None:
			# Swap the nodes at the given positions
			__magic_name__	:   List[Any]					     =					self.heap[nodea_pos][0]
			__magic_name__	:   Union[str, Any]					     =					self.heap[nodea_pos][0]
			__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					(
			    self.heap[nodea_pos],
			    self.heap[nodea_pos],
			)
			__magic_name__	:   Optional[Any]					     =					nodea_pos
			__magic_name__	:   Optional[Any]					     =					nodea_pos







class _lowerCamelCase	(						Generic[T]							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]      )  ->			None:
			__magic_name__	:   dict[T, dict[T, int]]					     =					{}
			__magic_name__	:   int					     =					0







		def __repr__( self		:						str      )  ->			str:
			return str(self.connections      )







		def __len__( self		:						Union[str, Any]      )  ->			int:
			return self.nodes







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T      )  ->			None:
			# Add a node in the graph if it is not in the graph
			if node not in self.connections:
				__magic_name__	:   str					     =					{}
				self.nodes += 1







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						T  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Add an edge between 2 nodes in the graph
			self.add_node(_A      )
			self.add_node(_A      )
			__magic_name__	:   Any					     =					weight
			__magic_name__	:   Dict					     =					weight







def lowerCamelCase      (      lowerCAmelCase    :     GraphUndirectedWeighted[T]		,		):





	"""simple docstring"""







	__magic_name__	:   dict[T, int]					     =					{node: maxsize for node in graph.connections}
	__magic_name__	:   dict[T, T | None]					     =					{node: None for node in graph.connections}

	__magic_name__	:   MinPriorityQueue[T]					     =					MinPriorityQueue()
	for node, weight in dist.items():
		priority_queue.push(lowerCAmelCase		,		lowerCAmelCase		)

	if priority_queue.is_empty():
		return dist, parent

	# initialization
	__magic_name__	:   Any					     =					priority_queue.extract_min()
	__magic_name__	:   List[str]					     =					0
	for neighbour in graph.connections[node]:
		if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
			__magic_name__	:   Any					     =					dist[node] + graph.connections[node][neighbour]
			priority_queue.update_key(lowerCAmelCase		,		dist[neighbour]		)
			__magic_name__	:   Tuple					     =					node

    # running prim's algorithm
	while not priority_queue.is_empty():
		__magic_name__	:   Tuple					     =					priority_queue.extract_min()
		for neighbour in graph.connections[node]:
			if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
				__magic_name__	:   int					     =					dist[node] + graph.connections[node][neighbour]
				priority_queue.update_key(lowerCAmelCase		,		dist[neighbour]		)
				__magic_name__	:   Union[str, Any]					     =					node
	return dist, parent