


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(	lowercase__	)						->	Optional[Any]:

		'''simple docstring'''







		__lowercase=			3_8_4
		if "tiny" in model_name:
				__lowercase=			[3, 3, 9, 3]
				__lowercase=			[9_6, 1_9_2, 3_8_4, 7_6_8]
		if "small" in model_name:
				__lowercase=			[3, 3, 2_7, 3]
				__lowercase=			[9_6, 1_9_2, 3_8_4, 7_6_8]
		if "base" in model_name:
				__lowercase=			[3, 3, 2_7, 3]
				__lowercase=			[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
				__lowercase=			5_1_2
		if "large" in model_name:
				__lowercase=			[3, 3, 2_7, 3]
				__lowercase=			[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
				__lowercase=			7_6_8
		if "xlarge" in model_name:
				__lowercase=			[3, 3, 2_7, 3]
				__lowercase=			[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
				__lowercase=			1_0_2_4

		# set label information
		__lowercase=			1_5_0
		__lowercase=			'huggingface/label-files'
		__lowercase=			'ade20k-id2label.json'
		__lowercase=			json.load(open(hf_hub_download(lowercase__   , lowercase__   , repo_type='dataset'	)   , 'r'	)	)
		__lowercase=			{int(lowercase__	): v for k, v in idalabel.items()}
		__lowercase=			{v: k for k, v in idalabel.items()}

		__lowercase=			ConvNextConfig(
		    depths=lowercase__   , hidden_sizes=lowercase__   , out_features=['stage1', 'stage2', 'stage3', 'stage4']	)
		__lowercase=			UperNetConfig(
		    backbone_config=lowercase__   , auxiliary_in_channels=lowercase__   , num_labels=lowercase__   , idalabel=lowercase__   , labelaid=lowercase__   , )

		return config







def        _lowerCamelCase(	lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			[]

		# 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(	lowercase__   , lowercase__   , lowercase__	)						->	Dict:

		'''simple docstring'''







		__lowercase=			dct.pop(lowercase__	)
		__lowercase=			val







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	int:

		'''simple docstring'''







		__lowercase=			{
		    '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',
		}
		__lowercase=			model_name_to_url[model_name]
		__lowercase=			torch.hub.load_state_dict_from_url(lowercase__   , map_location='cpu'	)['state_dict']

		__lowercase=			get_upernet_config(lowercase__	)
		__lowercase=			UperNetForSemanticSegmentation(lowercase__	)
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				__lowercase=			state_dict.pop(lowercase__	)
				if "bn" in key:
						__lowercase=			key.replace('bn'   , 'batch_norm'	)
				__lowercase=			val

		# rename keys
		__lowercase=			create_rename_keys(lowercase__	)
		for src, dest in rename_keys:
				rename_key(lowercase__   , lowercase__   , lowercase__	)

		model.load_state_dict(lowercase__	)

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

		__lowercase=			SegformerImageProcessor()
		__lowercase=			processor(lowercase__   , return_tensors='pt'	).pixel_values

		with torch.no_grad():
				__lowercase=			model(lowercase__	)

		if model_name == "upernet-convnext-tiny":
				__lowercase=			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":
				__lowercase=			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":
				__lowercase=			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":
				__lowercase=			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":
				__lowercase=			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]   , lowercase__   , 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(lowercase__	)
				print(F'Saving processor to {pytorch_dump_folder_path}'	)
				processor.save_pretrained(lowercase__	)

		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)










from __future__ import annotations







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Any:

		'''simple docstring'''







		if len(lowercase__	) <= 1 or n <= 1:
				return

		insert_next(lowercase__   , n - 1	)
		rec_insertion_sort(lowercase__   , n - 1	)







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Any:

		'''simple docstring'''







		if index >= len(lowercase__	) or collection[index - 1] <= collection[index]:
				return

		# Swaps adjacent elements since they are not in ascending order
		__lowercase,							__lowercase=			(
		    collection[index],
		    collection[index - 1],
		)

		insert_next(lowercase__   , index + 1	)


if __name__ == "__main__":
	lowerCAmelCase           =		input('''Enter integers separated by spaces: ''')
	lowerCAmelCase           =		[int(num) for num in numbers.split()]
	rec_insertion_sort(number_list, len(number_list))
	print(number_list)






