





from math import pi






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
  print(arc_length(9_0, 1_0))




import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			384
	snake_case_   		=			7
	if "tiny" in model_name:
		snake_case_   		=			96
		snake_case_   		=			(2, 2, 6, 2)
		snake_case_   		=			(3, 6, 12, 24)
	elif "small" in model_name:
		snake_case_   		=			96
		snake_case_   		=			(2, 2, 18, 2)
		snake_case_   		=			(3, 6, 12, 24)
	elif "base" in model_name:
		snake_case_   		=			128
		snake_case_   		=			(2, 2, 18, 2)
		snake_case_   		=			(4, 8, 16, 32)
		snake_case_   		=			12
		snake_case_   		=			512
	elif "large" in model_name:
		snake_case_   		=			192
		snake_case_   		=			(2, 2, 18, 2)
		snake_case_   		=			(6, 12, 24, 48)
		snake_case_   		=			12
		snake_case_   		=			768

	# set label information
	snake_case_   		=			150
	snake_case_   		=			'huggingface/label-files'
	snake_case_   		=			'ade20k-id2label.json'
	snake_case_   		=			json.load(open(hf_hub_download(lowercase__      ,			lowercase__      ,			repo_type='dataset'						)      ,			'r'						)						)
	snake_case_   		=			{int(lowercase__						): v for k, v in idalabel.items()}
	snake_case_   		=			{v: k for k, v in idalabel.items()}

	snake_case_   		=			SwinConfig(
	    embed_dim=lowercase__      ,			depths=lowercase__      ,			num_heads=lowercase__      ,			window_size=lowercase__      ,			out_features=['stage1', 'stage2', 'stage3', 'stage4']      ,			)
	snake_case_   		=			UperNetConfig(
	    backbone_config=lowercase__      ,			auxiliary_in_channels=lowercase__      ,			num_labels=lowercase__      ,			idalabel=lowercase__      ,			labelaid=lowercase__      ,			)

	return config





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			[]

	# fmt: off
	# stem
	rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')						)
	rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')						)
	rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')						)
	rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.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.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")						)

		if i < 3:
			rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.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 a(lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			dct.pop(lowercase__						)
	snake_case_   		=			val





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			[int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
	for i in range(len(backbone_config.depths						)						):
		snake_case_   		=			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)
			snake_case_   		=			state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""						)
			snake_case_   		=			state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""						)
			# next, add query, keys and values (in that order) to the state dict
			snake_case_   		=			in_proj_weight[:dim, :]
			snake_case_   		=			in_proj_bias[: dim]
			snake_case_   		=			in_proj_weight[
			    dim : dim * 2, :
			]
			snake_case_   		=			in_proj_bias[
			    dim : dim * 2
			]
			snake_case_   		=			in_proj_weight[
			    -dim :, :
			]
			snake_case_   		=			in_proj_bias[-dim :]
			# fmt: on





def a(lowercase__						):





	'''simple docstring'''
	snake_case_		,							snake_case_   		=			x.shape
	snake_case_   		=			x.reshape(lowercase__      ,			4      ,			in_channel // 4						)
	snake_case_   		=			x[:, [0, 2, 1, 3], :].transpose(1      ,			2						).reshape(lowercase__      ,			lowercase__						)
	return x





def a(lowercase__						):





	'''simple docstring'''
	snake_case_		,							snake_case_   		=			x.shape
	snake_case_   		=			x.reshape(lowercase__      ,			in_channel // 4      ,			4						)
	snake_case_   		=			x[:, :, [0, 2, 1, 3]].transpose(1      ,			2						).reshape(lowercase__      ,			lowercase__						)

	return x





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			x.shape[0]
	snake_case_   		=			x.reshape(4      ,			in_channel // 4						)
	snake_case_   		=			x[[0, 2, 1, 3], :].transpose(0      ,			1						).reshape(lowercase__						)
	return x





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			x.shape[0]
	snake_case_   		=			x.reshape(in_channel // 4      ,			4						)
	snake_case_   		=			x[:, [0, 2, 1, 3]].transpose(0      ,			1						).reshape(lowercase__						)
	return x





def a(lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			{
	    'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
	    'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
	    'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
	    'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
	}
	snake_case_   		=			model_name_to_url[model_name]
	snake_case_   		=			torch.hub.load_state_dict_from_url(lowercase__      ,			map_location='cpu'      ,			file_name=lowercase__						)[
	    'state_dict'
	]

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

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

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

	# rename keys
	snake_case_   		=			create_rename_keys(lowercase__						)
	for src, dest in rename_keys:
		rename_key(lowercase__      ,			lowercase__      ,			lowercase__						)
	read_in_q_k_v(lowercase__      ,			config.backbone_config						)

	# fix downsample parameters
	for key, value in state_dict.items():
		if "downsample" in key:
			if "reduction" in key:
				snake_case_   		=			reverse_correct_unfold_reduction_order(lowercase__						)
			if "norm" in key:
				snake_case_   		=			reverse_correct_unfold_norm_order(lowercase__						)

	model.load_state_dict(lowercase__						)

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

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

	with torch.no_grad():
		snake_case_   		=			model(lowercase__						)
		snake_case_   		=			outputs.logits

	print(logits.shape						)
	print('First values of logits:'      ,			logits[0, 0, :3, :3]						)
	# assert values
	if model_name == "upernet-swin-tiny":
		snake_case_   		=			torch.tensor(
		    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]						)
	elif model_name == "upernet-swin-small":
		snake_case_   		=			torch.tensor(
		    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]						)
	elif model_name == "upernet-swin-base":
		snake_case_   		=			torch.tensor(
		    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]						)
	elif model_name == "upernet-swin-large":
		snake_case_   		=			torch.tensor(
		    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]						)
	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__":
		A									=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='upernet-swin-tiny',
		    type=str,
		    choices=[f"""upernet-swin-{size}""" for size in ['tiny', 'small', 'base', 'large']],
		    help='Name of the Swin + 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.'
		)

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