





'''simple docstring'''


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  _lowerCamelCase	(__lowerCamelCase	:  int  ) ->     int:
			a__	 =			384
			a__	 =			7
			if "tiny" in model_name:
						a__	 =			96
						a__	 =			(2, 2, 6, 2)
						a__	 =			(3, 6, 12, 24)
			elif "small" in model_name:
						a__	 =			96
						a__	 =			(2, 2, 18, 2)
						a__	 =			(3, 6, 12, 24)
			elif "base" in model_name:
						a__	 =			128
						a__	 =			(2, 2, 18, 2)
						a__	 =			(4, 8, 16, 32)
						a__	 =			12
						a__	 =			512
			elif "large" in model_name:
						a__	 =			192
						a__	 =			(2, 2, 18, 2)
						a__	 =			(6, 12, 24, 48)
						a__	 =			12
						a__	 =			768

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

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

			return config







def  _lowerCamelCase	(__lowerCamelCase	:  Tuple  ) ->     Tuple:
			a__	 =			[]

			# 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  _lowerCamelCase	(__lowerCamelCase	:  Any       ,	__lowerCamelCase	:  Union[str, Any]       ,	__lowerCamelCase	:  Tuple  ) ->     str:
			a__	 =			dct.pop(snake_case_  )
			a__	 =			val







def  _lowerCamelCase	(__lowerCamelCase	:  List[str]       ,	__lowerCamelCase	:  str  ) ->     Optional[int]:
			a__	 =			[int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
			for i in range(len(backbone_config.depths  )  ):
						a__	 =			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)
									a__	 =			state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''  )
									a__	 =			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
									a__	 =			in_proj_weight[:dim, :]
									a__	 =			in_proj_bias[: dim]
									a__	 =			in_proj_weight[
									    dim : dim * 2, :
									]
									a__	 =			in_proj_bias[
									    dim : dim * 2
									]
									a__	 =			in_proj_weight[
									    -dim :, :
									]
									a__	 =			in_proj_bias[-dim :]
									# fmt: on







def  _lowerCamelCase	(__lowerCamelCase	:  Optional[Any]  ) ->     Tuple:
			a__	 =			x.shape
			a__	 =			x.reshape(snake_case_       ,	4       ,	in_channel // 4  )
			a__	 =			x[:, [0, 2, 1, 3], :].transpose(1       ,	2  ).reshape(snake_case_       ,	snake_case_  )
			return x







def  _lowerCamelCase	(__lowerCamelCase	:  Union[str, Any]  ) ->     Any:
			a__	 =			x.shape
			a__	 =			x.reshape(snake_case_       ,	in_channel // 4       ,	4  )
			a__	 =			x[:, :, [0, 2, 1, 3]].transpose(1       ,	2  ).reshape(snake_case_       ,	snake_case_  )

			return x







def  _lowerCamelCase	(__lowerCamelCase	:  List[Any]  ) ->     Any:
			a__	 =			x.shape[0]
			a__	 =			x.reshape(4       ,	in_channel // 4  )
			a__	 =			x[[0, 2, 1, 3], :].transpose(0       ,	1  ).reshape(snake_case_  )
			return x







def  _lowerCamelCase	(__lowerCamelCase	:  Optional[int]  ) ->     Optional[Any]:
			a__	 =			x.shape[0]
			a__	 =			x.reshape(in_channel // 4       ,	4  )
			a__	 =			x[:, [0, 2, 1, 3]].transpose(0       ,	1  ).reshape(snake_case_  )
			return x







def  _lowerCamelCase	(__lowerCamelCase	:  str       ,	__lowerCamelCase	:  int       ,	__lowerCamelCase	:  Optional[Any]  ) ->     Union[str, Any]:
			a__	 =			{
			    '''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''',
			}
			a__	 =			model_name_to_url[model_name]
			a__	 =			torch.hub.load_state_dict_from_url(snake_case_       ,	map_location="cpu"       ,	file_name=snake_case_  )[
			    '''state_dict'''
			]

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

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

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

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

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

			model.load_state_dict(snake_case_  )

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

			a__	 =			SegformerImageProcessor()
			a__	 =			processor(snake_case_       ,	return_tensors="pt"  ).pixel_values

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

			print(logits.shape  )
			print("First values of logits:"       ,	logits[0, 0, :3, :3]  )
			# assert values
			if model_name == "upernet-swin-tiny":
						a__	 =			torch.tensor(
						    [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]  )
			elif model_name == "upernet-swin-small":
						a__	 =			torch.tensor(
						    [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]  )
			elif model_name == "upernet-swin-base":
						a__	 =			torch.tensor(
						    [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]  )
			elif model_name == "upernet-swin-large":
						a__	 =			torch.tensor(
						    [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]  )
			print("Logits:"       ,	outputs.logits[0, 0, :3, :3]  )
			assert torch.allclose(outputs.logits[0, 0, :3, :3]       ,	snake_case_       ,	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(snake_case_  )
						print(f'''Saving processor to {pytorch_dump_folder_path}'''  )
						processor.save_pretrained(snake_case_  )

			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_ : List[str]	  =       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."
		)

		lowerCAmelCase_ : List[Any]	  =       parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""



from collections import namedtuple

import requests
from lxml import html  # type: ignore

a_					=     namedtuple("""covid_data""", """cases deaths recovered""")







def 						__lowercase	(  snake_case_    :    str = "https://www.worldometers.info/coronavirus/"		)  ->covid_data:



    '''simple docstring'''







    __A       :		List[str]							=	'''//div[@class = "maincounter-number"]/span/text()'''
    return covid_data(*html.fromstring(requests.get(snake_case_		).content		).xpath(snake_case_		)		)


a_					=     """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""




print(fmt.format(*covid_stats()))




