

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 						UpperCamelCase (lowercase_:			Any )					->				int:
	A__							:   Optional[Any]					       =		384
	A__							:   Union[str, Any]					       =		7
	if "tiny" in model_name:
		A__							:   List[str]					       =		96
		A__							:   Optional[int]					       =		(2, 2, 6, 2)
		A__							:   str					       =		(3, 6, 12, 24)
	elif "small" in model_name:
		A__							:   List[str]					       =		96
		A__							:   Optional[int]					       =		(2, 2, 18, 2)
		A__							:   str					       =		(3, 6, 12, 24)
	elif "base" in model_name:
		A__							:   Tuple					       =		128
		A__							:   Optional[Any]					       =		(2, 2, 18, 2)
		A__							:   str					       =		(4, 8, 16, 32)
		A__							:   Optional[int]					       =		12
		A__							:   Union[str, Any]					       =		512
	elif "large" in model_name:
		A__							:   Dict					       =		192
		A__							:   str					       =		(2, 2, 18, 2)
		A__							:   List[str]					       =		(6, 12, 24, 48)
		A__							:   str					       =		12
		A__							:   Tuple					       =		768

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

	A__							:   str					       =		SwinConfig(
	    embed_dim=lowercase_   ,				depths=lowercase_   ,				num_heads=lowercase_   ,				window_size=lowercase_   ,				out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]   ,				)
	A__							:   Union[str, Any]					       =		UperNetConfig(
	    backbone_config=lowercase_   ,				auxiliary_in_channels=lowercase_   ,				num_labels=lowercase_   ,				idalabel=lowercase_   ,				labelaid=lowercase_   ,				)

	return config

def 						UpperCamelCase (lowercase_:			int )					->				Optional[int]:
	A__							:   List[Any]					       =		[]

	# 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 						UpperCamelCase (lowercase_:			Any   ,				lowercase_:			Tuple   ,				lowercase_:			Any )					->				int:
	A__							:   Dict					       =		dct.pop(lowercase_ )
	A__							:   str					       =		val

def 						UpperCamelCase (lowercase_:			Union[str, Any]   ,				lowercase_:			List[str] )					->				Tuple:
	A__							:   Tuple					       =		[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
	for i in range(len(backbone_config.depths ) ):
		A__							:   Union[str, Any]					       =		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__							:   Optional[Any]					       =		state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight""" )
			A__							:   Optional[int]					       =		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__							:   List[str]					       =		in_proj_weight[:dim, :]
			A__							:   List[str]					       =		in_proj_bias[: dim]
			A__							:   str					       =		in_proj_weight[
			    dim : dim * 2, :
			]
			A__							:   Optional[int]					       =		in_proj_bias[
			    dim : dim * 2
			]
			A__							:   Tuple					       =		in_proj_weight[
			    -dim :, :
			]
			A__							:   List[str]					       =		in_proj_bias[-dim :]
			# fmt: on

def 						UpperCamelCase (lowercase_:			Union[str, Any] )					->				Any:
	A__       ,       A__							:   List[Any]					       =		x.shape
	A__							:   Optional[int]					       =		x.reshape(lowercase_   ,				4   ,				in_channel // 4 )
	A__							:   Tuple					       =		x[:, [0, 2, 1, 3], :].transpose(1   ,				2 ).reshape(lowercase_   ,				lowercase_ )
	return x

def 						UpperCamelCase (lowercase_:			Union[str, Any] )					->				Optional[Any]:
	A__       ,       A__							:   Any					       =		x.shape
	A__							:   List[Any]					       =		x.reshape(lowercase_   ,				in_channel // 4   ,				4 )
	A__							:   Any					       =		x[:, :, [0, 2, 1, 3]].transpose(1   ,				2 ).reshape(lowercase_   ,				lowercase_ )

	return x

def 						UpperCamelCase (lowercase_:			Union[str, Any] )					->				List[Any]:
	A__							:   int					       =		x.shape[0]
	A__							:   Dict					       =		x.reshape(4   ,				in_channel // 4 )
	A__							:   str					       =		x[[0, 2, 1, 3], :].transpose(0   ,				1 ).reshape(lowercase_ )
	return x

def 						UpperCamelCase (lowercase_:			str )					->				Tuple:
	A__							:   int					       =		x.shape[0]
	A__							:   Tuple					       =		x.reshape(in_channel // 4   ,				4 )
	A__							:   str					       =		x[:, [0, 2, 1, 3]].transpose(0   ,				1 ).reshape(lowercase_ )
	return x

def 						UpperCamelCase (lowercase_:			List[Any]   ,				lowercase_:			Dict   ,				lowercase_:			int )					->				List[str]:
	A__							:   Union[str, Any]					       =		{
	    """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__							:   Optional[int]					       =		model_name_to_url[model_name]
	A__							:   Tuple					       =		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 )

	A__							:   Optional[int]					       =		get_upernet_config(lowercase_ )
	A__							:   str					       =		UperNetForSemanticSegmentation(lowercase_ )
	model.eval()

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

	# rename keys
	A__							:   Tuple					       =		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:
				A__							:   List[Any]					       =		reverse_correct_unfold_reduction_order(lowercase_ )
			if "norm" in key:
				A__							:   Optional[Any]					       =		reverse_correct_unfold_norm_order(lowercase_ )

	model.load_state_dict(lowercase_ )

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

	A__							:   int					       =		SegformerImageProcessor()
	A__							:   int					       =		processor(lowercase_   ,				return_tensors="""pt""" ).pixel_values

	with torch.no_grad():
		A__							:   Union[str, Any]					       =		model(lowercase_ )
		A__							:   Dict					       =		outputs.logits

	print(logits.shape )
	print("""First values of logits:"""   ,				logits[0, 0, :3, :3] )
	# assert values
	if model_name == "upernet-swin-tiny":
		A__							:   Tuple					       =		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":
		A__							:   Dict					       =		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":
		A__							:   List[str]					       =		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":
		A__							:   List[Any]					       =		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_	:			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.'
	)

	A_	:			Any  	=      parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






from __future__ import annotations

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				tuple[float, list[float]]:
	A__							:   Tuple					       =		list(range(len(lowercase_ ) ) )
	A__							:   Union[str, Any]					       =		[v / w for v, w in zip(lowercase_   ,				lowercase_ )]
	index.sort(key=lambda lowercase_ : ratio[i]   ,				reverse=lowercase_ )

	A__							:   float					       =		0
	A__							:   list[float]					       =		[0] * len(lowercase_ )
	for i in index:
		if weight[i] <= capacity:
			A__							:   Optional[int]					       =		1
			max_value += value[i]
			capacity -= weight[i]
		else:
			A__							:   Union[str, Any]					       =		capacity / weight[i]
			max_value += value[i] * capacity / weight[i]
			break

	return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()



