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     UpperCamelCase						(	__lowercase						:			Any     ) ->    Optional[Any]:
		'''simple docstring'''




		A_		:    List[str]         =				3_84
		if "tiny" in model_name:
				A_		:    int         =				[3, 3, 9, 3]
				A_		:    Dict         =				[96, 1_92, 3_84, 7_68]
		if "small" in model_name:
				A_		:    Any         =				[3, 3, 27, 3]
				A_		:    Union[str, Any]         =				[96, 1_92, 3_84, 7_68]
		if "base" in model_name:
				A_		:    str         =				[3, 3, 27, 3]
				A_		:    List[str]         =				[1_28, 2_56, 5_12, 10_24]
				A_		:    Optional[Any]         =				5_12
		if "large" in model_name:
				A_		:    str         =				[3, 3, 27, 3]
				A_		:    List[str]         =				[1_92, 3_84, 7_68, 15_36]
				A_		:    Union[str, Any]         =				7_68
		if "xlarge" in model_name:
				A_		:    Optional[Any]         =				[3, 3, 27, 3]
				A_		:    List[str]         =				[2_56, 5_12, 10_24, 20_48]
				A_		:    int         =				10_24

		# set label information
		A_		:    Tuple         =				1_50
		A_		:    Tuple         =				'huggingface/label-files'
		A_		:    Optional[int]         =				'ade20k-id2label.json'
		A_		:    Tuple         =				json.load(open(hf_hub_download(__lowercase						,__lowercase						,repo_type='dataset'     )						,'r'     )     )
		A_		:    List[Any]         =				{int(__lowercase     ): v for k, v in idalabel.items()}
		A_		:    List[Any]         =				{v: k for k, v in idalabel.items()}

		A_		:    Tuple         =				ConvNextConfig(
		    depths=__lowercase						,hidden_sizes=__lowercase						,out_features=['stage1', 'stage2', 'stage3', 'stage4']     )
		A_		:    Optional[int]         =				UperNetConfig(
		    backbone_config=__lowercase						,auxiliary_in_channels=__lowercase						,num_labels=__lowercase						,idalabel=__lowercase						,labelaid=__lowercase						,)

		return config




def     UpperCamelCase						(	__lowercase						:			List[str]     ) ->    Dict:
		'''simple docstring'''




		A_		:    int         =				[]

		# 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     UpperCamelCase						(	__lowercase						:			Union[str, Any]						,__lowercase						:			Optional[int]						,__lowercase						:			int     ) ->    Optional[int]:
		'''simple docstring'''




		A_		:    Any         =				dct.pop(__lowercase     )
		A_		:    Union[str, Any]         =				val




def     UpperCamelCase						(	__lowercase						:			Union[str, Any]						,__lowercase						:			Tuple						,__lowercase						:			int     ) ->    Dict:
		'''simple docstring'''




		A_		:    Optional[Any]         =				{
		    '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',
		}
		A_		:    Tuple         =				model_name_to_url[model_name]
		A_		:    List[str]         =				torch.hub.load_state_dict_from_url(__lowercase						,map_location='cpu'     )['state_dict']

		A_		:    Tuple         =				get_upernet_config(__lowercase     )
		A_		:    Tuple         =				UperNetForSemanticSegmentation(__lowercase     )
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				A_		:    Optional[Any]         =				state_dict.pop(__lowercase     )
				if "bn" in key:
						A_		:    List[str]         =				key.replace('bn'						,'batch_norm'     )
				A_		:    List[Any]         =				val

		# rename keys
		A_		:    Optional[int]         =				create_rename_keys(__lowercase     )
		for src, dest in rename_keys:
				rename_key(__lowercase						,__lowercase						,__lowercase     )

		model.load_state_dict(__lowercase     )

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

		A_		:    Tuple         =				SegformerImageProcessor()
		A_		:    List[str]         =				processor(__lowercase						,return_tensors='pt'     ).pixel_values

		with torch.no_grad():
				A_		:    List[Any]         =				model(__lowercase     )

		if model_name == "upernet-convnext-tiny":
				A_		:    Tuple         =				torch.tensor(
				    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]     )
		elif model_name == "upernet-convnext-small":
				A_		:    Optional[Any]         =				torch.tensor(
				    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]     )
		elif model_name == "upernet-convnext-base":
				A_		:    str         =				torch.tensor(
				    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]     )
		elif model_name == "upernet-convnext-large":
				A_		:    Optional[int]         =				torch.tensor(
				    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]     )
		elif model_name == "upernet-convnext-xlarge":
				A_		:    Optional[Any]         =				torch.tensor(
				    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]     )
		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__":
	_UpperCAmelCase            =		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."""
	)

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







import math




def     UpperCamelCase						(	__lowercase						:			int = 1_00     ):
		'''simple docstring'''




		A_		:    List[Any]         =				sum(i * i for i in range(1						,n + 1     )     )
		A_		:    int         =				int(math.pow(sum(range(1						,n + 1     )     )						,2     )     )
		return square_of_sum - sum_of_squares


if __name__ == "__main__":
	print(F"""{solution() = }""")






